From c88ec3d676260ca5c5060470394a3183e892152c Mon Sep 17 00:00:00 2001
From: Aaron Lisman <lisman.aaron@gmail.com>
Date: Fri, 23 Oct 2026 07:57:10 +0000
Subject: [PATCH] Every view kind's output is pinned, and output may not depend
 on which query finished first

test/view-snapshots.test.ts runs one gallery example per view kind (30 kinds; the oncoprint has
its own golden SVG), plus four that combine studies, through the whole engine with the warehouse
replaced by answers recorded from it, and compares four files per case in test/snapshots/:
  <case>.sql   each cohort's compiled SQL and each view's queries, by label and study
  <case>.json  what each view computed (result without timings or queries)
  <case>.svg   each view's figure;  <case>.txt  the results text MCP clients and AI tools read
A change to shared code (a SQL fragment in federation-views.ts, a figure helper such as km.ts,
stats.ts, result-text.ts) that moves another view's queries, numbers, figure or text fails in
`npm test`, named by case, with no database. A changed query has no recorded answer and fails as
"not recorded". Comparison is exact: no rounding, no tolerance.

Each case runs twice, answers delivered in the order the queries were sent and then reversed,
and the two runs must agree. That found order-dependence in four runners, each combining answers
inside a Promise.all callback as they arrived, so the method record listed cohorts or studies in
arrival order ("Colorectal" first in one run, "Pancreatic" in the next) and the stratified
log-rank took its studies in arrival order:
- survival: per-study counts; enrichment: per-site steps; diffexpr: moments, hence gene and
  density-grid order; assays: per-site steps, and entity names, first-come across studies.
Each now asks through askEachSite (lib/views/shared.ts), which returns answers in the view's
order (cohorts, then studies) whatever order they finish in, and combines afterwards. Reverting any
of the four makes its case fail with "depends on the order database answers arrive"; every
combining runner moves to the helper in the per-kind reorganization, and the check covers new ones.
No other output changed. A view's queries are listed sorted in <case>.sql: independent queries
form a set; the snapshot pins which are sent, not the order they happen to be sent in.

lib/ch.ts gains setChTransport(): every statement already passes through post(); a transport can
answer it or watch it reach the database, with extra settings for that statement. Only this test
sets one. Answers are keyed by instance, format, statement text and every external table sent with
it, stored gzipped under a fixed database name, without ClickHouse's timing statistics, and pooled
across cases so any case runs alone. Recording (RECORD_SNAPSHOTS=1, .env.local) reads the public
warehouse only (the second instance is unset first) with max_threads=1: ClickHouse otherwise adds
floats across threads in varying order and returns unordered rows in varying order; two
recordings in a row are now byte-identical. The answers hold public studies' rows, patient-level
ones included (table, timeline, alterations), as the public deployment shows them.

Long arrays, and objects of more than 50 keys (a method's check values), keep their first 20 items
and stand in for the rest by count and digest, so the files stay reviewable while any change still
fails. The run's wall-clock time is left out of the text.

Verified: three replays in a row identical; npm test 1471/1471 (53 s; the snapshot file runs the
engine 68 times, ~33 s, in parallel with the rest). Size: 34 gzipped answer files and 136
snapshot files, 6.3 MB. CLAUDE.md says how to update and re-record.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CuBjiBEjYispDSEYDZDWM1
---
 CLAUDE.md                                     |   10 +
 lib/ch.ts                                     |   22 +-
 lib/views/assays.ts                           |   58 +-
 lib/views/diffexpr.ts                         |   10 +-
 lib/views/enrichment.ts                       |   17 +-
 lib/views/shared.ts                           |   13 +
 lib/views/survival.ts                         |    7 +-
 test/snapshots/alterations.json               | 1003 ++++++
 test/snapshots/alterations.sql                |  382 +++
 test/snapshots/alterations.svg                |    2 +
 test/snapshots/alterations.txt                |   59 +
 test/snapshots/answers/alterations.json.gz    |  Bin 0 -> 2417 bytes
 test/snapshots/answers/assays.json.gz         |  Bin 0 -> 2442 bytes
 test/snapshots/answers/attrition.json.gz      |  Bin 0 -> 359 bytes
 test/snapshots/answers/cancertypes.json.gz    |  Bin 0 -> 5731 bytes
 test/snapshots/answers/clinical.json.gz       |  Bin 0 -> 1708 bytes
 test/snapshots/answers/clonality.json.gz      |  Bin 0 -> 1985 bytes
 test/snapshots/answers/coexpression.json.gz   |  Bin 0 -> 674 bytes
 test/snapshots/answers/compare.json.gz        |  Bin 0 -> 45555 bytes
 test/snapshots/answers/composition.json.gz    |  Bin 0 -> 10014 bytes
 test/snapshots/answers/cooccurrence.json.gz   |  Bin 0 -> 558 bytes
 test/snapshots/answers/correlates.json.gz     |  Bin 0 -> 952042 bytes
 test/snapshots/answers/course.json.gz         |  Bin 0 -> 1907 bytes
 test/snapshots/answers/cox.json.gz            |  Bin 0 -> 2130 bytes
 test/snapshots/answers/diffexpr.json.gz       |  Bin 0 -> 848591 bytes
 test/snapshots/answers/enrichment.json.gz     |  Bin 0 -> 12220 bytes
 test/snapshots/answers/genome.json.gz         |  Bin 0 -> 25956 bytes
 test/snapshots/answers/hotspots.json.gz       |  Bin 0 -> 5458 bytes
 test/snapshots/answers/incidence.json.gz      |  Bin 0 -> 44283 bytes
 test/snapshots/answers/labs.json.gz           |  Bin 0 -> 10439 bytes
 test/snapshots/answers/logistic.json.gz       |  Bin 0 -> 702 bytes
 test/snapshots/answers/mutations.json.gz      |  Bin 0 -> 1653 bytes
 test/snapshots/answers/overlap.json.gz        |  Bin 0 -> 508 bytes
 test/snapshots/answers/paired.json.gz         |  Bin 0 -> 496 bytes
 test/snapshots/answers/pathways.json.gz       |  Bin 0 -> 8025 bytes
 test/snapshots/answers/plot.json.gz           |  Bin 0 -> 20030 bytes
 test/snapshots/answers/pooled-paired.json.gz  |  Bin 0 -> 5103 bytes
 test/snapshots/answers/riskmodel.json.gz      |  Bin 0 -> 462421 bytes
 test/snapshots/answers/sequence.json.gz       |  Bin 0 -> 11037 bytes
 test/snapshots/answers/survival.json.gz       |  Bin 0 -> 1256 bytes
 test/snapshots/answers/table.json.gz          |  Bin 0 -> 3960 bytes
 test/snapshots/answers/timeline.json.gz       |  Bin 0 -> 7085 bytes
 .../answers/two-studies-four-views.json.gz    |  Bin 0 -> 139966 bytes
 .../answers/two-studies-plot.json.gz          |  Bin 0 -> 21229 bytes
 .../answers/two-studies-survival.json.gz      |  Bin 0 -> 70102 bytes
 test/snapshots/assays.json                    |  697 ++++
 test/snapshots/assays.sql                     |   60 +
 test/snapshots/assays.svg                     |    2 +
 test/snapshots/assays.txt                     |   46 +
 test/snapshots/attrition.json                 |  154 +
 test/snapshots/attrition.sql                  |  176 +
 test/snapshots/attrition.svg                  |    2 +
 test/snapshots/attrition.txt                  |   27 +
 test/snapshots/cancertypes.json               | 1562 +++++++++
 test/snapshots/cancertypes.sql                |   78 +
 test/snapshots/cancertypes.svg                |    2 +
 test/snapshots/cancertypes.txt                |   44 +
 test/snapshots/clinical.json                  |  254 ++
 test/snapshots/clinical.sql                   |  123 +
 test/snapshots/clinical.svg                   |    2 +
 test/snapshots/clinical.txt                   |   21 +
 test/snapshots/clonality.json                 |  731 +++++
 test/snapshots/clonality.sql                  |  198 ++
 test/snapshots/clonality.svg                  |    2 +
 test/snapshots/clonality.txt                  |   34 +
 test/snapshots/coexpression.json              |  256 ++
 test/snapshots/coexpression.sql               |   35 +
 test/snapshots/coexpression.svg               |    2 +
 test/snapshots/coexpression.txt               |   23 +
 test/snapshots/compare.json                   |  143 +
 test/snapshots/compare.sql                    |   58 +
 test/snapshots/compare.svg                    |    2 +
 test/snapshots/compare.txt                    |   13 +
 test/snapshots/composition.json               | 1944 +++++++++++
 test/snapshots/composition.sql                | 1642 ++++++++++
 test/snapshots/composition.svg                |    4 +
 test/snapshots/composition.txt                |   32 +
 test/snapshots/cooccurrence.json              |  367 +++
 test/snapshots/cooccurrence.sql               |   42 +
 test/snapshots/cooccurrence.svg               |    2 +
 test/snapshots/cooccurrence.txt               |   29 +
 test/snapshots/correlates.json                |  679 ++++
 test/snapshots/correlates.sql                 |   43 +
 test/snapshots/correlates.svg                 |    2 +
 test/snapshots/correlates.txt                 |  216 ++
 test/snapshots/course.json                    |  782 +++++
 test/snapshots/course.sql                     |  187 ++
 test/snapshots/course.svg                     |    2 +
 test/snapshots/course.txt                     |   22 +
 test/snapshots/cox.json                       |  138 +
 test/snapshots/cox.sql                        |  100 +
 test/snapshots/cox.svg                        |    2 +
 test/snapshots/cox.txt                        |   22 +
 test/snapshots/diffexpr.json                  | 2865 +++++++++++++++++
 test/snapshots/diffexpr.sql                   |   52 +
 test/snapshots/diffexpr.svg                   |    2 +
 test/snapshots/diffexpr.txt                   |   33 +
 test/snapshots/enrichment.json                |  737 +++++
 test/snapshots/enrichment.sql                 |  162 +
 test/snapshots/enrichment.svg                 |    2 +
 test/snapshots/enrichment.txt                 |   59 +
 test/snapshots/genome.json                    |  475 +++
 test/snapshots/genome.sql                     |  102 +
 test/snapshots/genome.svg                     |    2 +
 test/snapshots/genome.txt                     |   20 +
 test/snapshots/hotspots.json                  | 2279 +++++++++++++
 test/snapshots/hotspots.sql                   |   62 +
 test/snapshots/hotspots.svg                   |  355 ++
 test/snapshots/hotspots.txt                   |   12 +
 test/snapshots/incidence.json                 |  836 +++++
 test/snapshots/incidence.sql                  |  170 +
 test/snapshots/incidence.svg                  |    2 +
 test/snapshots/incidence.txt                  |   32 +
 test/snapshots/labs.json                      |  568 ++++
 test/snapshots/labs.sql                       |  325 ++
 test/snapshots/labs.svg                       |    2 +
 test/snapshots/labs.txt                       |   25 +
 test/snapshots/logistic.json                  |  140 +
 test/snapshots/logistic.sql                   |   84 +
 test/snapshots/logistic.svg                   |    2 +
 test/snapshots/logistic.txt                   |   22 +
 test/snapshots/mutations.json                 |  709 ++++
 test/snapshots/mutations.sql                  |   77 +
 test/snapshots/mutations.svg                  |    2 +
 test/snapshots/mutations.txt                  |   39 +
 test/snapshots/overlap.json                   |  237 ++
 test/snapshots/overlap.sql                    |   82 +
 test/snapshots/overlap.svg                    |    2 +
 test/snapshots/overlap.txt                    |   32 +
 test/snapshots/paired.json                    |  233 ++
 test/snapshots/paired.sql                     |   61 +
 test/snapshots/paired.svg                     |    2 +
 test/snapshots/paired.txt                     |   22 +
 test/snapshots/pathways.json                  | 1557 +++++++++
 test/snapshots/pathways.sql                   |  203 ++
 test/snapshots/pathways.svg                   |  161 +
 test/snapshots/pathways.txt                   |   26 +
 test/snapshots/plot.json                      |  681 ++++
 test/snapshots/plot.sql                       |  232 ++
 test/snapshots/plot.svg                       |    4 +
 test/snapshots/plot.txt                       |   23 +
 test/snapshots/pooled-paired.json             |  431 +++
 test/snapshots/pooled-paired.sql              |  322 ++
 test/snapshots/pooled-paired.svg              |    2 +
 test/snapshots/pooled-paired.txt              |   33 +
 test/snapshots/riskmodel.json                 | 1242 +++++++
 test/snapshots/riskmodel.sql                  |  115 +
 test/snapshots/riskmodel.svg                  |    2 +
 test/snapshots/riskmodel.txt                  |   83 +
 test/snapshots/sequence.json                  |  810 +++++
 test/snapshots/sequence.sql                   |  262 ++
 test/snapshots/sequence.svg                   |    2 +
 test/snapshots/sequence.txt                   |   24 +
 test/snapshots/survival.json                  |  399 +++
 test/snapshots/survival.sql                   |   51 +
 test/snapshots/survival.svg                   |    2 +
 test/snapshots/survival.txt                   |   22 +
 test/snapshots/table.json                     |  888 +++++
 test/snapshots/table.sql                      |   80 +
 test/snapshots/table.svg                      |    2 +
 test/snapshots/table.txt                      |   39 +
 test/snapshots/timeline.json                  | 2243 +++++++++++++
 test/snapshots/timeline.sql                   |  511 +++
 test/snapshots/timeline.svg                   |    4 +
 test/snapshots/timeline.txt                   |  290 ++
 test/snapshots/two-studies-four-views.json    | 1474 +++++++++
 test/snapshots/two-studies-four-views.sql     |  553 ++++
 test/snapshots/two-studies-four-views.svg     |    8 +
 test/snapshots/two-studies-four-views.txt     |   87 +
 test/snapshots/two-studies-plot.json          |  873 +++++
 test/snapshots/two-studies-plot.sql           |  713 ++++
 test/snapshots/two-studies-plot.svg           |    4 +
 test/snapshots/two-studies-plot.txt           |   16 +
 test/snapshots/two-studies-survival.json      |  467 +++
 test/snapshots/two-studies-survival.sql       |  155 +
 test/snapshots/two-studies-survival.svg       |    2 +
 test/snapshots/two-studies-survival.txt       |   28 +
 test/view-snapshots.test.ts                   |  229 ++
 178 files changed, 38817 insertions(+), 50 deletions(-)
 create mode 100644 test/snapshots/alterations.json
 create mode 100644 test/snapshots/alterations.sql
 create mode 100644 test/snapshots/alterations.svg
 create mode 100644 test/snapshots/alterations.txt
 create mode 100644 test/snapshots/answers/alterations.json.gz
 create mode 100644 test/snapshots/answers/assays.json.gz
 create mode 100644 test/snapshots/answers/attrition.json.gz
 create mode 100644 test/snapshots/answers/cancertypes.json.gz
 create mode 100644 test/snapshots/answers/clinical.json.gz
 create mode 100644 test/snapshots/answers/clonality.json.gz
 create mode 100644 test/snapshots/answers/coexpression.json.gz
 create mode 100644 test/snapshots/answers/compare.json.gz
 create mode 100644 test/snapshots/answers/composition.json.gz
 create mode 100644 test/snapshots/answers/cooccurrence.json.gz
 create mode 100644 test/snapshots/answers/correlates.json.gz
 create mode 100644 test/snapshots/answers/course.json.gz
 create mode 100644 test/snapshots/answers/cox.json.gz
 create mode 100644 test/snapshots/answers/diffexpr.json.gz
 create mode 100644 test/snapshots/answers/enrichment.json.gz
 create mode 100644 test/snapshots/answers/genome.json.gz
 create mode 100644 test/snapshots/answers/hotspots.json.gz
 create mode 100644 test/snapshots/answers/incidence.json.gz
 create mode 100644 test/snapshots/answers/labs.json.gz
 create mode 100644 test/snapshots/answers/logistic.json.gz
 create mode 100644 test/snapshots/answers/mutations.json.gz
 create mode 100644 test/snapshots/answers/overlap.json.gz
 create mode 100644 test/snapshots/answers/paired.json.gz
 create mode 100644 test/snapshots/answers/pathways.json.gz
 create mode 100644 test/snapshots/answers/plot.json.gz
 create mode 100644 test/snapshots/answers/pooled-paired.json.gz
 create mode 100644 test/snapshots/answers/riskmodel.json.gz
 create mode 100644 test/snapshots/answers/sequence.json.gz
 create mode 100644 test/snapshots/answers/survival.json.gz
 create mode 100644 test/snapshots/answers/table.json.gz
 create mode 100644 test/snapshots/answers/timeline.json.gz
 create mode 100644 test/snapshots/answers/two-studies-four-views.json.gz
 create mode 100644 test/snapshots/answers/two-studies-plot.json.gz
 create mode 100644 test/snapshots/answers/two-studies-survival.json.gz
 create mode 100644 test/snapshots/assays.json
 create mode 100644 test/snapshots/assays.sql
 create mode 100644 test/snapshots/assays.svg
 create mode 100644 test/snapshots/assays.txt
 create mode 100644 test/snapshots/attrition.json
 create mode 100644 test/snapshots/attrition.sql
 create mode 100644 test/snapshots/attrition.svg
 create mode 100644 test/snapshots/attrition.txt
 create mode 100644 test/snapshots/cancertypes.json
 create mode 100644 test/snapshots/cancertypes.sql
 create mode 100644 test/snapshots/cancertypes.svg
 create mode 100644 test/snapshots/cancertypes.txt
 create mode 100644 test/snapshots/clinical.json
 create mode 100644 test/snapshots/clinical.sql
 create mode 100644 test/snapshots/clinical.svg
 create mode 100644 test/snapshots/clinical.txt
 create mode 100644 test/snapshots/clonality.json
 create mode 100644 test/snapshots/clonality.sql
 create mode 100644 test/snapshots/clonality.svg
 create mode 100644 test/snapshots/clonality.txt
 create mode 100644 test/snapshots/coexpression.json
 create mode 100644 test/snapshots/coexpression.sql
 create mode 100644 test/snapshots/coexpression.svg
 create mode 100644 test/snapshots/coexpression.txt
 create mode 100644 test/snapshots/compare.json
 create mode 100644 test/snapshots/compare.sql
 create mode 100644 test/snapshots/compare.svg
 create mode 100644 test/snapshots/compare.txt
 create mode 100644 test/snapshots/composition.json
 create mode 100644 test/snapshots/composition.sql
 create mode 100644 test/snapshots/composition.svg
 create mode 100644 test/snapshots/composition.txt
 create mode 100644 test/snapshots/cooccurrence.json
 create mode 100644 test/snapshots/cooccurrence.sql
 create mode 100644 test/snapshots/cooccurrence.svg
 create mode 100644 test/snapshots/cooccurrence.txt
 create mode 100644 test/snapshots/correlates.json
 create mode 100644 test/snapshots/correlates.sql
 create mode 100644 test/snapshots/correlates.svg
 create mode 100644 test/snapshots/correlates.txt
 create mode 100644 test/snapshots/course.json
 create mode 100644 test/snapshots/course.sql
 create mode 100644 test/snapshots/course.svg
 create mode 100644 test/snapshots/course.txt
 create mode 100644 test/snapshots/cox.json
 create mode 100644 test/snapshots/cox.sql
 create mode 100644 test/snapshots/cox.svg
 create mode 100644 test/snapshots/cox.txt
 create mode 100644 test/snapshots/diffexpr.json
 create mode 100644 test/snapshots/diffexpr.sql
 create mode 100644 test/snapshots/diffexpr.svg
 create mode 100644 test/snapshots/diffexpr.txt
 create mode 100644 test/snapshots/enrichment.json
 create mode 100644 test/snapshots/enrichment.sql
 create mode 100644 test/snapshots/enrichment.svg
 create mode 100644 test/snapshots/enrichment.txt
 create mode 100644 test/snapshots/genome.json
 create mode 100644 test/snapshots/genome.sql
 create mode 100644 test/snapshots/genome.svg
 create mode 100644 test/snapshots/genome.txt
 create mode 100644 test/snapshots/hotspots.json
 create mode 100644 test/snapshots/hotspots.sql
 create mode 100644 test/snapshots/hotspots.svg
 create mode 100644 test/snapshots/hotspots.txt
 create mode 100644 test/snapshots/incidence.json
 create mode 100644 test/snapshots/incidence.sql
 create mode 100644 test/snapshots/incidence.svg
 create mode 100644 test/snapshots/incidence.txt
 create mode 100644 test/snapshots/labs.json
 create mode 100644 test/snapshots/labs.sql
 create mode 100644 test/snapshots/labs.svg
 create mode 100644 test/snapshots/labs.txt
 create mode 100644 test/snapshots/logistic.json
 create mode 100644 test/snapshots/logistic.sql
 create mode 100644 test/snapshots/logistic.svg
 create mode 100644 test/snapshots/logistic.txt
 create mode 100644 test/snapshots/mutations.json
 create mode 100644 test/snapshots/mutations.sql
 create mode 100644 test/snapshots/mutations.svg
 create mode 100644 test/snapshots/mutations.txt
 create mode 100644 test/snapshots/overlap.json
 create mode 100644 test/snapshots/overlap.sql
 create mode 100644 test/snapshots/overlap.svg
 create mode 100644 test/snapshots/overlap.txt
 create mode 100644 test/snapshots/paired.json
 create mode 100644 test/snapshots/paired.sql
 create mode 100644 test/snapshots/paired.svg
 create mode 100644 test/snapshots/paired.txt
 create mode 100644 test/snapshots/pathways.json
 create mode 100644 test/snapshots/pathways.sql
 create mode 100644 test/snapshots/pathways.svg
 create mode 100644 test/snapshots/pathways.txt
 create mode 100644 test/snapshots/plot.json
 create mode 100644 test/snapshots/plot.sql
 create mode 100644 test/snapshots/plot.svg
 create mode 100644 test/snapshots/plot.txt
 create mode 100644 test/snapshots/pooled-paired.json
 create mode 100644 test/snapshots/pooled-paired.sql
 create mode 100644 test/snapshots/pooled-paired.svg
 create mode 100644 test/snapshots/pooled-paired.txt
 create mode 100644 test/snapshots/riskmodel.json
 create mode 100644 test/snapshots/riskmodel.sql
 create mode 100644 test/snapshots/riskmodel.svg
 create mode 100644 test/snapshots/riskmodel.txt
 create mode 100644 test/snapshots/sequence.json
 create mode 100644 test/snapshots/sequence.sql
 create mode 100644 test/snapshots/sequence.svg
 create mode 100644 test/snapshots/sequence.txt
 create mode 100644 test/snapshots/survival.json
 create mode 100644 test/snapshots/survival.sql
 create mode 100644 test/snapshots/survival.svg
 create mode 100644 test/snapshots/survival.txt
 create mode 100644 test/snapshots/table.json
 create mode 100644 test/snapshots/table.sql
 create mode 100644 test/snapshots/table.svg
 create mode 100644 test/snapshots/table.txt
 create mode 100644 test/snapshots/timeline.json
 create mode 100644 test/snapshots/timeline.sql
 create mode 100644 test/snapshots/timeline.svg
 create mode 100644 test/snapshots/timeline.txt
 create mode 100644 test/snapshots/two-studies-four-views.json
 create mode 100644 test/snapshots/two-studies-four-views.sql
 create mode 100644 test/snapshots/two-studies-four-views.svg
 create mode 100644 test/snapshots/two-studies-four-views.txt
 create mode 100644 test/snapshots/two-studies-plot.json
 create mode 100644 test/snapshots/two-studies-plot.sql
 create mode 100644 test/snapshots/two-studies-plot.svg
 create mode 100644 test/snapshots/two-studies-plot.txt
 create mode 100644 test/snapshots/two-studies-survival.json
 create mode 100644 test/snapshots/two-studies-survival.sql
 create mode 100644 test/snapshots/two-studies-survival.svg
 create mode 100644 test/snapshots/two-studies-survival.txt
 create mode 100644 test/view-snapshots.test.ts

diff --git a/CLAUDE.md b/CLAUDE.md
index c25147b2..e5d4a2ad 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -695,6 +695,16 @@ they can't tell you:
   `check:splitby-perf`) are stages of `scripts/gate.sh` of their own, and the boundary suite is in
   `npm test`.
   `npm run report:coverage` — the unit suites' coverage (CI shows it on the reports job).
+  **Every view kind's output is pinned** by `test/view-snapshots.test.ts` (in `npm test`, no
+  database): one gallery example per kind runs through the whole engine against warehouse answers
+  recorded in `test/snapshots/answers/`, and its compiled SQL and queries, computed result, figure
+  and results text are compared with `test/snapshots/<kind>.{sql,json,svg,txt}`. A diff there is a
+  change to review, not noise: `UPDATE_SNAPSHOTS=1` accepts output changes, and a changed query
+  needs `RECORD_SNAPSHOTS=1` with `.env.local` (public warehouse only; the answers hold public
+  studies' patient-level rows). A new view kind adds its case there. Each case also runs with its
+  answers delivered in reverse, and must not change: a runner combines what it asked for after
+  every answer is in, in the view's order (`askEachSite` in `lib/views/shared.ts`), never inside
+  the callback as each one lands.
   Secrets come from `.env.local` at runtime; never hardcode them anywhere, including tests.
 - Lint: `npm run lint` (~2 min, the gate's first stage) — see "Code standards" above for what it
   holds code to. While iterating, `npx eslint <files you touched>` is enough.
diff --git a/lib/ch.ts b/lib/ch.ts
index c64b03a0..12b7f00b 100644
--- a/lib/ch.ts
+++ b/lib/ch.ts
@@ -226,10 +226,19 @@ export function directConnections(): ChConn[] {
   return connections().filter((c) => c.id !== peer?.id);
 }
 
+/** One statement as the database would receive it: its text, and the tables that ride along with it. */
+export interface ChStatement { conn: ChConn; format: string; query: string; externals: { name: string; structure: string; blob: Blob }[] }
+/**
+ * Answers statements instead of the database, or watches them on their way to it (`send`, which
+ * takes extra ClickHouse settings for that statement, e.g. "max_threads=1"). Only
+ * test/view-snapshots.test.ts sets one: it records the warehouse's answers, then replays them.
+ */
+export type ChTransport = (st: ChStatement, send: (settings?: string) => Promise<string>) => Promise<string>;
+let transport: ChTransport | null = null;
+export function setChTransport(t: ChTransport | null): void { transport = t; }
+
 async function post(sql: string, conn: ChConn, format: string): Promise<string> {
   assertNotLoopbackDirect(sql, conn);
-  if (!conn.url || !conn.user || !conn.password || !conn.db)
-    throw new Error("ClickHouse instance '" + conn.id + "' is missing configuration (url/password/db)");
   const ctx = ctxStore.getStore();
   const query = sql + "\nFORMAT " + format;
   // uploaded custom data and stored id sets ride along only with a statement that names them
@@ -237,10 +246,17 @@ async function post(sql: string, conn: ChConn, format: string): Promise<string>
   const externals = [...externalsFor(query),
     ...[...customExternalsFor(query, conn.id), ...idSetExternalsFor(query, conn.id)]
       .map((e) => ({ name: e.name, structure: e.structure, blob: new Blob([e.data]) }))];
+  if (transport) return transport({ conn, format, query, externals }, (settings) => postToDatabase(conn, query, externals, ctx, settings));
+  return postToDatabase(conn, query, externals, ctx);
+}
+
+async function postToDatabase(conn: ChConn, query: string, externals: ChStatement["externals"], ctx: ChCtx | undefined, settings?: string): Promise<string> {
+  if (!conn.url || !conn.user || !conn.password || !conn.db)
+    throw new Error("ClickHouse instance '" + conn.id + "' is missing configuration (url/password/db)");
   // Tell ClickHouse to stop a query whose client has gone away. Without it an abandoned
   // request keeps its share of the database until it finishes, which is most of what an
   // abandoned request costs.
-  const url = conn.url.replace(/\/+$/, "") + "/?cancel_http_readonly_queries_on_client_close=1";
+  const url = conn.url.replace(/\/+$/, "") + "/?cancel_http_readonly_queries_on_client_close=1" + (settings ? "&" + settings : "");
   const send = async (): Promise<string> => {
     const auth = "Basic " + Buffer.from(conn.user + ":" + conn.password).toString("base64");
     let init: RequestInit;
diff --git a/lib/views/assays.ts b/lib/views/assays.ts
index d38ec0ce..d6cbc016 100644
--- a/lib/views/assays.ts
+++ b/lib/views/assays.ts
@@ -4,7 +4,7 @@ import { assayProfileSql, assayMomentsSql, assayValueCountsSql, assayEntityNames
 import { chiSquareTest, benjaminiHochberg, welchT, welchAnovaTest, varianceFromMoments } from "../stats.ts";
 import { assaysMethod } from "../method.ts";
 import type { ViewRun } from "./run.ts";
-import { overLimit, answering, refuse, niceCeil, PROFILE_NAME } from "./shared.ts";
+import { overLimit, answering, refuse, niceCeil, PROFILE_NAME, askEachSite } from "./shared.ts";
 
 const ASSAY_ROWS = 300;
 /** past this many entities a profile is refused rather than compared: the per-cohort answer
@@ -54,35 +54,35 @@ export async function runAssays(view: AssaysView, ctx: ViewRun): Promise<ViewRes
   const names = new Map<string, string>();
   let missing = 0;
   const perSite: { cohort: string; study: string; rows: number }[] = [];
-  await Promise.all([
-    ...holders.map(async (st) => {
-      for (const r of await ok[st].viewMeta("assays:names", (s, d) => assayEntityNamesSql(s, d, pt))) if (!names.has(String(r.entity))) names.set(String(r.entity), String(r.name));
-    }),
-    ...vNames.flatMap((n) => cohorts[n].studies.filter((st) => holders.includes(st)).map(async (st) => {
-      const def = doc.cohorts[n];
-      if (isCat) {
-        const rows = await ok[st].viewAggregate("assays:values:" + n, def, (c, s, d) => assayValueCountsSql(c, s, d, pt));
-        for (const r of rows) {
-          const v = String(r.value ?? "").trim(), c = Number(r.n);
-          if (MISSING_VALUES.has(v)) { missing += c; continue; }
-          const k = n + "\t" + r.entity;
-          entities.add(String(r.entity));
-          const m = counts.get(k) || new Map<string, number>();
-          m.set(v, (m.get(v) || 0) + c); counts.set(k, m);
-        }
-        perSite.push({ cohort: n, study: st, rows: rows.length });
-      } else {
-        const rows = await ok[st].viewAggregate("assays:moments:" + n, def, (c, s, d) => assayMomentsSql(c, s, d, pt));
-        for (const r of rows) {
-          const k = n + "\t" + r.entity, cur = mom.get(k) || { n: 0, sx: 0, sxx: 0 };
-          cur.n += Number(r.n); cur.sx += Number(r.sx); cur.sxx += Number(r.sxx); mom.set(k, cur);
-          missing += Number(r.other);
-          entities.add(String(r.entity));
-        }
-        perSite.push({ cohort: n, study: st, rows: rows.length });
-      }
-    }))
+  const holding = Object.fromEntries(holders.map((st) => [st, ok[st]]));
+  const [nameRows, answers] = await Promise.all([
+    Promise.all(holders.map((st) => ok[st].viewMeta("assays:names", (s, d) => assayEntityNamesSql(s, d, pt)))),
+    askEachSite(ctx, holding, (n, _st, r) => isCat
+      ? r.viewAggregate("assays:values:" + n, doc.cohorts[n], (c, s, d) => assayValueCountsSql(c, s, d, pt))
+      : r.viewAggregate("assays:moments:" + n, doc.cohorts[n], (c, s, d) => assayMomentsSql(c, s, d, pt)))
   ]);
+  // the first study (in the view's order) to name an entity names it
+  for (const rows of nameRows) for (const r of rows) if (!names.has(String(r.entity))) names.set(String(r.entity), String(r.name));
+  for (const { cohort: n, study: st, answer: rows } of answers) {
+    if (isCat) {
+      for (const r of rows) {
+        const v = String(r.value ?? "").trim(), c = Number(r.n);
+        if (MISSING_VALUES.has(v)) { missing += c; continue; }
+        const k = n + "\t" + r.entity;
+        entities.add(String(r.entity));
+        const m = counts.get(k) || new Map<string, number>();
+        m.set(v, (m.get(v) || 0) + c); counts.set(k, m);
+      }
+    } else {
+      for (const r of rows) {
+        const k = n + "\t" + r.entity, cur = mom.get(k) || { n: 0, sx: 0, sxx: 0 };
+        cur.n += Number(r.n); cur.sx += Number(r.sx); cur.sxx += Number(r.sxx); mom.set(k, cur);
+        missing += Number(r.other);
+        entities.add(String(r.entity));
+      }
+    }
+    perSite.push({ cohort: n, study: st, rows: rows.length });
+  }
   if (!entities.size) return refuse("assays", ctx, "none of these cohorts' " + unit + " has a value in “" + pt + "”");
   if (entities.size > ASSAY_MAX_ENTITIES) return refuse("assays", ctx, "“" + pt + "” has " + entities.size.toLocaleString("en-US") + " entities with values here; an assays view compares at most " + ASSAY_MAX_ENTITIES.toLocaleString("en-US"));
 
diff --git a/lib/views/diffexpr.ts b/lib/views/diffexpr.ts
index 94b2271d..67f92b08 100644
--- a/lib/views/diffexpr.ts
+++ b/lib/views/diffexpr.ts
@@ -4,7 +4,7 @@ import { profileMomentsAllSql, genesetMembersSql } from "../federation-views.ts"
 import { benjaminiHochberg, varianceFromMoments, welchT, preRankedGsea } from "../stats.ts";
 import { diffexprMethod } from "../method.ts";
 import type { ViewRun } from "./run.ts";
-import { overLimit, answering, refuse, niceCeil } from "./shared.ts";
+import { overLimit, answering, refuse, niceCeil, askEachSite } from "./shared.ts";
 
 const DE_MIN_N = 3, DE_POINT_CAP = 1500, DE_PERMUTATIONS = 1000;
 const SET_NAME = /^[A-Z0-9_]{3,120}$/;
@@ -51,15 +51,15 @@ export async function runDiffexpr(view: DiffexprView, ctx: ViewRun): Promise<Vie
       " profile — available: " + ([...offered].sort().join(", ") || "none"));
 
   const moments = new Map<string, { n: number; sx: number; sxx: number }>(); // cohort|gene
-  await Promise.all(vNames.flatMap((n) => cohorts[n].studies.filter((st) => ok[st]).map(async (st) => {
-    const rows = await ok[st].viewAggregate("diffexpr:moments:" + n, doc.cohorts[n],
-      (c, s, d) => profileMomentsAllSql(c, s, d, profileType, log2));
+  const answers = await askEachSite(ctx, ok, (n, _st, r) => r.viewAggregate("diffexpr:moments:" + n, doc.cohorts[n],
+    (c, s, d) => profileMomentsAllSql(c, s, d, profileType, log2)));
+  for (const { cohort: n, answer: rows } of answers) {
     for (const r of rows) {
       const k = n + "|" + String(r.gene), cur = moments.get(k) || { n: 0, sx: 0, sxx: 0 };
       cur.n += Number(r.n); cur.sx += Number(r.sx); cur.sxx += Number(r.sxx);
       moments.set(k, cur);
     }
-  })));
+  }
   const [A, B] = vNames;
   const genes = [...new Set([...moments.keys()].map((k) => k.slice(k.indexOf("|") + 1)))];
   const tested: DiffexprGene[] = [];
diff --git a/lib/views/enrichment.ts b/lib/views/enrichment.ts
index df3349b5..3bfa6f81 100644
--- a/lib/views/enrichment.ts
+++ b/lib/views/enrichment.ts
@@ -4,7 +4,7 @@ import { enrichAlteredSql, enrichPanelProfiledSql, enrichWesSql, type EnrichAlte
 import { fisherExact, chiSquareTest, benjaminiHochberg } from "../stats.ts";
 import { enrichmentMethod } from "../method.ts";
 import type { ViewRun } from "./run.ts";
-import { overLimit, cytobands, answering, refuse, niceCeil } from "./shared.ts";
+import { overLimit, cytobands, answering, refuse, niceCeil, askEachSite } from "./shared.ts";
 
 const ENRICH_ROWS = 300;
 const ALTERATION_WORD: Record<EnrichAlteration, string> = {
@@ -32,19 +32,18 @@ export async function runEnrichment(view: EnrichmentView, ctx: ViewRun): Promise
   const altered = new Map<string, number>(), panel = new Map<string, number>(), wes = new Map<string, number>();
   const genes = new Set<string>();
   const perSite: { cohort: string; study: string; genes: number; wes: number }[] = [];
-  await Promise.all(vNames.flatMap((n) => cohorts[n].studies.filter((st) => ok[st]).map(async (st) => {
-    const def = doc.cohorts[n];
-    const [a, pr, w] = await Promise.all([
-      ok[st].viewAggregate("enrichment:altered:" + n, def, (c, s, d) => enrichAlteredSql(c, s, d, alteration, pin, measure === "driver")),
-      ok[st].viewAggregate("enrichment:panels:" + n, def, (c, s, d) => enrichPanelProfiledSql(c, s, d, alteration)),
-      ok[st].viewAggregate("enrichment:wes:" + n, def, (c, s, d) => enrichWesSql(c, s, d, alteration))
-    ]);
+  const answers = await askEachSite(ctx, ok, (n, st, r) => Promise.all([
+    r.viewAggregate("enrichment:altered:" + n, doc.cohorts[n], (c, s, d) => enrichAlteredSql(c, s, d, alteration, pin, measure === "driver")),
+    r.viewAggregate("enrichment:panels:" + n, doc.cohorts[n], (c, s, d) => enrichPanelProfiledSql(c, s, d, alteration)),
+    r.viewAggregate("enrichment:wes:" + n, doc.cohorts[n], (c, s, d) => enrichWesSql(c, s, d, alteration))
+  ]));
+  for (const { cohort: n, study: st, answer: [a, pr, w] } of answers) {
     for (const r of a) { const k = n + "\t" + r.gene; altered.set(k, (altered.get(k) || 0) + Number(r.altered)); genes.add(String(r.gene)); }
     for (const r of pr) { const k = n + "\t" + r.gene; panel.set(k, (panel.get(k) || 0) + Number(r.profiled)); }
     const wn = Number(w[0]?.n || 0);
     wes.set(n, (wes.get(n) || 0) + wn);
     perSite.push({ cohort: n, study: st, genes: a.length, wes: wn });
-  })));
+  }
   const two = vNames.length === 2;
   const tested: EnrichmentGene[] = [];
   let untested = 0;
diff --git a/lib/views/shared.ts b/lib/views/shared.ts
index 0aba84f4..715b4d4e 100644
--- a/lib/views/shared.ts
+++ b/lib/views/shared.ts
@@ -67,3 +67,16 @@ export async function cytobands(ok: Record<string, ViewResolver>, genes: string[
 export async function studyCatalogs(c: ViewRun, studies: string[]): Promise<StudyCatalog[]> {
   return Promise.all(studies.map(async (st) => ({ study: st, catalog: (await c.studyProfiles(st)).catalog || [] })));
 }
+
+/**
+ * Asks every (cohort, study) the view draws that `resolvers` holds, concurrently, and returns the
+ * answers in the view's order (its cohorts, then each cohort's studies), never in the order they
+ * finish. Combine them after this returns: combining inside `ask` as each answer lands makes the
+ * result depend on which query finished first (test/view-snapshots.test.ts checks for that).
+ */
+export async function askEachSite<R, T>(ctx: ViewRun, resolvers: Record<string, R>,
+  ask: (cohort: string, study: string, r: R) => Promise<T>): Promise<{ cohort: string; study: string; answer: T }[]> {
+  const sites = ctx.vNames.flatMap((cohort) => ctx.cohorts[cohort].studies.filter((st) => resolvers[st]).map((study) => ({ cohort, study })));
+  const answers = await Promise.all(sites.map((s) => ask(s.cohort, s.study, resolvers[s.study])));
+  return sites.map((s, i) => ({ ...s, answer: answers[i] }));
+}
diff --git a/lib/views/survival.ts b/lib/views/survival.ts
index 72e81f8e..73e9229c 100644
--- a/lib/views/survival.ts
+++ b/lib/views/survival.ts
@@ -5,6 +5,7 @@ import type { ViewResult, LogRank, LogRankK, SurvivalView } from "../types.ts";
 import { expandSurvivalCounts, type SurvivalCount, type EndpointColumns } from "../federation.ts";
 import { survivalMethod } from "../method.ts";
 import { untimed, type Untimed, type ViewRun, type DocView } from "./run.ts";
+import { askEachSite } from "./shared.ts";
 
 export async function runSurvival(view: DocView, c: ViewRun): Promise<ViewResult | Untimed> {
   const { doc, cohorts, resolvers, vNames, vStudies, participationFor } = c;
@@ -60,10 +61,8 @@ export async function runSurvival(view: DocView, c: ViewRun): Promise<ViewResult
     : null;
   const perSite = new Map<string, Map<string, SurvivalCount[]>>(); // cohort -> study -> triples
   for (const n of vNames) perSite.set(n, new Map());
-  await Promise.all(vNames.flatMap((n) =>
-    cohorts[n].studies.map(async (st) => {
-      perSite.get(n)!.set(st, await resolvers[st].survivalCounts(doc.cohorts[n], endpoint, anchor, columns));
-    })));
+  for (const { cohort: n, study: st, answer } of await askEachSite(c, resolvers, (n, _st, r) => r.survivalCounts(doc.cohorts[n], endpoint, anchor, columns)))
+    perSite.get(n)!.set(st, answer);
   // An EMPTY COHORT also yields no rows, and must not be reported as a
   // missing endpoint — that sends the reader (or a model) off to conclude the
   // study lacks OS when it does not. Separate the two causes before blaming
diff --git a/test/snapshots/alterations.json b/test/snapshots/alterations.json
new file mode 100644
index 00000000..17a1c467
--- /dev/null
+++ b/test/snapshots/alterations.json
@@ -0,0 +1,1003 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "Prostate": {
+      "count": 3211,
+      "text": "CANCER_TYPE in [Prostate Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "alterations",
+      "cohorts": [
+        "Prostate"
+      ],
+      "alterations": {
+        "columns": [
+          {
+            "id": "patient",
+            "name": "Patient ID",
+            "numeric": false
+          },
+          {
+            "id": "source",
+            "name": "Source",
+            "numeric": false
+          },
+          {
+            "id": "samples",
+            "name": "Samples",
+            "numeric": true
+          },
+          {
+            "id": "mutations",
+            "name": "Mutations",
+            "numeric": true
+          },
+          {
+            "id": "cna",
+            "name": "CNAs",
+            "numeric": true
+          },
+          {
+            "id": "sv",
+            "name": "Structural Variants",
+            "numeric": true
+          }
+        ],
+        "shown": [
+          "patient",
+          "source",
+          "samples",
+          "mutations",
+          "cna",
+          "sv"
+        ],
+        "defaults": [
+          "patient",
+          "source",
+          "samples",
+          "mutations",
+          "cna",
+          "sv"
+        ],
+        "query": {
+          "sort": {
+            "column": "sv",
+            "desc": true
+          }
+        },
+        "rows": [
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0030872",
+            "sample": "",
+            "values": [
+              "P-0030872",
+              "this portal (primary)",
+              1,
+              4,
+              0,
+              5
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0000631",
+            "sample": "",
+            "values": [
+              "P-0000631",
+              "this portal (primary)",
+              1,
+              3,
+              4,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0013729",
+            "sample": "",
+            "values": [
+              "P-0013729",
+              "this portal (primary)",
+              1,
+              0,
+              7,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0017262",
+            "sample": "",
+            "values": [
+              "P-0017262",
+              "this portal (primary)",
+              1,
+              2,
+              1,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0020078",
+            "sample": "",
+            "values": [
+              "P-0020078",
+              "this portal (primary)",
+              1,
+              5,
+              7,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0025988",
+            "sample": "",
+            "values": [
+              "P-0025988",
+              "this portal (primary)",
+              1,
+              5,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0030109",
+            "sample": "",
+            "values": [
+              "P-0030109",
+              "this portal (primary)",
+              1,
+              2,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0037022",
+            "sample": "",
+            "values": [
+              "P-0037022",
+              "this portal (primary)",
+              1,
+              3,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0038349",
+            "sample": "",
+            "values": [
+              "P-0038349",
+              "this portal (primary)",
+              1,
+              3,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0043336",
+            "sample": "",
+            "values": [
+              "P-0043336",
+              "this portal (primary)",
+              1,
+              26,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0046943",
+            "sample": "",
+            "values": [
+              "P-0046943",
+              "this portal (primary)",
+              1,
+              2,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0049524",
+            "sample": "",
+            "values": [
+              "P-0049524",
+              "this portal (primary)",
+              1,
+              2,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0069200",
+            "sample": "",
+            "values": [
+              "P-0069200",
+              "this portal (primary)",
+              1,
+              9,
+              0,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0081157",
+            "sample": "",
+            "values": [
+              "P-0081157",
+              "this portal (primary)",
+              1,
+              3,
+              4,
+              4
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0003511",
+            "sample": "",
+            "values": [
+              "P-0003511",
+              "this portal (primary)",
+              1,
+              3,
+              4,
+              3
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0004946",
+            "sample": "",
+            "values": [
+              "P-0004946",
+              "this portal (primary)",
+              2,
+              7,
+              0,
+              3
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0006689",
+            "sample": "",
+            "values": [
+              "P-0006689",
+              "this portal (primary)",
+              1,
+              0,
+              0,
+              3
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0008960",
+            "sample": "",
+            "values": [
+              "P-0008960",
+              "this portal (primary)",
+              1,
+              0,
+              3,
+              3
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0012605",
+            "sample": "",
+            "values": [
+              "P-0012605",
+              "this portal (primary)",
+              1,
+              2,
+              0,
+              3
+            ]
+          },
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "patient": "P-0017882",
+            "sample": "",
+            "values": [
+              "P-0017882",
+              "this portal (primary)",
+              1,
+              3,
+              9,
+              3
+            ]
+          },
+          {
+            "…": "5 more",
+            "sha256": "5a1562f61b57960e"
+          }
+        ],
+        "offset": 0,
+        "pageSize": 25,
+        "lists": [
+          {
+            "cohort": "Prostate",
+            "study": "msk_chord_2024",
+            "total": 3211,
+            "all": 3211
+          }
+        ],
+        "total": 3211,
+        "all": 3211,
+        "patient": {
+          "study": "msk_chord_2024",
+          "id": "P-0024249",
+          "samples": [
+            {
+              "id": "P-0024249-T01-IM6",
+              "type": "Primary"
+            },
+            {
+              "id": "P-0024249-T02-IM7",
+              "type": "Primary"
+            }
+          ]
+        },
+        "position": {
+          "index": 21,
+          "prev": {
+            "study": "msk_chord_2024",
+            "id": "P-0019663"
+          },
+          "next": {
+            "study": "msk_chord_2024",
+            "id": "P-0024679"
+          }
+        },
+        "tables": {
+          "mutations": {
+            "columns": [
+              {
+                "id": "samples",
+                "name": "Samples",
+                "numeric": false
+              },
+              {
+                "id": "gene",
+                "name": "Gene",
+                "numeric": false
+              },
+              {
+                "id": "proteinChange",
+                "name": "Protein Change",
+                "numeric": false
+              },
+              {
+                "id": "annotation",
+                "name": "Annotation (OncoKB)",
+                "numeric": false
+              },
+              {
+                "id": "mutationType",
+                "name": "Mutation Type",
+                "numeric": false
+              },
+              {
+                "id": "copy",
+                "name": "Copy #",
+                "numeric": false
+              },
+              {
+                "id": "freq",
+                "name": "Cohort %",
+                "numeric": true
+              },
+              {
+                "id": "vaf",
+                "name": "Allele Freq",
+                "numeric": true
+              },
+              {
+                "id": "altCount",
+                "name": "Variant Reads",
+                "numeric": true
+              },
+              {
+                "id": "refCount",
+                "name": "Ref Reads",
+                "numeric": true
+              },
+              {
+                "id": "variantType",
+                "name": "Variant Type",
+                "numeric": false
+              },
+              {
+                "id": "chr",
+                "name": "Chromosome",
+                "numeric": false
+              },
+              {
+                "id": "start",
+                "name": "Start Pos",
+                "numeric": true
+              },
+              {
+                "id": "end",
+                "name": "End Pos",
+                "numeric": true
+              },
+              {
+                "id": "ref",
+                "name": "Ref",
+                "numeric": false
+              },
+              {
+                "id": "var",
+                "name": "Var",
+                "numeric": false
+              },
+              {
+                "id": "status",
+                "name": "MS",
+                "numeric": false
+              }
+            ],
+            "shown": [
+              "samples",
+              "gene",
+              "proteinChange",
+              "annotation",
+              "mutationType",
+              "copy",
+              "freq",
+              "vaf"
+            ],
+            "defaults": [
+              "samples",
+              "gene",
+              "proteinChange",
+              "annotation",
+              "mutationType",
+              "copy",
+              "freq",
+              "vaf"
+            ],
+            "query": {
+              "filters": {
+                "annotation": {
+                  "op": "contains",
+                  "value": "Oncogenic"
+                }
+              }
+            },
+            "rows": [
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T02-IM7",
+                "values": [
+                  "P-0024249-T02-IM7",
+                  "KRAS",
+                  "G12V",
+                  "Oncogenic · Level 1",
+                  "Missense_Mutation",
+                  "",
+                  28.5,
+                  0.076,
+                  42,
+                  512,
+                  "SNP",
+                  "12",
+                  25398284,
+                  25398284,
+                  "C",
+                  "A",
+                  "SOMATIC"
+                ]
+              },
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "PTEN",
+                  "G127V",
+                  "Likely Oncogenic · Level 1",
+                  "Missense_Mutation",
+                  "",
+                  4.8,
+                  0.619,
+                  262,
+                  161,
+                  "SNP",
+                  "10",
+                  89692896,
+                  89692896,
+                  "G",
+                  "T",
+                  "SOMATIC"
+                ]
+              },
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "TP53",
+                  "P151R",
+                  "Likely Oncogenic",
+                  "Missense_Mutation",
+                  "",
+                  52.4,
+                  0.761,
+                  761,
+                  239,
+                  "SNP",
+                  "17",
+                  7578478,
+                  7578478,
+                  "G",
+                  "C",
+                  "SOMATIC"
+                ]
+              }
+            ],
+            "offset": 0,
+            "pageSize": 25,
+            "lists": [
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "total": 3,
+                "all": 7
+              }
+            ],
+            "total": 3,
+            "all": 7
+          },
+          "cna": {
+            "columns": [
+              {
+                "id": "samples",
+                "name": "Samples",
+                "numeric": false
+              },
+              {
+                "id": "gene",
+                "name": "Gene",
+                "numeric": false
+              },
+              {
+                "id": "cna",
+                "name": "CNA",
+                "numeric": false
+              },
+              {
+                "id": "annotation",
+                "name": "Annotation (OncoKB)",
+                "numeric": false
+              },
+              {
+                "id": "cytoband",
+                "name": "Cytoband",
+                "numeric": false
+              },
+              {
+                "id": "freq",
+                "name": "Cohort %",
+                "numeric": true
+              }
+            ],
+            "shown": [
+              "samples",
+              "gene",
+              "cna",
+              "annotation",
+              "cytoband",
+              "freq"
+            ],
+            "defaults": [
+              "samples",
+              "gene",
+              "cna",
+              "annotation",
+              "cytoband",
+              "freq"
+            ],
+            "query": {},
+            "rows": [
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "MAP3K1",
+                  "HOMDEL",
+                  "Likely Oncogenic",
+                  "5q11.2",
+                  0.3
+                ]
+              },
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "RAD51B",
+                  "HOMDEL",
+                  "Likely Oncogenic · Level 1",
+                  "14q24.1",
+                  0.1
+                ]
+              },
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "PLK2",
+                  "HOMDEL",
+                  "Unknown",
+                  "5q11.2",
+                  0.2
+                ]
+              }
+            ],
+            "offset": 0,
+            "pageSize": 25,
+            "lists": [
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "total": 3,
+                "all": 3
+              }
+            ],
+            "total": 3,
+            "all": 3
+          },
+          "sv": {
+            "columns": [
+              {
+                "id": "samples",
+                "name": "Samples",
+                "numeric": false
+              },
+              {
+                "id": "gene1",
+                "name": "Gene 1",
+                "numeric": false
+              },
+              {
+                "id": "gene2",
+                "name": "Gene 2",
+                "numeric": false
+              },
+              {
+                "id": "class",
+                "name": "Variant Class",
+                "numeric": false
+              },
+              {
+                "id": "eventInfo",
+                "name": "Event Info",
+                "numeric": false
+              },
+              {
+                "id": "breakpointType",
+                "name": "Breakpoint Type",
+                "numeric": false
+              },
+              {
+                "id": "connectionType",
+                "name": "Connection Type",
+                "numeric": false
+              },
+              {
+                "id": "frame",
+                "name": "Effect on Frame",
+                "numeric": false
+              },
+              {
+                "id": "site1",
+                "name": "Site 1 Description",
+                "numeric": false
+              },
+              {
+                "id": "site2",
+                "name": "Site 2 Description",
+                "numeric": false
+              },
+              {
+                "id": "pos1",
+                "name": "Site 1 Position",
+                "numeric": false
+              },
+              {
+                "id": "pos2",
+                "name": "Site 2 Position",
+                "numeric": false
+              },
+              {
+                "id": "length",
+                "name": "Length",
+                "numeric": true
+              },
+              {
+                "id": "tumorVariantCount",
+                "name": "Tumor Variant Count",
+                "numeric": true
+              },
+              {
+                "id": "tumorReadCount",
+                "name": "Tumor Read Count",
+                "numeric": true
+              },
+              {
+                "id": "dnaSupport",
+                "name": "DNA Support",
+                "numeric": false
+              },
+              {
+                "id": "rnaSupport",
+                "name": "RNA Support",
+                "numeric": false
+              },
+              {
+                "id": "status",
+                "name": "Status",
+                "numeric": false
+              },
+              {
+                "id": "svAnnotation",
+                "name": "Study Annotation",
+                "numeric": false
+              },
+              {
+                "id": "comments",
+                "name": "Comments",
+                "numeric": false
+              }
+            ],
+            "shown": [
+              "samples",
+              "gene1",
+              "gene2",
+              "class",
+              "eventInfo",
+              "breakpointType",
+              "connectionType"
+            ],
+            "defaults": [
+              "samples",
+              "gene1",
+              "gene2",
+              "class",
+              "eventInfo",
+              "breakpointType",
+              "connectionType"
+            ],
+            "query": {},
+            "rows": [
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "IRS2",
+                  "CHMP1A",
+                  "TRANSLOCATION",
+                  "Protein Fusion: mid-exon  {IRS2:CHMP1A}",
+                  "IMPPRECISE",
+                  "3to5",
+                  "NA",
+                  "Intron of CHMP1A(-):1Kb before exon 2",
+                  "Exon 1 of IRS2(-)",
+                  "16:89721445",
+                  "13:110437384",
+                  0,
+                  10,
+                  0,
+                  "NA",
+                  "NA",
+                  "SOMATIC",
+                  "IRS2 (NM_003749) - CHMP1A (NM_002768) rearrangement: t(13;16)(q34;q24.3)(chr13:g.110437384::chr16:g.89721445)",
+                  "NA"
+                ]
+              },
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "TCF7L2",
+                  "",
+                  "DELETION",
+                  "TCF7L2-intragenic",
+                  "PRECISE",
+                  "3to5",
+                  "NA",
+                  "Intron of TCF7L2(+):23bp after exon 12",
+                  "IGR: 296Kb before HABP2(+)",
+                  "10:114917851",
+                  "10:115014274",
+                  96423,
+                  23,
+                  4714,
+                  "NA",
+                  "NA",
+                  "SOMATIC",
+                  "TCF7L2 (NM_001146274) rearrangement: c.1318+23_chr10:g.115014274del",
+                  "NA"
+                ]
+              },
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "patient": "P-0024249",
+                "sample": "P-0024249-T01-IM6",
+                "values": [
+                  "P-0024249-T01-IM6",
+                  "TMPRSS2",
+                  "ERG",
+                  "DELETION",
+                  "Protein Fusion: out of frame  {TMPRSS2:ERG}",
+                  "IMPPRECISE",
+                  "3to5",
+                  "NA",
+                  "Intron of ERG(-):57Kb after exon 3",
+                  "Intron of TMPRSS2(-):608bp before exon 2",
+                  "21:39890681",
+                  "21:42870724",
+                  2980043,
+                  105,
+                  41084,
+                  "NA",
+                  "NA",
+                  "SOMATIC",
+                  "TMPRSS2 (NM_001135099) - ERG (NM_004449) fusion ( TMPRSS2 exon 1 fused to ERG exons 4 -11): c.56-608:TMPRSS2_c.39+56905:ERGdel",
+                  "NA"
+                ]
+              }
+            ],
+            "offset": 0,
+            "pageSize": 25,
+            "lists": [
+              {
+                "cohort": "Prostate",
+                "study": "msk_chord_2024",
+                "total": 3,
+                "all": 3
+              }
+            ],
+            "total": 3,
+            "all": 3
+          }
+        },
+        "oncokbPin": "v7.5"
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": true,
+        "aggregateOnly": []
+      },
+      "caveat": "P-0024249 — Mutations: 3 of 7 rows (rows Annotation (OncoKB) contains “Oncogenic”); Copy Number Alterations: 3 rows; Structural Variants: 3 rows. Each table lists a variant, a gene's call or an event once however many of the patient's samples carry it (Samples names them); allele frequency and read counts are the highest among those samples. Copy number is the deep calls only (AMP, HOMDEL), as the portal's patient view lists them; Copy # on a mutation is the gene's deep calls anywhere in the patient. Cohort % is the share of the study's samples profiled for the gene by their panel with a mutation in it, or with the same copy-number call. Annotation is OncoKB v7.5 (oncogenicity and its highest sensitivity level); structural variants carry the study's own annotation, not OncoKB's. The patient list: 3,211 patients. Sorted by Structural Variants (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count the patients per cohort and study",
+            "detail": "The alterations:counts query above: every patient of each cohort, with their sample count and how many rows each of the three tables lists for them (a mutation, a gene's deep copy-number call, a structural variant — each counted once however many samples carry it).",
+            "params": [
+              [
+                "Prostate · msk_chord_2024",
+                "3,211"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Sort them together and take one page",
+            "detail": "The alterations:rows query above: the patients of every cohort and study combined in one query, sorted by Structural Variants descending with missing values last, then patient id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset."
+          },
+          {
+            "kind": "fetch",
+            "title": "Read P-0024249's three tables",
+            "detail": "The alterations:mutations, alterations:cna and alterations:sv queries above, each paged, searched, filtered and sorted in the database like any table view. Mutations: every called mutation of the patient's samples (uncalled calls left out), one row per variant with the samples carrying it, the highest allele frequency among them (variant / (variant + reference) reads) with that sample's reads, and the gene's deep copy-number calls in the patient (Copy #). Copy-number alterations: the deep calls (AMP, HOMDEL), one row per gene and call. Structural variants: one row per event (both genes and breakpoints, class, event info), the highest read counts among its samples. Cohort % is the share of the study's samples profiled for the gene (by their gene panel) with any mutation in it, or with the same copy-number call; the annotation is the OncoKB v7.5 call for the protein change, or for AMPLIFICATION / DELETION.",
+            "params": [
+              [
+                "Mutations",
+                "3 of 7 rows (kept: rows Annotation (OncoKB) contains “Oncogenic”)"
+              ],
+              [
+                "Copy Number Alterations",
+                "3 rows"
+              ],
+              [
+                "Structural Variants",
+                "3 rows"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "runAlterations",
+            "alterationPatientRowSource",
+            "patientMutationsSource",
+            "patientCnaSource",
+            "patientSvSource",
+            "pageSql",
+            "countsSql",
+            "rankSql"
+          ],
+          "file": "lib/views/alterations.ts, lib/patient-alterations.ts, lib/table-query.ts",
+          "commit": null
+        },
+        "verification": "A listing: no statistic is computed, so there is no script — the queries are the numbers."
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/alterations.sql b/test/snapshots/alterations.sql
new file mode 100644
index 00000000..9549adeb
--- /dev/null
+++ b/test/snapshots/alterations.sql
@@ -0,0 +1,382 @@
+-- cohort Prostate
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+
+-- view 0 alterations: alterations:cna:counts · msk_chord_2024
+WITH c AS (
+  SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, hugo_gene_symbol AS gene, cna_alteration AS alt, cna_cytoband AS cytoband
+  FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0024249' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+),
+g AS (SELECT gene, alt, any(cytoband) AS cytoband, arrayStringConcat(arraySort(x -> if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid))) AS so FROM c GROUP BY gene, alt),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'CNA' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+fa AS (SELECT hugo_gene_symbol AS gene, cna_alteration AS alt, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alt),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'COPY_NUMBER_ALTERATION' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, if(g.alt > 0, 'AMP', 'HOMDEL') AS cna, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.cytoband AS cytoband, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = if(g.alt > 0, 'AMPLIFICATION', 'DELETION')
+  LEFT JOIN fa ON fa.gene = g.gene AND fa.alt = g.alt LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 alterations: alterations:cna:rows · msk_chord_2024
+WITH c AS (
+  SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, hugo_gene_symbol AS gene, cna_alteration AS alt, cna_cytoband AS cytoband
+  FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0024249' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+),
+g AS (SELECT gene, alt, any(cytoband) AS cytoband, arrayStringConcat(arraySort(x -> if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid))) AS so FROM c GROUP BY gene, alt),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'CNA' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+fa AS (SELECT hugo_gene_symbol AS gene, cna_alteration AS alt, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alt),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'COPY_NUMBER_ALTERATION' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, if(g.alt > 0, 'AMP', 'HOMDEL') AS cna, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.cytoband AS cytoband, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = if(g.alt > 0, 'AMPLIFICATION', 'DELETION')
+  LEFT JOIN fa ON fa.gene = g.gene AND fa.alt = g.alt LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT *, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY gdrv DESC, gene, so, gene, cna
+LIMIT 25 OFFSET 0
+
+-- view 0 alterations: alterations:counts · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 alterations: alterations:mutations:counts · msk_chord_2024
+WITH m AS (
+  SELECT sampleId AS sid, `GENE.hugoGeneSymbol` AS gene, proteinChange, mutationType, variantType, chr, startPosition AS start, endPosition AS end,
+         referenceAllele AS ref, tumorSeqAllele AS var, tumorAltCount AS ac, tumorRefCount AS rc, mutationStatus AS ms, proteinPosStart AS codon,
+         if(tumorAltCount + tumorRefCount > 0, tumorAltCount / (tumorAltCount + tumorRefCount), NULL) AS f
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND patientId = 'P-0024249' AND molecularProfileId NOT LIKE '%uncalled%'
+),
+g AS (
+  SELECT gene, proteinChange, mutationType, variantType, chr, start, end, ref, var, any(ms) AS status, min(codon) AS pos, arrayStringConcat(arraySort(x -> if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid))) AS so,
+         max(f) AS vaf, argMax(ac, ifNull(f, -1)) AS altCount, argMax(rc, ifNull(f, -1)) AS refCount
+  FROM m GROUP BY gene, proteinChange, mutationType, variantType, chr, start, end, ref, var
+),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'MUTATION' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+cn AS (SELECT hugo_gene_symbol AS gene, arrayStringConcat(arraySort(groupUniqArray(if(cna_alteration > 0, 'AMP', 'HOMDEL'))), ', ') AS copy
+  FROM snapshot_db.genomic_event_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0024249'
+    AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fa AS (SELECT hugo_gene_symbol AS gene, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'mutation' AND genetic_profile_stable_id NOT LIKE '%uncalled%'
+    AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, g.proteinChange AS proteinChange, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.mutationType AS mutationType, ifNull(cn.copy, '') AS copy, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq,
+         g.vaf AS vaf, g.altCount AS altCount, g.refCount AS refCount, g.variantType AS variantType, g.chr AS chr, g.start AS start, g.end AS end,
+         g.ref AS ref, g.var AS var, g.status AS status, g.pos AS pos
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = g.proteinChange
+  LEFT JOIN cn ON cn.gene = g.gene LEFT JOIN fa ON fa.gene = g.gene LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT cohort, study, countIf((positionCaseInsensitiveUTF8(if(oncogenicity = '', '', concat(oncogenicity, if(level = '', '', concat(' · ', replaceOne(level, 'LEVEL_', 'Level '))))), 'Oncogenic') > 0)) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 alterations: alterations:mutations:rows · msk_chord_2024
+WITH m AS (
+  SELECT sampleId AS sid, `GENE.hugoGeneSymbol` AS gene, proteinChange, mutationType, variantType, chr, startPosition AS start, endPosition AS end,
+         referenceAllele AS ref, tumorSeqAllele AS var, tumorAltCount AS ac, tumorRefCount AS rc, mutationStatus AS ms, proteinPosStart AS codon,
+         if(tumorAltCount + tumorRefCount > 0, tumorAltCount / (tumorAltCount + tumorRefCount), NULL) AS f
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND patientId = 'P-0024249' AND molecularProfileId NOT LIKE '%uncalled%'
+),
+g AS (
+  SELECT gene, proteinChange, mutationType, variantType, chr, start, end, ref, var, any(ms) AS status, min(codon) AS pos, arrayStringConcat(arraySort(x -> if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid))) AS so,
+         max(f) AS vaf, argMax(ac, ifNull(f, -1)) AS altCount, argMax(rc, ifNull(f, -1)) AS refCount
+  FROM m GROUP BY gene, proteinChange, mutationType, variantType, chr, start, end, ref, var
+),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'MUTATION' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+cn AS (SELECT hugo_gene_symbol AS gene, arrayStringConcat(arraySort(groupUniqArray(if(cna_alteration > 0, 'AMP', 'HOMDEL'))), ', ') AS copy
+  FROM snapshot_db.genomic_event_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0024249'
+    AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fa AS (SELECT hugo_gene_symbol AS gene, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'mutation' AND genetic_profile_stable_id NOT LIKE '%uncalled%'
+    AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, g.proteinChange AS proteinChange, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.mutationType AS mutationType, ifNull(cn.copy, '') AS copy, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq,
+         g.vaf AS vaf, g.altCount AS altCount, g.refCount AS refCount, g.variantType AS variantType, g.chr AS chr, g.start AS start, g.end AS end,
+         g.ref AS ref, g.var AS var, g.status AS status, g.pos AS pos
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = g.proteinChange
+  LEFT JOIN cn ON cn.gene = g.gene LEFT JOIN fa ON fa.gene = g.gene LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT *, count() OVER () AS total FROM rows
+WHERE (positionCaseInsensitiveUTF8(if(oncogenicity = '', '', concat(oncogenicity, if(level = '', '', concat(' · ', replaceOne(level, 'LEVEL_', 'Level '))))), 'Oncogenic') > 0)
+ORDER BY gdrv DESC, gene, so, pos ASC NULLS LAST, gene, chr, start, end, ref, var, proteinChange, mutationType
+LIMIT 25 OFFSET 0
+
+-- view 0 alterations: alterations:rank · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT idx FROM (SELECT study, patient, row_number() OVER (ORDER BY CAST(sv AS Nullable(Float64)) DESC NULLS LAST, patient, ord) - 1 AS idx FROM rows
+WHERE 1)
+WHERE study = 'msk_chord_2024' AND patient = 'P-0024249' LIMIT 1
+
+-- view 0 alterations: alterations:rows · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT cohort, study, patient, sample, source, samples, mutations, cna, sv, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY CAST(sv AS Nullable(Float64)) DESC NULLS LAST, patient, ord
+LIMIT 25 OFFSET 0
+
+-- view 0 alterations: alterations:rows · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT cohort, study, patient, sample, source, samples, mutations, cna, sv, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY CAST(sv AS Nullable(Float64)) DESC NULLS LAST, patient, ord
+LIMIT 3 OFFSET 20
+
+-- view 0 alterations: alterations:samples:counts · msk_chord_2024
+WITH s AS (SELECT replaceOne(s.sample_unique_id, 'msk_chord_2024_', '') AS id, any(c.attribute_value) AS type
+FROM snapshot_db.sample_derived s
+LEFT JOIN snapshot_db.clinical_data_derived c ON c.sample_unique_id = s.sample_unique_id AND c.type = 'sample'
+  AND c.cancer_study_identifier = 'msk_chord_2024' AND c.attribute_name = 'SAMPLE_TYPE'
+WHERE s.cancer_study_identifier = 'msk_chord_2024' AND s.patient_unique_id = 'msk_chord_2024_P-0024249'
+GROUP BY id ORDER BY id),
+rows AS (SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, s.id AS sid, ifNull(s.type, '') AS type FROM s)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 alterations: alterations:samples:rows · msk_chord_2024
+WITH s AS (SELECT replaceOne(s.sample_unique_id, 'msk_chord_2024_', '') AS id, any(c.attribute_value) AS type
+FROM snapshot_db.sample_derived s
+LEFT JOIN snapshot_db.clinical_data_derived c ON c.sample_unique_id = s.sample_unique_id AND c.type = 'sample'
+  AND c.cancer_study_identifier = 'msk_chord_2024' AND c.attribute_name = 'SAMPLE_TYPE'
+WHERE s.cancer_study_identifier = 'msk_chord_2024' AND s.patient_unique_id = 'msk_chord_2024_P-0024249'
+GROUP BY id ORDER BY id),
+rows AS (SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, s.id AS sid, ifNull(s.type, '') AS type FROM s)
+SELECT *, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY sid, sid
+LIMIT 1000 OFFSET 0
+
+-- view 0 alterations: alterations:sv:counts · msk_chord_2024
+WITH s AS (
+  SELECT smp.stable_id AS sid, ifNull(g1.hugo_gene_symbol, '') AS gene1, ifNull(g2.hugo_gene_symbol, '') AS gene2,
+         ifNull(sv.class, '') AS class, ifNull(sv.event_info, '') AS eventInfo, sv.breakpoint_type AS breakpointType, sv.connection_type AS connectionType,
+         sv.site2_effect_on_frame AS frame, sv.site1_description AS site1, sv.site2_description AS site2,
+         if(sv.site1_position IS NULL, '', concat(ifNull(sv.site1_chromosome, ''), ':', toString(sv.site1_position))) AS pos1,
+         if(sv.site2_position IS NULL, '', concat(ifNull(sv.site2_chromosome, ''), ':', toString(sv.site2_position))) AS pos2,
+         sv.length AS length, sv.tumor_variant_count AS tumorVariantCount, sv.tumor_read_count AS tumorReadCount,
+         sv.dna_support AS dnaSupport, sv.rna_support AS rnaSupport, sv.sv_status AS status, sv.annotation AS svAnnotation, sv.comments AS comments
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample smp ON smp.internal_id = sv.sample_id
+  LEFT JOIN snapshot_db.gene g1 ON g1.entrez_gene_id = sv.site1_entrez_gene_id
+  LEFT JOIN snapshot_db.gene g2 ON g2.entrez_gene_id = sv.site2_entrez_gene_id
+  WHERE sv.sample_id IN (SELECT s.internal_id FROM snapshot_db.sample s JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+    JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+    WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id = 'P-0024249')
+),
+g AS (
+  SELECT gene1, gene2, class, eventInfo, pos1, pos2, arrayStringConcat(arraySort(x -> if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid))) AS so,
+         any(breakpointType) AS breakpointType, any(connectionType) AS connectionType, any(frame) AS frame, any(site1) AS site1, any(site2) AS site2,
+         any(length) AS length, max(tumorVariantCount) AS tumorVariantCount, max(tumorReadCount) AS tumorReadCount,
+         any(dnaSupport) AS dnaSupport, any(rnaSupport) AS rnaSupport, any(status) AS status, any(svAnnotation) AS svAnnotation, any(comments) AS comments
+  FROM s GROUP BY gene1, gene2, class, eventInfo, pos1, pos2
+),
+rows AS (SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, splitByString(', ', g.samples)[1] AS sid, g.* FROM g)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 alterations: alterations:sv:rows · msk_chord_2024
+WITH s AS (
+  SELECT smp.stable_id AS sid, ifNull(g1.hugo_gene_symbol, '') AS gene1, ifNull(g2.hugo_gene_symbol, '') AS gene2,
+         ifNull(sv.class, '') AS class, ifNull(sv.event_info, '') AS eventInfo, sv.breakpoint_type AS breakpointType, sv.connection_type AS connectionType,
+         sv.site2_effect_on_frame AS frame, sv.site1_description AS site1, sv.site2_description AS site2,
+         if(sv.site1_position IS NULL, '', concat(ifNull(sv.site1_chromosome, ''), ':', toString(sv.site1_position))) AS pos1,
+         if(sv.site2_position IS NULL, '', concat(ifNull(sv.site2_chromosome, ''), ':', toString(sv.site2_position))) AS pos2,
+         sv.length AS length, sv.tumor_variant_count AS tumorVariantCount, sv.tumor_read_count AS tumorReadCount,
+         sv.dna_support AS dnaSupport, sv.rna_support AS rnaSupport, sv.sv_status AS status, sv.annotation AS svAnnotation, sv.comments AS comments
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample smp ON smp.internal_id = sv.sample_id
+  LEFT JOIN snapshot_db.gene g1 ON g1.entrez_gene_id = sv.site1_entrez_gene_id
+  LEFT JOIN snapshot_db.gene g2 ON g2.entrez_gene_id = sv.site2_entrez_gene_id
+  WHERE sv.sample_id IN (SELECT s.internal_id FROM snapshot_db.sample s JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+    JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+    WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id = 'P-0024249')
+),
+g AS (
+  SELECT gene1, gene2, class, eventInfo, pos1, pos2, arrayStringConcat(arraySort(x -> if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid) = 0, 1000000, indexOf(['P-0024249-T01-IM6', 'P-0024249-T02-IM7'], sid))) AS so,
+         any(breakpointType) AS breakpointType, any(connectionType) AS connectionType, any(frame) AS frame, any(site1) AS site1, any(site2) AS site2,
+         any(length) AS length, max(tumorVariantCount) AS tumorVariantCount, max(tumorReadCount) AS tumorReadCount,
+         any(dnaSupport) AS dnaSupport, any(rnaSupport) AS rnaSupport, any(status) AS status, any(svAnnotation) AS svAnnotation, any(comments) AS comments
+  FROM s GROUP BY gene1, gene2, class, eventInfo, pos1, pos2
+),
+rows AS (SELECT 'Prostate' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0024249' AS patient, splitByString(', ', g.samples)[1] AS sid, g.* FROM g)
+SELECT *, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY gene1, so, gene2, gene1, gene2, pos1, pos2, class, eventInfo
+LIMIT 25 OFFSET 0
diff --git a/test/snapshots/alterations.svg b/test/snapshots/alterations.svg
new file mode 100644
index 00000000..b6a38522
--- /dev/null
+++ b/test/snapshots/alterations.svg
@@ -0,0 +1,2 @@
+<!-- view 0 alterations -->
+(no figure)
diff --git a/test/snapshots/alterations.txt b/test/snapshots/alterations.txt
new file mode 100644
index 00000000..5c04e0a1
--- /dev/null
+++ b/test/snapshots/alterations.txt
@@ -0,0 +1,59 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Prostate: 3,211 patients — CANCER_TYPE in [Prostate Cancer] (any sample)
+
+Patient alterations — patients to choose from: 3,211; the first 25, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	patient	source	samples	mutations	cna	sv
+  Prostate	P-0030872	this portal (primary)	1	4	0	5
+  Prostate	P-0000631	this portal (primary)	1	3	4	4
+  Prostate	P-0013729	this portal (primary)	1	0	7	4
+  Prostate	P-0017262	this portal (primary)	1	2	1	4
+  Prostate	P-0020078	this portal (primary)	1	5	7	4
+  Prostate	P-0025988	this portal (primary)	1	5	0	4
+  Prostate	P-0030109	this portal (primary)	1	2	0	4
+  Prostate	P-0037022	this portal (primary)	1	3	0	4
+  Prostate	P-0038349	this portal (primary)	1	3	0	4
+  Prostate	P-0043336	this portal (primary)	1	26	0	4
+  Prostate	P-0046943	this portal (primary)	1	2	0	4
+  Prostate	P-0049524	this portal (primary)	1	2	0	4
+  Prostate	P-0069200	this portal (primary)	1	9	0	4
+  Prostate	P-0081157	this portal (primary)	1	3	4	4
+  Prostate	P-0003511	this portal (primary)	1	3	4	3
+  Prostate	P-0004946	this portal (primary)	2	7	0	3
+  Prostate	P-0006689	this portal (primary)	1	0	0	3
+  Prostate	P-0008960	this portal (primary)	1	0	3	3
+  Prostate	P-0012605	this portal (primary)	1	2	0	3
+  Prostate	P-0017882	this portal (primary)	1	3	9	3
+  Prostate	P-0019663	this portal (primary)	1	0	0	3
+  Prostate	P-0024249	this portal (primary)	2	7	3	3
+  Prostate	P-0024679	this portal (primary)	1	1	0	3
+  Prostate	P-0025139	this portal (primary)	1	3	4	3
+  Prostate	P-0025823	this portal (primary)	1	2	17	3
+  … 3,186 more rows
+P-0024249 (msk_chord_2024); samples: P-0024249-T01-IM6 (Primary), P-0024249-T02-IM7 (Primary)
+Mutations: 3 of 7 rows kept by the query; the first 3, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	samples	gene	proteinChange	annotation	mutationType	copy	freq	vaf
+  Prostate	P-0024249-T02-IM7	KRAS	G12V	Oncogenic · Level 1	Missense_Mutation		28.5	0.076
+  Prostate	P-0024249-T01-IM6	PTEN	G127V	Likely Oncogenic · Level 1	Missense_Mutation		4.8	0.619
+  Prostate	P-0024249-T01-IM6	TP53	P151R	Likely Oncogenic	Missense_Mutation		52.4	0.761
+Copy Number Alterations: 3 rows; the first 3, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	samples	gene	cna	annotation	cytoband	freq
+  Prostate	P-0024249-T01-IM6	MAP3K1	HOMDEL	Likely Oncogenic	5q11.2	0.3
+  Prostate	P-0024249-T01-IM6	RAD51B	HOMDEL	Likely Oncogenic · Level 1	14q24.1	0.1
+  Prostate	P-0024249-T01-IM6	PLK2	HOMDEL	Unknown	5q11.2	0.2
+Structural Variants: 3 rows; the first 3, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	samples	gene1	gene2	class	eventInfo	breakpointType	connectionType
+  Prostate	P-0024249-T01-IM6	IRS2	CHMP1A	TRANSLOCATION	Protein Fusion: mid-exon  {IRS2:CHMP1A}	IMPPRECISE	3to5
+  Prostate	P-0024249-T01-IM6	TCF7L2		DELETION	TCF7L2-intragenic	PRECISE	3to5
+  Prostate	P-0024249-T01-IM6	TMPRSS2	ERG	DELETION	Protein Fusion: out of frame  {TMPRSS2:ERG}	IMPPRECISE	3to5
+  CAVEAT (tell the user): P-0024249 — Mutations: 3 of 7 rows (rows Annotation (OncoKB) contains “Oncogenic”); Copy Number Alterations: 3 rows; Structural Variants: 3 rows. Each table lists a variant, a gene's call or an event once however many of the patient's samples carry it (Samples names them); allele frequency and read counts are the highest among those samples. Copy number is the deep calls only (AMP, HOMDEL), as the portal's patient view lists them; Copy # on a mutation is the gene's deep calls anywhere in the patient. Cohort % is the share of the study's samples profiled for the gene by their panel with a mutation in it, or with the same copy-number call. Annotation is OncoKB v7.5 (oncogenicity and its highest sensitivity level); structural variants carry the study's own annotation, not OncoKB's. The patient list: 3,211 patients. Sorted by Structural Variants (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. 
+  METHOD:
+    - Count the patients per cohort and study: The alterations:counts query above: every patient of each cohort, with their sample count and how many rows each of the three tables lists for them (a mutation, a gene's deep copy-number call, a structural variant — each counted once however many samples carry it).
+        Prostate · msk_chord_2024: 3,211
+    - Sort them together and take one page: The alterations:rows query above: the patients of every cohort and study combined in one query, sorted by Structural Variants descending with missing values last, then patient id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset.
+    - Read P-0024249's three tables: The alterations:mutations, alterations:cna and alterations:sv queries above, each paged, searched, filtered and sorted in the database like any table view. Mutations: every called mutation of the patient's samples (uncalled calls left out), one row per variant with the samples carrying it, the highest allele frequency among them (variant / (variant + reference) reads) with that sample's reads, and the gene's deep copy-number calls in the patient (Copy #). Copy-number alterations: the deep calls (AMP, HOMDEL), one row per gene and call. Structural variants: one row per event (both genes and breakpoints, class, event info), the highest read counts among its samples. Cohort % is the share of the study's samples profiled for the gene (by their gene panel) with any mutation in it, or with the same copy-number call; the annotation is the OncoKB v7.5 call for the protein change, or for AMPLIFICATION / DELETION.
+        Mutations: 3 of 7 rows (kept: rows Annotation (OncoKB) contains “Oncogenic”)
+        Copy Number Alterations: 3 rows
+        Structural Variants: 3 rows
+    A listing: no statistic is computed, so there is no script — the queries are the numbers.
diff --git a/test/snapshots/answers/alterations.json.gz b/test/snapshots/answers/alterations.json.gz
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diff --git a/test/snapshots/assays.json b/test/snapshots/assays.json
new file mode 100644
index 00000000..098b6642
--- /dev/null
+++ b/test/snapshots/assays.json
@@ -0,0 +1,697 @@
+{
+  "study": "msk_impact_50k_2026",
+  "cohorts": {
+    "Melanoma": {
+      "count": 1706,
+      "text": "CANCER_TYPE in [Melanoma] (any sample)",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    },
+    "Non-small cell lung": {
+      "count": 6633,
+      "text": "CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "assays",
+      "cohorts": [
+        "Melanoma",
+        "Non-small cell lung"
+      ],
+      "assays": {
+        "profileType": "mutational_signatures_contribution_v2",
+        "profileName": "Mutational Signature Contribution Scores (COSMIC v2)",
+        "assayType": "MUTATIONAL_SIGNATURE",
+        "datatype": "numeric",
+        "unit": "samples",
+        "groups": [
+          "Melanoma",
+          "Non-small cell lung"
+        ],
+        "tested": 30,
+        "untested": 0,
+        "significant": 26,
+        "minN": 3,
+        "test": "Welch's t-test",
+        "missing": 0,
+        "rows": [
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature4",
+            "name": "Signature4 (Smoking)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.010298716665451143,
+                "sd": 0.058030565429624245
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.16622830059170146,
+                "sd": 0.2429434174798955
+              }
+            ],
+            "diff": -0.1559295839262503,
+            "statistic": -51.5445,
+            "p": 0,
+            "q": 0,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature7",
+            "name": "Signature7 (UV)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.5187993341391373,
+                "sd": 0.38449006711437517
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.023843506796059934,
+                "sd": 0.08326193650974895
+              }
+            ],
+            "diff": 0.4949558273430773,
+            "statistic": 55.2198,
+            "p": 0,
+            "q": 0,
+            "higherIn": "Melanoma"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature24",
+            "name": "Signature24 (Aflatoxin)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.009138891998722847,
+                "sd": 0.057083701696028374
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.06971042637090322,
+                "sd": 0.14364314155913735
+              }
+            ],
+            "diff": -0.060571534372180366,
+            "statistic": -29.1484,
+            "p": 4.257392752150982e-177,
+            "q": 4.257392752150982e-176,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature13",
+            "name": "Signature13 (APOBEC)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.00956848138721051,
+                "sd": 0.049038566059724766
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.05888704485121312,
+                "sd": 0.12005389818751065
+              }
+            ],
+            "diff": -0.04931856346400261,
+            "statistic": -28.0766,
+            "p": 7.819767583291453e-165,
+            "q": 5.86482568746859e-164,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature18",
+            "name": "Signature18 (Unknown)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.008721388163558217,
+                "sd": 0.05475951701062739
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.03888048199856076,
+                "sd": 0.10851084936848984
+              }
+            ],
+            "diff": -0.030159093835002544,
+            "statistic": -17.1941,
+            "p": 1.2383597382494074e-64,
+            "q": 7.430158429496444e-64,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature29",
+            "name": "Signature29 (Tobacco)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.0075951464783650165,
+                "sd": 0.05997980259981962
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.036515687120827596,
+                "sd": 0.11050042911728776
+              }
+            ],
+            "diff": -0.028920540642462578,
+            "statistic": -15.5638,
+            "p": 2.0399699792092248e-53,
+            "q": 9.140126119414008e-53,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature2",
+            "name": "Signature2 (APOBEC)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.01574707484289951,
+                "sd": 0.07207156259435718
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.04828051007857093,
+                "sd": 0.11181289363691815
+              }
+            ],
+            "diff": -0.032533435235671426,
+            "statistic": -15.6028,
+            "p": 2.132696094529935e-53,
+            "q": 9.140126119414008e-53,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature20",
+            "name": "Signature20 (MMR)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.012741072656891395,
+                "sd": 0.06996952278802417
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.03148789629481505,
+                "sd": 0.10402448882076575
+              }
+            ],
+            "diff": -0.018746823637923654,
+            "statistic": -9.3993,
+            "p": 8.978520304776075e-21,
+            "q": 3.366945114291028e-20,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature16",
+            "name": "Signature16 (Unknown)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.007451000313366118,
+                "sd": 0.04686758323534038
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.018510648716710272,
+                "sd": 0.07452376696028855
+              }
+            ],
+            "diff": -0.011059648403344155,
+            "statistic": -8.0839,
+            "p": 8.068458736467697e-16,
+            "q": 2.6894862454892323e-15,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature8",
+            "name": "Signature8 (Unknown)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.006187496702424302,
+                "sd": 0.047053354018776014
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.016258978835554536,
+                "sd": 0.07034220328342541
+              }
+            ],
+            "diff": -0.010071482133130234,
+            "statistic": -7.4949,
+            "p": 8.113222960127824e-14,
+            "q": 2.433966888038347e-13,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature3",
+            "name": "Signature3 (HRD)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.033382070490749524,
+                "sd": 0.10985459784098602
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.050057430234104486,
+                "sd": 0.12366731742703801
+              }
+            ],
+            "diff": -0.01667535974335496,
+            "statistic": -5.7568,
+            "p": 9.422091209324467e-9,
+            "q": 2.5696612389066728e-8,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature15",
+            "name": "Signature15 (MMR)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.008898449962132352,
+                "sd": 0.05376045204068322
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.017503401319110528,
+                "sd": 0.0751290088094514
+              }
+            ],
+            "diff": -0.008604951356978176,
+            "statistic": -5.7291,
+            "p": 1.089058255916334e-8,
+            "q": 2.722645639790835e-8,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature14",
+            "name": "Signature14 (MMR)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.003375244796579444,
+                "sd": 0.032469849908769825
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.008217846958261008,
+                "sd": 0.04910702349579657
+              }
+            ],
+            "diff": -0.004842602161681564,
+            "statistic": -5.2015,
+            "p": 2.0737585057441852e-7,
+            "q": 4.78559655171735e-7,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature11",
+            "name": "Signature11 (TMZ)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.025012240905447253,
+                "sd": 0.08301104223943147
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.014629153004696164,
+                "sd": 0.07381865739502905
+              }
+            ],
+            "diff": 0.01038308790075109,
+            "statistic": 4.9598,
+            "p": 7.514323940558246e-7,
+            "q": 0.000001610212272976767,
+            "higherIn": "Melanoma"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature22",
+            "name": "Signature22 (Aristolochic acid)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.030421841758504636,
+                "sd": 0.12065985491117996
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.0174121127144547,
+                "sd": 0.0701378460140468
+              }
+            ],
+            "diff": 0.013009729044049935,
+            "statistic": 4.4784,
+            "p": 0.000007914499451777197,
+            "q": 0.000015828998903554394,
+            "higherIn": "Melanoma"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature26",
+            "name": "Signature26 (MMR)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.005959323302291891,
+                "sd": 0.043912441051391006
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.011484670137244484,
+                "sd": 0.06572043429115658
+              }
+            ],
+            "diff": -0.005525346834952593,
+            "statistic": -4.4042,
+            "p": 0.000010893049287469575,
+            "q": 0.000020424467414005453,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature6",
+            "name": "Signature6 (MMR)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.015807337209066928,
+                "sd": 0.0817667918971231
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.023866173976835513,
+                "sd": 0.09133133211610106
+              }
+            ],
+            "diff": -0.008058836767768585,
+            "statistic": -3.7444,
+            "p": 0.00018415566527705678,
+            "q": 0.00032498058578304137,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature1",
+            "name": "Signature1 (Aging)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.04333237406593814,
+                "sd": 0.1312946832832878
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.05599605732477383,
+                "sd": 0.13541974869028547
+              }
+            ],
+            "diff": -0.012663683258835691,
+            "statistic": -3.7264,
+            "p": 0.00019799745329043342,
+            "q": 0.0003299957554840557,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature19",
+            "name": "Signature19 (Unknown)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.004369047392522002,
+                "sd": 0.03509725788077581
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.00757593414630822,
+                "sd": 0.04613297579534556
+              }
+            ],
+            "diff": -0.0032068867537862183,
+            "statistic": -3.3308,
+            "p": 0.0008748917823274928,
+            "q": 0.001381408077359199,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "entity": "mutational_signatures_contribution_v2_Signature9",
+            "name": "Signature9 (IGHV)",
+            "cells": [
+              {
+                "cohort": "Melanoma",
+                "n": 1860,
+                "mean": 0.006908302333128928,
+                "sd": 0.04331609198242078
+              },
+              {
+                "cohort": "Non-small cell lung",
+                "n": 8040,
+                "mean": 0.010629742521844341,
+                "sd": 0.056740018863434244
+              }
+            ],
+            "diff": -0.0037214401887154134,
+            "statistic": -3.1349,
+            "p": 0.00173320076233686,
+            "q": 0.002543008405928216,
+            "higherIn": "Non-small cell lung"
+          },
+          {
+            "…": "10 more",
+            "sha256": "7e4e84a5665d9825"
+          }
+        ],
+        "grid": {
+          "xMax": 0.5,
+          "yMax": 500,
+          "nx": 80,
+          "ny": 50,
+          "cells": []
+        },
+        "labels": [
+          "mutational_signatures_contribution_v2_Signature4",
+          "mutational_signatures_contribution_v2_Signature7",
+          "mutational_signatures_contribution_v2_Signature24",
+          "mutational_signatures_contribution_v2_Signature13",
+          "mutational_signatures_contribution_v2_Signature18",
+          "mutational_signatures_contribution_v2_Signature29",
+          "mutational_signatures_contribution_v2_Signature2",
+          "mutational_signatures_contribution_v2_Signature20",
+          "mutational_signatures_contribution_v2_Signature16",
+          "mutational_signatures_contribution_v2_Signature8",
+          "mutational_signatures_contribution_v2_Signature3",
+          "mutational_signatures_contribution_v2_Signature15"
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_impact_50k_2026",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Every entity of “Mutational Signature Contribution Scores (COSMIC v2)” (mutational_signatures_contribution_v2) compared across the cohorts, one value per sample as the study recorded it, from every sample of each cohort's patients. Values that are not numbers (a limit such as \">8\", or NA) are left out (0 of them). Each entity is tested with Welch's t-test from each cohort's n, mean and variance; with thousands of samples a tiny difference is significant, so read the means too. 30 entities tested; q is Benjamini–Hochberg across all of them. 0 were not tested: fewer than 3 samples with a value in some cohort, or no spread. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Sum each entity's values",
+            "detail": "The assays:moments query above, on “mutational_signatures_contribution_v2”: per entity, over the cohort's samples, n, Σv and Σv² of the values that read as numbers, and how many did not. No individual value leaves the database.",
+            "scope": "Melanoma · msk_impact_50k_2026",
+            "rowsOut": 30
+          },
+          {
+            "kind": "fetch",
+            "title": "Sum each entity's values",
+            "detail": "The assays:moments query above, on “mutational_signatures_contribution_v2”: per entity, over the cohort's samples, n, Σv and Σv² of the values that read as numbers, and how many did not. No individual value leaves the database.",
+            "scope": "Non-small cell lung · msk_impact_50k_2026",
+            "rowsOut": 30
+          },
+          {
+            "kind": "transform",
+            "title": "Leave out what cannot be tested",
+            "detail": "Values that are not numbers: 0 left out. An entity with fewer than 3 samples with a value in any cohort, or no spread in any cohort (variance ≤ 1e-12 · (1 + mean²)), is not tested.",
+            "params": [
+              [
+                "tested",
+                "30"
+              ],
+              [
+                "not tested",
+                "0"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Welch's t-test per entity, then Benjamini–Hochberg",
+            "detail": "Unequal-variance t from each cohort's n, mean and variance (Σv² − (Σv)²/n)/(n − 1), Welch–Satterthwaite df, two-sided. q across every entity tested, one value per sample."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "welchT",
+            "varianceFromMoments",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Welch's t and ANOVA from moments match scipy 1.18.1 ttest_ind(equal_var=False) and statsmodels 0.15.0 anova_generic(use_var=\"unequal\") and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/moments-golden.test.ts, test/tables-golden.test.ts).",
+        "check": {
+          "assays:tested": {
+            "v": 30
+          },
+          "assays:mutational_signatures_contribution_v2_Signature4:p": {
+            "v": 0,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature4:q": {
+            "v": 0,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature7:p": {
+            "v": 0,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature7:q": {
+            "v": 0,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature24:p": {
+            "v": 4.257392752150982e-177,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature24:q": {
+            "v": 4.257392752150982e-176,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature13:p": {
+            "v": 7.819767583291453e-165,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature13:q": {
+            "v": 5.86482568746859e-164,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature18:p": {
+            "v": 1.2383597382494074e-64,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature18:q": {
+            "v": 7.430158429496444e-64,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature29:p": {
+            "v": 2.0399699792092248e-53,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature29:q": {
+            "v": 9.140126119414008e-53,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature2:p": {
+            "v": 2.132696094529935e-53,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature2:q": {
+            "v": 9.140126119414008e-53,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature20:p": {
+            "v": 8.978520304776075e-21,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature20:q": {
+            "v": 3.366945114291028e-20,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature16:p": {
+            "v": 8.068458736467697e-16,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature16:q": {
+            "v": 2.6894862454892323e-15,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "assays:mutational_signatures_contribution_v2_Signature8:p": {
+            "v": 8.113222960127824e-14,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "…": {
+            "more": 41,
+            "sha256": "89b71828088bd533"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/assays.sql b/test/snapshots/assays.sql
new file mode 100644
index 00000000..a70ec430
--- /dev/null
+++ b/test/snapshots/assays.sql
@@ -0,0 +1,60 @@
+-- cohort Melanoma
+-- msk_impact_50k_2026
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Melanoma'))))
+
+-- cohort Non-small cell lung
+-- msk_impact_50k_2026
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))
+
+-- view 0 assays: assays:moments:Melanoma · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Melanoma'))))),
+rows AS (
+  SELECT entity_stable_id AS entity, value FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'mutational_signatures_contribution_v2'
+    AND profile_stable_id = 'msk_impact_50k_2026_mutational_signatures_contribution_v2'
+    AND patient_unique_id IN (SELECT concat('msk_impact_50k_2026_', pid) FROM cohort)
+)
+SELECT entity, countIf(isFinite(toFloat64OrNull(value))) AS n,
+       sumIf(toFloat64OrNull(value), isFinite(toFloat64OrNull(value))) AS sx,
+       sumIf(pow(toFloat64OrNull(value), 2), isFinite(toFloat64OrNull(value))) AS sxx,
+       countIf(NOT isFinite(ifNull(toFloat64OrNull(value), nan))) AS other
+FROM rows GROUP BY entity
+
+-- view 0 assays: assays:moments:Non-small cell lung · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))),
+rows AS (
+  SELECT entity_stable_id AS entity, value FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'mutational_signatures_contribution_v2'
+    AND profile_stable_id = 'msk_impact_50k_2026_mutational_signatures_contribution_v2'
+    AND patient_unique_id IN (SELECT concat('msk_impact_50k_2026_', pid) FROM cohort)
+)
+SELECT entity, countIf(isFinite(toFloat64OrNull(value))) AS n,
+       sumIf(toFloat64OrNull(value), isFinite(toFloat64OrNull(value))) AS sx,
+       sumIf(pow(toFloat64OrNull(value), 2), isFinite(toFloat64OrNull(value))) AS sxx,
+       countIf(NOT isFinite(ifNull(toFloat64OrNull(value), nan))) AS other
+FROM rows GROUP BY entity
+
+-- view 0 assays: assays:names · msk_impact_50k_2026
+WITH named AS (
+  SELECT ge.stable_id AS entity, p.value AS name FROM snapshot_db.generic_entity_properties p
+  JOIN snapshot_db.genetic_entity ge ON ge.id = p.genetic_entity_id
+  WHERE p.name = 'NAME' AND ge.stable_id IN (SELECT DISTINCT entity_stable_id FROM snapshot_db.generic_assay_data_derived
+    WHERE profile_type = 'mutational_signatures_contribution_v2' AND profile_stable_id = 'msk_impact_50k_2026_mutational_signatures_contribution_v2')
+)
+SELECT entity, name FROM named
+
+-- view 0 assays: assays:profile · msk_impact_50k_2026
+SELECT datatype, generic_assay_type, ifNull(patient_level, 0) AS patient_level, name FROM snapshot_db.genetic_profile
+WHERE stable_id = 'msk_impact_50k_2026_mutational_signatures_contribution_v2' AND genetic_alteration_type = 'GENERIC_ASSAY'
diff --git a/test/snapshots/assays.svg b/test/snapshots/assays.svg
new file mode 100644
index 00000000..8e8187ed
--- /dev/null
+++ b/test/snapshots/assays.svg
@@ -0,0 +1,2 @@
+<!-- view 0 assays -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 512" width="100%" role="img" aria-label="Generic assays — Mutational Signature Contribution Scores (COSMIC v2) — Melanoma vs Non-small cell lung" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Generic assays — Mutational Signature Contribution Scores (COSMIC v2) — Melanoma vs Non-small cell lung</title><rect width="960" height="512" fill="#ffffff"/><text x="8" y="14" font-size="11" fill="#5b6b76">30 Mutational Signature Contribution Scores (COSMIC v2) entities tested (Welch's t-test) · 26 at q &lt; 0.05</text><rect x="51" y="43" width="362" height="302" fill="none" stroke="#dde4e8" stroke-width="0.75"/><line x1="51" y1="340" x2="413" y2="340" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="343.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="51" y1="281.6" x2="413" y2="281.6" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="285.1" font-size="10" fill="#5b6b76" text-anchor="end">100</text><line x1="51" y1="223.2" x2="413" y2="223.2" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="226.7" font-size="10" fill="#5b6b76" text-anchor="end">200</text><line x1="51" y1="164.8" x2="413" y2="164.8" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="168.3" font-size="10" fill="#5b6b76" text-anchor="end">300</text><line x1="51" y1="106.4" x2="413" y2="106.4" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="109.9" font-size="10" fill="#5b6b76" text-anchor="end">400</text><line x1="51" y1="48" x2="413" y2="48" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="51.5" font-size="10" fill="#5b6b76" text-anchor="end">500</text><text x="14.8" y="194" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 14.8 194)">−log₁₀ p</text><line x1="91.2" y1="43" x2="91.2" y2="345" stroke="#dde4e8" stroke-width="0.75"/><line x1="91.2" y1="345" x2="91.2" y2="348" stroke="#5b6b76" stroke-width="1"/><text x="80.8" y="361" font-size="10" fill="#5b6b76">−0.4</text><line x1="161.6" y1="43" x2="161.6" y2="345" stroke="#dde4e8" stroke-width="0.75"/><line x1="161.6" y1="345" x2="161.6" y2="348" stroke="#5b6b76" stroke-width="1"/><text x="151.2" y="361" font-size="10" fill="#5b6b76">−0.2</text><line x1="232" y1="43" x2="232" y2="345" stroke="#dde4e8" stroke-width="0.75"/><line x1="232" y1="345" x2="232" y2="348" stroke="#5b6b76" stroke-width="1"/><text x="224.6" y="361" font-size="10" fill="#5b6b76">0.0</text><line x1="302.4" y1="43" x2="302.4" y2="345" stroke="#dde4e8" stroke-width="0.75"/><line x1="302.4" y1="345" x2="302.4" y2="348" stroke="#5b6b76" stroke-width="1"/><text x="295" y="361" font-size="10" fill="#5b6b76">0.2</text><line x1="372.8" y1="43" x2="372.8" y2="345" stroke="#dde4e8" stroke-width="0.75"/><line x1="372.8" y1="345" x2="372.8" y2="348" stroke="#5b6b76" stroke-width="1"/><text x="365.4" y="361" font-size="10" fill="#5b6b76">0.4</text><line x1="56" y1="345" x2="408" y2="345" stroke="#5b6b76" stroke-width="1"/><text x="232" y="380" font-size="11" fill="#16232b" text-anchor="middle">difference in mean (Melanoma − Non-small cell lung)</text><line x1="232" y1="43" x2="232" y2="345" stroke="#5b6b76" stroke-width="0.75"/><circle cx="177.1" cy="164.8" r="3" fill="#E69F00" opacity="0.8"><title>Signature4 (Smoking): −0.156 · q &lt;1e-300</title></circle><circle cx="406.2" cy="164.8" r="3" fill="#0072B2" opacity="0.8"><title>Signature7 (UV): 0.495 · q &lt;1e-300</title></circle><circle cx="210.7" cy="237" r="3" fill="#E69F00" opacity="0.8"><title>Signature24 (Aflatoxin): −0.0606 · q 4.3e-176</title></circle><circle cx="214.6" cy="244.2" r="3" fill="#E69F00" opacity="0.8"><title>Signature13 (APOBEC): −0.0493 · q 5.9e-164</title></circle><circle cx="221.4" cy="302.7" r="3" fill="#E69F00" opacity="0.8"><title>Signature18 (Unknown): −0.0302 · q 7.4e-64</title></circle><circle cx="221.8" cy="309.2" r="3" fill="#E69F00" opacity="0.8"><title>Signature29 (Tobacco): −0.0289 · q 9.1e-53</title></circle><circle cx="220.5" cy="309.2" r="3" fill="#E69F00" opacity="0.8"><title>Signature2 (APOBEC): −0.0325 · q 9.1e-53</title></circle><circle cx="225.4" cy="328.3" r="3" fill="#E69F00" opacity="0.8"><title>Signature20 (MMR): −0.0187 · q 3.4e-20</title></circle><circle cx="228.1" cy="331.2" r="3" fill="#E69F00" opacity="0.8"><title>Signature16 (Unknown): −0.0111 · q 2.7e-15</title></circle><circle cx="228.5" cy="332.4" r="3" fill="#E69F00" opacity="0.8"><title>Signature8 (Unknown): −0.0101 · q 2.4e-13</title></circle><circle cx="226.1" cy="335.3" r="3" fill="#E69F00" opacity="0.8"><title>Signature3 (HRD): −0.0167 · q 2.6e-8</title></circle><circle cx="229" cy="335.3" r="3" fill="#E69F00" opacity="0.8"><title>Signature15 (MMR): −0.0086 · q 2.7e-8</title></circle><circle cx="230.3" cy="336.1" r="3" fill="#E69F00" opacity="0.8"><title>Signature14 (MMR): −0.00484 · q 4.8e-7</title></circle><circle cx="235.7" cy="336.4" r="3" fill="#0072B2" opacity="0.8"><title>Signature11 (TMZ): 0.0104 · q 1.6e-6</title></circle><circle cx="236.6" cy="337" r="3" fill="#0072B2" opacity="0.8"><title>Signature22 (Aristolochic acid): 0.013 · q 1.6e-5</title></circle><circle cx="230.1" cy="337.1" r="3" fill="#E69F00" opacity="0.8"><title>Signature26 (MMR): −0.00553 · q 2e-5</title></circle><circle cx="229.2" cy="337.8" r="3" fill="#E69F00" opacity="0.8"><title>Signature6 (MMR): −0.00806 · q 3.2e-4</title></circle><circle cx="227.5" cy="337.8" r="3" fill="#E69F00" opacity="0.8"><title>Signature1 (Aging): −0.0127 · q 3.3e-4</title></circle><circle cx="230.9" cy="338.2" r="3" fill="#E69F00" opacity="0.8"><title>Signature19 (Unknown): −0.00321 · q 0.0014</title></circle><circle cx="230.7" cy="338.4" r="3" fill="#E69F00" opacity="0.8"><title>Signature9 (IGHV): −0.00372 · q 0.0025</title></circle><circle cx="230.4" cy="338.4" r="3" fill="#E69F00" opacity="0.8"><title>Signature10 (POLE): −0.00458 · q 0.0025</title></circle><circle cx="231" cy="339" r="3" fill="#E69F00" opacity="0.8"><title>Signature17 (Unknown): −0.00285 · q 0.027</title></circle><circle cx="230.6" cy="339" r="3" fill="#E69F00" opacity="0.8"><title>Signature30 (Unknown): −0.00392 · q 0.03</title></circle><circle cx="230.7" cy="339.1" r="3" fill="#E69F00" opacity="0.8"><title>Signature21 (MMR): −0.00382 · q 0.03</title></circle><circle cx="230.8" cy="339.1" r="3" fill="#E69F00" opacity="0.8"><title>Signature12 (Unknown): −0.00346 · q 0.032</title></circle><circle cx="231.6" cy="339.2" r="3" fill="#E69F00" opacity="0.8"><title>Signature5 (Unknown): −0.00126 · q 0.043</title></circle><circle cx="231.4" cy="339.3" r="3" fill="#5b6b76" opacity="0.8"><title>Signature27 (Unknown): −0.00159 · q 0.064</title></circle><circle cx="231.7" cy="339.8" r="3" fill="#5b6b76" opacity="0.8"><title>Signature25 (Unknown): −0.000981 · q 0.45</title></circle><circle cx="232.6" cy="339.8" r="3" fill="#5b6b76" opacity="0.8"><title>Signature28 (Unknown): 0.00164 · q 0.45</title></circle><circle cx="231.9" cy="340" r="3" fill="#5b6b76" opacity="0.8"><title>Signature23 (Unknown): −0.000289 · q 0.90</title></circle><circle cx="177.1" cy="164.8" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="406.2" cy="164.8" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="210.7" cy="237" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="214.6" cy="244.2" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="221.4" cy="302.7" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="221.8" cy="309.2" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="220.5" cy="309.2" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="225.4" cy="328.3" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="228.1" cy="331.2" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="228.5" cy="332.4" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="226.1" cy="335.3" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="229" cy="335.3" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><text x="183.1" y="167.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature4 (Smoking)</text><text x="329.3" y="167.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature7 (UV)</text><text x="100.6" y="239.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature24 (Aflatoxin)</text><text x="220.6" y="246.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature13 (APOBEC)</text><text x="227.4" y="305.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature18 (Unknown)</text><line x1="217.3" y1="309.2" x2="208.8" y2="309.2" stroke="#5b6b76" stroke-width="0.75"/><text x="105.7" y="311.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature29 (Tobacco)</text><line x1="217.4" y1="306.1" x2="211.4" y2="300.1" stroke="#5b6b76" stroke-width="0.75"/><text x="114.3" y="296.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature2 (APOBEC)</text><text x="132.7" y="330.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature20 (MMR)</text><line x1="232.6" y1="331.2" x2="241.1" y2="331.2" stroke="#5b6b76" stroke-width="0.75"/><text x="242.1" y="333.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature16 (Unknown)</text><line x1="231.6" y1="329.2" x2="237.6" y2="323.3" stroke="#5b6b76" stroke-width="0.75"/><text x="238.6" y="319.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature8 (Unknown)</text><line x1="226.1" y1="330.8" x2="226.1" y2="280.3" stroke="#5b6b76" stroke-width="0.75"/><text x="187" y="276.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Signature3 (HRD)</text><text x="51" y="396" font-size="10" fill="#5b6b76">← higher in Non-small cell lung</text><text x="413" y="396" font-size="10" fill="#5b6b76" text-anchor="end">higher in Melanoma →</text><text x="4" y="414" font-size="10" fill="#5b6b76">1 of 12 entities named on this volcano could not be labelled without overprinting another</text><text x="4" y="428" font-size="10" fill="#5b6b76">label or point and is left unlabelled: Signature15 (MMR).</text><text x="447" y="43" font-size="10" fill="#5b6b76">entity · mean</text><rect x="618.6" y="35" width="8" height="8" rx="1" fill="#0072B2"/><text x="630.6" y="43" font-size="10" fill="#16232b" font-weight="600">Melanoma</text><rect x="759.2" y="35" width="8" height="8" rx="1" fill="#E69F00"/><text x="771.2" y="43" font-size="10" fill="#16232b" font-weight="600">Non-small cell lung</text><text x="952" y="43" font-size="10" fill="#5b6b76" text-anchor="end">q</text><line x1="447" y1="47" x2="952" y2="47" stroke="#dde4e8" stroke-width="1"/><text x="447" y="62.9" font-size="11" fill="#16232b" font-weight="600">Signature4 (Smoking)</text><rect x="618.6" y="54" width="1.8" height="10" fill="#0072B2" opacity="0.85"/><text x="624.4" y="62.5" font-size="10" fill="#5b6b76">0.0103</text><rect x="759.2" y="54" width="28.8" height="10" fill="#E69F00" opacity="0.85"/><text x="792" y="62.5" font-size="10" fill="#5b6b76">0.166</text><text x="952" y="62.5" font-size="10" fill="#16232b" text-anchor="end">&lt;1e-300</text><text x="447" y="80.9" font-size="11" fill="#16232b" font-weight="600">Signature7 (UV)</text><rect x="618.6" y="72" width="89.9" height="10" fill="#0072B2" opacity="0.85"/><text x="712.5" y="80.5" font-size="10" fill="#5b6b76">0.519</text><rect x="759.2" y="72" width="4.1" height="10" fill="#E69F00" opacity="0.85"/><text x="767.3" y="80.5" font-size="10" fill="#5b6b76">0.0238</text><text x="952" y="80.5" font-size="10" fill="#16232b" text-anchor="end">&lt;1e-300</text><text x="447" y="98.9" font-size="11" fill="#16232b" font-weight="600">Signature24 (Aflatoxin)</text><rect x="618.6" y="90" width="1.6" height="10" fill="#0072B2" opacity="0.85"/><text x="624.2" y="98.5" font-size="10" fill="#5b6b76">0.00914</text><rect x="759.2" y="90" width="12.1" height="10" fill="#E69F00" opacity="0.85"/><text x="775.3" y="98.5" font-size="10" fill="#5b6b76">0.0697</text><text x="952" y="98.5" font-size="10" fill="#16232b" text-anchor="end">4.3e-176</text><text x="447" y="116.9" font-size="11" fill="#16232b" font-weight="600">Signature13 (APOBEC)</text><rect x="618.6" y="108" width="1.7" height="10" fill="#0072B2" opacity="0.85"/><text x="624.3" y="116.5" font-size="10" fill="#5b6b76">0.00957</text><rect x="759.2" y="108" width="10.2" height="10" fill="#E69F00" opacity="0.85"/><text x="773.4" y="116.5" font-size="10" fill="#5b6b76">0.0589</text><text x="952" y="116.5" font-size="10" fill="#16232b" text-anchor="end">5.9e-164</text><text x="447" y="134.9" font-size="11" fill="#16232b" font-weight="600">Signature18 (Unknown)</text><rect x="618.6" y="126" width="1.5" height="10" fill="#0072B2" opacity="0.85"/><text x="624.1" y="134.5" font-size="10" fill="#5b6b76">0.00872</text><rect x="759.2" y="126" width="6.7" height="10" fill="#E69F00" opacity="0.85"/><text x="769.9" y="134.5" font-size="10" fill="#5b6b76">0.0389</text><text x="952" y="134.5" font-size="10" fill="#16232b" text-anchor="end">7.4e-64</text><text x="447" y="152.9" font-size="11" fill="#16232b" font-weight="600">Signature29 (Tobacco)</text><rect x="618.6" y="144" width="1.3" height="10" fill="#0072B2" opacity="0.85"/><text x="623.9" y="152.5" font-size="10" fill="#5b6b76">0.0076</text><rect x="759.2" y="144" width="6.3" height="10" fill="#E69F00" opacity="0.85"/><text x="769.5" y="152.5" font-size="10" fill="#5b6b76">0.0365</text><text x="952" y="152.5" font-size="10" fill="#16232b" text-anchor="end">9.1e-53</text><text x="447" y="170.9" font-size="11" fill="#16232b" font-weight="600">Signature2 (APOBEC)</text><rect x="618.6" y="162" width="2.7" height="10" fill="#0072B2" opacity="0.85"/><text x="625.3" y="170.5" font-size="10" fill="#5b6b76">0.0157</text><rect x="759.2" y="162" width="8.4" height="10" fill="#E69F00" opacity="0.85"/><text x="771.6" y="170.5" font-size="10" fill="#5b6b76">0.0483</text><text x="952" y="170.5" font-size="10" fill="#16232b" text-anchor="end">9.1e-53</text><text x="447" y="188.9" font-size="11" fill="#16232b" font-weight="600">Signature20 (MMR)</text><rect x="618.6" y="180" width="2.2" height="10" fill="#0072B2" opacity="0.85"/><text x="624.8" y="188.5" font-size="10" fill="#5b6b76">0.0127</text><rect x="759.2" y="180" width="5.5" height="10" fill="#E69F00" opacity="0.85"/><text x="768.7" y="188.5" font-size="10" fill="#5b6b76">0.0315</text><text x="952" y="188.5" font-size="10" fill="#16232b" text-anchor="end">3.4e-20</text><text x="447" y="206.9" font-size="11" fill="#16232b" font-weight="600">Signature16 (Unknown)</text><rect x="618.6" y="198" width="1.3" height="10" fill="#0072B2" opacity="0.85"/><text x="623.9" y="206.5" font-size="10" fill="#5b6b76">0.00745</text><rect x="759.2" y="198" width="3.2" height="10" fill="#E69F00" opacity="0.85"/><text x="766.4" y="206.5" font-size="10" fill="#5b6b76">0.0185</text><text x="952" y="206.5" font-size="10" fill="#16232b" text-anchor="end">2.7e-15</text><text x="447" y="224.9" font-size="11" fill="#16232b" font-weight="600">Signature8 (Unknown)</text><rect x="618.6" y="216" width="1.1" height="10" fill="#0072B2" opacity="0.85"/><text x="623.7" y="224.5" font-size="10" fill="#5b6b76">0.00619</text><rect x="759.2" y="216" width="2.8" height="10" fill="#E69F00" opacity="0.85"/><text x="766" y="224.5" font-size="10" fill="#5b6b76">0.0163</text><text x="952" y="224.5" font-size="10" fill="#16232b" text-anchor="end">2.4e-13</text><text x="447" y="242.9" font-size="11" fill="#16232b" font-weight="600">Signature3 (HRD)</text><rect x="618.6" y="234" width="5.8" height="10" fill="#0072B2" opacity="0.85"/><text x="628.4" y="242.5" font-size="10" fill="#5b6b76">0.0334</text><rect x="759.2" y="234" width="8.7" height="10" fill="#E69F00" opacity="0.85"/><text x="771.9" y="242.5" font-size="10" fill="#5b6b76">0.0501</text><text x="952" y="242.5" font-size="10" fill="#16232b" text-anchor="end">2.6e-8</text><text x="447" y="260.9" font-size="11" fill="#16232b" font-weight="600">Signature15 (MMR)</text><rect x="618.6" y="252" width="1.5" height="10" fill="#0072B2" opacity="0.85"/><text x="624.1" y="260.5" font-size="10" fill="#5b6b76">0.0089</text><rect x="759.2" y="252" width="3" height="10" fill="#E69F00" opacity="0.85"/><text x="766.2" y="260.5" font-size="10" fill="#5b6b76">0.0175</text><text x="952" y="260.5" font-size="10" fill="#16232b" text-anchor="end">2.7e-8</text><text x="447" y="278.9" font-size="11" fill="#16232b" font-weight="600">Signature14 (MMR)</text><rect x="618.6" y="270" width="0.6" height="10" fill="#0072B2" opacity="0.85"/><text x="623.2" y="278.5" font-size="10" fill="#5b6b76">0.00338</text><rect x="759.2" y="270" width="1.4" height="10" fill="#E69F00" opacity="0.85"/><text x="764.6" y="278.5" font-size="10" fill="#5b6b76">0.00822</text><text x="952" y="278.5" font-size="10" fill="#16232b" text-anchor="end">4.8e-7</text><text x="447" y="296.9" font-size="11" fill="#16232b" font-weight="600">Signature11 (TMZ)</text><rect x="618.6" y="288" width="4.3" height="10" fill="#0072B2" opacity="0.85"/><text x="626.9" y="296.5" font-size="10" fill="#5b6b76">0.025</text><rect x="759.2" y="288" width="2.5" height="10" fill="#E69F00" opacity="0.85"/><text x="765.7" y="296.5" font-size="10" fill="#5b6b76">0.0146</text><text x="952" y="296.5" font-size="10" fill="#16232b" text-anchor="end">1.6e-6</text><text x="447" y="314.9" font-size="11" fill="#16232b" font-weight="600">Signature22 (Aristolochic acid)</text><rect x="618.6" y="306" width="5.3" height="10" fill="#0072B2" opacity="0.85"/><text x="627.9" y="314.5" font-size="10" fill="#5b6b76">0.0304</text><rect x="759.2" y="306" width="3" height="10" fill="#E69F00" opacity="0.85"/><text x="766.2" y="314.5" font-size="10" fill="#5b6b76">0.0174</text><text x="952" y="314.5" font-size="10" fill="#16232b" text-anchor="end">1.6e-5</text><text x="447" y="332.9" font-size="11" fill="#16232b" font-weight="600">Signature26 (MMR)</text><rect x="618.6" y="324" width="1" height="10" fill="#0072B2" opacity="0.85"/><text x="623.6" y="332.5" font-size="10" fill="#5b6b76">0.00596</text><rect x="759.2" y="324" width="2" height="10" fill="#E69F00" opacity="0.85"/><text x="765.2" y="332.5" font-size="10" fill="#5b6b76">0.0115</text><text x="952" y="332.5" font-size="10" fill="#16232b" text-anchor="end">2e-5</text><text x="447" y="350.9" font-size="11" fill="#16232b" font-weight="600">Signature6 (MMR)</text><rect x="618.6" y="342" width="2.7" height="10" fill="#0072B2" opacity="0.85"/><text x="625.3" y="350.5" font-size="10" fill="#5b6b76">0.0158</text><rect x="759.2" y="342" width="4.1" height="10" fill="#E69F00" opacity="0.85"/><text x="767.3" y="350.5" font-size="10" fill="#5b6b76">0.0239</text><text x="952" y="350.5" font-size="10" fill="#16232b" text-anchor="end">3.2e-4</text><text x="447" y="368.9" font-size="11" fill="#16232b" font-weight="600">Signature1 (Aging)</text><rect x="618.6" y="360" width="7.5" height="10" fill="#0072B2" opacity="0.85"/><text x="630.1" y="368.5" font-size="10" fill="#5b6b76">0.0433</text><rect x="759.2" y="360" width="9.7" height="10" fill="#E69F00" opacity="0.85"/><text x="772.9" y="368.5" font-size="10" fill="#5b6b76">0.056</text><text x="952" y="368.5" font-size="10" fill="#16232b" text-anchor="end">3.3e-4</text><text x="447" y="390" font-size="10" fill="#5b6b76">+12 more entities tested (the full table is in the result and the MCP text)</text><text x="8" y="452" font-size="10" fill="#5b6b76">Every entity of “Mutational Signature Contribution Scores (COSMIC v2)” (mutational_signatures_contribution_v2) compared across the cohorts, one value per sample as</text><text x="8" y="466" font-size="10" fill="#5b6b76">the study recorded it, from every sample of each cohort's patients. Values that are not numbers (a limit such as &quot;&gt;8&quot;, or NA) are left out (0 of them). Each entity is tested</text><text x="8" y="480" font-size="10" fill="#5b6b76">with Welch's t-test from each cohort's n, mean and variance; with thousands of samples a tiny difference is significant, so read the means too. 30 entities tested; q is</text><text x="8" y="494" font-size="10" fill="#5b6b76">Benjamini–Hochberg across all of them. 0 were not tested: fewer than 3 samples with a value in some cohort, or no spread.</text></svg>
diff --git a/test/snapshots/assays.txt b/test/snapshots/assays.txt
new file mode 100644
index 00000000..2b1349e4
--- /dev/null
+++ b/test/snapshots/assays.txt
@@ -0,0 +1,46 @@
+Analysis over msk_impact_50k_2026 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Melanoma: 1,706 patients — CANCER_TYPE in [Melanoma] (any sample)
+- Non-small cell lung: 6,633 patients — CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)
+
+Generic-assay comparison, Mutational Signature Contribution Scores (COSMIC v2) (mutational_signatures_contribution_v2; numeric, per sample; Welch's t-test, BH q over 30 entities; 26 at q<0.05):
+  Signature4 (Smoking): Melanoma mean 0.0103 (n=1860) | Non-small cell lung mean 0.166 (n=8040), difference -0.156, q=<1e-300, higher in Non-small cell lung
+  Signature7 (UV): Melanoma mean 0.519 (n=1860) | Non-small cell lung mean 0.0238 (n=8040), difference 0.495, q=<1e-300, higher in Melanoma
+  Signature24 (Aflatoxin): Melanoma mean 0.00914 (n=1860) | Non-small cell lung mean 0.0697 (n=8040), difference -0.0606, q=4.3e-176, higher in Non-small cell lung
+  Signature13 (APOBEC): Melanoma mean 0.00957 (n=1860) | Non-small cell lung mean 0.0589 (n=8040), difference -0.0493, q=5.9e-164, higher in Non-small cell lung
+  Signature18 (Unknown): Melanoma mean 0.00872 (n=1860) | Non-small cell lung mean 0.0389 (n=8040), difference -0.0302, q=7.4e-64, higher in Non-small cell lung
+  Signature29 (Tobacco): Melanoma mean 0.00760 (n=1860) | Non-small cell lung mean 0.0365 (n=8040), difference -0.0289, q=9.1e-53, higher in Non-small cell lung
+  Signature2 (APOBEC): Melanoma mean 0.0157 (n=1860) | Non-small cell lung mean 0.0483 (n=8040), difference -0.0325, q=9.1e-53, higher in Non-small cell lung
+  Signature20 (MMR): Melanoma mean 0.0127 (n=1860) | Non-small cell lung mean 0.0315 (n=8040), difference -0.0187, q=3.4e-20, higher in Non-small cell lung
+  Signature16 (Unknown): Melanoma mean 0.00745 (n=1860) | Non-small cell lung mean 0.0185 (n=8040), difference -0.0111, q=2.7e-15, higher in Non-small cell lung
+  Signature8 (Unknown): Melanoma mean 0.00619 (n=1860) | Non-small cell lung mean 0.0163 (n=8040), difference -0.0101, q=2.4e-13, higher in Non-small cell lung
+  Signature3 (HRD): Melanoma mean 0.0334 (n=1860) | Non-small cell lung mean 0.0501 (n=8040), difference -0.0167, q=2.6e-8, higher in Non-small cell lung
+  Signature15 (MMR): Melanoma mean 0.00890 (n=1860) | Non-small cell lung mean 0.0175 (n=8040), difference -0.00860, q=2.7e-8, higher in Non-small cell lung
+  Signature14 (MMR): Melanoma mean 0.00338 (n=1860) | Non-small cell lung mean 0.00822 (n=8040), difference -0.00484, q=4.8e-7, higher in Non-small cell lung
+  Signature11 (TMZ): Melanoma mean 0.0250 (n=1860) | Non-small cell lung mean 0.0146 (n=8040), difference 0.0104, q=1.6e-6, higher in Melanoma
+  Signature22 (Aristolochic acid): Melanoma mean 0.0304 (n=1860) | Non-small cell lung mean 0.0174 (n=8040), difference 0.0130, q=1.6e-5, higher in Melanoma
+  Signature26 (MMR): Melanoma mean 0.00596 (n=1860) | Non-small cell lung mean 0.0115 (n=8040), difference -0.00553, q=2.0e-5, higher in Non-small cell lung
+  Signature6 (MMR): Melanoma mean 0.0158 (n=1860) | Non-small cell lung mean 0.0239 (n=8040), difference -0.00806, q=0.0003, higher in Non-small cell lung
+  Signature1 (Aging): Melanoma mean 0.0433 (n=1860) | Non-small cell lung mean 0.0560 (n=8040), difference -0.0127, q=0.0003, higher in Non-small cell lung
+  Signature19 (Unknown): Melanoma mean 0.00437 (n=1860) | Non-small cell lung mean 0.00758 (n=8040), difference -0.00321, q=0.0014, higher in Non-small cell lung
+  Signature9 (IGHV): Melanoma mean 0.00691 (n=1860) | Non-small cell lung mean 0.0106 (n=8040), difference -0.00372, q=0.0025, higher in Non-small cell lung
+  Signature10 (POLE): Melanoma mean 0.00837 (n=1860) | Non-small cell lung mean 0.0129 (n=8040), difference -0.00458, q=0.0025, higher in Non-small cell lung
+  Signature17 (Unknown): Melanoma mean 0.00671 (n=1860) | Non-small cell lung mean 0.00956 (n=8040), difference -0.00285, q=0.0269, higher in Non-small cell lung
+  Signature30 (Unknown): Melanoma mean 0.0122 (n=1860) | Non-small cell lung mean 0.0162 (n=8040), difference -0.00392, q=0.0298, higher in Non-small cell lung
+  Signature21 (MMR): Melanoma mean 0.0145 (n=1860) | Non-small cell lung mean 0.0183 (n=8040), difference -0.00382, q=0.0305, higher in Non-small cell lung
+  Signature12 (Unknown): Melanoma mean 0.0105 (n=1860) | Non-small cell lung mean 0.0140 (n=8040), difference -0.00346, q=0.0320, higher in Non-small cell lung
+  Signature5 (Unknown): Melanoma mean 0.00172 (n=1860) | Non-small cell lung mean 0.00298 (n=8040), difference -0.00126, q=0.0431, higher in Non-small cell lung
+  Signature27 (Unknown): Melanoma mean 0.00413 (n=1860) | Non-small cell lung mean 0.00572 (n=8040), difference -0.00159, q=0.0635, higher in Non-small cell lung
+  Signature25 (Unknown): Melanoma mean 0.00715 (n=1860) | Non-small cell lung mean 0.00813 (n=8040), difference -0.000981, q=0.4548, higher in Non-small cell lung
+  Signature28 (Unknown): Melanoma mean 0.0148 (n=1860) | Non-small cell lung mean 0.0132 (n=8040), difference 0.00164, q=0.4548, higher in Melanoma
+  Signature23 (Unknown): Melanoma mean 0.0218 (n=1860) | Non-small cell lung mean 0.0221 (n=8040), difference -0.000289, q=0.8979, higher in Non-small cell lung
+  CAVEAT (tell the user): Every entity of “Mutational Signature Contribution Scores (COSMIC v2)” (mutational_signatures_contribution_v2) compared across the cohorts, one value per sample as the study recorded it, from every sample of each cohort's patients. Values that are not numbers (a limit such as ">8", or NA) are left out (0 of them). Each entity is tested with Welch's t-test from each cohort's n, mean and variance; with thousands of samples a tiny difference is significant, so read the means too. 30 entities tested; q is Benjamini–Hochberg across all of them. 0 were not tested: fewer than 3 samples with a value in some cohort, or no spread. 
+  METHOD:
+    - Sum each entity's values [Melanoma · msk_impact_50k_2026]: The assays:moments query above, on “mutational_signatures_contribution_v2”: per entity, over the cohort's samples, n, Σv and Σv² of the values that read as numbers, and how many did not. No individual value leaves the database.
+    - Sum each entity's values [Non-small cell lung · msk_impact_50k_2026]: The assays:moments query above, on “mutational_signatures_contribution_v2”: per entity, over the cohort's samples, n, Σv and Σv² of the values that read as numbers, and how many did not. No individual value leaves the database.
+    - Leave out what cannot be tested: Values that are not numbers: 0 left out. An entity with fewer than 3 samples with a value in any cohort, or no spread in any cohort (variance ≤ 1e-12 · (1 + mean²)), is not tested.
+        tested: 30
+        not tested: 0
+    - Welch's t-test per entity, then Benjamini–Hochberg: Unequal-variance t from each cohort's n, mean and variance (Σv² − (Σv)²/n)/(n − 1), Welch–Satterthwaite df, two-sided. q across every entity tested, one value per sample.
+    Welch's t and ANOVA from moments match scipy 1.18.1 ttest_ind(equal_var=False) and statsmodels 0.15.0 anova_generic(use_var="unequal") and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/moments-golden.test.ts, test/tables-golden.test.ts).
diff --git a/test/snapshots/attrition.json b/test/snapshots/attrition.json
new file mode 100644
index 00000000..229858ae
--- /dev/null
+++ b/test/snapshots/attrition.json
@@ -0,0 +1,154 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "LUAD KRAS G12C, immunotherapy": {
+      "count": 279,
+      "text": "CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample) AND OQL[KRAS: G12C] AND has Immuno treatment AND followupMonths ≥ 6 months",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "LUAD KRAS wild-type": {
+      "count": 3976,
+      "text": "CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample) AND NOT OQL[KRAS]",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "attrition",
+      "cohorts": [
+        "LUAD KRAS G12C, immunotherapy",
+        "LUAD KRAS wild-type"
+      ],
+      "attrition": [
+        {
+          "cohort": "LUAD KRAS G12C, immunotherapy",
+          "base": 24950,
+          "steps": [
+            {
+              "label": "CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample)",
+              "n": 5957,
+              "excluded": 18993,
+              "pctOfBase": 23.9
+            },
+            {
+              "label": "OQL[KRAS: G12C]",
+              "n": 793,
+              "excluded": 5164,
+              "pctOfBase": 3.2
+            },
+            {
+              "label": "has Immuno treatment",
+              "n": 357,
+              "excluded": 436,
+              "pctOfBase": 1.4
+            },
+            {
+              "label": "followupMonths ≥ 6 months",
+              "n": 279,
+              "excluded": 78,
+              "pctOfBase": 1.1
+            }
+          ],
+          "studies": [
+            "msk_chord_2024"
+          ]
+        },
+        {
+          "cohort": "LUAD KRAS wild-type",
+          "base": 24950,
+          "steps": [
+            {
+              "label": "CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample)",
+              "n": 5957,
+              "excluded": 18993,
+              "pctOfBase": 23.9
+            },
+            {
+              "label": "NOT OQL[KRAS]",
+              "n": 3976,
+              "excluded": 1981,
+              "pctOfBase": 15.9
+            }
+          ],
+          "studies": [
+            "msk_chord_2024"
+          ]
+        }
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Each step counts the patients left after ANDing that clause onto every clause above it, in the order the cohort lists them. The order changes the path, never the final count: reorder the clauses to see a different attrition sequence. Each view further drops patients who lack its own data (survival without follow-up, genes never profiled), and says how many in its own caption.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count the cohort clause by clause",
+            "detail": "One attrition query per step above, per study: the patients left after ANDing that clause onto every clause before it, in the order the cohort lists them, summed across msk_chord_2024. Nothing is computed from these counts beyond the differences between steps.",
+            "scope": "LUAD KRAS G12C, immunotherapy",
+            "params": [
+              [
+                "every patient",
+                "24,950"
+              ],
+              [
+                "CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample)",
+                "5,957 (−18,993)"
+              ],
+              [
+                "OQL[KRAS: G12C]",
+                "793 (−5,164)"
+              ],
+              [
+                "has Immuno treatment",
+                "357 (−436)"
+              ],
+              [
+                "followupMonths ≥ 6 months",
+                "279 (−78)"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count the cohort clause by clause",
+            "detail": "One attrition query per step above, per study: the patients left after ANDing that clause onto every clause before it, in the order the cohort lists them, summed across msk_chord_2024. Nothing is computed from these counts beyond the differences between steps.",
+            "scope": "LUAD KRAS wild-type",
+            "params": [
+              [
+                "every patient",
+                "24,950"
+              ],
+              [
+                "CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample)",
+                "5,957 (−18,993)"
+              ],
+              [
+                "NOT OQL[KRAS]",
+                "3,976 (−1,981)"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [],
+          "file": "lib/views/attrition.ts",
+          "commit": null
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/attrition.sql b/test/snapshots/attrition.sql
new file mode 100644
index 00000000..573a8c20
--- /dev/null
+++ b/test/snapshots/attrition.sql
@@ -0,0 +1,176 @@
+-- cohort LUAD KRAS G12C, immunotherapy
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation' AND g.mutation_variant = 'G12C'))
+INTERSECT DISTINCT
+(WITH
+study_patients AS (
+  SELECT p.internal_id FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+),
+sel_a AS (
+SELECT ce.patient_id, ce.start_date, ce.stop_date
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.clinical_event_data a0 ON a0.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(a0.key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(a0.value)) = lowerUTF8(trimBoth('Immuno'))
+  WHERE lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.patient_id IN (SELECT internal_id FROM study_patients)
+)
+SELECT DISTINCT p.stable_id AS pid
+FROM sel_a
+  JOIN snapshot_db.patient p ON p.internal_id = sel_a.patient_id
+WHERE 1
+ORDER BY pid LIMIT 1000000)
+INTERSECT DISTINCT
+(SELECT pid FROM (
+SELECT p.stable_id AS pid, max(ce.start_date) / 30.4375 AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+  GROUP BY pid
+)
+  WHERE v >= 6)
+
+-- cohort LUAD KRAS wild-type
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+
+-- view 0 attrition: attrition:LUAD KRAS G12C, immunotherapy:step 0 · msk_chord_2024
+SELECT count() AS n FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+
+-- view 0 attrition: attrition:LUAD KRAS G12C, immunotherapy:step 1 · msk_chord_2024
+SELECT count() AS n FROM ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma')))))
+
+-- view 0 attrition: attrition:LUAD KRAS G12C, immunotherapy:step 2 · msk_chord_2024
+SELECT count() AS n FROM ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation' AND g.mutation_variant = 'G12C')))
+
+-- view 0 attrition: attrition:LUAD KRAS G12C, immunotherapy:step 3 · msk_chord_2024
+SELECT count() AS n FROM ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation' AND g.mutation_variant = 'G12C'))
+INTERSECT DISTINCT
+(WITH
+study_patients AS (
+  SELECT p.internal_id FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+),
+sel_a AS (
+SELECT ce.patient_id, ce.start_date, ce.stop_date
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.clinical_event_data a0 ON a0.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(a0.key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(a0.value)) = lowerUTF8(trimBoth('Immuno'))
+  WHERE lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.patient_id IN (SELECT internal_id FROM study_patients)
+)
+SELECT DISTINCT p.stable_id AS pid
+FROM sel_a
+  JOIN snapshot_db.patient p ON p.internal_id = sel_a.patient_id
+WHERE 1
+ORDER BY pid LIMIT 1000000))
+
+-- view 0 attrition: attrition:LUAD KRAS G12C, immunotherapy:step 4 · msk_chord_2024
+SELECT count() AS n FROM ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation' AND g.mutation_variant = 'G12C'))
+INTERSECT DISTINCT
+(WITH
+study_patients AS (
+  SELECT p.internal_id FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+),
+sel_a AS (
+SELECT ce.patient_id, ce.start_date, ce.stop_date
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.clinical_event_data a0 ON a0.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(a0.key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(a0.value)) = lowerUTF8(trimBoth('Immuno'))
+  WHERE lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.patient_id IN (SELECT internal_id FROM study_patients)
+)
+SELECT DISTINCT p.stable_id AS pid
+FROM sel_a
+  JOIN snapshot_db.patient p ON p.internal_id = sel_a.patient_id
+WHERE 1
+ORDER BY pid LIMIT 1000000)
+INTERSECT DISTINCT
+(SELECT pid FROM (
+SELECT p.stable_id AS pid, max(ce.start_date) / 30.4375 AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+  GROUP BY pid
+)
+  WHERE v >= 6))
+
+-- view 0 attrition: attrition:LUAD KRAS wild-type:step 0 · msk_chord_2024
+SELECT count() AS n FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+
+-- view 0 attrition: attrition:LUAD KRAS wild-type:step 1 · msk_chord_2024
+SELECT count() AS n FROM ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma')))))
+
+-- view 0 attrition: attrition:LUAD KRAS wild-type:step 2 · msk_chord_2024
+SELECT count() AS n FROM ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Lung Adenocarcinoma'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)))))
diff --git a/test/snapshots/attrition.svg b/test/snapshots/attrition.svg
new file mode 100644
index 00000000..4c732e00
--- /dev/null
+++ b/test/snapshots/attrition.svg
@@ -0,0 +1,2 @@
+<!-- view 0 attrition -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 760 406" width="100%" role="img" aria-label="Cohort attrition — LUAD KRAS G12C, immunotherapy, LUAD KRAS wild-type" style="max-width:760px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Cohort attrition — LUAD KRAS G12C, immunotherapy, LUAD KRAS wild-type</title><rect width="760" height="406" fill="#ffffff"/><text x="8" y="14" font-size="12" fill="#16232b" font-weight="600">LUAD KRAS G12C, immunotherapy</text><g><rect x="8" y="22" width="340" height="42" rx="4" fill="#ffffff" stroke="#dde4e8" stroke-width="1"/><text x="16" y="39" font-size="11" fill="#5b6b76">Every patient in msk_chord_2024</text><text x="16" y="55" font-size="12" font-weight="600" fill="#16232b">24,950<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">patients</tspan></text></g><line x1="32" y1="64" x2="32" y2="86" stroke="#5b6b76" stroke-width="1"/><path d="M 28 81 L 32 87 L 36 81" fill="none" stroke="#5b6b76" stroke-width="1"/><text x="42" y="79.5" font-size="10" fill="#5b6b76">−18,993 excluded</text><g><rect x="8" y="88" width="340" height="56" rx="4" fill="#ffffff" stroke="#dde4e8" stroke-width="1"/><text x="16" y="105" font-size="11" fill="#5b6b76">CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any</text><text x="16" y="119" font-size="11" fill="#5b6b76">sample)</text><text x="16" y="135" font-size="12" font-weight="600" fill="#16232b">5,957<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">(23.9% of base)</tspan></text></g><line x1="32" y1="144" x2="32" y2="166" stroke="#5b6b76" stroke-width="1"/><path d="M 28 161 L 32 167 L 36 161" fill="none" stroke="#5b6b76" stroke-width="1"/><text x="42" y="159.5" font-size="10" fill="#5b6b76">−5,164 excluded</text><g><rect x="8" y="168" width="340" height="42" rx="4" fill="#ffffff" stroke="#dde4e8" stroke-width="1"/><text x="16" y="185" font-size="11" fill="#5b6b76">OQL[KRAS: G12C]</text><text x="16" y="201" font-size="12" font-weight="600" fill="#16232b">793<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">(3.2% of base)</tspan></text></g><line x1="32" y1="210" x2="32" y2="232" stroke="#5b6b76" stroke-width="1"/><path d="M 28 227 L 32 233 L 36 227" fill="none" stroke="#5b6b76" stroke-width="1"/><text x="42" y="225.5" font-size="10" fill="#5b6b76">−436 excluded</text><g><rect x="8" y="234" width="340" height="42" rx="4" fill="#ffffff" stroke="#dde4e8" stroke-width="1"/><text x="16" y="251" font-size="11" fill="#5b6b76">has Immuno treatment</text><text x="16" y="267" font-size="12" font-weight="600" fill="#16232b">357<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">(1.4% of base)</tspan></text></g><line x1="32" y1="276" x2="32" y2="298" stroke="#5b6b76" stroke-width="1"/><path d="M 28 293 L 32 299 L 36 293" fill="none" stroke="#5b6b76" stroke-width="1"/><text x="42" y="291.5" font-size="10" fill="#5b6b76">−78 excluded</text><g><rect x="8" y="300" width="340" height="42" rx="4" fill="#ffffff" stroke="#0072B2" stroke-width="2"/><text x="16" y="317" font-size="11" fill="#5b6b76">followupMonths ≥ 6 months</text><text x="16" y="333" font-size="12" font-weight="600" fill="#16232b">279<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">(1.1% of base)</tspan></text></g><text x="368" y="14" font-size="12" fill="#16232b" font-weight="600">LUAD KRAS wild-type</text><g><rect x="368" y="22" width="340" height="42" rx="4" fill="#ffffff" stroke="#dde4e8" stroke-width="1"/><text x="376" y="39" font-size="11" fill="#5b6b76">Every patient in msk_chord_2024</text><text x="376" y="55" font-size="12" font-weight="600" fill="#16232b">24,950<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">patients</tspan></text></g><line x1="392" y1="64" x2="392" y2="86" stroke="#5b6b76" stroke-width="1"/><path d="M 388 81 L 392 87 L 396 81" fill="none" stroke="#5b6b76" stroke-width="1"/><text x="402" y="79.5" font-size="10" fill="#5b6b76">−18,993 excluded</text><g><rect x="368" y="88" width="340" height="56" rx="4" fill="#ffffff" stroke="#dde4e8" stroke-width="1"/><text x="376" y="105" font-size="11" fill="#5b6b76">CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any</text><text x="376" y="119" font-size="11" fill="#5b6b76">sample)</text><text x="376" y="135" font-size="12" font-weight="600" fill="#16232b">5,957<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">(23.9% of base)</tspan></text></g><line x1="392" y1="144" x2="392" y2="166" stroke="#5b6b76" stroke-width="1"/><path d="M 388 161 L 392 167 L 396 161" fill="none" stroke="#5b6b76" stroke-width="1"/><text x="402" y="159.5" font-size="10" fill="#5b6b76">−1,981 excluded</text><g><rect x="368" y="168" width="340" height="42" rx="4" fill="#ffffff" stroke="#E69F00" stroke-width="2"/><text x="376" y="185" font-size="11" fill="#5b6b76">NOT OQL[KRAS]</text><text x="376" y="201" font-size="12" font-weight="600" fill="#16232b">3,976<tspan dx="5" font-size="10" font-weight="400" fill="#5b6b76">(15.9% of base)</tspan></text></g><text x="8" y="360" font-size="10" fill="#5b6b76">Each step counts the patients left after ANDing that clause onto every clause above it, in the order the cohort lists them. The order changes the path, never the final</text><text x="8" y="374" font-size="10" fill="#5b6b76">count: reorder the clauses to see a different attrition sequence. Each view further drops patients who lack its own data (survival without follow-up, genes never</text><text x="8" y="388" font-size="10" fill="#5b6b76">profiled), and says how many in its own caption.</text></svg>
diff --git a/test/snapshots/attrition.txt b/test/snapshots/attrition.txt
new file mode 100644
index 00000000..aec3dc4e
--- /dev/null
+++ b/test/snapshots/attrition.txt
@@ -0,0 +1,27 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- LUAD KRAS G12C, immunotherapy: 279 patients — CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample) AND OQL[KRAS: G12C] AND has Immuno treatment AND followupMonths ≥ 6 months
+- LUAD KRAS wild-type: 3,976 patients — CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample) AND NOT OQL[KRAS]
+
+Cohort attrition (patients left after each clause, in order):
+  LUAD KRAS G12C, immunotherapy: study base 24,950
+    → CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample): 5,957 (−18,993)
+    → OQL[KRAS: G12C]: 793 (−5,164)
+    → has Immuno treatment: 357 (−436)
+    → followupMonths ≥ 6 months: 279 (−78)
+  LUAD KRAS wild-type: study base 24,950
+    → CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample): 5,957 (−18,993)
+    → NOT OQL[KRAS]: 3,976 (−1,981)
+  CAVEAT (tell the user): Each step counts the patients left after ANDing that clause onto every clause above it, in the order the cohort lists them. The order changes the path, never the final count: reorder the clauses to see a different attrition sequence. Each view further drops patients who lack its own data (survival without follow-up, genes never profiled), and says how many in its own caption.
+  METHOD:
+    - Count the cohort clause by clause [LUAD KRAS G12C, immunotherapy]: One attrition query per step above, per study: the patients left after ANDing that clause onto every clause before it, in the order the cohort lists them, summed across msk_chord_2024. Nothing is computed from these counts beyond the differences between steps.
+        every patient: 24,950
+        CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample): 5,957 (−18,993)
+        OQL[KRAS: G12C]: 793 (−5,164)
+        has Immuno treatment: 357 (−436)
+        followupMonths ≥ 6 months: 279 (−78)
+    - Count the cohort clause by clause [LUAD KRAS wild-type]: One attrition query per step above, per study: the patients left after ANDing that clause onto every clause before it, in the order the cohort lists them, summed across msk_chord_2024. Nothing is computed from these counts beyond the differences between steps.
+        every patient: 24,950
+        CANCER_TYPE_DETAILED in [Lung Adenocarcinoma] (any sample): 5,957 (−18,993)
+        NOT OQL[KRAS]: 3,976 (−1,981)
diff --git a/test/snapshots/cancertypes.json b/test/snapshots/cancertypes.json
new file mode 100644
index 00000000..5ccdd938
--- /dev/null
+++ b/test/snapshots/cancertypes.json
@@ -0,0 +1,1562 @@
+{
+  "study": "msk_impact_50k_2026",
+  "cohorts": {
+    "all patients": {
+      "count": 48179,
+      "text": "all patients in the study",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "cancertypes",
+      "cohorts": [
+        "all patients"
+      ],
+      "cancertypes": {
+        "groupBy": "CANCER_TYPE",
+        "measure": "any",
+        "genes": [
+          "KRAS",
+          "NRAS",
+          "HRAS"
+        ],
+        "minSamples": 200,
+        "show": "frequency",
+        "unit": "patient",
+        "panels": [
+          {
+            "cohort": "all patients",
+            "samples": 48179,
+            "profiled": 48179,
+            "altered": 10796,
+            "groups": [
+              {
+                "key": "pancreatic cancer",
+                "label": "Pancreatic Cancer",
+                "samples": 2729,
+                "profiled": 2729,
+                "altered": 2263,
+                "mutation": 2211,
+                "amp": 3,
+                "homdel": 0,
+                "sv": 0,
+                "multiple": 49,
+                "profiledFor": {
+                  "mutation": 2729,
+                  "cna": 2729,
+                  "sv": 2729
+                }
+              },
+              {
+                "key": "appendiceal cancer",
+                "label": "Appendiceal Cancer",
+                "samples": 269,
+                "profiled": 269,
+                "altered": 163,
+                "mutation": 163,
+                "amp": 0,
+                "homdel": 0,
+                "sv": 0,
+                "multiple": 0,
+                "profiledFor": {
+                  "mutation": 269,
+                  "cna": 269,
+                  "sv": 269
+                }
+              },
+              {
+                "key": "colorectal cancer",
+                "label": "Colorectal Cancer",
+                "samples": 4993,
+                "profiled": 4993,
+                "altered": 2450,
+                "mutation": 2383,
+                "amp": 48,
+                "homdel": 4,
+                "sv": 1,
+                "multiple": 14,
+                "profiledFor": {
+                  "mutation": 4993,
+                  "cna": 4993,
+                  "sv": 4993
+                }
+              },
+              {
+                "key": "non-small cell lung cancer",
+                "label": "Non-Small Cell Lung Cancer",
+                "samples": 6633,
+                "profiled": 6633,
+                "altered": 2103,
+                "mutation": 1943,
+                "amp": 71,
+                "homdel": 20,
+                "sv": 1,
+                "multiple": 68,
+                "profiledFor": {
+                  "mutation": 6633,
+                  "cna": 6633,
+                  "sv": 6633
+                }
+              },
+              {
+                "key": "germ cell tumor",
+                "label": "Germ Cell Tumor",
+                "samples": 663,
+                "profiled": 663,
+                "altered": 208,
+                "mutation": 78,
+                "amp": 114,
+                "homdel": 0,
+                "sv": 0,
+                "multiple": 16,
+                "profiledFor": {
+                  "mutation": 663,
+                  "cna": 663,
+                  "sv": 663
+                }
+              },
+              {
+                "key": "melanoma",
+                "label": "Melanoma",
+                "samples": 1706,
+                "profiled": 1706,
+                "altered": 506,
+                "mutation": 452,
+                "amp": 17,
+                "homdel": 4,
+                "sv": 0,
+                "multiple": 33,
+                "profiledFor": {
+                  "mutation": 1706,
+                  "cna": 1706,
+                  "sv": 1706
+                }
+              },
+              {
+                "key": "cancer of unknown primary",
+                "label": "Cancer of Unknown Primary",
+                "samples": 1535,
+                "profiled": 1535,
+                "altered": 395,
+                "mutation": 348,
+                "amp": 25,
+                "homdel": 7,
+                "sv": 1,
+                "multiple": 14,
+                "profiledFor": {
+                  "mutation": 1535,
+                  "cna": 1535,
+                  "sv": 1535
+                }
+              },
+              {
+                "key": "endometrial cancer",
+                "label": "Endometrial Cancer",
+                "samples": 2373,
+                "profiled": 2373,
+                "altered": 570,
+                "mutation": 541,
+                "amp": 19,
+                "homdel": 6,
+                "sv": 0,
+                "multiple": 4,
+                "profiledFor": {
+                  "mutation": 2373,
+                  "cna": 2373,
+                  "sv": 2373
+                }
+              },
+              {
+                "key": "thyroid cancer",
+                "label": "Thyroid Cancer",
+                "samples": 803,
+                "profiled": 803,
+                "altered": 172,
+                "mutation": 164,
+                "amp": 2,
+                "homdel": 1,
+                "sv": 0,
+                "multiple": 5,
+                "profiledFor": {
+                  "mutation": 803,
+                  "cna": 803,
+                  "sv": 803
+                }
+              },
+              {
+                "key": "esophagogastric cancer",
+                "label": "Esophagogastric Cancer",
+                "samples": 1671,
+                "profiled": 1671,
+                "altered": 311,
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+                    "sv": 1,
+                    "multiple": 1,
+                    "profiledFor": {
+                      "mutation": 6633,
+                      "cna": 6633,
+                      "sv": 6633
+                    }
+                  },
+                  {
+                    "key": "head and neck cancer",
+                    "label": "Head and Neck Cancer",
+                    "samples": 610,
+                    "profiled": 610,
+                    "altered": 6,
+                    "mutation": 6,
+                    "amp": 0,
+                    "homdel": 0,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 610,
+                      "cna": 610,
+                      "sv": 610
+                    }
+                  },
+                  {
+                    "key": "small cell lung cancer",
+                    "label": "Small Cell Lung Cancer",
+                    "samples": 442,
+                    "profiled": 442,
+                    "altered": 4,
+                    "mutation": 3,
+                    "amp": 0,
+                    "homdel": 1,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 442,
+                      "cna": 442,
+                      "sv": 442
+                    }
+                  },
+                  {
+                    "…": "8 more",
+                    "sha256": "98fc00fd9877bd84"
+                  }
+                ],
+                "pooled": {
+                  "key": "__pooled__",
+                  "label": "38 smaller groups",
+                  "samples": 1842,
+                  "profiled": 1842,
+                  "altered": 17,
+                  "mutation": 16,
+                  "amp": 0,
+                  "homdel": 1,
+                  "sv": 0,
+                  "multiple": 0,
+                  "profiledFor": {
+                    "mutation": 1842,
+                    "cna": 1842,
+                    "sv": 1842
+                  },
+                  "count": 38
+                }
+              },
+              {
+                "gene": "HRAS",
+                "groups": [
+                  {
+                    "key": "salivary gland cancer",
+                    "label": "Salivary Gland Cancer",
+                    "samples": 352,
+                    "profiled": 352,
+                    "altered": 24,
+                    "mutation": 22,
+                    "amp": 1,
+                    "homdel": 0,
+                    "sv": 0,
+                    "multiple": 1,
+                    "profiledFor": {
+                      "mutation": 352,
+                      "cna": 352,
+                      "sv": 352
+                    }
+                  },
+                  {
+                    "key": "skin cancer, non-melanoma",
+                    "label": "Skin Cancer, Non-Melanoma",
+                    "samples": 429,
+                    "profiled": 429,
+                    "altered": 23,
+                    "mutation": 23,
+                    "amp": 0,
+                    "homdel": 0,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 429,
+                      "cna": 429,
+                      "sv": 429
+                    }
+                  },
+                  {
+                    "key": "bladder cancer",
+                    "label": "Bladder Cancer",
+                    "samples": 2046,
+                    "profiled": 2046,
+                    "altered": 99,
+                    "mutation": 88,
+                    "amp": 0,
+                    "homdel": 11,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 2046,
+                      "cna": 2046,
+                      "sv": 2046
+                    }
+                  },
+                  {
+                    "key": "thyroid cancer",
+                    "label": "Thyroid Cancer",
+                    "samples": 803,
+                    "profiled": 803,
+                    "altered": 34,
+                    "mutation": 28,
+                    "amp": 2,
+                    "homdel": 1,
+                    "sv": 0,
+                    "multiple": 3,
+                    "profiledFor": {
+                      "mutation": 803,
+                      "cna": 803,
+                      "sv": 803
+                    }
+                  },
+                  {
+                    "key": "head and neck cancer",
+                    "label": "Head and Neck Cancer",
+                    "samples": 610,
+                    "profiled": 610,
+                    "altered": 23,
+                    "mutation": 22,
+                    "amp": 1,
+                    "homdel": 0,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 610,
+                      "cna": 610,
+                      "sv": 610
+                    }
+                  },
+                  {
+                    "key": "melanoma",
+                    "label": "Melanoma",
+                    "samples": 1706,
+                    "profiled": 1706,
+                    "altered": 48,
+                    "mutation": 42,
+                    "amp": 1,
+                    "homdel": 2,
+                    "sv": 0,
+                    "multiple": 3,
+                    "profiledFor": {
+                      "mutation": 1706,
+                      "cna": 1706,
+                      "sv": 1706
+                    }
+                  },
+                  {
+                    "key": "small cell lung cancer",
+                    "label": "Small Cell Lung Cancer",
+                    "samples": 442,
+                    "profiled": 442,
+                    "altered": 10,
+                    "mutation": 4,
+                    "amp": 1,
+                    "homdel": 5,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 442,
+                      "cna": 442,
+                      "sv": 442
+                    }
+                  },
+                  {
+                    "key": "soft tissue sarcoma",
+                    "label": "Soft Tissue Sarcoma",
+                    "samples": 2128,
+                    "profiled": 2128,
+                    "altered": 33,
+                    "mutation": 11,
+                    "amp": 9,
+                    "homdel": 13,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 2128,
+                      "cna": 2128,
+                      "sv": 2128
+                    }
+                  },
+                  {
+                    "key": "prostate cancer",
+                    "label": "Prostate Cancer",
+                    "samples": 2893,
+                    "profiled": 2893,
+                    "altered": 44,
+                    "mutation": 35,
+                    "amp": 1,
+                    "homdel": 7,
+                    "sv": 1,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 2893,
+                      "cna": 2893,
+                      "sv": 2893
+                    }
+                  },
+                  {
+                    "key": "uterine sarcoma",
+                    "label": "Uterine Sarcoma",
+                    "samples": 337,
+                    "profiled": 337,
+                    "altered": 5,
+                    "mutation": 0,
+                    "amp": 2,
+                    "homdel": 3,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 337,
+                      "cna": 337,
+                      "sv": 337
+                    }
+                  },
+                  {
+                    "key": "cancer of unknown primary",
+                    "label": "Cancer of Unknown Primary",
+                    "samples": 1535,
+                    "profiled": 1535,
+                    "altered": 19,
+                    "mutation": 12,
+                    "amp": 2,
+                    "homdel": 5,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 1535,
+                      "cna": 1535,
+                      "sv": 1535
+                    }
+                  },
+                  {
+                    "key": "cervical cancer",
+                    "label": "Cervical Cancer",
+                    "samples": 326,
+                    "profiled": 326,
+                    "altered": 4,
+                    "mutation": 3,
+                    "amp": 0,
+                    "homdel": 1,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 326,
+                      "cna": 326,
+                      "sv": 326
+                    }
+                  },
+                  {
+                    "key": "endometrial cancer",
+                    "label": "Endometrial Cancer",
+                    "samples": 2373,
+                    "profiled": 2373,
+                    "altered": 29,
+                    "mutation": 19,
+                    "amp": 4,
+                    "homdel": 6,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 2373,
+                      "cna": 2373,
+                      "sv": 2373
+                    }
+                  },
+                  {
+                    "key": "glioma",
+                    "label": "Glioma",
+                    "samples": 2042,
+                    "profiled": 2042,
+                    "altered": 20,
+                    "mutation": 7,
+                    "amp": 2,
+                    "homdel": 11,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 2042,
+                      "cna": 2042,
+                      "sv": 2042
+                    }
+                  },
+                  {
+                    "key": "ovarian cancer",
+                    "label": "Ovarian Cancer",
+                    "samples": 2075,
+                    "profiled": 2075,
+                    "altered": 20,
+                    "mutation": 5,
+                    "amp": 2,
+                    "homdel": 13,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 2075,
+                      "cna": 2075,
+                      "sv": 2075
+                    }
+                  },
+                  {
+                    "key": "peripheral nervous system",
+                    "label": "Peripheral Nervous System",
+                    "samples": 324,
+                    "profiled": 324,
+                    "altered": 3,
+                    "mutation": 2,
+                    "amp": 0,
+                    "homdel": 1,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 324,
+                      "cna": 324,
+                      "sv": 324
+                    }
+                  },
+                  {
+                    "key": "breast cancer",
+                    "label": "Breast Cancer",
+                    "samples": 5803,
+                    "profiled": 5803,
+                    "altered": 49,
+                    "mutation": 20,
+                    "amp": 2,
+                    "homdel": 27,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 5803,
+                      "cna": 5803,
+                      "sv": 5803
+                    }
+                  },
+                  {
+                    "key": "gastrointestinal stromal tumor",
+                    "label": "Gastrointestinal Stromal Tumor",
+                    "samples": 520,
+                    "profiled": 520,
+                    "altered": 4,
+                    "mutation": 0,
+                    "amp": 0,
+                    "homdel": 4,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 520,
+                      "cna": 520,
+                      "sv": 520
+                    }
+                  },
+                  {
+                    "key": "colorectal cancer",
+                    "label": "Colorectal Cancer",
+                    "samples": 4993,
+                    "profiled": 4993,
+                    "altered": 37,
+                    "mutation": 33,
+                    "amp": 4,
+                    "homdel": 0,
+                    "sv": 0,
+                    "multiple": 0,
+                    "profiledFor": {
+                      "mutation": 4993,
+                      "cna": 4993,
+                      "sv": 4993
+                    }
+                  },
+                  {
+                    "key": "non-small cell lung cancer",
+                    "label": "Non-Small Cell Lung Cancer",
+                    "samples": 6633,
+                    "profiled": 6633,
+                    "altered": 39,
+                    "mutation": 24,
+                    "amp": 3,
+                    "homdel": 10,
+                    "sv": 0,
+                    "multiple": 2,
+                    "profiledFor": {
+                      "mutation": 6633,
+                      "cna": 6633,
+                      "sv": 6633
+                    }
+                  },
+                  {
+                    "…": "8 more",
+                    "sha256": "2397ab07fdd60ed9"
+                  }
+                ],
+                "pooled": {
+                  "key": "__pooled__",
+                  "label": "38 smaller groups",
+                  "samples": 1842,
+                  "profiled": 1842,
+                  "altered": 21,
+                  "mutation": 12,
+                  "amp": 1,
+                  "homdel": 8,
+                  "sv": 0,
+                  "multiple": 0,
+                  "profiledFor": {
+                    "mutation": 1842,
+                    "cna": 1842,
+                    "sv": 1842
+                  },
+                  "count": 38
+                }
+              }
+            ]
+          }
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_impact_50k_2026",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Patients altered in at least one of 3 genes per CANCER_TYPE group, over the patients profiled for at least one of them (the union of their panels, as an oncoprint's merged row counts it). Any mutation, high-level copy-number change (amplification, deep deletion) or structural variant counts; low-level gains and losses do not. A patient with more than one kind of alteration across the genes counts once, under \"multiple\". A patient counts in every group one of their samples falls in, profiled if any of those samples is, altered if any is. CANCER_TYPE is read per sample and folded on case and whitespace; samples with no value form their own group. Groups with fewer than 200 profiled patients are pooled into one bar rather than dropped. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients per CANCER_TYPE group",
+            "detail": "The cancertypes queries above. Per group, for the genes together and for each gene: the patients profiled for it, and of those the patients with any mutation, amplification, deep deletion or structural variant, split by kind (one kind only, or more than one); and the whole cohort's, each patient once. A patient counts in each group one of their samples is in, profiled if any of those samples is, altered if any is. Per group too, how many were profiled for mutations, copy number and structural variants at all. Counts only.",
+            "scope": "all patients · msk_impact_50k_2026",
+            "rowsOut": 67,
+            "params": [
+              [
+                "genes",
+                "KRAS, NRAS, HRAS"
+              ],
+              [
+                "profiled patients",
+                "48,892"
+              ]
+            ]
+          },
+          {
+            "kind": "transform",
+            "title": "Pool the small groups",
+            "scope": "all patients",
+            "detail": "38 groups with fewer than 200 profiled patients drawn as one pooled bar; 28 drawn on their own.",
+            "params": [
+              [
+                "profiled",
+                "48,179"
+              ],
+              [
+                "altered",
+                "10,796"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [],
+          "file": "lib/views/cancertypes.ts",
+          "commit": null
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/cancertypes.sql b/test/snapshots/cancertypes.sql
new file mode 100644
index 00000000..417d1194
--- /dev/null
+++ b/test/snapshots/cancertypes.sql
@@ -0,0 +1,78 @@
+-- cohort all patients
+-- msk_impact_50k_2026
+SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026'
+
+-- view 0 cancertypes: cancertypes:all patients · msk_impact_50k_2026
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026'),
+samp AS (
+  SELECT sample_unique_id AS suid, patient_unique_id AS puid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+grp AS (
+  SELECT sample_unique_id AS suid, any(lowerUTF8(trimBoth(attribute_value))) AS g, any(attribute_value) AS label FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'CANCER_TYPE'
+    AND sample_unique_id IN (SELECT suid FROM samp) GROUP BY suid
+),
+alt AS (
+  SELECT g.sample_unique_id AS suid, g.hugo_gene_symbol AS gene,
+         multiIf(g.variant_type = 'mutation', 'mutation', g.variant_type = 'cna' AND g.cna_alteration >= 2, 'amp',
+                 g.variant_type = 'cna', 'homdel', 'sv') AS t
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_impact_50k_2026' AND g.hugo_gene_symbol IN ('KRAS','NRAS','HRAS') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND g.sample_unique_id IN (SELECT suid FROM samp)
+  GROUP BY suid, gene, t
+),
+cov AS (
+  SELECT DISTINCT sp.sample_unique_id AS suid, pg.gene AS gene FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('KRAS','NRAS','HRAS')
+  WHERE sp.cancer_study_identifier = 'msk_impact_50k_2026' AND sp.sample_unique_id IN (SELECT suid FROM samp)
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+  UNION DISTINCT SELECT DISTINCT suid, gene FROM alt
+),
+sg AS (
+  SELECT cov.suid AS suid, cov.gene AS gene, groupUniqArrayIf(alt.t, alt.t != '') AS types
+  FROM cov LEFT JOIN alt ON alt.suid = cov.suid AND alt.gene = cov.gene GROUP BY suid, gene
+),
+sg2 AS (SELECT suid, gene, types FROM sg UNION ALL SELECT suid, '*' AS gene, arrayDistinct(arrayFlatten(groupArray(types))) AS types FROM sg GROUP BY suid),
+u AS (
+  SELECT samp.puid AS k, ifNull(grp.g, '') AS g, any(ifNull(grp.label, '')) AS label, sg2.gene AS gene,
+         arrayDistinct(arrayFlatten(groupArray(sg2.types))) AS types
+  FROM sg2 JOIN samp ON samp.suid = sg2.suid LEFT JOIN grp ON grp.suid = sg2.suid
+  GROUP BY k, g, gene
+),
+uall AS (SELECT k, '\x01all' AS g, '' AS label, gene, arrayDistinct(arrayFlatten(groupArray(types))) AS types FROM u GROUP BY k, gene),
+uu AS (SELECT k, g, label, gene, types FROM u UNION ALL SELECT k, g, label, gene, types FROM uall)
+SELECT g AS grp, gene, any(label) AS label, count() AS profiled,
+       countIf(length(types) > 0) AS altered,
+       countIf(types = ['mutation']) AS mutation, countIf(types = ['amp']) AS amp,
+       countIf(types = ['homdel']) AS homdel, countIf(types = ['sv']) AS sv,
+       countIf(length(types) > 1) AS multiple
+FROM uu GROUP BY grp, gene
+
+-- view 0 cancertypes: cancertypes:samples:all patients · msk_impact_50k_2026
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026'),
+samp AS (
+  SELECT sample_unique_id AS suid, patient_unique_id AS puid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+prof AS (
+  SELECT sample_unique_id AS suid, groupUniqArray(alteration_type) AS types FROM snapshot_db.sample_to_gene_panel_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND sample_unique_id IN (SELECT suid FROM samp) GROUP BY suid
+)
+SELECT ifNull(grp.g, '') AS grp, uniqExact(samp.puid) AS samples,
+       uniqExactIf(samp.puid, has(prof.types, 'MUTATION_EXTENDED')) AS mut,
+       uniqExactIf(samp.puid, has(prof.types, 'COPY_NUMBER_ALTERATION')) AS cna,
+       uniqExactIf(samp.puid, has(prof.types, 'STRUCTURAL_VARIANT')) AS sv
+FROM samp LEFT JOIN prof ON prof.suid = samp.suid
+LEFT JOIN (
+  SELECT sample_unique_id AS suid, any(lowerUTF8(trimBoth(attribute_value))) AS g FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'CANCER_TYPE'
+    AND sample_unique_id IN (SELECT suid FROM samp) GROUP BY suid
+) grp ON grp.suid = samp.suid
+GROUP BY grp
+UNION ALL SELECT '\x01all' AS grp, uniqExact(samp.puid), uniqExactIf(samp.puid, has(prof.types, 'MUTATION_EXTENDED')),
+       uniqExactIf(samp.puid, has(prof.types, 'COPY_NUMBER_ALTERATION')), uniqExactIf(samp.puid, has(prof.types, 'STRUCTURAL_VARIANT'))
+FROM samp LEFT JOIN prof ON prof.suid = samp.suid
diff --git a/test/snapshots/cancertypes.svg b/test/snapshots/cancertypes.svg
new file mode 100644
index 00000000..4f673daa
--- /dev/null
+++ b/test/snapshots/cancertypes.svg
@@ -0,0 +1,2 @@
+<!-- view 0 cancertypes -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 860 541" width="100%" role="img" aria-label="Cancer types summary — KRAS, NRAS, HRAS" style="max-width:860px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Cancer types summary — KRAS, NRAS, HRAS</title><rect width="860" height="541" fill="#ffffff"/><g transform="translate(0 0)"><text x="8" y="18" font-size="12" fill="#16232b" font-weight="600">all patients</text><text x="79.6" y="18" font-size="10" fill="#5b6b76">10,796 of 48,179 profiled patients altered (22.4%)</text><line x1="91" y1="274" x2="848" y2="274" stroke="#dde4e8" stroke-width="0.75"/><text x="85" y="277.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="91" y1="226" x2="848" y2="226" stroke="#dde4e8" stroke-width="0.75"/><text x="85" y="229.5" font-size="10" fill="#5b6b76" text-anchor="end">20%</text><line x1="91" y1="178" x2="848" y2="178" stroke="#dde4e8" stroke-width="0.75"/><text x="85" y="181.5" font-size="10" fill="#5b6b76" text-anchor="end">40%</text><line x1="91" y1="130" x2="848" y2="130" stroke="#dde4e8" stroke-width="0.75"/><text x="85" y="133.5" font-size="10" fill="#5b6b76" text-anchor="end">60%</text><line x1="91" y1="82" x2="848" y2="82" stroke="#dde4e8" stroke-width="0.75"/><text x="85" y="85.5" font-size="10" fill="#5b6b76" text-anchor="end">80%</text><line x1="91" y1="34" x2="848" y2="34" stroke="#dde4e8" stroke-width="0.75"/><text x="85" y="37.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><text x="44.8" y="154" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 44.8 154)">Alteration Frequency</text><line x1="91" y1="274" x2="848" y2="274" stroke="#5b6b76" stroke-width="1"/><rect x="97.9" y="79.6" width="20.3" height="194.4" fill="#008000"><title>Pancreatic Cancer — Mutation: 2,211 of 2,729 profiled patients</title></rect><rect x="97.9" y="79.3" width="20.3" height="0.3" fill="#ff0000"><title>Pancreatic Cancer — Amplification: 3 of 2,729 profiled patients</title></rect><rect x="97.9" y="75" width="20.3" height="4.3" fill="#6e7a83"><title>Pancreatic Cancer — Multiple Alterations: 49 of 2,729 profiled patients</title></rect><rect x="97.9" y="75" width="20.3" height="199" fill="none"><title>Pancreatic Cancer: 82.9% (2,263 / 2,729 patients)</title></rect><rect x="123.8" y="128.6" width="20.3" height="145.4" fill="#008000"><title>Appendiceal Cancer — Mutation: 163 of 269 profiled patients</title></rect><rect x="123.8" y="128.6" width="20.3" height="145.4" fill="none"><title>Appendiceal Cancer: 60.6% (163 / 269 patients)</title></rect><rect x="149.8" y="159.5" width="20.3" height="114.5" fill="#008000"><title>Colorectal Cancer — Mutation: 2,383 of 4,993 profiled patients</title></rect><rect x="149.8" y="157.1" width="20.3" height="2.3" fill="#ff0000"><title>Colorectal Cancer — Amplification: 48 of 4,993 profiled patients</title></rect><rect x="149.8" y="157" width="20.3" height="0.2" fill="#0000ff"><title>Colorectal Cancer — Deep Deletion: 4 of 4,993 profiled patients</title></rect><rect x="149.8" y="156.9" width="20.3" height="0" fill="#8b00c9"><title>Colorectal Cancer — Structural Variant: 1 of 4,993 profiled patients</title></rect><rect x="149.8" y="156.2" width="20.3" height="0.7" fill="#6e7a83"><title>Colorectal Cancer — Multiple Alterations: 14 of 4,993 profiled patients</title></rect><rect x="149.8" y="156.2" width="20.3" height="117.8" fill="none"><title>Colorectal Cancer: 49.1% (2,450 / 4,993 patients)</title></rect><rect x="175.8" y="203.7" width="20.3" height="70.3" fill="#008000"><title>Non-Small Cell Lung Cancer — Mutation: 1,943 of 6,633 profiled patients</title></rect><rect x="175.8" y="201.1" width="20.3" height="2.6" fill="#ff0000"><title>Non-Small Cell Lung Cancer — Amplification: 71 of 6,633 profiled patients</title></rect><rect x="175.8" y="200.4" width="20.3" height="0.7" fill="#0000ff"><title>Non-Small Cell Lung Cancer — Deep Deletion: 20 of 6,633 profiled patients</title></rect><rect x="175.8" y="200.4" width="20.3" height="0" fill="#8b00c9"><title>Non-Small Cell Lung Cancer — Structural Variant: 1 of 6,633 profiled patients</title></rect><rect x="175.8" y="197.9" width="20.3" height="2.5" fill="#6e7a83"><title>Non-Small Cell Lung Cancer — Multiple Alterations: 68 of 6,633 profiled patients</title></rect><rect x="175.8" y="197.9" width="20.3" height="76.1" fill="none"><title>Non-Small Cell Lung Cancer: 31.7% (2,103 / 6,633 patients)</title></rect><rect x="201.7" y="245.8" width="20.3" height="28.2" fill="#008000"><title>Germ Cell Tumor — Mutation: 78 of 663 profiled patients</title></rect><rect x="201.7" y="204.5" width="20.3" height="41.3" fill="#ff0000"><title>Germ Cell Tumor — Amplification: 114 of 663 profiled patients</title></rect><rect x="201.7" y="198.7" width="20.3" height="5.8" fill="#6e7a83"><title>Germ Cell Tumor — Multiple Alterations: 16 of 663 profiled patients</title></rect><rect x="201.7" y="198.7" width="20.3" height="75.3" fill="none"><title>Germ Cell Tumor: 31.4% (208 / 663 patients)</title></rect><rect x="227.7" y="210.4" width="20.3" height="63.6" fill="#008000"><title>Melanoma — Mutation: 452 of 1,706 profiled patients</title></rect><rect x="227.7" y="208" width="20.3" height="2.4" fill="#ff0000"><title>Melanoma — Amplification: 17 of 1,706 profiled patients</title></rect><rect x="227.7" y="207.5" width="20.3" height="0.6" fill="#0000ff"><title>Melanoma — Deep Deletion: 4 of 1,706 profiled patients</title></rect><rect x="227.7" y="202.8" width="20.3" height="4.6" fill="#6e7a83"><title>Melanoma — Multiple Alterations: 33 of 1,706 profiled patients</title></rect><rect x="227.7" y="202.8" width="20.3" height="71.2" fill="none"><title>Melanoma: 29.7% (506 / 1,706 patients)</title></rect><rect x="253.6" y="219.6" width="20.3" height="54.4" fill="#008000"><title>Cancer of Unknown Primary — Mutation: 348 of 1,535 profiled patients</title></rect><rect x="253.6" y="215.7" width="20.3" height="3.9" fill="#ff0000"><title>Cancer of Unknown Primary — Amplification: 25 of 1,535 profiled patients</title></rect><rect x="253.6" y="214.6" width="20.3" height="1.1" fill="#0000ff"><title>Cancer of Unknown Primary — Deep Deletion: 7 of 1,535 profiled patients</title></rect><rect x="253.6" y="214.4" width="20.3" height="0.2" fill="#8b00c9"><title>Cancer of Unknown Primary — Structural Variant: 1 of 1,535 profiled patients</title></rect><rect x="253.6" y="212.2" width="20.3" height="2.2" fill="#6e7a83"><title>Cancer of Unknown Primary — Multiple Alterations: 14 of 1,535 profiled patients</title></rect><rect x="253.6" y="212.2" width="20.3" height="61.8" fill="none"><title>Cancer of Unknown Primary: 25.7% (395 / 1,535 patients)</title></rect><rect x="279.6" y="219.3" width="20.3" height="54.7" fill="#008000"><title>Endometrial Cancer — Mutation: 541 of 2,373 profiled patients</title></rect><rect x="279.6" y="217.4" width="20.3" height="1.9" fill="#ff0000"><title>Endometrial Cancer — Amplification: 19 of 2,373 profiled patients</title></rect><rect x="279.6" y="216.8" width="20.3" height="0.6" fill="#0000ff"><title>Endometrial Cancer — Deep Deletion: 6 of 2,373 profiled patients</title></rect><rect x="279.6" y="216.4" width="20.3" height="0.4" fill="#6e7a83"><title>Endometrial Cancer — Multiple Alterations: 4 of 2,373 profiled patients</title></rect><rect x="279.6" y="216.4" width="20.3" height="57.6" fill="none"><title>Endometrial Cancer: 24.0% (570 / 2,373 patients)</title></rect><rect x="305.6" y="225" width="20.3" height="49" fill="#008000"><title>Thyroid Cancer — Mutation: 164 of 803 profiled patients</title></rect><rect x="305.6" y="224.4" width="20.3" height="0.6" fill="#ff0000"><title>Thyroid Cancer — Amplification: 2 of 803 profiled patients</title></rect><rect x="305.6" y="224.1" width="20.3" height="0.3" fill="#0000ff"><title>Thyroid Cancer — Deep Deletion: 1 of 803 profiled patients</title></rect><rect x="305.6" y="222.6" width="20.3" height="1.5" fill="#6e7a83"><title>Thyroid Cancer — Multiple Alterations: 5 of 803 profiled patients</title></rect><rect x="305.6" y="222.6" width="20.3" height="51.4" fill="none"><title>Thyroid Cancer: 21.4% (172 / 803 patients)</title></rect><rect x="331.5" y="254.8" width="20.3" height="19.2" fill="#008000"><title>Esophagogastric Cancer — Mutation: 134 of 1,671 profiled patients</title></rect><rect x="331.5" y="230.5" width="20.3" height="24.3" fill="#ff0000"><title>Esophagogastric Cancer — Amplification: 169 of 1,671 profiled patients</title></rect><rect x="331.5" y="230.1" width="20.3" height="0.4" fill="#0000ff"><title>Esophagogastric Cancer — Deep Deletion: 3 of 1,671 profiled patients</title></rect><rect x="331.5" y="229.3" width="20.3" height="0.7" fill="#6e7a83"><title>Esophagogastric Cancer — Multiple Alterations: 5 of 1,671 profiled patients</title></rect><rect x="331.5" y="229.3" width="20.3" height="44.7" fill="none"><title>Esophagogastric Cancer: 18.6% (311 / 1,671 patients)</title></rect><rect x="357.5" y="251" width="20.3" height="23" fill="#008000"><title>Ovarian Cancer — Mutation: 199 of 2,075 profiled patients</title></rect><rect x="357.5" y="242.3" width="20.3" height="8.7" fill="#ff0000"><title>Ovarian Cancer — Amplification: 75 of 2,075 profiled patients</title></rect><rect x="357.5" y="240.8" width="20.3" height="1.5" fill="#0000ff"><title>Ovarian Cancer — Deep Deletion: 13 of 2,075 profiled patients</title></rect><rect x="357.5" y="240.7" width="20.3" height="0.1" fill="#8b00c9"><title>Ovarian Cancer — Structural Variant: 1 of 2,075 profiled patients</title></rect><rect x="357.5" y="239.6" width="20.3" height="1" fill="#6e7a83"><title>Ovarian Cancer — Multiple Alterations: 9 of 2,075 profiled patients</title></rect><rect x="357.5" y="239.6" width="20.3" height="34.4" fill="none"><title>Ovarian Cancer: 14.3% (297 / 2,075 patients)</title></rect><rect x="383.5" y="243.3" width="20.3" height="30.7" fill="#008000"><title>Hepatobiliary Cancer — Mutation: 173 of 1,353 profiled patients</title></rect><rect x="383.5" y="240.5" width="20.3" height="2.8" fill="#ff0000"><title>Hepatobiliary Cancer — Amplification: 16 of 1,353 profiled patients</title></rect><rect x="383.5" y="240.1" width="20.3" height="0.4" fill="#6e7a83"><title>Hepatobiliary Cancer — Multiple Alterations: 2 of 1,353 profiled patients</title></rect><rect x="383.5" y="240.1" width="20.3" height="33.9" fill="none"><title>Hepatobiliary Cancer: 14.1% (191 / 1,353 patients)</title></rect><rect x="409.4" y="244.6" width="20.3" height="29.4" fill="#008000"><title>Cervical Cancer — Mutation: 40 of 326 profiled patients</title></rect><rect x="409.4" y="243.1" width="20.3" height="1.5" fill="#0000ff"><title>Cervical Cancer — Deep Deletion: 2 of 326 profiled patients</title></rect><rect x="409.4" y="241.6" width="20.3" height="1.5" fill="#6e7a83"><title>Cervical Cancer — Multiple Alterations: 2 of 326 profiled patients</title></rect><rect x="409.4" y="241.6" width="20.3" height="32.4" fill="none"><title>Cervical Cancer: 13.5% (44 / 326 patients)</title></rect><rect x="435.4" y="248.8" width="20.3" height="25.2" fill="#008000"><title>Bladder Cancer — Mutation: 215 of 2,046 profiled patients</title></rect><rect x="435.4" y="245.6" width="20.3" height="3.2" fill="#ff0000"><title>Bladder Cancer — Amplification: 27 of 2,046 profiled patients</title></rect><rect x="435.4" y="244.1" width="20.3" height="1.5" fill="#0000ff"><title>Bladder Cancer — Deep Deletion: 13 of 2,046 profiled patients</title></rect><rect x="435.4" y="244" width="20.3" height="0.1" fill="#8b00c9"><title>Bladder Cancer — Structural Variant: 1 of 2,046 profiled patients</title></rect><rect x="435.4" y="243.6" width="20.3" height="0.4" fill="#6e7a83"><title>Bladder Cancer — Multiple Alterations: 3 of 2,046 profiled patients</title></rect><rect x="435.4" y="243.6" width="20.3" height="30.4" fill="none"><title>Bladder Cancer: 12.7% (259 / 2,046 patients)</title></rect><rect x="461.4" y="252.7" width="20.3" height="21.3" fill="#008000"><title>Skin Cancer, Non-Melanoma — Mutation: 38 of 429 profiled patients</title></rect><rect x="461.4" y="251.6" width="20.3" height="1.1" fill="#0000ff"><title>Skin Cancer, Non-Melanoma — Deep Deletion: 2 of 429 profiled patients</title></rect><rect x="461.4" y="251.6" width="20.3" height="22.4" fill="none"><title>Skin Cancer, Non-Melanoma: 9.3% (40 / 429 patients)</title></rect><rect x="487.3" y="257.6" width="20.3" height="16.4" fill="#008000"><title>Salivary Gland Cancer — Mutation: 24 of 352 profiled patients</title></rect><rect x="487.3" y="255.6" width="20.3" height="2" fill="#ff0000"><title>Salivary Gland Cancer — Amplification: 3 of 352 profiled patients</title></rect><rect x="487.3" y="254.2" width="20.3" height="1.4" fill="#6e7a83"><title>Salivary Gland Cancer — Multiple Alterations: 2 of 352 profiled patients</title></rect><rect x="487.3" y="254.2" width="20.3" height="19.8" fill="none"><title>Salivary Gland Cancer: 8.2% (29 / 352 patients)</title></rect><rect x="513.3" y="258.7" width="20.3" height="15.3" fill="#008000"><title>Head and Neck Cancer — Mutation: 39 of 610 profiled patients</title></rect><rect x="513.3" y="255.5" width="20.3" height="3.1" fill="#ff0000"><title>Head and Neck Cancer — Amplification: 8 of 610 profiled patients</title></rect><rect x="513.3" y="255.5" width="20.3" height="18.5" fill="none"><title>Head and Neck Cancer: 7.7% (47 / 610 patients)</title></rect><rect x="539.3" y="264.8" width="20.3" height="9.2" fill="#008000"><title>Small Cell Lung Cancer — Mutation: 17 of 442 profiled patients</title></rect><rect x="539.3" y="262.6" width="20.3" height="2.2" fill="#ff0000"><title>Small Cell Lung Cancer — Amplification: 4 of 442 profiled patients</title></rect><rect x="539.3" y="259.3" width="20.3" height="3.3" fill="#0000ff"><title>Small Cell Lung Cancer — Deep Deletion: 6 of 442 profiled patients</title></rect><rect x="539.3" y="259.3" width="20.3" height="14.7" fill="none"><title>Small Cell Lung Cancer: 6.1% (27 / 442 patients)</title></rect><rect x="565.2" y="266.9" width="20.3" height="7.1" fill="#008000"><title>Soft Tissue Sarcoma — Mutation: 63 of 2,128 profiled patients</title></rect><rect x="565.2" y="265" width="20.3" height="1.9" fill="#ff0000"><title>Soft Tissue Sarcoma — Amplification: 17 of 2,128 profiled patients</title></rect><rect x="565.2" y="263.3" width="20.3" height="1.7" fill="#0000ff"><title>Soft Tissue Sarcoma — Deep Deletion: 15 of 2,128 profiled patients</title></rect><rect x="565.2" y="263.2" width="20.3" height="0.1" fill="#8b00c9"><title>Soft Tissue Sarcoma — Structural Variant: 1 of 2,128 profiled patients</title></rect><rect x="565.2" y="262.4" width="20.3" height="0.8" fill="#6e7a83"><title>Soft Tissue Sarcoma — Multiple Alterations: 7 of 2,128 profiled patients</title></rect><rect x="565.2" y="262.4" width="20.3" height="11.6" fill="none"><title>Soft Tissue Sarcoma: 4.8% (103 / 2,128 patients)</title></rect><rect x="591.2" y="269" width="20.3" height="5" fill="#008000"><title>Uterine Sarcoma — Mutation: 7 of 337 profiled patients</title></rect><rect x="591.2" y="266.2" width="20.3" height="2.8" fill="#ff0000"><title>Uterine Sarcoma — Amplification: 4 of 337 profiled patients</title></rect><rect x="591.2" y="264" width="20.3" height="2.1" fill="#0000ff"><title>Uterine Sarcoma — Deep Deletion: 3 of 337 profiled patients</title></rect><rect x="591.2" y="263.3" width="20.3" height="0.7" fill="#6e7a83"><title>Uterine Sarcoma — Multiple Alterations: 1 of 337 profiled patients</title></rect><rect x="591.2" y="263.3" width="20.3" height="10.7" fill="none"><title>Uterine Sarcoma: 4.5% (15 / 337 patients)</title></rect><rect x="617.2" y="266.6" width="20.3" height="7.4" fill="#008000"><title>Peripheral Nervous System — Mutation: 10 of 324 profiled patients</title></rect><rect x="617.2" y="265.1" width="20.3" height="1.5" fill="#ff0000"><title>Peripheral Nervous System — Amplification: 2 of 324 profiled patients</title></rect><rect x="617.2" y="264.4" width="20.3" height="0.7" fill="#0000ff"><title>Peripheral Nervous System — Deep Deletion: 1 of 324 profiled patients</title></rect><rect x="617.2" y="264.4" width="20.3" height="9.6" fill="none"><title>Peripheral Nervous System: 4.0% (13 / 324 patients)</title></rect><rect x="643.1" y="269.3" width="20.3" height="4.7" fill="#008000"><title>Glioma — Mutation: 40 of 2,042 profiled patients</title></rect><rect x="643.1" y="266.8" width="20.3" height="2.5" fill="#ff0000"><title>Glioma — Amplification: 21 of 2,042 profiled patients</title></rect><rect x="643.1" y="265.5" width="20.3" height="1.3" fill="#0000ff"><title>Glioma — Deep Deletion: 11 of 2,042 profiled patients</title></rect><rect x="643.1" y="265.4" width="20.3" height="0.1" fill="#8b00c9"><title>Glioma — Structural Variant: 1 of 2,042 profiled patients</title></rect><rect x="643.1" y="265" width="20.3" height="0.5" fill="#6e7a83"><title>Glioma — Multiple Alterations: 4 of 2,042 profiled patients</title></rect><rect x="643.1" y="265" width="20.3" height="9" fill="none"><title>Glioma: 3.8% (77 / 2,042 patients)</title></rect><rect x="669.1" y="270.2" width="20.3" height="3.8" fill="#008000"><title>Bone Cancer — Mutation: 8 of 506 profiled patients</title></rect><rect x="669.1" y="266.4" width="20.3" height="3.8" fill="#ff0000"><title>Bone Cancer — Amplification: 8 of 506 profiled patients</title></rect><rect x="669.1" y="265.5" width="20.3" height="0.9" fill="#0000ff"><title>Bone Cancer — Deep Deletion: 2 of 506 profiled patients</title></rect><rect x="669.1" y="265.5" width="20.3" height="8.5" fill="none"><title>Bone Cancer: 3.6% (18 / 506 patients)</title></rect><rect x="695.1" y="270.7" width="20.3" height="3.3" fill="#008000"><title>Breast Cancer — Mutation: 80 of 5,803 profiled patients</title></rect><rect x="695.1" y="267.5" width="20.3" height="3.2" fill="#ff0000"><title>Breast Cancer — Amplification: 78 of 5,803 profiled patients</title></rect><rect x="695.1" y="266.1" width="20.3" height="1.4" fill="#0000ff"><title>Breast Cancer — Deep Deletion: 33 of 5,803 profiled patients</title></rect><rect x="695.1" y="266.1" width="20.3" height="0" fill="#8b00c9"><title>Breast Cancer — Structural Variant: 1 of 5,803 profiled patients</title></rect><rect x="695.1" y="265.9" width="20.3" height="0.1" fill="#6e7a83"><title>Breast Cancer — Multiple Alterations: 3 of 5,803 profiled patients</title></rect><rect x="695.1" y="265.9" width="20.3" height="8.1" fill="none"><title>Breast Cancer: 3.4% (195 / 5,803 patients)</title></rect><rect x="721" y="268.9" width="20.3" height="5.1" fill="#008000"><title>Prostate Cancer — Mutation: 62 of 2,893 profiled patients</title></rect><rect x="721" y="267.9" width="20.3" height="0.9" fill="#ff0000"><title>Prostate Cancer — Amplification: 11 of 2,893 profiled patients</title></rect><rect x="721" y="266.9" width="20.3" height="1" fill="#0000ff"><title>Prostate Cancer — Deep Deletion: 12 of 2,893 profiled patients</title></rect><rect x="721" y="266.9" width="20.3" height="0.1" fill="#8b00c9"><title>Prostate Cancer — Structural Variant: 1 of 2,893 profiled patients</title></rect><rect x="721" y="266.9" width="20.3" height="7.1" fill="none"><title>Prostate Cancer: 3.0% (86 / 2,893 patients)</title></rect><rect x="747" y="270.9" width="20.3" height="3.1" fill="#008000"><title>Mesothelioma — Mutation: 5 of 382 profiled patients</title></rect><rect x="747" y="269.6" width="20.3" height="1.3" fill="#0000ff"><title>Mesothelioma — Deep Deletion: 2 of 382 profiled patients</title></rect><rect x="747" y="269" width="20.3" height="0.6" fill="#6e7a83"><title>Mesothelioma — Multiple Alterations: 1 of 382 profiled patients</title></rect><rect x="747" y="269" width="20.3" height="5" fill="none"><title>Mesothelioma: 2.1% (8 / 382 patients)</title></rect><rect x="773" y="273.1" width="20.3" height="0.9" fill="#008000"><title>Gastrointestinal Stromal Tumor — Mutation: 2 of 520 profiled patients</title></rect><rect x="773" y="270.3" width="20.3" height="2.8" fill="#0000ff"><title>Gastrointestinal Stromal Tumor — Deep Deletion: 6 of 520 profiled patients</title></rect><rect x="773" y="269.8" width="20.3" height="0.5" fill="#6e7a83"><title>Gastrointestinal Stromal Tumor — Multiple Alterations: 1 of 520 profiled patients</title></rect><rect x="773" y="269.8" width="20.3" height="4.2" fill="none"><title>Gastrointestinal Stromal Tumor: 1.7% (9 / 520 patients)</title></rect><rect x="798.9" y="270.3" width="20.3" height="3.7" fill="#008000"><title>Renal Cell Carcinoma — Mutation: 17 of 1,107 profiled patients</title></rect><rect x="798.9" y="270.1" width="20.3" height="0.2" fill="#6e7a83"><title>Renal Cell Carcinoma — Multiple Alterations: 1 of 1,107 profiled patients</title></rect><rect x="798.9" y="270.1" width="20.3" height="3.9" fill="none"><title>Renal Cell Carcinoma: 1.6% (18 / 1,107 patients)</title></rect><rect x="824.9" y="243.9" width="20.3" height="30.1" fill="#008000"><title>38 smaller groups — Mutation: 231 of 1,842 profiled patients</title></rect><rect x="824.9" y="242.9" width="20.3" height="1" fill="#ff0000"><title>38 smaller groups — Amplification: 8 of 1,842 profiled patients</title></rect><rect x="824.9" y="241.8" width="20.3" height="1" fill="#0000ff"><title>38 smaller groups — Deep Deletion: 8 of 1,842 profiled patients</title></rect><rect x="824.9" y="241.4" width="20.3" height="0.4" fill="#6e7a83"><title>38 smaller groups — Multiple Alterations: 3 of 1,842 profiled patients</title></rect><rect x="824.9" y="241.4" width="20.3" height="32.6" fill="none"><title>38 smaller groups: 13.6% (250 / 1,842 patients)</title></rect><text x="111" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 111 284)">Pancreatic Cancer</text><text x="136.9" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 136.9 284)">Appendiceal Cancer</text><text x="162.9" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 162.9 284)">Colorectal Cancer</text><text x="188.9" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 188.9 284)">Non-Small Cell Lung Cancer</text><text x="214.8" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 214.8 284)">Germ Cell Tumor</text><text x="240.8" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 240.8 284)">Melanoma</text><text x="266.8" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 266.8 284)">Cancer of Unknown Primary</text><text x="292.7" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 292.7 284)">Endometrial Cancer</text><text x="318.7" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 318.7 284)">Thyroid Cancer</text><text x="344.7" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 344.7 284)">Esophagogastric Cancer</text><text x="370.6" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 370.6 284)">Ovarian Cancer</text><text x="396.6" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 396.6 284)">Hepatobiliary Cancer</text><text x="422.6" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 422.6 284)">Cervical Cancer</text><text x="448.5" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 448.5 284)">Bladder Cancer</text><text x="474.5" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 474.5 284)">Skin Cancer, Non-Melanoma</text><text x="500.5" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 500.5 284)">Salivary Gland Cancer</text><text x="526.4" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 526.4 284)">Head and Neck Cancer</text><text x="552.4" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 552.4 284)">Small Cell Lung Cancer</text><text x="578.4" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 578.4 284)">Soft Tissue Sarcoma</text><text x="604.3" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 604.3 284)">Uterine Sarcoma</text><text x="630.3" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 630.3 284)">Peripheral Nervous System</text><text x="656.3" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 656.3 284)">Glioma</text><text x="682.2" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 682.2 284)">Bone Cancer</text><text x="708.2" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 708.2 284)">Breast Cancer</text><text x="734.2" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 734.2 284)">Prostate Cancer</text><text x="760.1" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 760.1 284)">Mesothelioma</text><text x="786.1" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 786.1 284)">Gastrointestinal Stromal Tumor</text><text x="812.1" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 812.1 284)">Renal Cell Carcinoma</text><text x="838" y="284" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 838 284)">38 smaller groups</text><rect x="95" y="404.5" width="10" height="10" rx="2" fill="#008000"/><text x="110" y="413" font-size="10" fill="#16232b">Mutation</text><rect x="165.94" y="404.5" width="10" height="10" rx="2" fill="#ff0000"/><text x="180.9" y="413" font-size="10" fill="#16232b">Amplification</text><rect x="256.5" y="404.5" width="10" height="10" rx="2" fill="#0000ff"/><text x="271.5" y="413" font-size="10" fill="#16232b">Deep Deletion</text><rect x="351.05" y="404.5" width="10" height="10" rx="2" fill="#8b00c9"/><text x="366.1" y="413" font-size="10" fill="#16232b">Structural Variant</text><rect x="461.16" y="404.5" width="10" height="10" rx="2" fill="#6e7a83"/><text x="476.2" y="413" font-size="10" fill="#16232b">Multiple Alterations</text><text x="95" y="431" font-size="10" fill="#5b6b76">Every group was profiled for mutation, CNA and structural variant data.</text></g><text x="8" y="465" font-size="10" fill="#5b6b76">Patients altered in at least one of 3 genes per CANCER_TYPE group, over the patients profiled for at least one of them (the union of their panels, as an oncoprint's merged</text><text x="8" y="479" font-size="10" fill="#5b6b76">row counts it). Any mutation, high-level copy-number change (amplification, deep deletion) or structural variant counts; low-level gains and losses do not. A patient with</text><text x="8" y="493" font-size="10" fill="#5b6b76">more than one kind of alteration across the genes counts once, under &quot;multiple&quot;. A patient counts in every group one of their samples falls in, profiled if any of those</text><text x="8" y="507" font-size="10" fill="#5b6b76">samples is, altered if any is. CANCER_TYPE is read per sample and folded on case and whitespace; samples with no value form their own group. Groups with fewer than</text><text x="8" y="521" font-size="10" fill="#5b6b76">200 profiled patients are pooled into one bar rather than dropped.</text></svg>
diff --git a/test/snapshots/cancertypes.txt b/test/snapshots/cancertypes.txt
new file mode 100644
index 00000000..1202dbcc
--- /dev/null
+++ b/test/snapshots/cancertypes.txt
@@ -0,0 +1,44 @@
+Analysis over msk_impact_50k_2026 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- all patients: 48,179 patients — all patients in the study
+
+Cancer types summary: KRAS, NRAS, HRAS by CANCER_TYPE (any alteration; samples profiled for at least one gene):
+  all patients: 10,796 of 48,179 profiled samples altered
+    Pancreatic Cancer: 82.9% (2263/2729; mutation 2211, amp 3, deep del 0, SV 0, multiple 49)
+    Appendiceal Cancer: 60.6% (163/269; mutation 163, amp 0, deep del 0, SV 0, multiple 0)
+    Colorectal Cancer: 49.1% (2450/4993; mutation 2383, amp 48, deep del 4, SV 1, multiple 14)
+    Non-Small Cell Lung Cancer: 31.7% (2103/6633; mutation 1943, amp 71, deep del 20, SV 1, multiple 68)
+    Germ Cell Tumor: 31.4% (208/663; mutation 78, amp 114, deep del 0, SV 0, multiple 16)
+    Melanoma: 29.7% (506/1706; mutation 452, amp 17, deep del 4, SV 0, multiple 33)
+    Cancer of Unknown Primary: 25.7% (395/1535; mutation 348, amp 25, deep del 7, SV 1, multiple 14)
+    Endometrial Cancer: 24.0% (570/2373; mutation 541, amp 19, deep del 6, SV 0, multiple 4)
+    Thyroid Cancer: 21.4% (172/803; mutation 164, amp 2, deep del 1, SV 0, multiple 5)
+    Esophagogastric Cancer: 18.6% (311/1671; mutation 134, amp 169, deep del 3, SV 0, multiple 5)
+    Ovarian Cancer: 14.3% (297/2075; mutation 199, amp 75, deep del 13, SV 1, multiple 9)
+    Hepatobiliary Cancer: 14.1% (191/1353; mutation 173, amp 16, deep del 0, SV 0, multiple 2)
+    Cervical Cancer: 13.5% (44/326; mutation 40, amp 0, deep del 2, SV 0, multiple 2)
+    Bladder Cancer: 12.7% (259/2046; mutation 215, amp 27, deep del 13, SV 1, multiple 3)
+    Skin Cancer, Non-Melanoma: 9.3% (40/429; mutation 38, amp 0, deep del 2, SV 0, multiple 0)
+    Salivary Gland Cancer: 8.2% (29/352; mutation 24, amp 3, deep del 0, SV 0, multiple 2)
+    Head and Neck Cancer: 7.7% (47/610; mutation 39, amp 8, deep del 0, SV 0, multiple 0)
+    Small Cell Lung Cancer: 6.1% (27/442; mutation 17, amp 4, deep del 6, SV 0, multiple 0)
+    Soft Tissue Sarcoma: 4.8% (103/2128; mutation 63, amp 17, deep del 15, SV 1, multiple 7)
+    Uterine Sarcoma: 4.5% (15/337; mutation 7, amp 4, deep del 3, SV 0, multiple 1)
+    Peripheral Nervous System: 4.0% (13/324; mutation 10, amp 2, deep del 1, SV 0, multiple 0)
+    Glioma: 3.8% (77/2042; mutation 40, amp 21, deep del 11, SV 1, multiple 4)
+    Bone Cancer: 3.6% (18/506; mutation 8, amp 8, deep del 2, SV 0, multiple 0)
+    Breast Cancer: 3.4% (195/5803; mutation 80, amp 78, deep del 33, SV 1, multiple 3)
+    Prostate Cancer: 3.0% (86/2893; mutation 62, amp 11, deep del 12, SV 1, multiple 0)
+    Mesothelioma: 2.1% (8/382; mutation 5, amp 0, deep del 2, SV 0, multiple 1)
+    Gastrointestinal Stromal Tumor: 1.7% (9/520; mutation 2, amp 0, deep del 6, SV 0, multiple 1)
+    Renal Cell Carcinoma: 1.6% (18/1107; mutation 17, amp 0, deep del 0, SV 0, multiple 1)
+    38 smaller groups: 13.6% (250/1842; mutation 231, amp 8, deep del 8, SV 0, multiple 3)
+  CAVEAT (tell the user): Patients altered in at least one of 3 genes per CANCER_TYPE group, over the patients profiled for at least one of them (the union of their panels, as an oncoprint's merged row counts it). Any mutation, high-level copy-number change (amplification, deep deletion) or structural variant counts; low-level gains and losses do not. A patient with more than one kind of alteration across the genes counts once, under "multiple". A patient counts in every group one of their samples falls in, profiled if any of those samples is, altered if any is. CANCER_TYPE is read per sample and folded on case and whitespace; samples with no value form their own group. Groups with fewer than 200 profiled patients are pooled into one bar rather than dropped. 
+  METHOD:
+    - Count patients per CANCER_TYPE group [all patients · msk_impact_50k_2026]: The cancertypes queries above. Per group, for the genes together and for each gene: the patients profiled for it, and of those the patients with any mutation, amplification, deep deletion or structural variant, split by kind (one kind only, or more than one); and the whole cohort's, each patient once. A patient counts in each group one of their samples is in, profiled if any of those samples is, altered if any is. Per group too, how many were profiled for mutations, copy number and structural variants at all. Counts only.
+        genes: KRAS, NRAS, HRAS
+        profiled patients: 48,892
+    - Pool the small groups [all patients]: 38 groups with fewer than 200 profiled patients drawn as one pooled bar; 28 drawn on their own.
+        profiled: 48,179
+        altered: 10,796
diff --git a/test/snapshots/clinical.json b/test/snapshots/clinical.json
new file mode 100644
index 00000000..b8a6db61
--- /dev/null
+++ b/test/snapshots/clinical.json
@@ -0,0 +1,254 @@
+{
+  "study": "crc_apc_impact_2020",
+  "cohorts": {
+    "liver mets": {
+      "count": 227,
+      "text": "OQL[APC: MUT] AND METASTASIS_LIVER in [Yes]",
+      "studies": [
+        "crc_apc_impact_2020"
+      ]
+    },
+    "no liver mets": {
+      "count": 98,
+      "text": "OQL[APC: MUT] AND METASTASIS_LIVER in [No]",
+      "studies": [
+        "crc_apc_impact_2020"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "clinical",
+      "cohorts": [
+        "liver mets",
+        "no liver mets"
+      ],
+      "clinical": [
+        {
+          "attribute": "MSI_SCORE",
+          "per": "sample",
+          "kind": "num",
+          "q": 0.06716509090323068,
+          "cells": [
+            {
+              "cohort": "liver mets",
+              "n": 227,
+              "mean": 1.09,
+              "sd": 2.264,
+              "min": -1,
+              "max": 21.96,
+              "median": 0.6,
+              "q1": 0.26,
+              "q3": 1.29,
+              "histogramTruncated": false
+            },
+            {
+              "cohort": "no liver mets",
+              "n": 98,
+              "mean": 2.892,
+              "sd": 8.147,
+              "min": 0,
+              "max": 48.45,
+              "median": 0.595,
+              "q1": 0.205,
+              "q3": 1.248,
+              "histogramTruncated": false
+            }
+          ],
+          "test": "Welch t",
+          "statistic": -2.1537,
+          "df": 103.52,
+          "p": 0.03358254545161534,
+          "rankStatistic": 10602,
+          "rankP": 0.5029628540374218
+        },
+        {
+          "attribute": "AGE_AT_MET",
+          "per": "patient",
+          "kind": "num",
+          "q": 0.7656913780960357,
+          "cells": [
+            {
+              "cohort": "liver mets",
+              "n": 227,
+              "mean": 58.952,
+              "sd": 13.101,
+              "min": 23,
+              "max": 93,
+              "median": 59,
+              "q1": 51,
+              "q3": 68,
+              "histogramTruncated": false
+            },
+            {
+              "cohort": "no liver mets",
+              "n": 98,
+              "mean": 59.439,
+              "sd": 13.677,
+              "min": 24,
+              "max": 95,
+              "median": 58.5,
+              "q1": 50.25,
+              "q3": 68.75,
+              "histogramTruncated": false
+            }
+          ],
+          "test": "Welch t",
+          "statistic": -0.2985,
+          "df": 177.14,
+          "p": 0.7656913780960357,
+          "rankStatistic": 11113.5,
+          "rankP": 0.9907597366429025
+        }
+      ],
+      "studies": [
+        "crc_apc_impact_2020"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "crc_apc_impact_2020",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "Categorical attributes are compared with a Pearson chi-squared over the full value vocabulary; numeric ones with Welch's unequal-variance t-test (2 cohorts) or Welch's ANOVA (3+) computed from (n, Σx, Σx²) per cohort — those totals are ADDITIVE across sites, so the pooled mean and variance are exact from per-site aggregates and no individual value is ever transmitted. Counting unit follows the attribute: patients for patient-level, samples for sample-level. q-values are Benjamini–Hochberg across the 2 tested attributes. Numeric attributes also show median/IQR and a rank-based test alongside the moment-based one — a Mann-Whitney U for 2 cohorts, Kruskal-Wallis for 3+ (neither itself part of the q-value correction) — computed from the attribute's complete value distribution, which is only exact and only requested because this view is confined to a single study.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Describe each attribute",
+            "detail": "The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies), plus its complete value distribution as (value, count) pairs for the median, IQR and rank tests. Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database except as a count of how many share it.",
+            "scope": "liver mets · crc_apc_impact_2020",
+            "rowsIn": 227
+          },
+          {
+            "kind": "fetch",
+            "title": "Describe each attribute",
+            "detail": "The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies), plus its complete value distribution as (value, count) pairs for the median, IQR and rank tests. Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database except as a count of how many share it.",
+            "scope": "no liver mets · crc_apc_impact_2020",
+            "rowsIn": 98
+          },
+          {
+            "kind": "statistic",
+            "title": "Welch's t-test for each numeric attribute",
+            "detail": "Unequal-variance t from each cohort's n, mean and variance, with Welch–Satterthwaite degrees of freedom; two-sided. Mean and variance are from the summed moments: variance = (Σx² − (Σx)²/n) / (n − 1).",
+            "params": [
+              [
+                "MSI_SCORE",
+                "t -2.1537 on 103.52 df · p 0.0336"
+              ],
+              [
+                "AGE_AT_MET",
+                "t -0.2985 on 177.14 df · p 0.766"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Mann–Whitney U alongside",
+            "detail": "From each cohort's complete value distribution: ranks averaged within tied values, the variance tie-corrected, the normal approximation with a 0.5 continuity correction; two-sided. Medians and quartiles from the same distribution, interpolating linearly between ranks. A check against skew and outliers — not part of the q-value correction.",
+            "params": [
+              [
+                "MSI_SCORE",
+                "U 10602 · p 0.503"
+              ],
+              [
+                "AGE_AT_MET",
+                "U 11113.5 · p 0.991"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across attributes",
+            "detail": "q-values over the 2 tested attributes, from the χ² and Welch p-values together."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "welchT",
+            "mannWhitneyU",
+            "quantilesFromHistogram",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "χ² matches scipy 1.18.1 chi2_contingency; Welch's t scipy ttest_ind(equal_var=False) and Welch's ANOVA statsmodels 0.15.0 anova_generic from the same moments; Mann–Whitney, Kruskal–Wallis and quantiles scipy/numpy on the expanded distribution; Benjamini–Hochberg statsmodels multipletests — all to a relative 1e-6 (test/tables-golden.test.ts, test/moments-golden.test.ts).",
+        "check": {
+          "clinical:MSI_SCORE:p": {
+            "v": 0.03358254545161534
+          },
+          "clinical:MSI_SCORE:q": {
+            "v": 0.06716509090323068
+          },
+          "clinical:MSI_SCORE:rank_p": {
+            "v": 0.5029628540374218
+          },
+          "clinical:AGE_AT_MET:p": {
+            "v": 0.7656913780960357
+          },
+          "clinical:AGE_AT_MET:q": {
+            "v": 0.7656913780960357
+          },
+          "clinical:AGE_AT_MET:rank_p": {
+            "v": 0.9907597366429025
+          },
+          "clinical:MSI_SCORE:liver mets:median": {
+            "v": 0.6,
+            "abs": 0.000500001
+          },
+          "clinical:MSI_SCORE:liver mets:q1": {
+            "v": 0.26,
+            "abs": 0.000500001
+          },
+          "clinical:MSI_SCORE:liver mets:q3": {
+            "v": 1.29,
+            "abs": 0.000500001
+          },
+          "clinical:MSI_SCORE:no liver mets:median": {
+            "v": 0.595,
+            "abs": 0.000500001
+          },
+          "clinical:MSI_SCORE:no liver mets:q1": {
+            "v": 0.205,
+            "abs": 0.000500001
+          },
+          "clinical:MSI_SCORE:no liver mets:q3": {
+            "v": 1.248,
+            "abs": 0.000500001
+          },
+          "clinical:AGE_AT_MET:liver mets:median": {
+            "v": 59,
+            "abs": 0.000500001
+          },
+          "clinical:AGE_AT_MET:liver mets:q1": {
+            "v": 51,
+            "abs": 0.000500001
+          },
+          "clinical:AGE_AT_MET:liver mets:q3": {
+            "v": 68,
+            "abs": 0.000500001
+          },
+          "clinical:AGE_AT_MET:no liver mets:median": {
+            "v": 58.5,
+            "abs": 0.000500001
+          },
+          "clinical:AGE_AT_MET:no liver mets:q1": {
+            "v": 50.25,
+            "abs": 0.000500001
+          },
+          "clinical:AGE_AT_MET:no liver mets:q3": {
+            "v": 68.75,
+            "abs": 0.000500001
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/clinical.sql b/test/snapshots/clinical.sql
new file mode 100644
index 00000000..0ececcc0
--- /dev/null
+++ b/test/snapshots/clinical.sql
@@ -0,0 +1,123 @@
+-- cohort liver mets
+-- crc_apc_impact_2020
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'crc_apc_impact_2020'
+    AND g.hugo_gene_symbol = 'APC'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type = 'patient'
+    AND attribute_name = 'METASTASIS_LIVER' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Yes'))))
+
+-- cohort no liver mets
+-- crc_apc_impact_2020
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'crc_apc_impact_2020'
+    AND g.hugo_gene_symbol = 'APC'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type = 'patient'
+    AND attribute_name = 'METASTASIS_LIVER' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('No'))))
+
+-- view 0 clinical: clinicalHistogram · crc_apc_impact_2020
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'crc_apc_impact_2020'
+    AND g.hugo_gene_symbol = 'APC'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type = 'patient'
+    AND attribute_name = 'METASTASIS_LIVER' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('No'))))),
+     raw AS (
+       SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+       FROM snapshot_db.clinical_data_derived
+       WHERE cancer_study_identifier = 'crc_apc_impact_2020'
+         AND attribute_name IN ('MSI_SCORE','AGE_AT_MET')
+         AND attribute_value != ''
+         AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+     )
+SELECT a, per, x, count() AS n
+FROM raw
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per, x
+ORDER BY n DESC
+LIMIT 20001 BY a, per
+
+-- view 0 clinical: clinicalHistogram · crc_apc_impact_2020
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'crc_apc_impact_2020'
+    AND g.hugo_gene_symbol = 'APC'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type = 'patient'
+    AND attribute_name = 'METASTASIS_LIVER' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Yes'))))),
+     raw AS (
+       SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+       FROM snapshot_db.clinical_data_derived
+       WHERE cancer_study_identifier = 'crc_apc_impact_2020'
+         AND attribute_name IN ('MSI_SCORE','AGE_AT_MET')
+         AND attribute_value != ''
+         AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+     )
+SELECT a, per, x, count() AS n
+FROM raw
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per, x
+ORDER BY n DESC
+LIMIT 20001 BY a, per
+
+-- view 0 clinical: clinicalMoments · crc_apc_impact_2020
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'crc_apc_impact_2020'
+    AND g.hugo_gene_symbol = 'APC'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type = 'patient'
+    AND attribute_name = 'METASTASIS_LIVER' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('No')))))
+SELECT a, per, count() AS n, sum(x) AS sx, sum(x * x) AS sxx, min(x) AS mn, max(x) AS mx
+FROM (
+  SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'
+    AND attribute_name IN ('MSI_SCORE','AGE_AT_MET')
+    AND attribute_value != ''
+    AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+)
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per
+
+-- view 0 clinical: clinicalMoments · crc_apc_impact_2020
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'crc_apc_impact_2020'
+    AND g.hugo_gene_symbol = 'APC'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type = 'patient'
+    AND attribute_name = 'METASTASIS_LIVER' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Yes')))))
+SELECT a, per, count() AS n, sum(x) AS sx, sum(x * x) AS sxx, min(x) AS mn, max(x) AS mx
+FROM (
+  SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'
+    AND attribute_name IN ('MSI_SCORE','AGE_AT_MET')
+    AND attribute_value != ''
+    AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+)
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per
diff --git a/test/snapshots/clinical.svg b/test/snapshots/clinical.svg
new file mode 100644
index 00000000..acea42ce
--- /dev/null
+++ b/test/snapshots/clinical.svg
@@ -0,0 +1,2 @@
+<!-- view 0 clinical -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 250" width="100%" role="img" aria-label="Clinical comparison — −log₁₀ q" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Clinical comparison — −log₁₀ q</title><rect width="720" height="250" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Clinical comparison — −log₁₀ q</text><rect x="8" y="28.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="37" font-size="10" fill="#16232b">q &lt; 0.05</text><rect x="76.24000000000001" y="28.5" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="91.2" y="37" font-size="10" fill="#16232b">not significant</text><line x1="88.5" y1="61" x2="88.5" y2="105" stroke="#dde4e8" stroke-width="0.75"/><line x1="220.2" y1="61" x2="220.2" y2="105" stroke="#dde4e8" stroke-width="0.75"/><line x1="351.8" y1="61" x2="351.8" y2="105" stroke="#dde4e8" stroke-width="0.75"/><line x1="483.4" y1="61" x2="483.4" y2="105" stroke="#dde4e8" stroke-width="0.75"/><line x1="615.1" y1="61" x2="615.1" y2="105" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="61" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="75.9" font-size="11" fill="#16232b">MSI_SCORE</text><rect x="88.5" y="66" width="308.8" height="12" rx="1.5" fill="#9aa5ad"></rect><text x="712" y="75.5" font-size="10" fill="#16232b" text-anchor="end">Welch t · q = 0.067</text><text x="8" y="97.9" font-size="11" fill="#16232b">AGE_AT_MET</text><rect x="88.5" y="88" width="30.5" height="12" rx="1.5" fill="#9aa5ad"></rect><text x="712" y="97.5" font-size="10" fill="#16232b" text-anchor="end">Welch t · q = 0.77</text><line x1="431.1" y1="61" x2="431.1" y2="105" stroke="#5b6b76" stroke-width="1" stroke-dasharray="3 3"/><text x="434.1" y="58" font-size="10" fill="#5b6b76">q = 0.05</text><line x1="88.5" y1="105" x2="88.5" y2="108" stroke="#5b6b76" stroke-width="1"/><text x="81.2" y="121" font-size="10" fill="#5b6b76">0.0</text><line x1="220.2" y1="105" x2="220.2" y2="108" stroke="#5b6b76" stroke-width="1"/><text x="212.8" y="121" font-size="10" fill="#5b6b76">0.5</text><line x1="351.8" y1="105" x2="351.8" y2="108" stroke="#5b6b76" stroke-width="1"/><text x="344.4" y="121" font-size="10" fill="#5b6b76">1.0</text><line x1="483.4" y1="105" x2="483.4" y2="108" stroke="#5b6b76" stroke-width="1"/><text x="476.1" y="121" font-size="10" fill="#5b6b76">1.5</text><line x1="615.1" y1="105" x2="615.1" y2="108" stroke="#5b6b76" stroke-width="1"/><text x="607.7" y="121" font-size="10" fill="#5b6b76">2.0</text><line x1="88.519" y1="105" x2="615.08" y2="105" stroke="#5b6b76" stroke-width="1"/><text x="351.8" y="140" font-size="11" fill="#16232b" text-anchor="middle">−log₁₀ q (Benjamini–Hochberg) — longer is stronger evidence of a difference</text><text x="8" y="162" font-size="10" fill="#5b6b76">Categorical attributes are compared with a Pearson chi-squared over the full value vocabulary; numeric ones with Welch's unequal-variance t-test (2</text><text x="8" y="176" font-size="10" fill="#5b6b76">cohorts) or Welch's ANOVA (3+) computed from (n, Σx, Σx²) per cohort — those totals are ADDITIVE across sites, so the pooled mean and variance are exact</text><text x="8" y="190" font-size="10" fill="#5b6b76">from per-site aggregates and no individual value is ever transmitted. Counting unit follows the attribute: patients for patient-level, samples for sample-level.</text><text x="8" y="204" font-size="10" fill="#5b6b76">q-values are Benjamini–Hochberg across the 2 tested attributes. Numeric attributes also show median/IQR and a rank-based test alongside the</text><text x="8" y="218" font-size="10" fill="#5b6b76">moment-based one — a Mann-Whitney U for 2 cohorts, Kruskal-Wallis for 3+ (neither itself part of the q-value correction) — computed from the attribute's</text><text x="8" y="232" font-size="10" fill="#5b6b76">complete value distribution, which is only exact and only requested because this view is confined to a single study.</text></svg>
diff --git a/test/snapshots/clinical.txt b/test/snapshots/clinical.txt
new file mode 100644
index 00000000..db1a95a8
--- /dev/null
+++ b/test/snapshots/clinical.txt
@@ -0,0 +1,21 @@
+Analysis over crc_apc_impact_2020 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- liver mets: 227 patients — OQL[APC: MUT] AND METASTASIS_LIVER in [Yes]
+- no liver mets: 98 patients — OQL[APC: MUT] AND METASTASIS_LIVER in [No]
+
+Clinical comparison across cohorts:
+  MSI_SCORE [sample, num]: liver mets mean 1.09 sd 2.26 (n=227) | no liver mets mean 2.89 sd 8.15 (n=98)  Welch t p=0.0336 q=0.0672
+  AGE_AT_MET [patient, num]: liver mets mean 58.95 sd 13.10 (n=227) | no liver mets mean 59.44 sd 13.68 (n=98)  Welch t p=0.7657 q=0.7657
+  Categorical attributes are compared with a Pearson chi-squared over the full value vocabulary; numeric ones with Welch's unequal-variance t-test (2 cohorts) or Welch's ANOVA (3+) computed from (n, Σx, Σx²) per cohort — those totals are ADDITIVE across sites, so the pooled mean and variance are exact from per-site aggregates and no individual value is ever transmitted. Counting unit follows the attribute: patients for patient-level, samples for sample-level. q-values are Benjamini–Hochberg across the 2 tested attributes. Numeric attributes also show median/IQR and a rank-based test alongside the moment-based one — a Mann-Whitney U for 2 cohorts, Kruskal-Wallis for 3+ (neither itself part of the q-value correction) — computed from the attribute's complete value distribution, which is only exact and only requested because this view is confined to a single study.
+  METHOD:
+    - Describe each attribute [liver mets · crc_apc_impact_2020]: The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies), plus its complete value distribution as (value, count) pairs for the median, IQR and rank tests. Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database except as a count of how many share it.
+    - Describe each attribute [no liver mets · crc_apc_impact_2020]: The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies), plus its complete value distribution as (value, count) pairs for the median, IQR and rank tests. Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database except as a count of how many share it.
+    - Welch's t-test for each numeric attribute: Unequal-variance t from each cohort's n, mean and variance, with Welch–Satterthwaite degrees of freedom; two-sided. Mean and variance are from the summed moments: variance = (Σx² − (Σx)²/n) / (n − 1).
+        MSI_SCORE: t -2.1537 on 103.52 df · p 0.0336
+        AGE_AT_MET: t -0.2985 on 177.14 df · p 0.766
+    - Mann–Whitney U alongside: From each cohort's complete value distribution: ranks averaged within tied values, the variance tie-corrected, the normal approximation with a 0.5 continuity correction; two-sided. Medians and quartiles from the same distribution, interpolating linearly between ranks. A check against skew and outliers — not part of the q-value correction.
+        MSI_SCORE: U 10602 · p 0.503
+        AGE_AT_MET: U 11113.5 · p 0.991
+    - Benjamini–Hochberg across attributes: q-values over the 2 tested attributes, from the χ² and Welch p-values together.
+    χ² matches scipy 1.18.1 chi2_contingency; Welch's t scipy ttest_ind(equal_var=False) and Welch's ANOVA statsmodels 0.15.0 anova_generic from the same moments; Mann–Whitney, Kruskal–Wallis and quantiles scipy/numpy on the expanded distribution; Benjamini–Hochberg statsmodels multipletests — all to a relative 1e-6 (test/tables-golden.test.ts, test/moments-golden.test.ts).
diff --git a/test/snapshots/clonality.json b/test/snapshots/clonality.json
new file mode 100644
index 00000000..e9fcb272
--- /dev/null
+++ b/test/snapshots/clonality.json
@@ -0,0 +1,731 @@
+{
+  "study": "msk_impact_50k_2026",
+  "cohorts": {
+    "Colorectal": {
+      "count": 4993,
+      "text": "CANCER_TYPE in [Colorectal Cancer] (any sample)",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    },
+    "Breast": {
+      "count": 5803,
+      "text": "CANCER_TYPE in [Breast Cancer] (any sample)",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "clonality",
+      "cohorts": [
+        "Colorectal",
+        "Breast"
+      ],
+      "clonality": {
+        "measure": "driver",
+        "qc": true,
+        "panels": [
+          {
+            "cohort": "Colorectal",
+            "samples": 5395,
+            "ascnSamples": 5379,
+            "qcPass": 4912,
+            "qcFail": 451,
+            "wgd": {
+              "yes": 1749,
+              "no": 3163
+            },
+            "purity": [
+              0,
+              1,
+              114,
+              171,
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+              3
+            ],
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+              41,
+              6,
+              {
+                "…": "12 more",
+                "sha256": "92b15c3938b83399"
+              }
+            ],
+            "genes": [
+              {
+                "gene": "TP53",
+                "calls": 3839,
+                "samples": 3571,
+                "clonal": 3160,
+                "subclonal": 420,
+                "indeterminate": 255,
+                "uncalled": 4,
+                "loh": 2803,
+                "lohKnown": 3650,
+                "ccf": [
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+                "p": 0.5947092735836579,
+                "q": 0.5947092735836579
+              },
+              {
+                "gene": "KRAS",
+                "calls": 2145,
+                "samples": 2099,
+                "clonal": 1508,
+                "subclonal": 290,
+                "indeterminate": 346,
+                "uncalled": 1,
+                "loh": 423,
+                "lohKnown": 1973,
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+                "p": 0.1286939990837484,
+                "q": 0.22521449839655971
+              },
+              {
+                "gene": "APC",
+                "calls": 5344,
+                "samples": 3577,
+                "clonal": 3889,
+                "subclonal": 897,
+                "indeterminate": 554,
+                "uncalled": 4,
+                "loh": 1748,
+                "lohKnown": 5217,
+                "ccf": [
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+                  3011
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+                "p": 0.5160703794843146,
+                "q": 0.5947092735836579
+              },
+              {
+                "gene": "PIK3CA",
+                "calls": 1018,
+                "samples": 917,
+                "clonal": 634,
+                "subclonal": 302,
+                "indeterminate": 82,
+                "uncalled": 0,
+                "loh": 117,
+                "lohKnown": 1010,
+                "ccf": [
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+                ],
+                "p": 4.394795388579005e-24,
+                "q": 3.076356772005303e-23
+              },
+              {
+                "gene": "SMAD4",
+                "calls": 673,
+                "samples": 621,
+                "clonal": 494,
+                "subclonal": 164,
+                "indeterminate": 15,
+                "uncalled": 0,
+                "loh": 526,
+                "lohKnown": 664,
+                "ccf": [
+                  3,
+                  17,
+                  16,
+                  24,
+                  16,
+                  16,
+                  31,
+                  55,
+                  113,
+                  382
+                ],
+                "p": 0.00601102198333149,
+                "q": 0.01402571796110681
+              },
+              {
+                "gene": "CDH1",
+                "calls": 62,
+                "samples": 55,
+                "clonal": 42,
+                "subclonal": 17,
+                "indeterminate": 3,
+                "uncalled": 0,
+                "loh": 10,
+                "lohKnown": 61,
+                "ccf": [
+                  1,
+                  1,
+                  3,
+                  2,
+                  2,
+                  4,
+                  4,
+                  3,
+                  9,
+                  33
+                ],
+                "p": 0.0028190005173689085,
+                "q": 0.00986650181079118
+              },
+              {
+                "gene": "GATA3",
+                "calls": 42,
+                "samples": 41,
+                "clonal": 28,
+                "subclonal": 12,
+                "indeterminate": 2,
+                "uncalled": 0,
+                "loh": 3,
+                "lohKnown": 42,
+                "ccf": [
+                  0,
+                  0,
+                  1,
+                  2,
+                  3,
+                  2,
+                  3,
+                  3,
+                  12,
+                  16
+                ],
+                "p": 0.23422973174670686,
+                "q": 0.32792162444538964
+              }
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "samples": 6907,
+            "ascnSamples": 6703,
+            "qcPass": 6204,
+            "qcFail": 671,
+            "wgd": {
+              "yes": 2159,
+              "no": 4045
+            },
+            "purity": [
+              0,
+              2,
+              95,
+              160,
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+              202,
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+            ],
+            "ploidy": [
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+              0,
+              1,
+              1,
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+              2071,
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+              565,
+              352,
+              508,
+              300,
+              295,
+              120,
+              104,
+              38,
+              35,
+              11,
+              {
+                "…": "12 more",
+                "sha256": "8b7b77625894c711"
+              }
+            ],
+            "genes": [
+              {
+                "gene": "TP53",
+                "calls": 2672,
+                "samples": 2615,
+                "clonal": 2188,
+                "subclonal": 278,
+                "indeterminate": 195,
+                "uncalled": 11,
+                "loh": 2198,
+                "lohKnown": 2467,
+                "ccf": [
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+                "p": 0.5947092735836579,
+                "q": 0.5947092735836579
+              },
+              {
+                "gene": "KRAS",
+                "calls": 59,
+                "samples": 59,
+                "clonal": 40,
+                "subclonal": 13,
+                "indeterminate": 6,
+                "uncalled": 0,
+                "loh": 9,
+                "lohKnown": 52,
+                "ccf": [
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+                  1,
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+                  2,
+                  1,
+                  2,
+                  2,
+                  4,
+                  13,
+                  30
+                ],
+                "p": 0.1286939990837484,
+                "q": 0.22521449839655971
+              },
+              {
+                "gene": "APC",
+                "calls": 39,
+                "samples": 35,
+                "clonal": 27,
+                "subclonal": 8,
+                "indeterminate": 4,
+                "uncalled": 0,
+                "loh": 16,
+                "lohKnown": 36,
+                "ccf": [
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+                  0,
+                  1,
+                  1,
+                  1,
+                  0,
+                  2,
+                  5,
+                  6,
+                  23
+                ],
+                "p": 0.5160703794843146,
+                "q": 0.5947092735836579
+              },
+              {
+                "gene": "PIK3CA",
+                "calls": 2350,
+                "samples": 2130,
+                "clonal": 1793,
+                "subclonal": 334,
+                "indeterminate": 221,
+                "uncalled": 2,
+                "loh": 294,
+                "lohKnown": 2224,
+                "ccf": [
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+                ],
+                "p": 4.394795388579005e-24,
+                "q": 3.076356772005303e-23
+              },
+              {
+                "gene": "SMAD4",
+                "calls": 79,
+                "samples": 75,
+                "clonal": 46,
+                "subclonal": 31,
+                "indeterminate": 2,
+                "uncalled": 0,
+                "loh": 72,
+                "lohKnown": 78,
+                "ccf": [
+                  2,
+                  3,
+                  4,
+                  3,
+                  6,
+                  4,
+                  6,
+                  7,
+                  12,
+                  32
+                ],
+                "p": 0.00601102198333149,
+                "q": 0.01402571796110681
+              },
+              {
+                "gene": "CDH1",
+                "calls": 773,
+                "samples": 769,
+                "clonal": 639,
+                "subclonal": 96,
+                "indeterminate": 36,
+                "uncalled": 2,
+                "loh": 711,
+                "lohKnown": 750,
+                "ccf": [
+                  0,
+                  4,
+                  4,
+                  12,
+                  12,
+                  14,
+                  20,
+                  46,
+                  84,
+                  573
+                ],
+                "p": 0.0028190005173689085,
+                "q": 0.00986650181079118
+              },
+              {
+                "gene": "GATA3",
+                "calls": 847,
+                "samples": 836,
+                "clonal": 566,
+                "subclonal": 152,
+                "indeterminate": 123,
+                "uncalled": 6,
+                "loh": 58,
+                "lohKnown": 765,
+                "ccf": [
+                  0,
+                  0,
+                  13,
+                  15,
+                  19,
+                  31,
+                  38,
+                  79,
+                  144,
+                  499
+                ],
+                "p": 0.23422973174670686,
+                "q": 0.32792162444538964
+              }
+            ]
+          }
+        ],
+        "wgdP": 0.37889995587930314
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_impact_50k_2026",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "FACETS allele-specific copy number: each OncoKB driver (v7.5) mutation call — one mutation in one sample — is clonal, subclonal or indeterminate as FACETS called it from its cancer-cell fraction; uncalled calls have an estimate but no call. LOH at the locus is a minor copy number of 0 where one was estimated. Only samples whose FACETS_QC is Pass are used (Colorectal: 4,912 pass, 451 fail; Breast: 6,204 pass, 671 fail); qc: false uses every sample. Whole-genome doubling, purity and ploidy are FACETS's own per-sample calls. Clonality is only as good as the purity estimate behind it: a low-purity sample's clonal mutation can be called subclonal, and a panel's handful of mutations per sample makes each estimate coarse. Two cohorts: Fisher on clonal against subclonal calls per gene (BH across genes) and on WGD.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count FACETS's calls per gene",
+            "detail": "The clonality:summary, clonality:purity-ploidy and clonality:genes queries above. Per gene, each an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in one sample is one call, which FACETS labels clonal, subclonal or indeterminate from its cancer-cell fraction (or leaves uncalled); loss of heterozygosity is a minor copy number of 0 where one was estimated. Per sample: whole-genome doubling, purity and ploidy as FACETS called them. Counts only.",
+            "scope": "Colorectal · msk_impact_50k_2026",
+            "rowsIn": 4993
+          },
+          {
+            "kind": "fetch",
+            "title": "Count FACETS's calls per gene",
+            "detail": "The clonality:summary, clonality:purity-ploidy and clonality:genes queries above. Per gene, each an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in one sample is one call, which FACETS labels clonal, subclonal or indeterminate from its cancer-cell fraction (or leaves uncalled); loss of heterozygosity is a minor copy number of 0 where one was estimated. Per sample: whole-genome doubling, purity and ploidy as FACETS called them. Counts only.",
+            "scope": "Breast · msk_impact_50k_2026",
+            "rowsIn": 5803
+          },
+          {
+            "kind": "transform",
+            "title": "Keep samples that pass FACETS QC",
+            "detail": "Only samples whose FACETS_QC is Pass (matched ignoring case) contribute calls, WGD, purity and ploidy.",
+            "params": [
+              [
+                "Colorectal",
+                "4,912 pass, 451 fail, of 5,379 samples with allele-specific copy number"
+              ],
+              [
+                "Breast",
+                "6,204 pass, 671 fail, of 6,703 samples with allele-specific copy number"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Fisher's exact test per gene, and on WGD",
+            "detail": "Two-sided, on clonal against subclonal calls between the two cohorts, for each gene with at least one such call in both; indeterminate and uncalled calls do not enter. And on whole-genome doubled against not, per sample."
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across genes",
+            "detail": "q-values over the genes tested."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "fisherExact",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "clonality:Colorectal:TP53:clonal": {
+            "v": 3160,
+            "abs": 0
+          },
+          "clonality:Colorectal:TP53:subclonal": {
+            "v": 420,
+            "abs": 0
+          },
+          "clonality:Colorectal:KRAS:clonal": {
+            "v": 1508,
+            "abs": 0
+          },
+          "clonality:Colorectal:KRAS:subclonal": {
+            "v": 290,
+            "abs": 0
+          },
+          "clonality:Colorectal:APC:clonal": {
+            "v": 3889,
+            "abs": 0
+          },
+          "clonality:Colorectal:APC:subclonal": {
+            "v": 897,
+            "abs": 0
+          },
+          "clonality:Colorectal:PIK3CA:clonal": {
+            "v": 634,
+            "abs": 0
+          },
+          "clonality:Colorectal:PIK3CA:subclonal": {
+            "v": 302,
+            "abs": 0
+          },
+          "clonality:Colorectal:SMAD4:clonal": {
+            "v": 494,
+            "abs": 0
+          },
+          "clonality:Colorectal:SMAD4:subclonal": {
+            "v": 164,
+            "abs": 0
+          },
+          "clonality:Colorectal:CDH1:clonal": {
+            "v": 42,
+            "abs": 0
+          },
+          "clonality:Colorectal:CDH1:subclonal": {
+            "v": 17,
+            "abs": 0
+          },
+          "clonality:Colorectal:GATA3:clonal": {
+            "v": 28,
+            "abs": 0
+          },
+          "clonality:Colorectal:GATA3:subclonal": {
+            "v": 12,
+            "abs": 0
+          },
+          "clonality:Breast:TP53:clonal": {
+            "v": 2188,
+            "abs": 0
+          },
+          "clonality:Breast:TP53:subclonal": {
+            "v": 278,
+            "abs": 0
+          },
+          "clonality:Breast:KRAS:clonal": {
+            "v": 40,
+            "abs": 0
+          },
+          "clonality:Breast:KRAS:subclonal": {
+            "v": 13,
+            "abs": 0
+          },
+          "clonality:Breast:APC:clonal": {
+            "v": 27,
+            "abs": 0
+          },
+          "clonality:Breast:APC:subclonal": {
+            "v": 8,
+            "abs": 0
+          },
+          "clonality:Breast:PIK3CA:clonal": {
+            "v": 1793,
+            "abs": 0
+          },
+          "clonality:Breast:PIK3CA:subclonal": {
+            "v": 334,
+            "abs": 0
+          },
+          "clonality:Breast:SMAD4:clonal": {
+            "v": 46,
+            "abs": 0
+          },
+          "clonality:Breast:SMAD4:subclonal": {
+            "v": 31,
+            "abs": 0
+          },
+          "clonality:Breast:CDH1:clonal": {
+            "v": 639,
+            "abs": 0
+          },
+          "clonality:Breast:CDH1:subclonal": {
+            "v": 96,
+            "abs": 0
+          },
+          "clonality:Breast:GATA3:clonal": {
+            "v": 566,
+            "abs": 0
+          },
+          "clonality:Breast:GATA3:subclonal": {
+            "v": 152,
+            "abs": 0
+          },
+          "clonality:TP53:p": {
+            "v": 0.5947092735836579
+          },
+          "clonality:TP53:q": {
+            "v": 0.5947092735836579
+          },
+          "clonality:KRAS:p": {
+            "v": 0.1286939990837484
+          },
+          "clonality:KRAS:q": {
+            "v": 0.22521449839655971
+          },
+          "clonality:APC:p": {
+            "v": 0.5160703794843146
+          },
+          "clonality:APC:q": {
+            "v": 0.5947092735836579
+          },
+          "clonality:PIK3CA:p": {
+            "v": 4.394795388579005e-24
+          },
+          "clonality:PIK3CA:q": {
+            "v": 3.076356772005303e-23
+          },
+          "clonality:SMAD4:p": {
+            "v": 0.00601102198333149
+          },
+          "clonality:SMAD4:q": {
+            "v": 0.01402571796110681
+          },
+          "clonality:CDH1:p": {
+            "v": 0.0028190005173689085
+          },
+          "clonality:CDH1:q": {
+            "v": 0.00986650181079118
+          },
+          "clonality:GATA3:p": {
+            "v": 0.23422973174670686
+          },
+          "clonality:GATA3:q": {
+            "v": 0.32792162444538964
+          },
+          "clonality:wgd:p": {
+            "v": 0.37889995587930314
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/clonality.sql b/test/snapshots/clonality.sql
new file mode 100644
index 00000000..9f2f8724
--- /dev/null
+++ b/test/snapshots/clonality.sql
@@ -0,0 +1,198 @@
+-- cohort Colorectal
+-- msk_impact_50k_2026
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+
+-- cohort Breast
+-- msk_impact_50k_2026
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))
+
+-- view 0 clonality: clonality:ascn-studies · msk_impact_50k_2026
+SELECT DISTINCT cs.cancer_study_identifier AS study FROM snapshot_db.allele_specific_copy_number a
+JOIN snapshot_db.genetic_profile gp ON gp.genetic_profile_id = a.genetic_profile_id
+JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = gp.cancer_study_id ORDER BY study
+
+-- view 0 clonality: clonality:genes:Breast · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid, sample_unique_id AS suid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+    AND sample_unique_id IN (SELECT sample_unique_id FROM snapshot_db.clinical_data_derived
+      WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_QC'
+        AND lowerUTF8(trimBoth(attribute_value)) = 'pass')
+),
+prof AS (
+  SELECT gp.genetic_profile_id AS id FROM snapshot_db.genetic_profile gp
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = gp.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_impact_50k_2026' AND gp.genetic_alteration_type = 'MUTATION_EXTENDED'
+),
+calls AS (
+  SELECT g.hugo_gene_symbol AS gene, a.sample_id AS sid, a.clonal AS call_state, a.ccf_expected_copies AS ccf, a.minor_copy_number AS minor
+  FROM snapshot_db.allele_specific_copy_number a
+  JOIN snapshot_db.mutation_event me ON me.mutation_event_id = a.mutation_event_id
+  JOIN snapshot_db.gene g ON g.entrez_gene_id = me.entrez_gene_id
+  INNER JOIN oncokb_annotation.variant_annotation ann ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+    AND ann.hugo_gene_symbol = g.hugo_gene_symbol AND ann.alteration_type = 'MUTATION' AND ann.alteration = me.protein_change
+  WHERE a.genetic_profile_id IN (SELECT id FROM prof) AND a.sample_id IN (SELECT sid FROM samp)
+    AND g.hugo_gene_symbol IN ('TP53','KRAS','APC','PIK3CA','SMAD4','CDH1','GATA3')
+)
+SELECT gene, count() AS calls, uniqExact(sid) AS samples,
+       countIf(call_state = 'CLONAL') AS clonal, countIf(call_state = 'SUBCLONAL') AS subclonal,
+       countIf(call_state = 'INDETERMINATE') AS indeterminate,
+       countIf(minor = 0) AS loh, countIf(minor IS NOT NULL) AS loh_known,
+       countIf(ccf >= 0 AND ccf < 0.1) AS ccf0,
+       countIf(ccf >= 0.1 AND ccf < 0.2) AS ccf1,
+       countIf(ccf >= 0.2 AND ccf < 0.3) AS ccf2,
+       countIf(ccf >= 0.3 AND ccf < 0.4) AS ccf3,
+       countIf(ccf >= 0.4 AND ccf < 0.5) AS ccf4,
+       countIf(ccf >= 0.5 AND ccf < 0.6) AS ccf5,
+       countIf(ccf >= 0.6 AND ccf < 0.7) AS ccf6,
+       countIf(ccf >= 0.7 AND ccf < 0.8) AS ccf7,
+       countIf(ccf >= 0.8 AND ccf < 0.9) AS ccf8,
+       countIf(ccf >= 0.9) AS ccf9
+FROM calls GROUP BY gene
+
+-- view 0 clonality: clonality:genes:Colorectal · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid, sample_unique_id AS suid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+    AND sample_unique_id IN (SELECT sample_unique_id FROM snapshot_db.clinical_data_derived
+      WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_QC'
+        AND lowerUTF8(trimBoth(attribute_value)) = 'pass')
+),
+prof AS (
+  SELECT gp.genetic_profile_id AS id FROM snapshot_db.genetic_profile gp
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = gp.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_impact_50k_2026' AND gp.genetic_alteration_type = 'MUTATION_EXTENDED'
+),
+calls AS (
+  SELECT g.hugo_gene_symbol AS gene, a.sample_id AS sid, a.clonal AS call_state, a.ccf_expected_copies AS ccf, a.minor_copy_number AS minor
+  FROM snapshot_db.allele_specific_copy_number a
+  JOIN snapshot_db.mutation_event me ON me.mutation_event_id = a.mutation_event_id
+  JOIN snapshot_db.gene g ON g.entrez_gene_id = me.entrez_gene_id
+  INNER JOIN oncokb_annotation.variant_annotation ann ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+    AND ann.hugo_gene_symbol = g.hugo_gene_symbol AND ann.alteration_type = 'MUTATION' AND ann.alteration = me.protein_change
+  WHERE a.genetic_profile_id IN (SELECT id FROM prof) AND a.sample_id IN (SELECT sid FROM samp)
+    AND g.hugo_gene_symbol IN ('TP53','KRAS','APC','PIK3CA','SMAD4','CDH1','GATA3')
+)
+SELECT gene, count() AS calls, uniqExact(sid) AS samples,
+       countIf(call_state = 'CLONAL') AS clonal, countIf(call_state = 'SUBCLONAL') AS subclonal,
+       countIf(call_state = 'INDETERMINATE') AS indeterminate,
+       countIf(minor = 0) AS loh, countIf(minor IS NOT NULL) AS loh_known,
+       countIf(ccf >= 0 AND ccf < 0.1) AS ccf0,
+       countIf(ccf >= 0.1 AND ccf < 0.2) AS ccf1,
+       countIf(ccf >= 0.2 AND ccf < 0.3) AS ccf2,
+       countIf(ccf >= 0.3 AND ccf < 0.4) AS ccf3,
+       countIf(ccf >= 0.4 AND ccf < 0.5) AS ccf4,
+       countIf(ccf >= 0.5 AND ccf < 0.6) AS ccf5,
+       countIf(ccf >= 0.6 AND ccf < 0.7) AS ccf6,
+       countIf(ccf >= 0.7 AND ccf < 0.8) AS ccf7,
+       countIf(ccf >= 0.8 AND ccf < 0.9) AS ccf8,
+       countIf(ccf >= 0.9) AS ccf9
+FROM calls GROUP BY gene
+
+-- view 0 clonality: clonality:purity-ploidy:Breast · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid, sample_unique_id AS suid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+    AND sample_unique_id IN (SELECT sample_unique_id FROM snapshot_db.clinical_data_derived
+      WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_QC'
+        AND lowerUTF8(trimBoth(attribute_value)) = 'pass')
+)
+SELECT attribute_name AS attr,
+       toInt32(floor(least(if(attribute_name = 'FACETS_PURITY', 0.9999, 7.9999), greatest(0, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL))) /
+         if(attribute_name = 'FACETS_PURITY', 0.05, 0.25))) AS bin,
+       count() AS n
+FROM snapshot_db.clinical_data_derived
+WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name IN ('FACETS_PURITY', 'FACETS_PLOIDY')
+  AND sample_unique_id IN (SELECT suid FROM samp) AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+GROUP BY attr, bin ORDER BY attr, bin
+
+-- view 0 clonality: clonality:purity-ploidy:Colorectal · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid, sample_unique_id AS suid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+    AND sample_unique_id IN (SELECT sample_unique_id FROM snapshot_db.clinical_data_derived
+      WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_QC'
+        AND lowerUTF8(trimBoth(attribute_value)) = 'pass')
+)
+SELECT attribute_name AS attr,
+       toInt32(floor(least(if(attribute_name = 'FACETS_PURITY', 0.9999, 7.9999), greatest(0, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL))) /
+         if(attribute_name = 'FACETS_PURITY', 0.05, 0.25))) AS bin,
+       count() AS n
+FROM snapshot_db.clinical_data_derived
+WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name IN ('FACETS_PURITY', 'FACETS_PLOIDY')
+  AND sample_unique_id IN (SELECT suid FROM samp) AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+GROUP BY attr, bin ORDER BY attr, bin
+
+-- view 0 clonality: clonality:summary:Breast · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid, sample_unique_id AS suid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+prof AS (
+  SELECT gp.genetic_profile_id AS id FROM snapshot_db.genetic_profile gp
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = gp.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_impact_50k_2026' AND gp.genetic_alteration_type = 'MUTATION_EXTENDED'
+),
+wgd AS (SELECT sample_unique_id AS suid, lowerUTF8(trimBoth(attribute_value)) AS v FROM snapshot_db.clinical_data_derived
+    WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_WGD'),
+qcv AS (SELECT sample_unique_id AS suid, lowerUTF8(trimBoth(attribute_value)) AS v FROM snapshot_db.clinical_data_derived
+    WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_QC')
+SELECT count() AS samples,
+       countIf(sid IN (SELECT DISTINCT sample_id FROM snapshot_db.allele_specific_copy_number WHERE genetic_profile_id IN (SELECT id FROM prof))) AS ascn_samples,
+       countIf(suid IN (SELECT suid FROM qcv WHERE v = 'pass')) AS qc_pass,
+       countIf(suid IN (SELECT suid FROM qcv WHERE v = 'fail')) AS qc_fail,
+       countIf(suid IN (SELECT suid FROM wgd WHERE v = 'true') AND suid IN (SELECT suid FROM qcv WHERE v = 'pass')) AS wgd_true,
+       countIf(suid IN (SELECT suid FROM wgd WHERE v = 'false') AND suid IN (SELECT suid FROM qcv WHERE v = 'pass')) AS wgd_false
+FROM samp
+
+-- view 0 clonality: clonality:summary:Colorectal · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid, sample_unique_id AS suid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+prof AS (
+  SELECT gp.genetic_profile_id AS id FROM snapshot_db.genetic_profile gp
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = gp.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_impact_50k_2026' AND gp.genetic_alteration_type = 'MUTATION_EXTENDED'
+),
+wgd AS (SELECT sample_unique_id AS suid, lowerUTF8(trimBoth(attribute_value)) AS v FROM snapshot_db.clinical_data_derived
+    WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_WGD'),
+qcv AS (SELECT sample_unique_id AS suid, lowerUTF8(trimBoth(attribute_value)) AS v FROM snapshot_db.clinical_data_derived
+    WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'FACETS_QC')
+SELECT count() AS samples,
+       countIf(sid IN (SELECT DISTINCT sample_id FROM snapshot_db.allele_specific_copy_number WHERE genetic_profile_id IN (SELECT id FROM prof))) AS ascn_samples,
+       countIf(suid IN (SELECT suid FROM qcv WHERE v = 'pass')) AS qc_pass,
+       countIf(suid IN (SELECT suid FROM qcv WHERE v = 'fail')) AS qc_fail,
+       countIf(suid IN (SELECT suid FROM wgd WHERE v = 'true') AND suid IN (SELECT suid FROM qcv WHERE v = 'pass')) AS wgd_true,
+       countIf(suid IN (SELECT suid FROM wgd WHERE v = 'false') AND suid IN (SELECT suid FROM qcv WHERE v = 'pass')) AS wgd_false
+FROM samp
diff --git a/test/snapshots/clonality.svg b/test/snapshots/clonality.svg
new file mode 100644
index 00000000..253fe7db
--- /dev/null
+++ b/test/snapshots/clonality.svg
@@ -0,0 +1,2 @@
+<!-- view 0 clonality -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 480" width="100%" role="img" aria-label="Clonality (FACETS) — Colorectal, Breast" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Clonality (FACETS) — Colorectal, Breast</title><rect width="960" height="480" fill="#ffffff"/><text x="8" y="15" font-size="10" fill="#5b6b76">cohorts:</text><rect x="54.88" y="6.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="69.9" y="15" font-size="10" fill="#16232b">Colorectal</text><rect x="131.77" y="6.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="146.8" y="15" font-size="10" fill="#16232b">Breast</text><text x="8" y="32" font-size="10" fill="#5b6b76">driver mutation calls:</text><rect x="112.92" y="23.5" width="10" height="10" rx="2" fill="#5b6b76"/><text x="127.9" y="32" font-size="10" fill="#16232b">clonal</text><rect x="171.01" y="23.5" width="10" height="10" rx="2" fill="#5b6b76" opacity="0.4"/><text x="186" y="32" font-size="10" fill="#16232b">subclonal (paler)</text><rect x="277.53" y="23.5" width="10" height="10" rx="2" fill="#9aa5ad" opacity="0.5"/><text x="292.5" y="32" font-size="10" fill="#16232b">indeterminate</text><text x="621" y="54" font-size="10" fill="#5b6b76" text-anchor="end">clonal share differs</text><line x1="60.9" y1="58" x2="60.9" y2="324" stroke="#dde4e8" stroke-width="0.75"/><line x1="114.1" y1="58" x2="114.1" y2="324" stroke="#dde4e8" stroke-width="0.75"/><line x1="167.2" y1="58" x2="167.2" y2="324" stroke="#dde4e8" stroke-width="0.75"/><line x1="220.3" y1="58" x2="220.3" y2="324" stroke="#dde4e8" stroke-width="0.75"/><line x1="273.5" y1="58" x2="273.5" y2="324" stroke="#dde4e8" stroke-width="0.75"/><line x1="326.6" y1="58" x2="326.6" y2="324" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="58" width="621" height="38" fill="#dde4e8" opacity="0.3"/><text x="48.9" y="80.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">TP53</text><rect x="60.9" y="64.5" width="218.9" height="10" fill="#0072B2"/><rect x="279.9" y="64.5" width="29.1" height="10" fill="#0072B2" opacity="0.4"/><rect x="309" y="64.5" width="17.7" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="73" font-size="10" fill="#16232b" text-anchor="end">88.3% clonal</text><text x="461.6" y="73" font-size="10" fill="#5b6b76" text-anchor="end">LOH 76.8%</text><text x="523.3" y="73" font-size="10" fill="#5b6b76" text-anchor="end">3,839 calls</text><text x="621" y="73" font-size="10" fill="#5b6b76" text-anchor="end">q 0.59</text><rect x="60.9" y="79.5" width="218.5" height="10" fill="#E69F00"/><rect x="279.4" y="79.5" width="27.8" height="10" fill="#E69F00" opacity="0.4"/><rect x="307.1" y="79.5" width="19.5" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="88" font-size="10" fill="#16232b" text-anchor="end">88.7% clonal</text><text x="461.6" y="88" font-size="10" fill="#5b6b76" text-anchor="end">LOH 89.1%</text><text x="523.3" y="88" font-size="10" fill="#5b6b76" text-anchor="end">2,672 calls</text><text x="48.9" y="118.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">KRAS</text><rect x="60.9" y="102.5" width="186.9" height="10" fill="#0072B2"/><rect x="247.8" y="102.5" width="35.9" height="10" fill="#0072B2" opacity="0.4"/><rect x="283.7" y="102.5" width="42.9" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="111" font-size="10" fill="#16232b" text-anchor="end">83.9% clonal</text><text x="461.6" y="111" font-size="10" fill="#5b6b76" text-anchor="end">LOH 21.4%</text><text x="523.3" y="111" font-size="10" fill="#5b6b76" text-anchor="end">2,145 calls</text><text x="621" y="111" font-size="10" fill="#5b6b76" text-anchor="end">q 0.23</text><rect x="60.9" y="117.5" width="180.1" height="10" fill="#E69F00"/><rect x="241.1" y="117.5" width="58.5" height="10" fill="#E69F00" opacity="0.4"/><rect x="299.6" y="117.5" width="27" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="126" font-size="10" fill="#16232b" text-anchor="end">75.5% clonal</text><text x="461.6" y="126" font-size="10" fill="#5b6b76" text-anchor="end">LOH 17.3%</text><text x="523.3" y="126" font-size="10" fill="#5b6b76" text-anchor="end">59 calls</text><rect x="4" y="134" width="621" height="38" fill="#dde4e8" opacity="0.3"/><text x="48.9" y="156.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">APC</text><rect x="60.9" y="140.5" width="193.5" height="10" fill="#0072B2"/><rect x="254.4" y="140.5" width="44.6" height="10" fill="#0072B2" opacity="0.4"/><rect x="299.1" y="140.5" width="27.6" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="149" font-size="10" fill="#16232b" text-anchor="end">81.3% clonal</text><text x="461.6" y="149" font-size="10" fill="#5b6b76" text-anchor="end">LOH 33.5%</text><text x="523.3" y="149" font-size="10" fill="#5b6b76" text-anchor="end">5,344 calls</text><text x="621" y="149" font-size="10" fill="#5b6b76" text-anchor="end">q 0.59</text><rect x="60.9" y="155.5" width="183.9" height="10" fill="#E69F00"/><rect x="244.9" y="155.5" width="54.5" height="10" fill="#E69F00" opacity="0.4"/><rect x="299.4" y="155.5" width="27.3" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="164" font-size="10" fill="#16232b" text-anchor="end">77.1% clonal</text><text x="461.6" y="164" font-size="10" fill="#5b6b76" text-anchor="end">LOH 44.4%</text><text x="523.3" y="164" font-size="10" fill="#5b6b76" text-anchor="end">39 calls</text><text x="48.9" y="194.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">PIK3CA</text><rect x="60.9" y="178.5" width="165.5" height="10" fill="#0072B2"/><rect x="226.4" y="178.5" width="78.8" height="10" fill="#0072B2" opacity="0.4"/><rect x="305.2" y="178.5" width="21.4" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="187" font-size="10" fill="#16232b" text-anchor="end">67.7% clonal</text><text x="461.6" y="187" font-size="10" fill="#5b6b76" text-anchor="end">LOH 11.6%</text><text x="523.3" y="187" font-size="10" fill="#5b6b76" text-anchor="end">1,018 calls</text><text x="621" y="187" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">q 3.1e-23</text><rect x="60.9" y="193.5" width="202.9" height="10" fill="#E69F00"/><rect x="263.8" y="193.5" width="37.8" height="10" fill="#E69F00" opacity="0.4"/><rect x="301.6" y="193.5" width="25" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="202" font-size="10" fill="#16232b" text-anchor="end">84.3% clonal</text><text x="461.6" y="202" font-size="10" fill="#5b6b76" text-anchor="end">LOH 13.2%</text><text x="523.3" y="202" font-size="10" fill="#5b6b76" text-anchor="end">2,350 calls</text><rect x="4" y="210" width="621" height="38" fill="#dde4e8" opacity="0.3"/><text x="48.9" y="232.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">SMAD4</text><rect x="60.9" y="216.5" width="195" height="10" fill="#0072B2"/><rect x="256" y="216.5" width="64.7" height="10" fill="#0072B2" opacity="0.4"/><rect x="320.7" y="216.5" width="5.9" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="225" font-size="10" fill="#16232b" text-anchor="end">75.1% clonal</text><text x="461.6" y="225" font-size="10" fill="#5b6b76" text-anchor="end">LOH 79.2%</text><text x="523.3" y="225" font-size="10" fill="#5b6b76" text-anchor="end">673 calls</text><text x="621" y="225" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">q 0.014</text><rect x="60.9" y="231.5" width="154.7" height="10" fill="#E69F00"/><rect x="215.6" y="231.5" width="104.3" height="10" fill="#E69F00" opacity="0.4"/><rect x="319.9" y="231.5" width="6.7" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="240" font-size="10" fill="#16232b" text-anchor="end">59.7% clonal</text><text x="461.6" y="240" font-size="10" fill="#5b6b76" text-anchor="end">LOH 92.3%</text><text x="523.3" y="240" font-size="10" fill="#5b6b76" text-anchor="end">79 calls</text><text x="48.9" y="270.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">CDH1</text><rect x="60.9" y="254.5" width="180" height="10" fill="#0072B2"/><rect x="240.9" y="254.5" width="72.9" height="10" fill="#0072B2" opacity="0.4"/><rect x="313.8" y="254.5" width="12.9" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="263" font-size="10" fill="#16232b" text-anchor="end">71.2% clonal</text><text x="461.6" y="263" font-size="10" fill="#5b6b76" text-anchor="end">LOH 16.4%</text><text x="523.3" y="263" font-size="10" fill="#5b6b76" text-anchor="end">62 calls</text><text x="621" y="263" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">q 0.0099</text><rect x="60.9" y="269.5" width="220.2" height="10" fill="#E69F00"/><rect x="281.1" y="269.5" width="33.1" height="10" fill="#E69F00" opacity="0.4"/><rect x="314.2" y="269.5" width="12.4" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="278" font-size="10" fill="#16232b" text-anchor="end">86.9% clonal</text><text x="461.6" y="278" font-size="10" fill="#5b6b76" text-anchor="end">LOH 94.8%</text><text x="523.3" y="278" font-size="10" fill="#5b6b76" text-anchor="end">773 calls</text><rect x="4" y="286" width="621" height="38" fill="#dde4e8" opacity="0.3"/><text x="48.9" y="308.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">GATA3</text><rect x="60.9" y="292.5" width="177.1" height="10" fill="#0072B2"/><rect x="238.1" y="292.5" width="75.9" height="10" fill="#0072B2" opacity="0.4"/><rect x="314" y="292.5" width="12.7" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="301" font-size="10" fill="#16232b" text-anchor="end">70.0% clonal</text><text x="461.6" y="301" font-size="10" fill="#5b6b76" text-anchor="end">LOH 7.1%</text><text x="523.3" y="301" font-size="10" fill="#5b6b76" text-anchor="end">42 calls</text><text x="621" y="301" font-size="10" fill="#5b6b76" text-anchor="end">q 0.33</text><rect x="60.9" y="307.5" width="178.8" height="10" fill="#E69F00"/><rect x="239.7" y="307.5" width="48" height="10" fill="#E69F00" opacity="0.4"/><rect x="287.8" y="307.5" width="38.9" height="10" fill="#9aa5ad" opacity="0.5"/><text x="398.1" y="316" font-size="10" fill="#16232b" text-anchor="end">78.8% clonal</text><text x="461.6" y="316" font-size="10" fill="#5b6b76" text-anchor="end">LOH 7.6%</text><text x="523.3" y="316" font-size="10" fill="#5b6b76" text-anchor="end">847 calls</text><line x1="60.9" y1="324" x2="60.9" y2="327" stroke="#5b6b76" stroke-width="1"/><text x="53.3" y="340" font-size="10" fill="#5b6b76">0%</text><line x1="114.1" y1="324" x2="114.1" y2="327" stroke="#5b6b76" stroke-width="1"/><text x="103.4" y="340" font-size="10" fill="#5b6b76">20%</text><line x1="167.2" y1="324" x2="167.2" y2="327" stroke="#5b6b76" stroke-width="1"/><text x="156.6" y="340" font-size="10" fill="#5b6b76">40%</text><line x1="220.3" y1="324" x2="220.3" y2="327" stroke="#5b6b76" stroke-width="1"/><text x="209.7" y="340" font-size="10" fill="#5b6b76">60%</text><line x1="273.5" y1="324" x2="273.5" y2="327" stroke="#5b6b76" stroke-width="1"/><text x="262.8" y="340" font-size="10" fill="#5b6b76">80%</text><line x1="326.6" y1="324" x2="326.6" y2="327" stroke="#5b6b76" stroke-width="1"/><text x="311.4" y="340" font-size="10" fill="#5b6b76">100%</text><line x1="60.92" y1="324" x2="326.62" y2="324" stroke="#5b6b76" stroke-width="1"/><text x="193.8" y="359" font-size="11" fill="#16232b" text-anchor="middle">share of the gene's calls</text><text x="645" y="68" font-size="10" fill="#5b6b76">per sample (FACETS)</text><rect x="645" y="76" width="10" height="10" rx="2" fill="#0072B2"/><text x="660" y="85" font-size="11" fill="#16232b" font-weight="600">Colorectal</text><text x="645" y="100" font-size="10" fill="#16232b">whole-genome doubled: 35.6% of 4,912 samples</text><line x1="673" y1="154" x2="792.5" y2="154" stroke="#dde4e8" stroke-width="0.75"/><text x="667" y="157.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="673" y1="139.3" x2="792.5" y2="139.3" stroke="#dde4e8" stroke-width="0.75"/><text x="667" y="142.8" font-size="10" fill="#5b6b76" text-anchor="end">200</text><line x1="673" y1="124.7" x2="792.5" y2="124.7" stroke="#dde4e8" stroke-width="0.75"/><text x="667" y="128.2" font-size="10" fill="#5b6b76" text-anchor="end">400</text><line x1="673" y1="110" x2="792.5" y2="110" stroke="#dde4e8" stroke-width="0.75"/><text x="667" y="113.5" font-size="10" fill="#5b6b76" text-anchor="end">600</text><path d="M679 154V153.9h5.4V154zM685 154V145.6h5.4V154zM690.9 154V141.5h5.4V154zM696.9 154V129.4h5.4V154zM702.9 154V117.1h5.4V154zM708.9 154V115.7h5.4V154zM714.8 154V123.9h5.4V154zM720.8 154V116.3h5.4V154zM726.8 154V120.8h5.4V154zM732.8 154V120.3h5.4V154zM738.7 154V120.7h5.4V154zM744.7 154V132.4h5.4V154zM750.7 154V134.3h5.4V154zM756.7 154V144.1h5.4V154zM762.6 154V145.4h5.4V154zM768.6 154V148.2h5.4V154zM774.6 154V150.3h5.4V154zM780.6 154V152h5.4V154zM786.5 154V153.8h5.4V154z" fill="#0072B2"/><line x1="673" y1="154" 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stroke-width="1"/><text x="946" y="319" font-size="10" fill="#5b6b76">8</text><line x1="841.5" y1="303" x2="952" y2="303" stroke="#5b6b76" stroke-width="1"/><text x="896.8" y="338" font-size="11" fill="#16232b" text-anchor="middle">ploidy</text><text x="645" y="356" font-size="10" fill="#5b6b76">6,703 of 6,907 samples have FACETS estimates; 6,204 pass QC</text><text x="645" y="382" font-size="10" fill="#16232b">WGD differs: Fisher p = 0.38</text><text x="8" y="404" font-size="10" fill="#5b6b76">FACETS allele-specific copy number: each OncoKB driver (v7.5) mutation call — one mutation in one sample — is clonal, subclonal or indeterminate as FACETS called it</text><text x="8" y="418" font-size="10" fill="#5b6b76">from its cancer-cell fraction; uncalled calls have an estimate but no call. LOH at the locus is a minor copy number of 0 where one was estimated. Only samples whose</text><text x="8" y="432" font-size="10" fill="#5b6b76">FACETS_QC is Pass are used (Colorectal: 4,912 pass, 451 fail; Breast: 6,204 pass, 671 fail); qc: false uses every sample. Whole-genome doubling, purity and ploidy are</text><text x="8" y="446" font-size="10" fill="#5b6b76">FACETS's own per-sample calls. Clonality is only as good as the purity estimate behind it: a low-purity sample's clonal mutation can be called subclonal, and a panel's</text><text x="8" y="460" font-size="10" fill="#5b6b76">handful of mutations per sample makes each estimate coarse. Two cohorts: Fisher on clonal against subclonal calls per gene (BH across genes) and on WGD.</text></svg>
diff --git a/test/snapshots/clonality.txt b/test/snapshots/clonality.txt
new file mode 100644
index 00000000..399f595a
--- /dev/null
+++ b/test/snapshots/clonality.txt
@@ -0,0 +1,34 @@
+Analysis over msk_impact_50k_2026 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Colorectal: 4,993 patients — CANCER_TYPE in [Colorectal Cancer] (any sample)
+- Breast: 5,803 patients — CANCER_TYPE in [Breast Cancer] (any sample)
+
+Clonality (FACETS; driver mutation calls, QC-passing samples):
+  Colorectal: WGD 35.6% of 4,912 samples
+    TP53: 3839 calls, 88% clonal (3160 clonal, 420 subclonal, 255 indeterminate), LOH 77%, q=0.5947
+    KRAS: 2145 calls, 84% clonal (1508 clonal, 290 subclonal, 346 indeterminate), LOH 21%, q=0.2252
+    APC: 5344 calls, 81% clonal (3889 clonal, 897 subclonal, 554 indeterminate), LOH 34%, q=0.5947
+    PIK3CA: 1018 calls, 68% clonal (634 clonal, 302 subclonal, 82 indeterminate), LOH 12%, q=3.1e-23
+    SMAD4: 673 calls, 75% clonal (494 clonal, 164 subclonal, 15 indeterminate), LOH 79%, q=0.0140
+    CDH1: 62 calls, 71% clonal (42 clonal, 17 subclonal, 3 indeterminate), LOH 16%, q=0.0099
+    GATA3: 42 calls, 70% clonal (28 clonal, 12 subclonal, 2 indeterminate), LOH 7%, q=0.3279
+  Breast: WGD 34.8% of 6,204 samples
+    TP53: 2672 calls, 89% clonal (2188 clonal, 278 subclonal, 195 indeterminate), LOH 89%, q=0.5947
+    KRAS: 59 calls, 75% clonal (40 clonal, 13 subclonal, 6 indeterminate), LOH 17%, q=0.2252
+    APC: 39 calls, 77% clonal (27 clonal, 8 subclonal, 4 indeterminate), LOH 44%, q=0.5947
+    PIK3CA: 2350 calls, 84% clonal (1793 clonal, 334 subclonal, 221 indeterminate), LOH 13%, q=3.1e-23
+    SMAD4: 79 calls, 60% clonal (46 clonal, 31 subclonal, 2 indeterminate), LOH 92%, q=0.0140
+    CDH1: 773 calls, 87% clonal (639 clonal, 96 subclonal, 36 indeterminate), LOH 95%, q=0.0099
+    GATA3: 847 calls, 79% clonal (566 clonal, 152 subclonal, 123 indeterminate), LOH 8%, q=0.3279
+  WGD difference: Fisher p=0.3789
+  CAVEAT (tell the user): FACETS allele-specific copy number: each OncoKB driver (v7.5) mutation call — one mutation in one sample — is clonal, subclonal or indeterminate as FACETS called it from its cancer-cell fraction; uncalled calls have an estimate but no call. LOH at the locus is a minor copy number of 0 where one was estimated. Only samples whose FACETS_QC is Pass are used (Colorectal: 4,912 pass, 451 fail; Breast: 6,204 pass, 671 fail); qc: false uses every sample. Whole-genome doubling, purity and ploidy are FACETS's own per-sample calls. Clonality is only as good as the purity estimate behind it: a low-purity sample's clonal mutation can be called subclonal, and a panel's handful of mutations per sample makes each estimate coarse. Two cohorts: Fisher on clonal against subclonal calls per gene (BH across genes) and on WGD.
+  METHOD:
+    - Count FACETS's calls per gene [Colorectal · msk_impact_50k_2026]: The clonality:summary, clonality:purity-ploidy and clonality:genes queries above. Per gene, each an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in one sample is one call, which FACETS labels clonal, subclonal or indeterminate from its cancer-cell fraction (or leaves uncalled); loss of heterozygosity is a minor copy number of 0 where one was estimated. Per sample: whole-genome doubling, purity and ploidy as FACETS called them. Counts only.
+    - Count FACETS's calls per gene [Breast · msk_impact_50k_2026]: The clonality:summary, clonality:purity-ploidy and clonality:genes queries above. Per gene, each an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in one sample is one call, which FACETS labels clonal, subclonal or indeterminate from its cancer-cell fraction (or leaves uncalled); loss of heterozygosity is a minor copy number of 0 where one was estimated. Per sample: whole-genome doubling, purity and ploidy as FACETS called them. Counts only.
+    - Keep samples that pass FACETS QC: Only samples whose FACETS_QC is Pass (matched ignoring case) contribute calls, WGD, purity and ploidy.
+        Colorectal: 4,912 pass, 451 fail, of 5,379 samples with allele-specific copy number
+        Breast: 6,204 pass, 671 fail, of 6,703 samples with allele-specific copy number
+    - Fisher's exact test per gene, and on WGD: Two-sided, on clonal against subclonal calls between the two cohorts, for each gene with at least one such call in both; indeterminate and uncalled calls do not enter. And on whole-genome doubled against not, per sample.
+    - Benjamini–Hochberg across genes: q-values over the genes tested.
+    Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/coexpression.json b/test/snapshots/coexpression.json
new file mode 100644
index 00000000..20a366c4
--- /dev/null
+++ b/test/snapshots/coexpression.json
@@ -0,0 +1,256 @@
+{
+  "study": "brca_tcga_pan_can_atlas_2018",
+  "cohorts": {
+    "BRCA": {
+      "count": 1066,
+      "text": "profiled for MUTATION",
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "coexpression",
+      "cohorts": [
+        "BRCA"
+      ],
+      "coexpression": [
+        {
+          "a": "ERBB2",
+          "b": "GRB7",
+          "r": 0.8713,
+          "n": 1064,
+          "p": 0,
+          "q": 0
+        },
+        {
+          "a": "ESR1",
+          "b": "MKI67",
+          "r": -0.2334,
+          "n": 1064,
+          "p": 1.2418853229945448e-14,
+          "q": 6.209426614972723e-14
+        },
+        {
+          "a": "ESR1",
+          "b": "PGR",
+          "r": 0.2263,
+          "n": 1064,
+          "p": 8.025146361684293e-14,
+          "q": 2.6750487872280976e-13
+        },
+        {
+          "a": "PGR",
+          "b": "MKI67",
+          "r": -0.1839,
+          "n": 1064,
+          "p": 1.5183862366308729e-9,
+          "q": 3.7959655915771825e-9
+        },
+        {
+          "a": "GRB7",
+          "b": "ESR1",
+          "r": -0.1723,
+          "n": 1064,
+          "p": 1.560101548391098e-8,
+          "q": 3.120203096782196e-8
+        },
+        {
+          "a": "ERBB2",
+          "b": "ESR1",
+          "r": -0.1651,
+          "n": 1064,
+          "p": 6.030449902721193e-8,
+          "q": 1.0050749837868655e-7
+        },
+        {
+          "a": "ERBB2",
+          "b": "PGR",
+          "r": -0.127,
+          "n": 1064,
+          "p": 0.00003249163546036962,
+          "q": 0.00004641662208624231
+        },
+        {
+          "a": "GRB7",
+          "b": "PGR",
+          "r": -0.1185,
+          "n": 1064,
+          "p": 0.0001071034933253945,
+          "q": 0.00013387936665674313
+        },
+        {
+          "a": "GRB7",
+          "b": "MKI67",
+          "r": 0.0457,
+          "n": 1064,
+          "p": 0.13671251169317,
+          "q": 0.1425531010179386
+        },
+        {
+          "a": "ERBB2",
+          "b": "MKI67",
+          "r": 0.045,
+          "n": 1064,
+          "p": 0.1425531010179386,
+          "q": 0.1425531010179386
+        }
+      ],
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "brca_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "Pearson correlation on “rna_seq_v2_mrna_median_Zscores” within “BRCA”, over the patients with BOTH values (values averaged per patient, matching the heatmap convention). Each site reports six sums — n, Σx, Σy, Σxy, Σx², Σy² — and nothing else; those sums are additive, so the pooled correlation is EXACT rather than an approximation of a centralized one. q-values are Benjamini–Hochberg across the 10 pairs with a defined correlation.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Sum each gene pair's values",
+            "detail": "The pairMoments query above, on “rna_seq_v2_mrna_median_Zscores”. Per pair, over the patients with a value for both genes (a patient with several samples contributes their average): n, Σx, Σy, Σxy, Σx² and Σy² — six sums, no individual value.",
+            "scope": "BRCA · brca_tcga_pan_can_atlas_2018",
+            "rowsIn": 1066,
+            "params": [
+              [
+                "pairs",
+                "10"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Pearson correlation per pair",
+            "detail": "r = (nΣxy − ΣxΣy) / √((nΣx² − (Σx)²)(nΣy² − (Σy)²)); t = r√((n − 2)/(1 − r²)), two-sided p on n − 2 df. Undefined for n < 3 or a constant series."
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across pairs",
+            "detail": "q-values over the 10 pairs with a defined correlation, from the Pearson p-values."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "pearsonFromMoments",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Pearson's r and p from the six sums match scipy 1.18.1 pearsonr on the underlying values, to a relative 1e-6 (test/moments-golden.test.ts).",
+        "check": {
+          "coexpression:ERBB2/GRB7:r": {
+            "v": 0.8713,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:ERBB2/GRB7:p": {
+            "v": 0
+          },
+          "coexpression:ERBB2/GRB7:q": {
+            "v": 0
+          },
+          "coexpression:ESR1/MKI67:r": {
+            "v": -0.2334,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:ESR1/MKI67:p": {
+            "v": 1.2418853229945448e-14
+          },
+          "coexpression:ESR1/MKI67:q": {
+            "v": 6.209426614972723e-14
+          },
+          "coexpression:ESR1/PGR:r": {
+            "v": 0.2263,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:ESR1/PGR:p": {
+            "v": 8.025146361684293e-14
+          },
+          "coexpression:ESR1/PGR:q": {
+            "v": 2.6750487872280976e-13
+          },
+          "coexpression:PGR/MKI67:r": {
+            "v": -0.1839,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:PGR/MKI67:p": {
+            "v": 1.5183862366308729e-9
+          },
+          "coexpression:PGR/MKI67:q": {
+            "v": 3.7959655915771825e-9
+          },
+          "coexpression:GRB7/ESR1:r": {
+            "v": -0.1723,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:GRB7/ESR1:p": {
+            "v": 1.560101548391098e-8
+          },
+          "coexpression:GRB7/ESR1:q": {
+            "v": 3.120203096782196e-8
+          },
+          "coexpression:ERBB2/ESR1:r": {
+            "v": -0.1651,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:ERBB2/ESR1:p": {
+            "v": 6.030449902721193e-8
+          },
+          "coexpression:ERBB2/ESR1:q": {
+            "v": 1.0050749837868655e-7
+          },
+          "coexpression:ERBB2/PGR:r": {
+            "v": -0.127,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:ERBB2/PGR:p": {
+            "v": 0.00003249163546036962
+          },
+          "coexpression:ERBB2/PGR:q": {
+            "v": 0.00004641662208624231
+          },
+          "coexpression:GRB7/PGR:r": {
+            "v": -0.1185,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:GRB7/PGR:p": {
+            "v": 0.0001071034933253945
+          },
+          "coexpression:GRB7/PGR:q": {
+            "v": 0.00013387936665674313
+          },
+          "coexpression:GRB7/MKI67:r": {
+            "v": 0.0457,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:GRB7/MKI67:p": {
+            "v": 0.13671251169317
+          },
+          "coexpression:GRB7/MKI67:q": {
+            "v": 0.1425531010179386
+          },
+          "coexpression:ERBB2/MKI67:r": {
+            "v": 0.045,
+            "abs": 0.000050000001000000005
+          },
+          "coexpression:ERBB2/MKI67:p": {
+            "v": 0.1425531010179386
+          },
+          "coexpression:ERBB2/MKI67:q": {
+            "v": 0.1425531010179386
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/coexpression.sql b/test/snapshots/coexpression.sql
new file mode 100644
index 00000000..500ee616
--- /dev/null
+++ b/test/snapshots/coexpression.sql
@@ -0,0 +1,35 @@
+-- cohort BRCA
+-- brca_tcga_pan_can_atlas_2018
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND sp.alteration_type = 'MUTATION_EXTENDED')
+
+-- view 0 coexpression: pairMoments · brca_tcga_pan_can_atlas_2018
+WITH cohort AS ((SELECT DISTINCT replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND sp.alteration_type = 'MUTATION_EXTENDED')),
+vals AS (
+  SELECT replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid, ga.hugo_gene_symbol AS gene,
+         avg(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL)) AS v
+  FROM snapshot_db.genetic_alteration_derived ga
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+  WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna_median_Zscores'
+    AND ga.hugo_gene_symbol IN ('ERBB2','GRB7','ESR1','PGR','MKI67')
+    AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL
+    AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid, gene
+),
+pp AS (SELECT pid, groupArray(gene) AS gs, groupArray(v) AS vs FROM vals GROUP BY pid)
+SELECT ga, gb, countIf(ok) AS n,
+       sumIf(x, ok) AS sx, sumIf(y, ok) AS sy,
+       sumIf(x * y, ok) AS sxy, sumIf(x * x, ok) AS sxx, sumIf(y * y, ok) AS syy
+FROM (
+  SELECT ga, gb, indexOf(gs, ga) AS ia, indexOf(gs, gb) AS ib,
+         (ia > 0 AND ib > 0) AS ok,
+         if(ok, vs[ia], 0) AS x, if(ok, vs[ib], 0) AS y
+  FROM pp
+  ARRAY JOIN ['ERBB2','ERBB2','ERBB2','ERBB2','GRB7','GRB7','GRB7','ESR1','ESR1','PGR'] AS ga, ['GRB7','ESR1','PGR','MKI67','ESR1','PGR','MKI67','PGR','MKI67','MKI67'] AS gb
+)
+GROUP BY ga, gb
diff --git a/test/snapshots/coexpression.svg b/test/snapshots/coexpression.svg
new file mode 100644
index 00000000..294fcd93
--- /dev/null
+++ b/test/snapshots/coexpression.svg
@@ -0,0 +1,2 @@
+<!-- view 0 coexpression -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 370" width="100%" role="img" aria-label="Co-expression — Pearson r" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Co-expression — Pearson r</title><rect width="720" height="370" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Co-expression — Pearson r</text><rect x="8" y="28.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="37" font-size="10" fill="#16232b">positive, q &lt; 0.05</text><rect x="116.51" y="28.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="131.5" y="37" font-size="10" fill="#16232b">negative, q &lt; 0.05</text><rect x="228.04000000000002" y="28.5" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="243" y="37" font-size="10" fill="#16232b">not significant</text><line x1="97.3" y1="47" x2="97.3" y2="267" stroke="#dde4e8" stroke-width="0.75"/><line x1="209.8" y1="47" x2="209.8" y2="267" stroke="#dde4e8" stroke-width="0.75"/><line x1="322.2" y1="47" x2="322.2" y2="267" stroke="#dde4e8" stroke-width="0.75"/><line x1="434.7" y1="47" x2="434.7" y2="267" stroke="#dde4e8" stroke-width="0.75"/><line x1="547.1" y1="47" x2="547.1" y2="267" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="47" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="61.9" font-size="11" fill="#16232b">ERBB2 / GRB7</text><rect x="322.2" y="52" width="195.9" height="12" rx="1.5" fill="#0072B2"><title>ERBB2 / GRB7</title></rect><text x="712" y="61.5" font-size="10" fill="#16232b" text-anchor="end">r = 0.87 · n = 1,064 · q &lt; 1e-300</text><text x="8" y="83.9" font-size="11" fill="#16232b">ESR1 / MKI67</text><rect x="269.7" y="74" width="52.5" height="12" rx="1.5" fill="#E69F00"><title>ESR1 / MKI67</title></rect><text x="712" y="83.5" font-size="10" fill="#16232b" text-anchor="end">r = −0.23 · n = 1,064 · q = 6.2e-14</text><rect x="4" y="91" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="105.9" font-size="11" fill="#16232b">ESR1 / PGR</text><rect x="322.2" y="96" width="50.9" height="12" rx="1.5" fill="#0072B2"><title>ESR1 / PGR</title></rect><text x="712" y="105.5" font-size="10" fill="#16232b" text-anchor="end">r = 0.23 · n = 1,064 · q = 2.7e-13</text><text x="8" y="127.9" font-size="11" fill="#16232b">PGR / MKI67</text><rect x="280.9" y="118" width="41.4" height="12" rx="1.5" fill="#E69F00"><title>PGR / MKI67</title></rect><text x="712" y="127.5" font-size="10" fill="#16232b" text-anchor="end">r = −0.18 · n = 1,064 · q = 3.8e-9</text><rect x="4" y="135" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="149.9" font-size="11" fill="#16232b">GRB7 / ESR1</text><rect x="283.5" y="140" width="38.7" height="12" rx="1.5" fill="#E69F00"><title>GRB7 / ESR1</title></rect><text x="712" y="149.5" font-size="10" fill="#16232b" text-anchor="end">r = −0.17 · n = 1,064 · q = 3.1e-8</text><text x="8" y="171.9" font-size="11" fill="#16232b">ERBB2 / ESR1</text><rect x="285.1" y="162" width="37.1" height="12" rx="1.5" fill="#E69F00"><title>ERBB2 / ESR1</title></rect><text x="712" y="171.5" font-size="10" fill="#16232b" text-anchor="end">r = −0.17 · n = 1,064 · q = 1e-7</text><rect x="4" y="179" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="193.9" font-size="11" fill="#16232b">ERBB2 / PGR</text><rect x="293.7" y="184" width="28.6" height="12" rx="1.5" fill="#E69F00"><title>ERBB2 / PGR</title></rect><text x="712" y="193.5" font-size="10" fill="#16232b" text-anchor="end">r = −0.13 · n = 1,064 · q = 4.6e-5</text><text x="8" y="215.9" font-size="11" fill="#16232b">GRB7 / PGR</text><rect x="295.6" y="206" width="26.6" height="12" rx="1.5" fill="#E69F00"><title>GRB7 / PGR</title></rect><text x="712" y="215.5" font-size="10" fill="#16232b" text-anchor="end">r = −0.12 · n = 1,064 · q = 1.3e-4</text><rect x="4" y="223" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="237.9" font-size="11" fill="#16232b">GRB7 / MKI67</text><rect x="322.2" y="228" width="10.3" height="12" rx="1.5" fill="#9aa5ad"><title>GRB7 / MKI67</title></rect><text x="712" y="237.5" font-size="10" fill="#16232b" text-anchor="end">r = 0.046 · n = 1,064 · q = 0.14</text><text x="8" y="259.9" font-size="11" fill="#16232b">ERBB2 / MKI67</text><rect x="322.2" y="250" width="10.1" height="12" rx="1.5" fill="#9aa5ad"><title>ERBB2 / MKI67</title></rect><text x="712" y="259.5" font-size="10" fill="#16232b" text-anchor="end">r = 0.045 · n = 1,064 · q = 0.14</text><line x1="322.2" y1="45" x2="322.2" y2="269" stroke="#16232b" stroke-width="1"/><line x1="97.3" y1="267" x2="97.3" y2="270" stroke="#5b6b76" stroke-width="1"/><text x="87" y="283" font-size="10" fill="#5b6b76">−1.0</text><line x1="209.8" y1="267" x2="209.8" y2="270" stroke="#5b6b76" stroke-width="1"/><text x="199.4" y="283" font-size="10" fill="#5b6b76">−0.5</text><line x1="322.2" y1="267" x2="322.2" y2="270" stroke="#5b6b76" stroke-width="1"/><text x="314.9" y="283" font-size="10" fill="#5b6b76">0.0</text><line x1="434.7" y1="267" x2="434.7" y2="270" stroke="#5b6b76" stroke-width="1"/><text x="427.3" y="283" font-size="10" fill="#5b6b76">0.5</text><line x1="547.1" y1="267" x2="547.1" y2="270" stroke="#5b6b76" stroke-width="1"/><text x="539.7" y="283" font-size="10" fill="#5b6b76">1.0</text><line x1="97.33" y1="267" x2="547.09" y2="267" stroke="#5b6b76" stroke-width="1"/><text x="322.2" y="302" font-size="11" fill="#16232b" text-anchor="middle">Pearson r — left: anti-correlated, right: correlated</text><text x="8" y="324" font-size="10" fill="#5b6b76">Pearson correlation on “rna_seq_v2_mrna_median_Zscores” within “BRCA”, over the patients with BOTH values (values averaged per patient, matching the</text><text x="8" y="338" font-size="10" fill="#5b6b76">heatmap convention). Each site reports six sums — n, Σx, Σy, Σxy, Σx², Σy² — and nothing else; those sums are additive, so the pooled correlation is EXACT</text><text x="8" y="352" font-size="10" fill="#5b6b76">rather than an approximation of a centralized one. q-values are Benjamini–Hochberg across the 10 pairs with a defined correlation.</text></svg>
diff --git a/test/snapshots/coexpression.txt b/test/snapshots/coexpression.txt
new file mode 100644
index 00000000..629b89d2
--- /dev/null
+++ b/test/snapshots/coexpression.txt
@@ -0,0 +1,23 @@
+Analysis over brca_tcga_pan_can_atlas_2018 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- BRCA: 1,066 patients — profiled for MUTATION
+
+Co-expression (Pearson r, computed from six per-site sums so the pooled value is exact):
+  ERBB2/GRB7: r=0.871 (n=1,064) p=<1e-300 q=<1e-300
+  ESR1/MKI67: r=-0.233 (n=1,064) p=1.2e-14 q=6.2e-14
+  ESR1/PGR: r=0.226 (n=1,064) p=8.0e-14 q=2.7e-13
+  PGR/MKI67: r=-0.184 (n=1,064) p=1.5e-9 q=3.8e-9
+  GRB7/ESR1: r=-0.172 (n=1,064) p=1.6e-8 q=3.1e-8
+  ERBB2/ESR1: r=-0.165 (n=1,064) p=6.0e-8 q=1.0e-7
+  ERBB2/PGR: r=-0.127 (n=1,064) p=3.2e-5 q=4.6e-5
+  GRB7/PGR: r=-0.118 (n=1,064) p=0.0001 q=0.0001
+  GRB7/MKI67: r=0.046 (n=1,064) p=0.1367 q=0.1426
+  ERBB2/MKI67: r=0.045 (n=1,064) p=0.1426 q=0.1426
+  Pearson correlation on “rna_seq_v2_mrna_median_Zscores” within “BRCA”, over the patients with BOTH values (values averaged per patient, matching the heatmap convention). Each site reports six sums — n, Σx, Σy, Σxy, Σx², Σy² — and nothing else; those sums are additive, so the pooled correlation is EXACT rather than an approximation of a centralized one. q-values are Benjamini–Hochberg across the 10 pairs with a defined correlation.
+  METHOD:
+    - Sum each gene pair's values [BRCA · brca_tcga_pan_can_atlas_2018]: The pairMoments query above, on “rna_seq_v2_mrna_median_Zscores”. Per pair, over the patients with a value for both genes (a patient with several samples contributes their average): n, Σx, Σy, Σxy, Σx² and Σy² — six sums, no individual value.
+        pairs: 10
+    - Pearson correlation per pair: r = (nΣxy − ΣxΣy) / √((nΣx² − (Σx)²)(nΣy² − (Σy)²)); t = r√((n − 2)/(1 − r²)), two-sided p on n − 2 df. Undefined for n < 3 or a constant series.
+    - Benjamini–Hochberg across pairs: q-values over the 10 pairs with a defined correlation, from the Pearson p-values.
+    Pearson's r and p from the six sums match scipy 1.18.1 pearsonr on the underlying values, to a relative 1e-6 (test/moments-golden.test.ts).
diff --git a/test/snapshots/compare.json b/test/snapshots/compare.json
new file mode 100644
index 00000000..12fd0ed5
--- /dev/null
+++ b/test/snapshots/compare.json
@@ -0,0 +1,143 @@
+{
+  "study": "sarcoma_msk_2022",
+  "cohorts": {
+    "Well-diff liposarcoma": {
+      "count": 444,
+      "text": "CANCER_TYPE_DETAILED in [Well-Differentiated Liposarcoma] (any sample)",
+      "studies": [
+        "sarcoma_msk_2022"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "compare",
+      "cohorts": [
+        "Well-diff liposarcoma"
+      ],
+      "annotation": "OncoKB v7.5 (pinned)",
+      "rows": [
+        {
+          "gene": "MDM2",
+          "cells": [
+            {
+              "cohort": "Well-diff liposarcoma",
+              "profiled": 444,
+              "any": 0,
+              "driver": 0,
+              "anyPct": 0,
+              "driverPct": 0,
+              "byType": {
+                "mutation": {
+                  "profiled": 444,
+                  "altered": 0
+                },
+                "cna": {
+                  "profiled": 0,
+                  "altered": 0
+                },
+                "structuralVariant": {
+                  "profiled": 444,
+                  "altered": 0
+                }
+              }
+            }
+          ]
+        },
+        {
+          "gene": "TP53",
+          "cells": [
+            {
+              "cohort": "Well-diff liposarcoma",
+              "profiled": 444,
+              "any": 17,
+              "driver": 7,
+              "anyPct": 3.8,
+              "driverPct": 1.6,
+              "byType": {
+                "mutation": {
+                  "profiled": 444,
+                  "altered": 11
+                },
+                "cna": {
+                  "profiled": 0,
+                  "altered": 0
+                },
+                "structuralVariant": {
+                  "profiled": 444,
+                  "altered": 6
+                }
+              }
+            }
+          ]
+        }
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "sarcoma_msk_2022",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "no copy-number data exists for sarcoma_msk_2022 — altered% reflects only mutation/structural-variant status, not a real negative for the missing type(s)",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count profiled and altered patients per gene",
+            "detail": "The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.",
+            "scope": "Well-diff liposarcoma · sarcoma_msk_2022",
+            "rowsIn": 444,
+            "params": [
+              [
+                "genes",
+                "2"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "No test",
+            "detail": "One cohort: frequencies only, nothing to compare against."
+          }
+        ],
+        "implementation": {
+          "functions": [],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "check": {
+          "compare:MDM2:Well-diff liposarcoma:profiled": {
+            "v": 444,
+            "abs": 0
+          },
+          "compare:MDM2:Well-diff liposarcoma:altered": {
+            "v": 0,
+            "abs": 0
+          },
+          "compare:MDM2:Well-diff liposarcoma:driver": {
+            "v": 0,
+            "abs": 0
+          },
+          "compare:TP53:Well-diff liposarcoma:profiled": {
+            "v": 444,
+            "abs": 0
+          },
+          "compare:TP53:Well-diff liposarcoma:altered": {
+            "v": 17,
+            "abs": 0
+          },
+          "compare:TP53:Well-diff liposarcoma:driver": {
+            "v": 7,
+            "abs": 0
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/compare.sql b/test/snapshots/compare.sql
new file mode 100644
index 00000000..dec20f59
--- /dev/null
+++ b/test/snapshots/compare.sql
@@ -0,0 +1,58 @@
+-- cohort Well-diff liposarcoma
+-- sarcoma_msk_2022
+(SELECT DISTINCT replaceOne(patient_unique_id, 'sarcoma_msk_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'sarcoma_msk_2022' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Well-Differentiated Liposarcoma'))))
+
+-- view 0 compare: geneCounts · sarcoma_msk_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'sarcoma_msk_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'sarcoma_msk_2022' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE_DETAILED' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Well-Differentiated Liposarcoma'))))),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'sarcoma_msk_2022_', '') AS pid, g.variant_type AS vtype,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'sarcoma_msk_2022'
+    AND g.hugo_gene_symbol IN ('MDM2','TP53') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'sarcoma_msk_2022_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid, vtype
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'sarcoma_msk_2022_', '') AS pid,
+         multiIf(sp.alteration_type = 'MUTATION_EXTENDED', 'mutation',
+                 sp.alteration_type = 'COPY_NUMBER_ALTERATION', 'cna', 'structural_variant') AS vtype
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('MDM2','TP53')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'sarcoma_msk_2022'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'sarcoma_msk_2022_', '') IN (SELECT pid FROM cohort)
+),
+raw AS (
+  SELECT gene, pid, vtype, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, vtype, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, vtype, 0 AS is_alt, 0 AS is_drv FROM covered
+  )
+  GROUP BY gene, pid, vtype
+),
+perPatient AS (
+  SELECT gene, pid, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM raw GROUP BY gene, pid
+)
+SELECT gene, vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM raw GROUP BY gene, vtype
+UNION ALL
+SELECT gene, 'any' AS vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM perPatient GROUP BY gene
diff --git a/test/snapshots/compare.svg b/test/snapshots/compare.svg
new file mode 100644
index 00000000..88f06c56
--- /dev/null
+++ b/test/snapshots/compare.svg
@@ -0,0 +1,2 @@
+<!-- view 0 compare -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 201" width="100%" role="img" aria-label="Altered %, by cohort — Well-diff liposarcoma" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Altered %, by cohort — Well-diff liposarcoma</title><rect width="720" height="201" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Altered %, by cohort</text><rect x="8" y="29" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="37.9" font-size="11" fill="#16232b">Well-diff liposarcoma</text><line x1="52.3" y1="52" x2="52.3" y2="126" stroke="#dde4e8" stroke-width="0.75"/><line x1="207.2" y1="52" x2="207.2" y2="126" stroke="#dde4e8" stroke-width="0.75"/><line x1="362.1" y1="52" x2="362.1" y2="126" stroke="#dde4e8" stroke-width="0.75"/><line x1="517.1" y1="52" x2="517.1" y2="126" stroke="#dde4e8" stroke-width="0.75"/><line x1="672" y1="52" x2="672" y2="126" stroke="#dde4e8" stroke-width="0.75"/><text x="8" y="63" font-size="11" fill="#16232b" font-weight="600">MDM2</text><rect x="52.3" y="53" width="0" height="10" fill="#0072B2"><title>Well-diff liposarcoma — MDM2: 0% (0 of 444 profiled)</title></rect><text x="55.3" y="61.5" font-size="10" fill="#5b6b76">0%</text><text x="8" y="100" font-size="11" fill="#16232b" font-weight="600">TP53</text><rect x="52.3" y="90" width="588.7" height="10" fill="#0072B2"><title>Well-diff liposarcoma — TP53: 3.8% (17 of 444 profiled)</title></rect><text x="644" y="98.5" font-size="10" fill="#5b6b76">3.8%</text><line x1="52.3" y1="52" x2="52.3" y2="126" stroke="#5b6b76" stroke-width="1"/><line x1="52.3" y1="126" x2="52.3" y2="129" stroke="#5b6b76" stroke-width="1"/><text x="44.7" y="142" font-size="10" fill="#5b6b76">0%</text><line x1="207.2" y1="126" x2="207.2" y2="129" stroke="#5b6b76" stroke-width="1"/><text x="199.6" y="142" font-size="10" fill="#5b6b76">1%</text><line x1="362.1" y1="126" x2="362.1" y2="129" stroke="#5b6b76" stroke-width="1"/><text x="354.5" y="142" font-size="10" fill="#5b6b76">2%</text><line x1="517.1" y1="126" x2="517.1" y2="129" stroke="#5b6b76" stroke-width="1"/><text x="509.4" y="142" font-size="10" fill="#5b6b76">3%</text><line x1="672" y1="126" x2="672" y2="129" stroke="#5b6b76" stroke-width="1"/><text x="664.4" y="142" font-size="10" fill="#5b6b76">4%</text><line x1="52.285" y1="126" x2="672.01" y2="126" stroke="#5b6b76" stroke-width="1"/><text x="362.1" y="161" font-size="11" fill="#16232b" text-anchor="middle">altered, % of profiled</text><text x="8" y="183" font-size="10" fill="#5b6b76">no copy-number data exists for sarcoma_msk_2022 — altered% reflects only mutation/structural-variant status, not a real negative for the missing type(s)</text></svg>
diff --git a/test/snapshots/compare.txt b/test/snapshots/compare.txt
new file mode 100644
index 00000000..71e9851c
--- /dev/null
+++ b/test/snapshots/compare.txt
@@ -0,0 +1,13 @@
+Analysis over sarcoma_msk_2022 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Well-diff liposarcoma: 444 patients — CANCER_TYPE_DETAILED in [Well-Differentiated Liposarcoma] (any sample)
+
+Compare (driver%/any%, panel-aware denominators; annotation OncoKB v7.5 (pinned)):
+  MDM2: Well-diff liposarcoma 0%/0%
+  TP53: Well-diff liposarcoma 1.6%/3.8%
+  no copy-number data exists for sarcoma_msk_2022 — altered% reflects only mutation/structural-variant status, not a real negative for the missing type(s)
+  METHOD:
+    - Count profiled and altered patients per gene [Well-diff liposarcoma · sarcoma_msk_2022]: The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.
+        genes: 2
+    - No test: One cohort: frequencies only, nothing to compare against.
diff --git a/test/snapshots/composition.json b/test/snapshots/composition.json
new file mode 100644
index 00000000..15a352be
--- /dev/null
+++ b/test/snapshots/composition.json
@@ -0,0 +1,1944 @@
+{
+  "study": "msk_spectrum_tme_2022",
+  "cohorts": {
+    "HRD-Dup": {
+      "count": 16,
+      "text": "CONSENSUS_SIGNATURE in [HRD-Dup]",
+      "studies": [
+        "msk_spectrum_tme_2022"
+      ]
+    },
+    "HRD-Del": {
+      "count": 6,
+      "text": "CONSENSUS_SIGNATURE in [HRD-Del]",
+      "studies": [
+        "msk_spectrum_tme_2022"
+      ]
+    },
+    "FBI": {
+      "count": 14,
+      "text": "CONSENSUS_SIGNATURE in [FBI]",
+      "studies": [
+        "msk_spectrum_tme_2022"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "composition",
+      "cohorts": [
+        "HRD-Dup",
+        "HRD-Del",
+        "FBI"
+      ],
+      "composition": {
+        "source": "clinical",
+        "sourceName": "clinical attributes",
+        "unit": "patients",
+        "normalize": true,
+        "sort": "custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13",
+        "parts": [
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13",
+            "name": "CD4 T CXCL13"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_CD4_T_naive",
+            "name": "CD4 T naive"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_CD4_T_reg",
+            "name": "CD4 T reg"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13",
+            "name": "CD8 T CXCL13"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_CD8_T_ISG",
+            "name": "CD8 T ISG"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic",
+            "name": "CD8 T cytotoxic"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_Cycling_T_NK",
+            "name": "Cycling T NK"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_ILC",
+            "name": "ILC"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_NK_CD56",
+            "name": "NK CD56"
+          },
+          {
+            "id": "custom:spectrum-sc-tnk/TNK_NK_cytotoxic",
+            "name": "NK cytotoxic"
+          }
+        ],
+        "groups": [
+          {
+            "cohort": "HRD-Dup",
+            "n": 16,
+            "incomplete": 0,
+            "invalid": 0,
+            "entropy": {
+              "mean": 2.069917619184448,
+              "sd": 0.05895794661892703
+            },
+            "cases": [
+              [
+                0.130197265857417,
+                0.07582840760344031,
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+                0.09009810793973326,
+                0.02675943805180091
+              ],
+              [
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+                0.00902171271530451,
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+              ],
+              [
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+              ],
+              [
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+              [
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+              [
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+              ],
+              [
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+              ],
+              [
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+                0.074639703161411
+              ]
+            ]
+          },
+          {
+            "cohort": "HRD-Del",
+            "n": 6,
+            "incomplete": 0,
+            "invalid": 0,
+            "entropy": {
+              "mean": 1.96274443194148,
+              "sd": 0.05433303341805096
+            },
+            "cases": [
+              [
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+              ],
+              [
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+              [
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+              ],
+              [
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+              ],
+              [
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+              ],
+              [
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+                0.1134931904085755,
+                0.08268503889766614
+              ]
+            ]
+          },
+          {
+            "cohort": "FBI",
+            "n": 14,
+            "incomplete": 0,
+            "invalid": 0,
+            "entropy": {
+              "mean": 1.9822756783103324,
+              "sd": 0.07138785458820697
+            },
+            "cases": [
+              [
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+            "title": "Sum each part over the cohort's patients",
+            "detail": "The composition:moments query above: each patient's 10 parts read and put in part order inside the database; a patient with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those patients n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.",
+            "scope": "HRD-Dup · msk_spectrum_tme_2022",
+            "rowsOut": 16
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+          {
+            "kind": "fetch",
+            "title": "Sum each part over the cohort's patients",
+            "detail": "The composition:moments query above: each patient's 10 parts read and put in part order inside the database; a patient with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those patients n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.",
+            "scope": "HRD-Del · msk_spectrum_tme_2022",
+            "rowsOut": 6
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+          {
+            "kind": "fetch",
+            "title": "Sum each part over the cohort's patients",
+            "detail": "The composition:moments query above: each patient's 10 parts read and put in part order inside the database; a patient with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those patients n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.",
+            "scope": "FBI · msk_spectrum_tme_2022",
+            "rowsOut": 14
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+          {
+            "kind": "statistic",
+            "title": "Each cohort against the others pooled, per part; then Benjamini–Hochberg",
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+          {
+            "…": "10 more",
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+        "tested": 30,
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+      "participation": {
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+            "mode": "patient-level",
+            "instance": "primary",
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+        "requiresPatientLevel": false,
+        "aggregateOnly": []
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+      "caveat": "Each sample's 10 parts (entities of “Cell Type Relative Fractions”), divided by their sum so every bar is a whole; cases ordered by T_cell, highest first. Each cohort is compared with the view's other cohorts pooled, part by part: log2 of the ratio of means (each + 0.001), Welch's t from the cohorts' n, mean and variance, Benjamini–Hochberg across the 30 comparisons. Parts of a whole are not independent — one part's rise is the others' fall — and a case in two cohorts counts in both. Entropy is Shannon's (natural log) over each case's shares. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Sum each part over the cohort's samples",
+            "detail": "The composition:moments query above: each sample's 10 parts read and put in part order inside the database; a sample with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those samples n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.",
+            "scope": "HRD-Dup · msk_spectrum_tme_2022",
+            "rowsOut": 19
+          },
+          {
+            "kind": "fetch",
+            "title": "Sum each part over the cohort's samples",
+            "detail": "The composition:moments query above: each sample's 10 parts read and put in part order inside the database; a sample with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those samples n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.",
+            "scope": "HRD-Del · msk_spectrum_tme_2022",
+            "rowsOut": 8
+          },
+          {
+            "kind": "fetch",
+            "title": "Sum each part over the cohort's samples",
+            "detail": "The composition:moments query above: each sample's 10 parts read and put in part order inside the database; a sample with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those samples n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.",
+            "scope": "FBI · msk_spectrum_tme_2022",
+            "rowsOut": 19
+          },
+          {
+            "kind": "statistic",
+            "title": "Each cohort against the others pooled, per part; then Benjamini–Hochberg",
+            "detail": "The other cohorts' sums are added into one group. Effect: log2((mean + 0.001) / (others' mean + 0.001)). Test: Welch's unequal-variance t from each side's n, mean and variance (Σp² − (Σp)²/n)/(n − 1), two-sided; not tested when either side has under 2 samples or neither has any spread. q across every comparison tested.",
+            "params": [
+              [
+                "comparisons",
+                "30"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "welchT",
+            "varianceFromMoments",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Welch's t from moments matches scipy 1.18.1 ttest_ind(equal_var=False) and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/moments-golden.test.ts).",
+        "check": {
+          "composition:tested": {
+            "v": 30
+          },
+          "composition:HRD-Dup|B_cell:mean": {
+            "v": 0.025429784846068906,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|B_cell:log2": {
+            "v": 0.552089045579386,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|B_cell:p": {
+            "v": 0.4653273526789026,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|B_cell:q": {
+            "v": 0.7304459576478041,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|Dendritic_cell:mean": {
+            "v": 0.0033227942634539835,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Dendritic_cell:log2": {
+            "v": -0.4041004900557177,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Dendritic_cell:p": {
+            "v": 0.3325952043539977,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|Dendritic_cell:q": {
+            "v": 0.5869327135658783,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|Endothelial_cell:mean": {
+            "v": 0.02264195111891671,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Endothelial_cell:log2": {
+            "v": 0.3237036404485407,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Endothelial_cell:p": {
+            "v": 0.4732090127512971,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|Endothelial_cell:q": {
+            "v": 0.7304459576478041,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|Fibroblast:mean": {
+            "v": 0.18217998044196398,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Fibroblast:log2": {
+            "v": 0.6432653766744568,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Fibroblast:p": {
+            "v": 0.16053870710566653,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|Fibroblast:q": {
+            "v": 0.5869327135658783,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "composition:HRD-Dup|Mast_cell:mean": {
+            "v": 0.0008646280530261326,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Mast_cell:log2": {
+            "v": -0.5932869405889261,
+            "rel": 0.000001,
+            "abs": 1e-9
+          },
+          "composition:HRD-Dup|Mast_cell:p": {
+            "v": 0.10013306419957604,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "…": {
+            "more": 101,
+            "sha256": "bc3bde605013f805"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/composition.sql b/test/snapshots/composition.sql
new file mode 100644
index 00000000..c40cbe76
--- /dev/null
+++ b/test/snapshots/composition.sql
@@ -0,0 +1,1642 @@
+-- cohort HRD-Dup
+-- msk_spectrum_tme_2022
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Dup'))))
+
+-- cohort HRD-Del
+-- msk_spectrum_tme_2022
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Del'))))
+
+-- cohort FBI
+-- msk_spectrum_tme_2022
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('FBI'))))
+
+-- view 0 composition: composition:cases:FBI · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('FBI'))))),
+parts AS (
+SELECT id, toUInt16(0) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(1) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(2) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(3) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(4) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(5) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(6) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(7) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(8) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(9) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT ps, h FROM cases ORDER BY ps[4] DESC, cityHash64(id)
+LIMIT 2001
+
+-- view 0 composition: composition:cases:HRD-Del · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Del'))))),
+parts AS (
+SELECT id, toUInt16(0) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(1) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(2) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(3) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(4) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(5) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(6) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(7) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(8) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(9) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT ps, h FROM cases ORDER BY ps[4] DESC, cityHash64(id)
+LIMIT 2001
+
+-- view 0 composition: composition:cases:HRD-Dup · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Dup'))))),
+parts AS (
+SELECT id, toUInt16(0) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(1) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(2) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(3) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(4) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(5) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(6) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(7) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(8) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(9) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT ps, h FROM cases ORDER BY ps[4] DESC, cityHash64(id)
+LIMIT 2001
+
+-- view 0 composition: composition:moments:FBI · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('FBI'))))),
+parts AS (
+SELECT id, toUInt16(0) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(1) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(2) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(3) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(4) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(5) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(6) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(7) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(8) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(9) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT count() AS n, sumForEach(ps) AS sx, sumForEach(arrayMap(x -> x * x, ps)) AS sxx, sum(h) AS hx, sum(h * h) AS hxx,
+       (SELECT countIf(k < 10) FROM raw) AS incomplete,
+       (SELECT countIf(k = 10 AND NOT (lo >= 0 AND tot > 0)) FROM raw) AS invalid
+FROM cases
+
+-- view 0 composition: composition:moments:HRD-Del · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Del'))))),
+parts AS (
+SELECT id, toUInt16(0) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(1) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(2) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(3) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(4) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(5) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(6) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(7) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(8) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(9) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT count() AS n, sumForEach(ps) AS sx, sumForEach(arrayMap(x -> x * x, ps)) AS sxx, sum(h) AS hx, sum(h * h) AS hxx,
+       (SELECT countIf(k < 10) FROM raw) AS incomplete,
+       (SELECT countIf(k = 10 AND NOT (lo >= 0 AND tot > 0)) FROM raw) AS invalid
+FROM cases
+
+-- view 0 composition: composition:moments:HRD-Dup · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Dup'))))),
+parts AS (
+SELECT id, toUInt16(0) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_c69fe134)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(1) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e449dcfd)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_naive' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(2) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_f23281fa)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD4_T_reg' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(3) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d53efc38)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_CXCL13' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(4) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_36ce14b9)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_ISG' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(5) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e42f954c)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_CD8_T_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(6) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_e52f52c7)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_Cycling_T_NK' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(7) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_d3243663)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_ILC' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(8) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_33a1d461)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_CD56' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+UNION ALL
+SELECT id, toUInt16(9) AS c, toFloat64(assumeNotNull(av)) AS v FROM (SELECT id, v AS av FROM (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM (SELECT internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM snapshot_db.clinical_data_derived
+ UNION ALL
+ SELECT toInt32(0) AS internal_id, sample_unique_id, patient_unique_id, attribute_name, attribute_value, cancer_study_identifier, type
+   FROM _custom_clinical_a671983f)
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND attribute_name = 'custom:spectrum-sc-tnk/TNK_NK_cytotoxic' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+)) WHERE isFinite(ifNull(av, nan))
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT count() AS n, sumForEach(ps) AS sx, sumForEach(arrayMap(x -> x * x, ps)) AS sxx, sum(h) AS hx, sum(h * h) AS hxx,
+       (SELECT countIf(k < 10) FROM raw) AS incomplete,
+       (SELECT countIf(k = 10 AND NOT (lo >= 0 AND tot > 0)) FROM raw) AS invalid
+FROM cases
+
+-- view 1 composition: composition:cases:FBI · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('FBI'))))),
+parts AS (
+SELECT id, c, assumeNotNull(avg(val)) AS v FROM (
+  SELECT sample_unique_id AS id, toUInt16(indexOf(['B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell'], entity_stable_id) - 1) AS c,
+         if(isFinite(ifNull(toFloat64OrNull(value), nan)), toFloat64OrNull(value), NULL) AS val
+  FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts'
+    AND entity_stable_id IN ('B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell')
+    AND patient_unique_id IN (SELECT concat('msk_spectrum_tme_2022_', pid) FROM cohort)
+) WHERE isFinite(ifNull(val, nan)) GROUP BY id, c
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT ps, h FROM cases ORDER BY ps[10] DESC, cityHash64(id)
+LIMIT 2001
+
+-- view 1 composition: composition:cases:HRD-Del · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Del'))))),
+parts AS (
+SELECT id, c, assumeNotNull(avg(val)) AS v FROM (
+  SELECT sample_unique_id AS id, toUInt16(indexOf(['B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell'], entity_stable_id) - 1) AS c,
+         if(isFinite(ifNull(toFloat64OrNull(value), nan)), toFloat64OrNull(value), NULL) AS val
+  FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts'
+    AND entity_stable_id IN ('B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell')
+    AND patient_unique_id IN (SELECT concat('msk_spectrum_tme_2022_', pid) FROM cohort)
+) WHERE isFinite(ifNull(val, nan)) GROUP BY id, c
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT ps, h FROM cases ORDER BY ps[10] DESC, cityHash64(id)
+LIMIT 2001
+
+-- view 1 composition: composition:cases:HRD-Dup · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Dup'))))),
+parts AS (
+SELECT id, c, assumeNotNull(avg(val)) AS v FROM (
+  SELECT sample_unique_id AS id, toUInt16(indexOf(['B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell'], entity_stable_id) - 1) AS c,
+         if(isFinite(ifNull(toFloat64OrNull(value), nan)), toFloat64OrNull(value), NULL) AS val
+  FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts'
+    AND entity_stable_id IN ('B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell')
+    AND patient_unique_id IN (SELECT concat('msk_spectrum_tme_2022_', pid) FROM cohort)
+) WHERE isFinite(ifNull(val, nan)) GROUP BY id, c
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT ps, h FROM cases ORDER BY ps[10] DESC, cityHash64(id)
+LIMIT 2001
+
+-- view 1 composition: composition:entities · msk_spectrum_tme_2022
+WITH entities AS (
+  SELECT DISTINCT entity_stable_id AS entity FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts'
+)
+SELECT entity FROM entities ORDER BY entity
+
+-- view 1 composition: composition:moments:FBI · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('FBI'))))),
+parts AS (
+SELECT id, c, assumeNotNull(avg(val)) AS v FROM (
+  SELECT sample_unique_id AS id, toUInt16(indexOf(['B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell'], entity_stable_id) - 1) AS c,
+         if(isFinite(ifNull(toFloat64OrNull(value), nan)), toFloat64OrNull(value), NULL) AS val
+  FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts'
+    AND entity_stable_id IN ('B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell')
+    AND patient_unique_id IN (SELECT concat('msk_spectrum_tme_2022_', pid) FROM cohort)
+) WHERE isFinite(ifNull(val, nan)) GROUP BY id, c
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT count() AS n, sumForEach(ps) AS sx, sumForEach(arrayMap(x -> x * x, ps)) AS sxx, sum(h) AS hx, sum(h * h) AS hxx,
+       (SELECT countIf(k < 10) FROM raw) AS incomplete,
+       (SELECT countIf(k = 10 AND NOT (lo >= 0 AND tot > 0)) FROM raw) AS invalid
+FROM cases
+
+-- view 1 composition: composition:moments:HRD-Del · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Del'))))),
+parts AS (
+SELECT id, c, assumeNotNull(avg(val)) AS v FROM (
+  SELECT sample_unique_id AS id, toUInt16(indexOf(['B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell'], entity_stable_id) - 1) AS c,
+         if(isFinite(ifNull(toFloat64OrNull(value), nan)), toFloat64OrNull(value), NULL) AS val
+  FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts'
+    AND entity_stable_id IN ('B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell')
+    AND patient_unique_id IN (SELECT concat('msk_spectrum_tme_2022_', pid) FROM cohort)
+) WHERE isFinite(ifNull(val, nan)) GROUP BY id, c
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT count() AS n, sumForEach(ps) AS sx, sumForEach(arrayMap(x -> x * x, ps)) AS sxx, sum(h) AS hx, sum(h * h) AS hxx,
+       (SELECT countIf(k < 10) FROM raw) AS incomplete,
+       (SELECT countIf(k = 10 AND NOT (lo >= 0 AND tot > 0)) FROM raw) AS invalid
+FROM cases
+
+-- view 1 composition: composition:moments:HRD-Dup · msk_spectrum_tme_2022
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_spectrum_tme_2022_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_spectrum_tme_2022' AND type = 'patient'
+    AND attribute_name = 'CONSENSUS_SIGNATURE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('HRD-Dup'))))),
+parts AS (
+SELECT id, c, assumeNotNull(avg(val)) AS v FROM (
+  SELECT sample_unique_id AS id, toUInt16(indexOf(['B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell'], entity_stable_id) - 1) AS c,
+         if(isFinite(ifNull(toFloat64OrNull(value), nan)), toFloat64OrNull(value), NULL) AS val
+  FROM snapshot_db.generic_assay_data_derived
+  WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts'
+    AND entity_stable_id IN ('B_cell','Dendritic_cell','Endothelial_cell','Fibroblast','Mast_cell','Monocyte','Other','Ovarian_cancer_cell','Plasma_cell','T_cell')
+    AND patient_unique_id IN (SELECT concat('msk_spectrum_tme_2022_', pid) FROM cohort)
+) WHERE isFinite(ifNull(val, nan)) GROUP BY id, c
+),
+raw AS (
+  SELECT id, count() AS k, sum(v) AS tot, min(v) AS lo, arrayMap(x -> x.2, arraySort(x -> x.1, groupArray((c, v)))) AS vs
+  FROM parts GROUP BY id
+),
+cases AS (
+  SELECT id, arrayMap(x -> x / tot, vs) AS ps, arrayMap(x -> x / tot, vs) AS sh,
+         arraySum(arrayMap(x -> if(x > 0, -x * log(x), 0), sh)) AS h
+  FROM raw WHERE k = 10 AND lo >= 0 AND tot > 0
+)
+SELECT count() AS n, sumForEach(ps) AS sx, sumForEach(arrayMap(x -> x * x, ps)) AS sxx, sum(h) AS hx, sum(h * h) AS hxx,
+       (SELECT countIf(k < 10) FROM raw) AS incomplete,
+       (SELECT countIf(k = 10 AND NOT (lo >= 0 AND tot > 0)) FROM raw) AS invalid
+FROM cases
+
+-- view 1 composition: composition:names · msk_spectrum_tme_2022
+WITH named AS (
+  SELECT ge.stable_id AS entity, p.value AS name FROM snapshot_db.generic_entity_properties p
+  JOIN snapshot_db.genetic_entity ge ON ge.id = p.genetic_entity_id
+  WHERE p.name = 'NAME' AND ge.stable_id IN (SELECT DISTINCT entity_stable_id FROM snapshot_db.generic_assay_data_derived
+    WHERE profile_type = 'cell_type_relative_counts' AND profile_stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts')
+)
+SELECT entity, name FROM named
+
+-- view 1 composition: composition:profile · msk_spectrum_tme_2022
+SELECT datatype, generic_assay_type, ifNull(patient_level, 0) AS patient_level, name FROM snapshot_db.genetic_profile
+WHERE stable_id = 'msk_spectrum_tme_2022_cell_type_relative_counts' AND genetic_alteration_type = 'GENERIC_ASSAY'
diff --git a/test/snapshots/composition.svg b/test/snapshots/composition.svg
new file mode 100644
index 00000000..2edeb22e
--- /dev/null
+++ b/test/snapshots/composition.svg
@@ -0,0 +1,4 @@
+<!-- view 0 composition -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 760 625" width="100%" role="img" aria-label="Composition — clinical attributes by HRD-Dup, HRD-Del, FBI" style="max-width:760px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Composition — clinical attributes by HRD-Dup, HRD-Del, FBI</title><rect width="760" height="625" fill="#ffffff"/><defs><pattern id="cmpagurxpp8" patternUnits="userSpaceOnUse" width="5" height="5" patternTransform="rotate(45)"><rect width="5" height="5" fill="#ffffff"/><rect width="2.6" height="5" fill="#0072B2"/></pattern><pattern id="cmpagurxpp9" patternUnits="userSpaceOnUse" width="5" height="5" patternTransform="rotate(45)"><rect width="5" height="5" fill="#ffffff"/><rect width="2.6" height="5" fill="#E69F00"/></pattern></defs><rect x="8" y="10.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="19" font-size="10" fill="#16232b">CD4 T CXCL13</text><rect x="103.92" y="10.5" width="10" height="10" rx="2" 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height="36.13" fill="#E69F00"><title>FBI · CD4 T naive: mean 20.1%</title></rect><rect x="726" y="202.71" width="26" height="17.96" fill="#009E73"><title>FBI · CD4 T reg: mean 10.0%</title></rect><rect x="726" y="194.88" width="26" height="7.83" fill="#CC79A7"><title>FBI · CD8 T CXCL13: mean 4.4%</title></rect><rect x="726" y="182.08" width="26" height="12.8" fill="#D55E00"><title>FBI · CD8 T ISG: mean 7.1%</title></rect><rect x="726" y="136.14" width="26" height="45.94" fill="#56B4E9"><title>FBI · CD8 T cytotoxic: mean 25.5%</title></rect><rect x="726" y="125.95" width="26" height="10.18" fill="#F0E442"><title>FBI · Cycling T NK: mean 5.7%</title></rect><rect x="726" y="121.8" width="26" height="4.15" fill="#999999"><title>FBI · ILC: mean 2.3%</title></rect><rect x="726" y="101" width="26" height="20.81" fill="url(#cmpagurxpp8)"><title>FBI · NK CD56: mean 11.6%</title></rect><rect x="726" y="82" width="26" height="19" fill="url(#cmpagurxpp9)"><title>FBI · NK cytotoxic: mean 10.6%</title></rect><rect x="726" y="82" width="26" height="180" fill="none" stroke="#16232b" stroke-width="0.75"/><text x="739" y="276" font-size="10" fill="#5b6b76" text-anchor="middle">mean</text><text x="8" y="303" font-size="11" fill="#16232b" font-weight="600">Each cohort against the others, per part</text><text x="104.2" y="321" font-size="10" fill="#16232b" font-weight="600">HRD-Dup</text><text x="234.2" y="321" font-size="10" fill="#16232b" font-weight="600">HRD-Del</text><text x="364.2" y="321" font-size="10" fill="#16232b" font-weight="600">FBI</text><rect x="8" y="333" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="341.5" font-size="10" fill="#16232b">CD4 T CXCL13</text><text x="123.2" y="341.5" font-size="10" fill="#5b6b76">4.2%</text><circle cx="112.2" cy="338" r="3.28" fill="rgb(237,201,206)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · CD4 T CXCL13: mean 4.2%, log2 vs others 0.47, p = 0.17, q = 0.33</title></circle><text x="253.2" y="341.5" font-size="10" fill="#5b6b76">3.2%</text><circle cx="242.2" cy="338" r="2.69" fill="rgb(240,244,249)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · CD4 T CXCL13: mean 3.2%, log2 vs others −0.14, p = 0.66, q = 0.76</title></circle><text x="383.2" y="341.5" font-size="10" fill="#5b6b76">2.9%</text><circle cx="372.2" cy="338" r="3.28" fill="rgb(207,222,237)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · CD4 T CXCL13: mean 2.9%, log2 vs others −0.43, p = 0.19, q = 0.33</title></circle><rect x="8" y="351" width="10" height="10" rx="2" fill="#E69F00"/><text x="23" y="359.5" font-size="10" fill="#16232b">CD4 T naive</text><text x="123.2" y="359.5" font-size="10" fill="#5b6b76">15.2%</text><circle cx="112.2" cy="356" r="3.61" fill="rgb(212,225,239)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · CD4 T naive: mean 15.2%, log2 vs others −0.39, p = 0.039, q = 0.20</title></circle><text x="253.2" y="359.5" font-size="10" fill="#5b6b76">19.6%</text><circle cx="242.2" cy="356" r="2.69" fill="rgb(249,236,237)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · CD4 T naive: mean 19.6%, log2 vs others 0.17, p = 0.64, q = 0.76</title></circle><text x="383.2" y="359.5" font-size="10" fill="#5b6b76">20.1%</text><circle cx="372.2" cy="356" r="3.37" fill="rgb(244,222,224)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · CD4 T naive: mean 20.1%, log2 vs others 0.29, p = 0.14, q = 0.29</title></circle><rect x="8" y="369" width="10" height="10" rx="2" fill="#009E73"/><text x="23" y="377.5" font-size="10" fill="#16232b">CD4 T reg</text><text x="123.2" y="377.5" font-size="10" fill="#5b6b76">11.6%</text><circle cx="112.2" cy="374" r="2.51" fill="rgb(254,254,255)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · CD4 T reg: mean 11.6%, log2 vs others −0.0085, p = 0.97, q = 0.99</title></circle><text x="253.2" y="377.5" font-size="10" fill="#5b6b76">15.7%</text><circle cx="242.2" cy="374" r="3.17" fill="rgb(235,194,199)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · CD4 T reg: mean 15.7%, log2 vs others 0.52, p = 0.25, q = 0.38</title></circle><text x="383.2" y="377.5" font-size="10" fill="#5b6b76">10.0%</text><circle cx="372.2" cy="374" r="3.46" fill="rgb(217,228,241)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · CD4 T reg: mean 10.0%, log2 vs others −0.35, p = 0.092, q = 0.25</title></circle><rect x="8" y="387" width="10" height="10" rx="2" fill="#CC79A7"/><text x="23" y="395.5" font-size="10" fill="#16232b">CD8 T CXCL13</text><text x="123.2" y="395.5" font-size="10" fill="#5b6b76">7.0%</text><circle cx="112.2" cy="392" r="3.61" fill="rgb(231,184,190)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · CD8 T CXCL13: mean 7.0%, log2 vs others 0.61, p = 0.022, q = 0.20</title></circle><text x="253.2" y="395.5" font-size="10" fill="#5b6b76">4.9%</text><circle cx="242.2" cy="392" r="2.69" fill="rgb(231,238,246)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · CD8 T CXCL13: mean 4.9%, log2 vs others −0.22, p = 0.61, q = 0.76</title></circle><text x="383.2" y="395.5" font-size="10" fill="#5b6b76">4.4%</text><circle cx="372.2" cy="392" r="3.61" fill="rgb(194,213,232)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · CD8 T CXCL13: mean 4.4%, log2 vs others −0.55, p = 0.041, q = 0.20</title></circle><rect x="8" y="405" width="10" height="10" rx="2" fill="#D55E00"/><text x="23" y="413.5" font-size="10" fill="#16232b">CD8 T ISG</text><text x="123.2" y="413.5" font-size="10" fill="#5b6b76">9.2%</text><circle cx="112.2" cy="410" r="3.61" fill="rgb(239,208,212)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · CD8 T ISG: mean 9.2%, log2 vs others 0.41, p = 0.031, q = 0.20</title></circle><text x="253.2" y="413.5" font-size="10" fill="#5b6b76">6.4%</text><circle cx="242.2" cy="410" r="3.47" fill="rgb(216,228,240)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · CD8 T ISG: mean 6.4%, log2 vs others −0.35, p = 0.082, q = 0.25</title></circle><text x="383.2" y="413.5" font-size="10" fill="#5b6b76">7.1%</text><circle cx="372.2" cy="410" r="3.17" fill="rgb(228,237,245)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · CD8 T ISG: mean 7.1%, log2 vs others −0.24, p = 0.25, q = 0.38</title></circle><rect x="8" y="423" width="10" height="10" rx="2" fill="#56B4E9"/><text x="23" y="431.5" font-size="10" fill="#16232b">CD8 T cytotoxic</text><text x="123.2" y="431.5" font-size="10" fill="#5b6b76">21.4%</text><circle cx="112.2" cy="428" r="3.4" fill="rgb(232,239,246)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · CD8 T cytotoxic: mean 21.4%, log2 vs others −0.21, p = 0.13, q = 0.27</title></circle><text x="253.2" y="431.5" font-size="10" fill="#5b6b76">23.1%</text><circle cx="242.2" cy="428" r="2.51" fill="rgb(253,254,254)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · CD8 T cytotoxic: mean 23.1%, log2 vs others −0.016, p = 0.92, q = 0.99</title></circle><text x="383.2" y="431.5" font-size="10" fill="#5b6b76">25.5%</text><circle cx="372.2" cy="428" r="3.4" fill="rgb(246,229,231)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · CD8 T cytotoxic: mean 25.5%, log2 vs others 0.22, p = 0.12, q = 0.27</title></circle><rect x="8" y="441" width="10" height="10" rx="2" fill="#F0E442"/><text x="23" y="449.5" font-size="10" fill="#16232b">Cycling T NK</text><text x="123.2" y="449.5" font-size="10" fill="#5b6b76">8.6%</text><circle cx="112.2" cy="446" r="4.26" fill="rgb(232,187,192)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Cycling T NK: mean 8.6%, log2 vs others 0.59, p = 0.0027, q = 0.08</title></circle><text x="253.2" y="449.5" font-size="10" fill="#5b6b76">5.7%</text><circle cx="242.2" cy="446" r="3.47" fill="rgb(218,229,241)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Cycling T NK: mean 5.7%, log2 vs others −0.34, p = 0.072, q = 0.25</title></circle><text x="383.2" y="449.5" font-size="10" fill="#5b6b76">5.7%</text><circle cx="372.2" cy="446" r="3.61" fill="rgb(204,220,236)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Cycling T NK: mean 5.7%, log2 vs others −0.46, p = 0.029, q = 0.20</title></circle><rect x="8" y="459" width="10" height="10" rx="2" fill="#999999"/><text x="23" y="467.5" font-size="10" fill="#16232b">ILC</text><text x="123.2" y="467.5" font-size="10" fill="#5b6b76">2.1%</text><circle cx="112.2" cy="464" r="2.51" fill="rgb(255,255,255)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · ILC: mean 2.1%, log2 vs others −0.0041, p = 0.99, q = 0.99</title></circle><text x="253.2" y="467.5" font-size="10" fill="#5b6b76">1.8%</text><circle cx="242.2" cy="464" r="3.41" fill="rgb(222,232,243)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · ILC: mean 1.8%, log2 vs others −0.29, p = 0.11, q = 0.27</title></circle><text x="383.2" y="467.5" font-size="10" fill="#5b6b76">2.3%</text><circle cx="372.2" cy="464" r="2.81" fill="rgb(249,236,238)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · ILC: mean 2.3%, log2 vs others 0.16, p = 0.49, q = 0.64</title></circle><rect x="8" y="477" width="10" height="10" rx="2" fill="url(#cmpagurxpp8)"/><text x="23" y="485.5" font-size="10" fill="#16232b">NK CD56</text><text x="123.2" y="485.5" font-size="10" fill="#5b6b76">14.6%</text><circle cx="112.2" cy="482" r="3.13" fill="rgb(244,222,225)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · NK CD56: mean 14.6%, log2 vs others 0.28, p = 0.30, q = 0.41</title></circle><text x="253.2" y="485.5" font-size="10" fill="#5b6b76">13.0%</text><circle cx="242.2" cy="482" r="2.51" fill="rgb(253,254,254)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · NK CD56: mean 13.0%, log2 vs others −0.016, p = 0.98, q = 0.99</title></circle><text x="383.2" y="485.5" font-size="10" fill="#5b6b76">11.6%</text><circle cx="372.2" cy="482" r="3.17" fill="rgb(222,233,243)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · NK CD56: mean 11.6%, log2 vs others −0.29, p = 0.25, q = 0.38</title></circle><rect x="8" y="495" width="10" height="10" rx="2" fill="url(#cmpagurxpp9)"/><text x="23" y="503.5" font-size="10" fill="#16232b">NK cytotoxic</text><text x="123.2" y="503.5" font-size="10" fill="#5b6b76">6.1%</text><circle cx="112.2" cy="500" r="3.47" fill="rgb(188,209,230)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · NK cytotoxic: mean 6.1%, log2 vs others −0.6, p = 0.074, q = 0.25</title></circle><text x="253.2" y="503.5" font-size="10" fill="#5b6b76">6.6%</text><circle cx="242.2" cy="500" r="3.13" fill="rgb(220,231,242)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · NK cytotoxic: mean 6.6%, log2 vs others −0.32, p = 0.29, q = 0.41</title></circle><text x="383.2" y="503.5" font-size="10" fill="#5b6b76">10.6%</text><circle cx="372.2" cy="500" r="3.54" fill="rgb(226,169,176)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · NK cytotoxic: mean 10.6%, log2 vs others 0.75, p = 0.052, q = 0.22</title></circle><text x="8" y="523" font-size="10" fill="#5b6b76">Each cell: the cohort's mean share, and a dot whose colour is log2 of it over the other cohorts' mean (blue lower, red higher, clamped at ±2), size −log10 q, outlined</text><text x="8" y="537" font-size="10" fill="#5b6b76">when q &lt; 0.05. 30 comparisons tested, 0 at q &lt; 0.05.</text><text x="8" y="565" font-size="10" fill="#5b6b76">Each patient's 10 parts (clinical attributes), divided by their sum so every bar is a whole; cases ordered by CD8 T CXCL13, highest first. Each cohort is compared with</text><text x="8" y="579" font-size="10" fill="#5b6b76">the view's other cohorts pooled, part by part: log2 of the ratio of means (each + 0.001), Welch's t from the cohorts' n, mean and variance, Benjamini–Hochberg across</text><text x="8" y="593" font-size="10" fill="#5b6b76">the 30 comparisons. Parts of a whole are not independent — one part's rise is the others' fall — and a case in two cohorts counts in both. Entropy is Shannon's (natural</text><text x="8" y="607" font-size="10" fill="#5b6b76">log) over each case's shares.</text></svg>
+<!-- view 1 composition -->
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font-size="10" fill="#5b6b76">2.3%</text><circle cx="134.03" cy="374" r="2.72" fill="rgb(243,218,221)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Endothelial_cell: mean 2.3%, log2 vs others 0.32, p = 0.47, q = 0.73</title></circle><text x="275" y="377.5" font-size="10" fill="#5b6b76">1.0%</text><circle cx="264.03" cy="374" r="3.15" fill="rgb(133,171,210)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Endothelial_cell: mean 1.0%, log2 vs others −1.1, p = 0.013, q = 0.39</title></circle><text x="405" y="377.5" font-size="10" fill="#5b6b76">2.1%</text><circle cx="394.03" cy="374" r="2.66" fill="rgb(248,235,237)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Endothelial_cell: mean 2.1%, log2 vs others 0.17, p = 0.69, q = 0.79</title></circle><rect x="8" y="387" width="10" height="10" rx="2" fill="#CC79A7"/><text x="23" y="395.5" font-size="10" fill="#16232b">Fibroblast</text><text x="145" y="395.5" font-size="10" fill="#5b6b76">18.2%</text><circle cx="134.03" cy="392" r="2.87" fill="rgb(230,181,187)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Fibroblast: mean 18.2%, log2 vs others 0.64, p = 0.16, q = 0.59</title></circle><text x="275" y="395.5" font-size="10" fill="#5b6b76">15.1%</text><circle cx="264.03" cy="392" r="2.52" fill="rgb(252,245,246)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Fibroblast: mean 15.1%, log2 vs others 0.086, p = 0.88, q = 0.98</title></circle><text x="405" y="395.5" font-size="10" fill="#5b6b76">10.2%</text><circle cx="394.03" cy="392" r="2.92" fill="rgb(171,197,224)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Fibroblast: mean 10.2%, log2 vs others −0.76, p = 0.11, q = 0.55</title></circle><rect x="8" y="405" width="10" height="10" rx="2" fill="#D55E00"/><text x="23" y="413.5" font-size="10" fill="#16232b">Mast_cell</text><text x="145" y="413.5" font-size="10" fill="#5b6b76">&lt;0.1%</text><circle cx="134.03" cy="410" r="2.92" fill="rgb(189,210,230)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Mast_cell: mean &lt;0.1%, log2 vs others −0.59, p = 0.10, q = 0.55</title></circle><text x="275" y="413.5" font-size="10" fill="#5b6b76">&lt;0.1%</text><circle cx="264.03" cy="410" r="2.87" fill="rgb(190,210,231)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Mast_cell: mean &lt;0.1%, log2 vs others −0.59, p = 0.15, q = 0.59</title></circle><text x="405" y="413.5" font-size="10" fill="#5b6b76">0.2%</text><circle cx="394.03" cy="410" r="2.92" fill="rgb(222,157,165)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Mast_cell: mean 0.2%, log2 vs others 0.85, p = 0.051, q = 0.55</title></circle><rect x="8" y="423" width="10" height="10" rx="2" fill="#56B4E9"/><text x="23" y="431.5" font-size="10" fill="#16232b">Monocyte</text><text x="145" y="431.5" font-size="10" fill="#5b6b76">21.4%</text><circle cx="134.03" cy="428" r="2.51" fill="rgb(254,253,253)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Monocyte: mean 21.4%, log2 vs others 0.02, p = 0.95, q = 0.98</title></circle><text x="275" y="431.5" font-size="10" fill="#5b6b76">24.4%</text><circle cx="264.03" cy="428" r="2.72" fill="rgb(245,226,229)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Monocyte: mean 24.4%, log2 vs others 0.25, p = 0.54, q = 0.73</title></circle><text x="405" y="431.5" font-size="10" fill="#5b6b76">19.7%</text><circle cx="394.03" cy="428" r="2.69" fill="rgb(235,241,248)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Monocyte: mean 19.7%, log2 vs others −0.18, p = 0.61, q = 0.77</title></circle><rect x="8" y="441" width="10" height="10" rx="2" fill="#F0E442"/><text x="23" y="449.5" font-size="10" fill="#16232b">Other</text><text x="145" y="449.5" font-size="10" fill="#5b6b76">0%</text><circle cx="134.03" cy="446" r="2.87" fill="rgb(255,255,255)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Other: mean 0%, log2 vs others −0.0037, p = 0.33, q = 0.59</title></circle><text x="275" y="449.5" font-size="10" fill="#5b6b76">0%</text><circle cx="264.03" cy="446" r="2.87" fill="rgb(255,255,255)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Other: mean 0%, log2 vs others −0.0026, p = 0.32, q = 0.59</title></circle><text x="405" y="449.5" font-size="10" fill="#5b6b76">&lt;0.1%</text><circle cx="394.03" cy="446" r="2.87" fill="rgb(255,254,254)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Other: mean &lt;0.1%, log2 vs others 0.0053, p = 0.33, q = 0.59</title></circle><rect x="8" y="459" width="10" height="10" rx="2" fill="#999999"/><text x="23" y="467.5" font-size="10" fill="#16232b">Ovarian_cancer_cell</text><text x="145" y="467.5" font-size="10" fill="#5b6b76">26.6%</text><circle cx="134.03" cy="464" r="2.72" fill="rgb(231,239,246)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Ovarian_cancer_cell: mean 26.6%, log2 vs others −0.21, p = 0.51, q = 0.73</title></circle><text x="275" y="467.5" font-size="10" fill="#5b6b76">29.0%</text><circle cx="264.03" cy="464" r="2.5" fill="rgb(255,255,255)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Ovarian_cancer_cell: mean 29.0%, log2 vs others −0.0034, p = 0.99, q = 0.99</title></circle><text x="405" y="467.5" font-size="10" fill="#5b6b76">31.6%</text><circle cx="394.03" cy="464" r="2.72" fill="rgb(247,231,233)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Ovarian_cancer_cell: mean 31.6%, log2 vs others 0.21, p = 0.56, q = 0.73</title></circle><rect x="8" y="477" width="10" height="10" rx="2" fill="url(#cmp26jstap8)"/><text x="23" y="485.5" font-size="10" fill="#16232b">Plasma_cell</text><text x="145" y="485.5" font-size="10" fill="#5b6b76">4.7%</text><circle cx="134.03" cy="482" r="2.92" fill="rgb(192,66,82)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · Plasma_cell: mean 4.7%, log2 vs others 1.6, p = 0.085, q = 0.55</title></circle><text x="275" y="485.5" font-size="10" fill="#5b6b76">1.4%</text><circle cx="264.03" cy="482" r="2.87" fill="rgb(142,177,213)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · Plasma_cell: mean 1.4%, log2 vs others −1, p = 0.30, q = 0.59</title></circle><text x="405" y="485.5" font-size="10" fill="#5b6b76">1.4%</text><circle cx="394.03" cy="482" r="2.92" fill="rgb(108,154,200)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · Plasma_cell: mean 1.4%, log2 vs others −1.3, p = 0.098, q = 0.55</title></circle><rect x="8" y="495" width="10" height="10" rx="2" fill="url(#cmp26jstap9)"/><text x="23" y="503.5" font-size="10" fill="#16232b">T_cell</text><text x="145" y="503.5" font-size="10" fill="#5b6b76">23.9%</text><circle cx="134.03" cy="500" r="2.87" fill="rgb(214,227,240)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Dup · T_cell: mean 23.9%, log2 vs others −0.37, p = 0.23, q = 0.59</title></circle><text x="275" y="503.5" font-size="10" fill="#5b6b76">27.3%</text><circle cx="264.03" cy="500" r="2.51" fill="rgb(250,252,253)" stroke="#dde4e8" stroke-width="0.75"><title>HRD-Del · T_cell: mean 27.3%, log2 vs others −0.045, p = 0.93, q = 0.98</title></circle><text x="405" y="503.5" font-size="10" fill="#5b6b76">32.4%</text><circle cx="394.03" cy="500" r="2.87" fill="rgb(240,211,215)" stroke="#dde4e8" stroke-width="0.75"><title>FBI · T_cell: mean 32.4%, log2 vs others 0.38, p = 0.24, q = 0.59</title></circle><text x="8" y="523" font-size="10" fill="#5b6b76">Each cell: the cohort's mean share, and a dot whose colour is log2 of it over the other cohorts' mean (blue lower, red higher, clamped at ±2), size −log10 q, outlined</text><text x="8" y="537" font-size="10" fill="#5b6b76">when q &lt; 0.05. 30 comparisons tested, 0 at q &lt; 0.05.</text><text x="8" y="565" font-size="10" fill="#5b6b76">Each sample's 10 parts (entities of “Cell Type Relative Fractions”), divided by their sum so every bar is a whole; cases ordered by T_cell, highest first. Each cohort is</text><text x="8" y="579" font-size="10" fill="#5b6b76">compared with the view's other cohorts pooled, part by part: log2 of the ratio of means (each + 0.001), Welch's t from the cohorts' n, mean and variance,</text><text x="8" y="593" font-size="10" fill="#5b6b76">Benjamini–Hochberg across the 30 comparisons. Parts of a whole are not independent — one part's rise is the others' fall — and a case in two cohorts counts in both.</text><text x="8" y="607" font-size="10" fill="#5b6b76">Entropy is Shannon's (natural log) over each case's shares.</text></svg>
diff --git a/test/snapshots/composition.txt b/test/snapshots/composition.txt
new file mode 100644
index 00000000..3a59dbe9
--- /dev/null
+++ b/test/snapshots/composition.txt
@@ -0,0 +1,32 @@
+Analysis over msk_spectrum_tme_2022 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- HRD-Dup: 16 patients — CONSENSUS_SIGNATURE in [HRD-Dup]
+- HRD-Del: 6 patients — CONSENSUS_SIGNATURE in [HRD-Del]
+- FBI: 14 patients — CONSENSUS_SIGNATURE in [FBI]
+
+Composition of clinical attributes (10 parts, shares of each patient's total; each cohort against the others by Welch's t, BH q over 30 comparisons; 0 at q<0.05):
+  HRD-Dup (n=16, entropy 2.07): CD4 T CXCL13 4.2% (log2 0.47, q=0.3269), CD4 T naive 15.2% (log2 -0.39, q=0.2036), CD4 T reg 11.6% (log2 -0.01, q=0.9878), CD8 T CXCL13 7.0% (log2 0.61, q=0.2036), CD8 T ISG 9.2% (log2 0.41, q=0.2036), CD8 T cytotoxic 21.4% (log2 -0.21, q=0.2733), Cycling T NK 8.6% (log2 0.59, q=0.0797), ILC 2.1% (log2 -0.00, q=0.9878), NK CD56 14.6% (log2 0.28, q=0.4064), NK cytotoxic 6.1% (log2 -0.60, q=0.2458)
+  HRD-Del (n=6, entropy 1.96): CD4 T CXCL13 3.2% (log2 -0.14, q=0.7628), CD4 T naive 19.6% (log2 0.17, q=0.7628), CD4 T reg 15.7% (log2 0.52, q=0.3806), CD8 T CXCL13 4.9% (log2 -0.22, q=0.7614), CD8 T ISG 6.4% (log2 -0.35, q=0.2458), CD8 T cytotoxic 23.1% (log2 -0.02, q=0.9878), Cycling T NK 5.7% (log2 -0.34, q=0.2458), ILC 1.8% (log2 -0.29, q=0.2706), NK CD56 13.0% (log2 -0.02, q=0.9878), NK cytotoxic 6.6% (log2 -0.32, q=0.4064)
+  FBI (n=14, entropy 1.98): CD4 T CXCL13 2.9% (log2 -0.43, q=0.3269), CD4 T naive 20.1% (log2 0.29, q=0.2870), CD4 T reg 10.0% (log2 -0.35, q=0.2505), CD8 T CXCL13 4.4% (log2 -0.55, q=0.2036), CD8 T ISG 7.1% (log2 -0.24, q=0.3806), CD8 T cytotoxic 25.5% (log2 0.22, q=0.2733), Cycling T NK 5.7% (log2 -0.46, q=0.2036), ILC 2.3% (log2 0.16, q=0.6431), NK CD56 11.6% (log2 -0.29, q=0.3806), NK cytotoxic 10.6% (log2 0.75, q=0.2237)
+  CAVEAT (tell the user): Each patient's 10 parts (clinical attributes), divided by their sum so every bar is a whole; cases ordered by CD8 T CXCL13, highest first. Each cohort is compared with the view's other cohorts pooled, part by part: log2 of the ratio of means (each + 0.001), Welch's t from the cohorts' n, mean and variance, Benjamini–Hochberg across the 30 comparisons. Parts of a whole are not independent — one part's rise is the others' fall — and a case in two cohorts counts in both. Entropy is Shannon's (natural log) over each case's shares. 
+  METHOD:
+    - Sum each part over the cohort's patients [HRD-Dup · msk_spectrum_tme_2022]: The composition:moments query above: each patient's 10 parts read and put in part order inside the database; a patient with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those patients n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.
+    - Sum each part over the cohort's patients [HRD-Del · msk_spectrum_tme_2022]: The composition:moments query above: each patient's 10 parts read and put in part order inside the database; a patient with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those patients n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.
+    - Sum each part over the cohort's patients [FBI · msk_spectrum_tme_2022]: The composition:moments query above: each patient's 10 parts read and put in part order inside the database; a patient with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those patients n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.
+    - Each cohort against the others pooled, per part; then Benjamini–Hochberg: The other cohorts' sums are added into one group. Effect: log2((mean + 0.001) / (others' mean + 0.001)). Test: Welch's unequal-variance t from each side's n, mean and variance (Σp² − (Σp)²/n)/(n − 1), two-sided; not tested when either side has under 2 patients or neither has any spread. q across every comparison tested.
+        comparisons: 30
+    Welch's t from moments matches scipy 1.18.1 ttest_ind(equal_var=False) and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/moments-golden.test.ts).
+
+Composition of Cell Type Relative Fractions (10 parts, shares of each sample's total; each cohort against the others by Welch's t, BH q over 30 comparisons; 0 at q<0.05):
+  HRD-Dup (n=19, entropy 1.33): B_cell 2.5% (log2 0.55, q=0.7304), Dendritic_cell 0.3% (log2 -0.40, q=0.5869), Endothelial_cell 2.3% (log2 0.32, q=0.7304), Fibroblast 18.2% (log2 0.64, q=0.5869), Mast_cell 0.1% (log2 -0.59, q=0.5468), Monocyte 21.4% (log2 0.02, q=0.9842), Other 0.0% (log2 -0.00, q=0.5869), Ovarian_cancer_cell 26.6% (log2 -0.21, q=0.7304), Plasma_cell 4.7% (log2 1.64, q=0.5468), T_cell 23.9% (log2 -0.37, q=0.5869)
+  HRD-Del (n=8, entropy 1.20): B_cell 1.4% (log2 -0.58, q=0.7304), Dendritic_cell 0.3% (log2 -0.50, q=0.5869), Endothelial_cell 1.0% (log2 -1.10, q=0.3935), Fibroblast 15.1% (log2 0.09, q=0.9768), Mast_cell 0.1% (log2 -0.59, q=0.5869), Monocyte 24.4% (log2 0.25, q=0.7337), Other 0.0% (log2 -0.00, q=0.5869), Ovarian_cancer_cell 29.0% (log2 -0.00, q=0.9935), Plasma_cell 1.4% (log2 -1.02, q=0.5869), T_cell 27.3% (log2 -0.04, q=0.9842)
+  FBI (n=19, entropy 1.18): B_cell 1.8% (log2 -0.27, q=0.7905), Dendritic_cell 0.6% (log2 0.64, q=0.5869), Endothelial_cell 2.1% (log2 0.17, q=0.7905), Fibroblast 10.2% (log2 -0.76, q=0.5468), Mast_cell 0.2% (log2 0.85, q=0.5468), Monocyte 19.7% (log2 -0.18, q=0.7655), Other 0.0% (log2 0.01, q=0.5869), Ovarian_cancer_cell 31.6% (log2 0.21, q=0.7337), Plasma_cell 1.4% (log2 -1.32, q=0.5468), T_cell 32.4% (log2 0.38, q=0.5869)
+  CAVEAT (tell the user): Each sample's 10 parts (entities of “Cell Type Relative Fractions”), divided by their sum so every bar is a whole; cases ordered by T_cell, highest first. Each cohort is compared with the view's other cohorts pooled, part by part: log2 of the ratio of means (each + 0.001), Welch's t from the cohorts' n, mean and variance, Benjamini–Hochberg across the 30 comparisons. Parts of a whole are not independent — one part's rise is the others' fall — and a case in two cohorts counts in both. Entropy is Shannon's (natural log) over each case's shares. 
+  METHOD:
+    - Sum each part over the cohort's samples [HRD-Dup · msk_spectrum_tme_2022]: The composition:moments query above: each sample's 10 parts read and put in part order inside the database; a sample with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those samples n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.
+    - Sum each part over the cohort's samples [HRD-Del · msk_spectrum_tme_2022]: The composition:moments query above: each sample's 10 parts read and put in part order inside the database; a sample with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those samples n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.
+    - Sum each part over the cohort's samples [FBI · msk_spectrum_tme_2022]: The composition:moments query above: each sample's 10 parts read and put in part order inside the database; a sample with every part, none below zero and a sum above zero is kept, its parts divided by their sum; then over those samples n, Σp and Σp² per part, and Σ and Σ² of each one's Shannon entropy. No case's values leave the database in this query.
+    - Each cohort against the others pooled, per part; then Benjamini–Hochberg: The other cohorts' sums are added into one group. Effect: log2((mean + 0.001) / (others' mean + 0.001)). Test: Welch's unequal-variance t from each side's n, mean and variance (Σp² − (Σp)²/n)/(n − 1), two-sided; not tested when either side has under 2 samples or neither has any spread. q across every comparison tested.
+        comparisons: 30
+    Welch's t from moments matches scipy 1.18.1 ttest_ind(equal_var=False) and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/moments-golden.test.ts).
diff --git a/test/snapshots/cooccurrence.json b/test/snapshots/cooccurrence.json
new file mode 100644
index 00000000..0b21ee03
--- /dev/null
+++ b/test/snapshots/cooccurrence.json
@@ -0,0 +1,367 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "NSCLC": {
+      "count": 7809,
+      "text": "CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "cooccurrence",
+      "cohorts": [
+        "NSCLC"
+      ],
+      "pairs": [
+        {
+          "a": "STK11",
+          "b": "KEAP1",
+          "both": 572,
+          "onlyA": 537,
+          "onlyB": 512,
+          "neither": 6188,
+          "profiled": 7809,
+          "log2Odds": 3.686,
+          "p": 3.396549365554813e-249,
+          "q": 5.094824048332219e-248,
+          "tendency": "co-occurrence"
+        },
+        {
+          "a": "EGFR",
+          "b": "KRAS",
+          "both": 75,
+          "onlyA": 1947,
+          "onlyB": 2194,
+          "neither": 3593,
+          "profiled": 7809,
+          "log2Odds": -3.987,
+          "p": 1.5080466769626828e-238,
+          "q": 1.1310350077220122e-237,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "EGFR",
+          "b": "STK11",
+          "both": 52,
+          "onlyA": 1970,
+          "onlyB": 1057,
+          "neither": 4730,
+          "profiled": 7809,
+          "log2Odds": -3.082,
+          "p": 8.910206347433695e-88,
+          "q": 4.4551031737168476e-87,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "KRAS",
+          "b": "STK11",
+          "both": 598,
+          "onlyA": 1671,
+          "onlyB": 511,
+          "neither": 5029,
+          "profiled": 7809,
+          "log2Odds": 1.816,
+          "p": 4.9400796448155687e-79,
+          "q": 1.8525298668058381e-78,
+          "tendency": "co-occurrence"
+        },
+        {
+          "a": "EGFR",
+          "b": "KEAP1",
+          "both": 66,
+          "onlyA": 1956,
+          "onlyB": 1018,
+          "neither": 4769,
+          "profiled": 7809,
+          "log2Odds": -2.661,
+          "p": 1.503888597094316e-72,
+          "q": 4.511665791282948e-72,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "KRAS",
+          "b": "TP53",
+          "both": 873,
+          "onlyA": 1396,
+          "onlyB": 3215,
+          "neither": 2325,
+          "profiled": 7809,
+          "log2Odds": -1.145,
+          "p": 1.0558783450158271e-55,
+          "q": 2.6396958625395677e-55,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "EGFR",
+          "b": "ALK",
+          "both": 49,
+          "onlyA": 1973,
+          "onlyB": 503,
+          "neither": 5284,
+          "profiled": 7809,
+          "log2Odds": -1.938,
+          "p": 3.1984865484044725e-25,
+          "q": 6.853899746581012e-25,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "KRAS",
+          "b": "KEAP1",
+          "both": 456,
+          "onlyA": 1813,
+          "onlyB": 628,
+          "neither": 4912,
+          "profiled": 7809,
+          "log2Odds": 0.976,
+          "p": 5.47635802660293e-23,
+          "q": 1.0268171299880494e-22,
+          "tendency": "co-occurrence"
+        },
+        {
+          "a": "TP53",
+          "b": "STK11",
+          "both": 453,
+          "onlyA": 3635,
+          "onlyB": 656,
+          "neither": 3065,
+          "profiled": 7809,
+          "log2Odds": -0.78,
+          "p": 1.3052910427261104e-16,
+          "q": 2.175485071210184e-16,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "EGFR",
+          "b": "TP53",
+          "both": 1174,
+          "onlyA": 848,
+          "onlyB": 2914,
+          "neither": 2873,
+          "profiled": 7809,
+          "log2Odds": 0.449,
+          "p": 2.2614400356737777e-9,
+          "q": 3.3921600535106666e-9,
+          "tendency": "co-occurrence"
+        },
+        {
+          "a": "KRAS",
+          "b": "ALK",
+          "both": 118,
+          "onlyA": 2151,
+          "onlyB": 434,
+          "neither": 5106,
+          "profiled": 7809,
+          "log2Odds": -0.632,
+          "p": 0.000027654786501243727,
+          "q": 0.00003771107250169599,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "STK11",
+          "b": "ALK",
+          "both": 66,
+          "onlyA": 1043,
+          "onlyB": 486,
+          "neither": 6214,
+          "profiled": 7809,
+          "log2Odds": -0.306,
+          "p": 0.12879997517049538,
+          "q": 0.16099996896311922,
+          "tendency": "mutual exclusivity"
+        },
+        {
+          "a": "TP53",
+          "b": "ALK",
+          "both": 303,
+          "onlyA": 3785,
+          "onlyB": 249,
+          "neither": 3472,
+          "profiled": 7809,
+          "log2Odds": 0.159,
+          "p": 0.21623930193910998,
+          "q": 0.24172078956410842,
+          "tendency": "co-occurrence"
+        },
+        {
+          "a": "TP53",
+          "b": "KEAP1",
+          "both": 586,
+          "onlyA": 3502,
+          "onlyB": 498,
+          "neither": 3223,
+          "profiled": 7809,
+          "log2Odds": 0.115,
+          "p": 0.2256060702598345,
+          "q": 0.24172078956410842,
+          "tendency": "co-occurrence"
+        },
+        {
+          "a": "KEAP1",
+          "b": "ALK",
+          "both": 72,
+          "onlyA": 1012,
+          "onlyB": 480,
+          "neither": 6245,
+          "profiled": 7809,
+          "log2Odds": -0.111,
+          "p": 0.6094218694938038,
+          "q": 0.6094218694938038,
+          "tendency": "mutual exclusivity"
+        }
+      ],
+      "studies": [
+        "msk_chord_2024"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "Pairwise co-occurrence within “NSCLC”. Denominators are the patients profiled for BOTH genes, computed per study against that study's own panels — a pair is never scored on patients whose panel could not have detected it. Two-sided Fisher exact test. q-values are Benjamini–Hochberg across the 15 assessable pairs. Every site returns four counts per pair and nothing else.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count each gene pair's 2×2 table",
+            "detail": "The pairCounts query above. Of the patients with a sample on a mutation panel covering both genes: how many are altered in both, only the first, only the second, or neither (altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion)). Four counts per pair, nothing else.",
+            "scope": "NSCLC · msk_chord_2024",
+            "rowsIn": 7809,
+            "params": [
+              [
+                "pairs",
+                "15"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Log₂ odds ratio per pair",
+            "detail": "log₂((both · neither) / (only first · only second)) on the counts summed across studies, with 0.5 added to every cell when any cell is 0."
+          },
+          {
+            "kind": "statistic",
+            "title": "Fisher's exact test per pair",
+            "detail": "Two-sided, on the pair's 2×2 table."
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across pairs",
+            "detail": "q-values over the 15 assessable pairs, from the Fisher p-values."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "fisherExact",
+            "log2OddsRatio",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels 0.15.0 multipletests(method=\"fdr_bh\"), to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "cooccurrence:STK11/KEAP1:p": {
+            "v": 3.396549365554813e-249
+          },
+          "cooccurrence:STK11/KEAP1:q": {
+            "v": 5.094824048332219e-248
+          },
+          "cooccurrence:EGFR/KRAS:p": {
+            "v": 1.5080466769626828e-238
+          },
+          "cooccurrence:EGFR/KRAS:q": {
+            "v": 1.1310350077220122e-237
+          },
+          "cooccurrence:EGFR/STK11:p": {
+            "v": 8.910206347433695e-88
+          },
+          "cooccurrence:EGFR/STK11:q": {
+            "v": 4.4551031737168476e-87
+          },
+          "cooccurrence:KRAS/STK11:p": {
+            "v": 4.9400796448155687e-79
+          },
+          "cooccurrence:KRAS/STK11:q": {
+            "v": 1.8525298668058381e-78
+          },
+          "cooccurrence:EGFR/KEAP1:p": {
+            "v": 1.503888597094316e-72
+          },
+          "cooccurrence:EGFR/KEAP1:q": {
+            "v": 4.511665791282948e-72
+          },
+          "cooccurrence:KRAS/TP53:p": {
+            "v": 1.0558783450158271e-55
+          },
+          "cooccurrence:KRAS/TP53:q": {
+            "v": 2.6396958625395677e-55
+          },
+          "cooccurrence:EGFR/ALK:p": {
+            "v": 3.1984865484044725e-25
+          },
+          "cooccurrence:EGFR/ALK:q": {
+            "v": 6.853899746581012e-25
+          },
+          "cooccurrence:KRAS/KEAP1:p": {
+            "v": 5.47635802660293e-23
+          },
+          "cooccurrence:KRAS/KEAP1:q": {
+            "v": 1.0268171299880494e-22
+          },
+          "cooccurrence:TP53/STK11:p": {
+            "v": 1.3052910427261104e-16
+          },
+          "cooccurrence:TP53/STK11:q": {
+            "v": 2.175485071210184e-16
+          },
+          "cooccurrence:EGFR/TP53:p": {
+            "v": 2.2614400356737777e-9
+          },
+          "cooccurrence:EGFR/TP53:q": {
+            "v": 3.3921600535106666e-9
+          },
+          "cooccurrence:KRAS/ALK:p": {
+            "v": 0.000027654786501243727
+          },
+          "cooccurrence:KRAS/ALK:q": {
+            "v": 0.00003771107250169599
+          },
+          "cooccurrence:STK11/ALK:p": {
+            "v": 0.12879997517049538
+          },
+          "cooccurrence:STK11/ALK:q": {
+            "v": 0.16099996896311922
+          },
+          "cooccurrence:TP53/ALK:p": {
+            "v": 0.21623930193910998
+          },
+          "cooccurrence:TP53/ALK:q": {
+            "v": 0.24172078956410842
+          },
+          "cooccurrence:TP53/KEAP1:p": {
+            "v": 0.2256060702598345
+          },
+          "cooccurrence:TP53/KEAP1:q": {
+            "v": 0.24172078956410842
+          },
+          "cooccurrence:KEAP1/ALK:p": {
+            "v": 0.6094218694938038
+          },
+          "cooccurrence:KEAP1/ALK:q": {
+            "v": 0.6094218694938038
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/cooccurrence.sql b/test/snapshots/cooccurrence.sql
new file mode 100644
index 00000000..3851b9c5
--- /dev/null
+++ b/test/snapshots/cooccurrence.sql
@@ -0,0 +1,42 @@
+-- cohort NSCLC
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))
+
+-- view 0 cooccurrence: pairCounts · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))),
+altered AS (
+  SELECT DISTINCT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('EGFR','KRAS','TP53','STK11','KEAP1','ALK') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('EGFR','KRAS','TP53','STK11','KEAP1','ALK')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+pp AS (
+  SELECT pid,
+         groupUniqArrayIf(gene, k = 1) AS alt,
+         groupUniqArrayIf(gene, k = 0) AS cov
+  FROM (SELECT gene, pid, 1 AS k FROM altered UNION ALL SELECT gene, pid, 0 AS k FROM covered)
+  GROUP BY pid
+)
+SELECT ga, gb,
+       countIf(has(cov, ga) AND has(cov, gb)) AS profiled,
+       countIf(has(cov, ga) AND has(cov, gb) AND has(alt, ga) AND has(alt, gb)) AS both,
+       countIf(has(cov, ga) AND has(cov, gb) AND has(alt, ga) AND NOT has(alt, gb)) AS only_a,
+       countIf(has(cov, ga) AND has(cov, gb) AND NOT has(alt, ga) AND has(alt, gb)) AS only_b
+FROM pp
+ARRAY JOIN ['EGFR','EGFR','EGFR','EGFR','EGFR','KRAS','KRAS','KRAS','KRAS','TP53','TP53','TP53','STK11','STK11','KEAP1'] AS ga, ['KRAS','TP53','STK11','KEAP1','ALK','TP53','STK11','KEAP1','ALK','STK11','KEAP1','ALK','KEAP1','ALK','ALK'] AS gb
+GROUP BY ga, gb
diff --git a/test/snapshots/cooccurrence.svg b/test/snapshots/cooccurrence.svg
new file mode 100644
index 00000000..ad1bf67b
--- /dev/null
+++ b/test/snapshots/cooccurrence.svg
@@ -0,0 +1,2 @@
+<!-- view 0 cooccurrence -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 480" width="100%" role="img" aria-label="Co-occurrence — log₂ odds ratio" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Co-occurrence — log₂ odds ratio</title><rect width="720" height="480" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Co-occurrence — log₂ odds ratio</text><rect x="8" y="28.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="37" font-size="10" fill="#16232b">co-occur, q &lt; 0.05</text><rect x="120.95" y="28.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="136" y="37" font-size="10" fill="#16232b">mutually exclusive, q &lt; 0.05</text><rect x="277.77" y="28.5" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="292.8" y="37" font-size="10" fill="#16232b">not significant</text><line x1="96" y1="47" x2="96" y2="377" stroke="#dde4e8" stroke-width="0.75"/><line x1="223.6" y1="47" x2="223.6" y2="377" stroke="#dde4e8" stroke-width="0.75"/><line x1="351.3" y1="47" x2="351.3" y2="377" stroke="#dde4e8" stroke-width="0.75"/><line x1="478.9" y1="47" x2="478.9" y2="377" stroke="#dde4e8" stroke-width="0.75"/><line x1="606.6" y1="47" x2="606.6" y2="377" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="47" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="61.9" font-size="11" fill="#16232b">STK11 / KEAP1</text><rect x="351.3" y="52" width="235.3" height="12" rx="1.5" fill="#0072B2"><title>STK11 / KEAP1: both 572, STK11 only 537, KEAP1 only 512, neither 6,188 · p = 3.4e-249</title></rect><text x="712" y="61.5" font-size="10" fill="#16232b" text-anchor="end">3.69 · q = 5.1e-248</text><text x="8" y="83.9" font-size="11" fill="#16232b">EGFR / KRAS</text><rect x="96.8" y="74" width="254.5" height="12" rx="1.5" fill="#E69F00"><title>EGFR / KRAS: both 75, EGFR only 1,947, KRAS only 2,194, neither 3,593 · p = 1.5e-238</title></rect><text x="712" y="83.5" font-size="10" fill="#16232b" text-anchor="end">−3.99 · q = 1.1e-237</text><rect x="4" y="91" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="105.9" font-size="11" fill="#16232b">EGFR / STK11</text><rect x="154.6" y="96" width="196.7" height="12" rx="1.5" fill="#E69F00"><title>EGFR / STK11: both 52, EGFR only 1,970, STK11 only 1,057, neither 4,730 · p = 8.9e-88</title></rect><text x="712" y="105.5" font-size="10" fill="#16232b" text-anchor="end">−3.08 · q = 4.5e-87</text><text x="8" y="127.9" font-size="11" fill="#16232b">KRAS / STK11</text><rect x="351.3" y="118" width="115.9" height="12" rx="1.5" fill="#0072B2"><title>KRAS / STK11: both 598, KRAS only 1,671, STK11 only 511, neither 5,029 · p = 4.9e-79</title></rect><text x="712" y="127.5" font-size="10" fill="#16232b" text-anchor="end">1.82 · q = 1.9e-78</text><rect x="4" y="135" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="149.9" font-size="11" fill="#16232b">EGFR / KEAP1</text><rect x="181.4" y="140" width="169.8" height="12" rx="1.5" fill="#E69F00"><title>EGFR / KEAP1: both 66, EGFR only 1,956, KEAP1 only 1,018, neither 4,769 · p = 1.5e-72</title></rect><text x="712" y="149.5" font-size="10" fill="#16232b" text-anchor="end">−2.66 · q = 4.5e-72</text><text x="8" y="171.9" font-size="11" fill="#16232b">KRAS / TP53</text><rect x="278.2" y="162" width="73.1" height="12" rx="1.5" fill="#E69F00"><title>KRAS / TP53: both 873, KRAS only 1,396, TP53 only 3,215, neither 2,325 · p = 1.1e-55</title></rect><text x="712" y="171.5" font-size="10" fill="#16232b" text-anchor="end">−1.15 · q = 2.6e-55</text><rect x="4" y="179" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="193.9" font-size="11" fill="#16232b">EGFR / ALK</text><rect x="227.6" y="184" width="123.7" height="12" rx="1.5" fill="#E69F00"><title>EGFR / ALK: both 49, EGFR only 1,973, ALK only 503, neither 5,284 · p = 3.2e-25</title></rect><text x="712" y="193.5" font-size="10" fill="#16232b" text-anchor="end">−1.94 · q = 6.9e-25</text><text x="8" y="215.9" font-size="11" fill="#16232b">KRAS / KEAP1</text><rect x="351.3" y="206" width="62.3" height="12" rx="1.5" fill="#0072B2"><title>KRAS / KEAP1: both 456, KRAS only 1,813, KEAP1 only 628, neither 4,912 · p = 5.5e-23</title></rect><text x="712" y="215.5" font-size="10" fill="#16232b" text-anchor="end">0.976 · q = 1e-22</text><rect x="4" y="223" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="237.9" font-size="11" fill="#16232b">TP53 / STK11</text><rect x="301.5" y="228" width="49.8" height="12" rx="1.5" fill="#E69F00"><title>TP53 / STK11: both 453, TP53 only 3,635, STK11 only 656, neither 3,065 · p = 1.3e-16</title></rect><text x="712" y="237.5" font-size="10" fill="#16232b" text-anchor="end">−0.78 · q = 2.2e-16</text><text x="8" y="259.9" font-size="11" fill="#16232b">EGFR / TP53</text><rect x="351.3" y="250" width="28.7" height="12" rx="1.5" fill="#0072B2"><title>EGFR / TP53: both 1,174, EGFR only 848, TP53 only 2,914, neither 2,873 · p = 2.3e-9</title></rect><text x="712" y="259.5" font-size="10" fill="#16232b" text-anchor="end">0.449 · q = 3.4e-9</text><rect x="4" y="267" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="281.9" font-size="11" fill="#16232b">KRAS / ALK</text><rect x="310.9" y="272" width="40.3" height="12" rx="1.5" fill="#E69F00"><title>KRAS / ALK: both 118, KRAS only 2,151, ALK only 434, neither 5,106 · p = 2.8e-5</title></rect><text x="712" y="281.5" font-size="10" fill="#16232b" text-anchor="end">−0.632 · q = 3.8e-5</text><text x="8" y="303.9" font-size="11" fill="#16232b">STK11 / ALK</text><rect x="331.7" y="294" width="19.5" height="12" rx="1.5" fill="#9aa5ad"><title>STK11 / ALK: both 66, STK11 only 1,043, ALK only 486, neither 6,214 · p = 0.13</title></rect><text x="712" y="303.5" font-size="10" fill="#16232b" text-anchor="end">−0.306 · q = 0.16</text><rect x="4" y="311" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="325.9" font-size="11" fill="#16232b">TP53 / ALK</text><rect x="351.3" y="316" width="10.1" height="12" rx="1.5" fill="#9aa5ad"><title>TP53 / ALK: both 303, TP53 only 3,785, ALK only 249, neither 3,472 · p = 0.22</title></rect><text x="712" y="325.5" font-size="10" fill="#16232b" text-anchor="end">0.159 · q = 0.24</text><text x="8" y="347.9" font-size="11" fill="#16232b">TP53 / KEAP1</text><rect x="351.3" y="338" width="7.3" height="12" rx="1.5" fill="#9aa5ad"><title>TP53 / KEAP1: both 586, TP53 only 3,502, KEAP1 only 498, neither 3,223 · p = 0.23</title></rect><text x="712" y="347.5" font-size="10" fill="#16232b" text-anchor="end">0.115 · q = 0.24</text><rect x="4" y="355" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="369.9" font-size="11" fill="#16232b">KEAP1 / ALK</text><rect x="344.2" y="360" width="7.1" height="12" rx="1.5" fill="#9aa5ad"><title>KEAP1 / ALK: both 72, KEAP1 only 1,012, ALK only 480, neither 6,245 · p = 0.61</title></rect><text x="712" y="369.5" font-size="10" fill="#16232b" text-anchor="end">−0.111 · q = 0.61</text><line x1="351.3" y1="45" x2="351.3" y2="379" stroke="#16232b" stroke-width="1"/><line x1="96" y1="377" x2="96" y2="380" stroke="#5b6b76" stroke-width="1"/><text x="90" y="393" font-size="10" fill="#5b6b76">−4</text><line x1="223.6" y1="377" x2="223.6" y2="380" stroke="#5b6b76" stroke-width="1"/><text x="217.6" y="393" font-size="10" fill="#5b6b76">−2</text><line x1="351.3" y1="377" x2="351.3" y2="380" stroke="#5b6b76" stroke-width="1"/><text x="348.3" y="393" font-size="10" fill="#5b6b76">0</text><line x1="478.9" y1="377" x2="478.9" y2="380" stroke="#5b6b76" stroke-width="1"/><text x="475.9" y="393" font-size="10" fill="#5b6b76">2</text><line x1="606.6" y1="377" x2="606.6" y2="380" stroke="#5b6b76" stroke-width="1"/><text x="603.6" y="393" font-size="10" fill="#5b6b76">4</text><line x1="95.966" y1="377" x2="606.58" y2="377" stroke="#5b6b76" stroke-width="1"/><text x="351.3" y="412" font-size="11" fill="#16232b" text-anchor="middle">log₂ odds ratio — left: mutually exclusive, right: co-occurring</text><text x="8" y="434" font-size="10" fill="#5b6b76">Pairwise co-occurrence within “NSCLC”. Denominators are the patients profiled for BOTH genes, computed per study against that study's own panels — a</text><text x="8" y="448" font-size="10" fill="#5b6b76">pair is never scored on patients whose panel could not have detected it. Two-sided Fisher exact test. q-values are Benjamini–Hochberg across the 15</text><text x="8" y="462" font-size="10" fill="#5b6b76">assessable pairs. Every site returns four counts per pair and nothing else.</text></svg>
diff --git a/test/snapshots/cooccurrence.txt b/test/snapshots/cooccurrence.txt
new file mode 100644
index 00000000..c314c890
--- /dev/null
+++ b/test/snapshots/cooccurrence.txt
@@ -0,0 +1,29 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- NSCLC: 7,809 patients — CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)
+
+Co-occurrence (2x2 per pair, among patients profiled for BOTH genes):
+  STK11/KEAP1: both 572, STK11 only 537, KEAP1 only 512, neither 6,188 (of 7,809 profiled)  log2OR=3.69 (co-occur)  p=3.4e-249 q=5.1e-248
+  EGFR/KRAS: both 75, EGFR only 1,947, KRAS only 2,194, neither 3,593 (of 7,809 profiled)  log2OR=-3.99 (mutually exclusive)  p=1.5e-238 q=1.1e-237
+  EGFR/STK11: both 52, EGFR only 1,970, STK11 only 1,057, neither 4,730 (of 7,809 profiled)  log2OR=-3.08 (mutually exclusive)  p=8.9e-88 q=4.5e-87
+  KRAS/STK11: both 598, KRAS only 1,671, STK11 only 511, neither 5,029 (of 7,809 profiled)  log2OR=1.82 (co-occur)  p=4.9e-79 q=1.9e-78
+  EGFR/KEAP1: both 66, EGFR only 1,956, KEAP1 only 1,018, neither 4,769 (of 7,809 profiled)  log2OR=-2.66 (mutually exclusive)  p=1.5e-72 q=4.5e-72
+  KRAS/TP53: both 873, KRAS only 1,396, TP53 only 3,215, neither 2,325 (of 7,809 profiled)  log2OR=-1.15 (mutually exclusive)  p=1.1e-55 q=2.6e-55
+  EGFR/ALK: both 49, EGFR only 1,973, ALK only 503, neither 5,284 (of 7,809 profiled)  log2OR=-1.94 (mutually exclusive)  p=3.2e-25 q=6.9e-25
+  KRAS/KEAP1: both 456, KRAS only 1,813, KEAP1 only 628, neither 4,912 (of 7,809 profiled)  log2OR=0.98 (co-occur)  p=5.5e-23 q=1.0e-22
+  TP53/STK11: both 453, TP53 only 3,635, STK11 only 656, neither 3,065 (of 7,809 profiled)  log2OR=-0.78 (mutually exclusive)  p=1.3e-16 q=2.2e-16
+  EGFR/TP53: both 1,174, EGFR only 848, TP53 only 2,914, neither 2,873 (of 7,809 profiled)  log2OR=0.45 (co-occur)  p=2.3e-9 q=3.4e-9
+  KRAS/ALK: both 118, KRAS only 2,151, ALK only 434, neither 5,106 (of 7,809 profiled)  log2OR=-0.63 (mutually exclusive)  p=2.8e-5 q=3.8e-5
+  STK11/ALK: both 66, STK11 only 1,043, ALK only 486, neither 6,214 (of 7,809 profiled)  log2OR=-0.31 (mutually exclusive)  p=0.1288 q=0.1610
+  TP53/ALK: both 303, TP53 only 3,785, ALK only 249, neither 3,472 (of 7,809 profiled)  log2OR=0.16 (co-occur)  p=0.2162 q=0.2417
+  TP53/KEAP1: both 586, TP53 only 3,502, KEAP1 only 498, neither 3,223 (of 7,809 profiled)  log2OR=0.12 (co-occur)  p=0.2256 q=0.2417
+  KEAP1/ALK: both 72, KEAP1 only 1,012, ALK only 480, neither 6,245 (of 7,809 profiled)  log2OR=-0.11 (mutually exclusive)  p=0.6094 q=0.6094
+  Pairwise co-occurrence within “NSCLC”. Denominators are the patients profiled for BOTH genes, computed per study against that study's own panels — a pair is never scored on patients whose panel could not have detected it. Two-sided Fisher exact test. q-values are Benjamini–Hochberg across the 15 assessable pairs. Every site returns four counts per pair and nothing else.
+  METHOD:
+    - Count each gene pair's 2×2 table [NSCLC · msk_chord_2024]: The pairCounts query above. Of the patients with a sample on a mutation panel covering both genes: how many are altered in both, only the first, only the second, or neither (altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion)). Four counts per pair, nothing else.
+        pairs: 15
+    - Log₂ odds ratio per pair: log₂((both · neither) / (only first · only second)) on the counts summed across studies, with 0.5 added to every cell when any cell is 0.
+    - Fisher's exact test per pair: Two-sided, on the pair's 2×2 table.
+    - Benjamini–Hochberg across pairs: q-values over the 15 assessable pairs, from the Fisher p-values.
+    Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels 0.15.0 multipletests(method="fdr_bh"), to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/correlates.json b/test/snapshots/correlates.json
new file mode 100644
index 00000000..57218f26
--- /dev/null
+++ b/test/snapshots/correlates.json
@@ -0,0 +1,679 @@
+{
+  "study": "brca_tcga_pan_can_atlas_2018",
+  "cohorts": {
+    "all patients": {
+      "count": 1084,
+      "text": "all patients in the study",
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "correlates",
+      "cohorts": [
+        "all patients"
+      ],
+      "correlates": {
+        "gene": "ESR1",
+        "profileType": "rna_seq_v2_mrna",
+        "otherProfile": "rna_seq_v2_mrna",
+        "cohort": "all patients",
+        "tested": 20057,
+        "untested": 280,
+        "significant": 15572,
+        "minN": 10,
+        "rhoFrom": "rank",
+        "positive": [
+          {
+            "gene": "CCDC170",
+            "n": 1082,
+            "rho": 0.7917,
+            "rhoP": 2.2249983400385912e-233,
+            "q": 4.4626791706154024e-229,
+            "r": 0.6675,
+            "p": 1.86618036574955e-140,
+            "cytoband": "6q25.1"
+          },
+          {
+            "gene": "DCPH1",
+            "n": 1082,
+            "rho": 0.7841,
+            "rhoP": 5.089149809464745e-226,
+            "q": 5.103653886421719e-222,
+            "r": 0.5652,
+            "p": 2.3489389556771624e-92,
+            "cytoband": "6q25.1"
+          },
+          {
+            "gene": "THSD4",
+            "n": 1082,
+            "rho": 0.7776,
+            "rhoP": 6.808597091162043e-220,
+            "q": 4.55200106191457e-216,
+            "r": 0.6439,
+            "p": 1.0524911370004154e-127,
+            "cytoband": "15q23"
+          },
+          {
+            "gene": "FOXA1",
+            "n": 1082,
+            "rho": 0.7478,
+            "rhoP": 2.5074740593460398e-194,
+            "q": 1.257310180207588e-190,
+            "r": 0.686,
+            "p": 2.481862600299769e-151,
+            "cytoband": "14q21.1"
+          },
+          {
+            "gene": "TBC1D9",
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+            "q": 2.500629414875059e-189,
+            "r": 0.5514,
+            "p": 4.160301459187909e-87,
+            "cytoband": "4q31.21"
+          },
+          {
+            "gene": "RABEP1",
+            "n": 1082,
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+            "rhoP": 1.7768277814040784e-179,
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+            "p": 3.9827630128370835e-115,
+            "cytoband": "17p13.2"
+          },
+          {
+            "gene": "SLC39A6",
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+            "cytoband": "18q12.2"
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+          {
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+            "cytoband": "10p14"
+          },
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+            "cytoband": "15q22.2"
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+            "cytoband": "20q13.13"
+          },
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+            "cytoband": "7p21.1"
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+            "cytoband": "12p12.3"
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+            "cytoband": "17q21.31"
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+            "p": 6.0880783498218e-66,
+            "cytoband": "17q24.2"
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+            "gene": "XBP1",
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+            "p": 2.7960784409340003e-94,
+            "cytoband": "22q12.1"
+          },
+          {
+            "gene": "C5AR2",
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+            "cytoband": "19q13.32"
+          },
+          {
+            "gene": "ACADSB",
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+            "r": 0.4641,
+            "p": 6.801311968497827e-59,
+            "cytoband": "10q26.13"
+          },
+          {
+            "gene": "AFF3",
+            "n": 1082,
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+            "rhoP": 4.3889664583171614e-138,
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+            "r": 0.508,
+            "p": 4.629622349829199e-72,
+            "cytoband": "2q11.2"
+          },
+          {
+            "…": "80 more",
+            "sha256": "17a1ad5b7d8a703b"
+          }
+        ],
+        "negative": [
+          {
+            "gene": "HAPLN3",
+            "n": 1082,
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+            "rhoP": 1.9286476519055978e-147,
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+            "p": 6.0141506017266405e-36,
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+          },
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+            "cytoband": "1p34.2"
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+            "cytoband": "11q21"
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+          {
+            "gene": "PSAT1",
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+            "p": 1.0668131141943748e-33,
+            "cytoband": "9q21.2"
+          },
+          {
+            "gene": "ACOT9",
+            "n": 1082,
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+            "p": 3.0756367010551174e-60,
+            "cytoband": "Xp22.11"
+          },
+          {
+            "gene": "PPP1R14C",
+            "n": 1082,
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+            "r": -0.301,
+            "p": 4.21053493663196e-24,
+            "cytoband": "6q25.1"
+          },
+          {
+            "gene": "PADI2",
+            "n": 1082,
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+            "p": 1.049149496850871e-40,
+            "cytoband": "1p36.13"
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+          {
+            "gene": "NCK2",
+            "n": 1082,
+            "rho": -0.6349,
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+            "r": -0.4534,
+            "p": 5.539379416243532e-56,
+            "cytoband": "2q12.2"
+          },
+          {
+            "gene": "UBE2E3",
+            "n": 1082,
+            "rho": -0.6301,
+            "rhoP": 8.703162966833193e-121,
+            "q": 3.6366529088702784e-118,
+            "r": -0.5035,
+            "p": 1.2643614072929785e-70,
+            "cytoband": "2q31.3"
+          },
+          {
+            "gene": "BCL11A",
+            "n": 1082,
+            "rho": -0.6297,
+            "rhoP": 1.4616886426408042e-120,
+            "q": 5.983079409274818e-118,
+            "r": -0.3608,
+            "p": 1.3078776903970973e-34,
+            "cytoband": "2p16.1"
+          },
+          {
+            "gene": "CCDC71L",
+            "n": 1082,
+            "rho": -0.6296,
+            "rhoP": 1.5073665335434951e-120,
+            "q": 6.046650112656376e-118,
+            "r": -0.4197,
+            "p": 2.0824439735859627e-47,
+            "cytoband": "7q22.3"
+          },
+          {
+            "gene": "MSANTD3",
+            "n": 1082,
+            "rho": -0.6261,
+            "rhoP": 8.199876471874299e-119,
+            "q": 3.162786969161208e-116,
+            "r": -0.4676,
+            "p": 7.098801694277027e-60,
+            "cytoband": "9q31.1"
+          },
+          {
+            "gene": "NXN",
+            "n": 1082,
+            "rho": -0.6239,
+            "rhoP": 9.456765471161885e-118,
+            "q": 3.448624455547162e-115,
+            "r": -0.4446,
+            "p": 1.2405501727205802e-53,
+            "cytoband": "17p13.3"
+          },
+          {
+            "gene": "GLIPR2",
+            "n": 1082,
+            "rho": -0.6231,
+            "rhoP": 2.1299986841621278e-117,
+            "q": 7.628818501471392e-115,
+            "r": -0.387,
+            "p": 5.444979798636671e-40,
+            "cytoband": "9p13.3"
+          },
+          {
+            "gene": "MTHFD1L",
+            "n": 1082,
+            "rho": -0.6194,
+            "rhoP": 1.2583543296204185e-115,
+            "q": 4.2777648795248705e-113,
+            "r": -0.404,
+            "p": 9.667661724659982e-44,
+            "cytoband": "6q25.1"
+          },
+          {
+            "gene": "USB1",
+            "n": 1082,
+            "rho": -0.619,
+            "rhoP": 2.014366286114203e-115,
+            "q": 6.733690766765428e-113,
+            "r": -0.4453,
+            "p": 7.932625194451654e-54,
+            "cytoband": "16q21"
+          },
+          {
+            "gene": "MSL3",
+            "n": 1082,
+            "rho": -0.6177,
+            "rhoP": 8.090272347188528e-115,
+            "q": 2.6172031043154886e-112,
+            "r": -0.4725,
+            "p": 2.957035824955362e-61,
+            "cytoband": "Xp22.2"
+          },
+          {
+            "gene": "VGLL1",
+            "n": 1082,
+            "rho": -0.6137,
+            "rhoP": 6.022559869204369e-113,
+            "q": 1.7013307506567892e-110,
+            "r": -0.2137,
+            "p": 1.219860470145793e-12,
+            "cytoband": "Xq26.3"
+          },
+          {
+            "gene": "CSRP2",
+            "n": 1082,
+            "rho": -0.609,
+            "rhoP": 8.507424395223815e-111,
+            "q": 2.245176461776369e-108,
+            "r": -0.3422,
+            "p": 4.421146331412537e-31,
+            "cytoband": "12q21.2"
+          },
+          {
+            "gene": "KCNN4",
+            "n": 1082,
+            "rho": -0.6088,
+            "rhoP": 1.061542169475088e-110,
+            "q": 2.729660422200236e-108,
+            "r": -0.3303,
+            "p": 5.937877932918649e-29,
+            "cytoband": "19q13.31"
+          },
+          {
+            "…": "80 more",
+            "sha256": "a9c884cfa869dfcb"
+          }
+        ],
+        "histogram": [
+          [
+            -1,
+            0
+          ],
+          [
+            -0.95,
+            0
+          ],
+          [
+            -0.9,
+            0
+          ],
+          [
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+            0
+          ],
+          [
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+            0
+          ],
+          [
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+            0
+          ],
+          [
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+          [
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+            23
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+          [
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+            66
+          ],
+          [
+            -0.55,
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+          ],
+          [
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+          ],
+          [
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+          ],
+          [
+            -0.4,
+            638
+          ],
+          [
+            -0.35,
+            786
+          ],
+          [
+            -0.3,
+            1094
+          ],
+          [
+            -0.25,
+            1253
+          ],
+          [
+            -0.2,
+            1401
+          ],
+          [
+            -0.15,
+            1448
+          ],
+          [
+            -0.1,
+            1762
+          ],
+          [
+            -0.05,
+            1943
+          ],
+          {
+            "…": "20 more",
+            "sha256": "ddc5ef42075ece16"
+          }
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "brca_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "ESR1 on “rna_seq_v2_mrna” against every gene on “rna_seq_v2_mrna”, over the patients in “all patients” with both values (a patient's samples averaged, as the heatmap takes them). Ranked by Spearman's ρ, as the portal ranks, with Pearson's r beside it; q is Benjamini–Hochberg over ρ's p across all 20,057 genes tested. A correlation is not a regulatory link: genes that rise together across a cohort often both track a third thing — a subtype, the tumour's purity, proliferation. 280 genes were not tested: fewer than 10 patients with both values, or no spread.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Sum every gene's values and ranks against ESR1's",
+            "detail": "The correlates:moments query above. Per patient, ESR1 on “rna_seq_v2_mrna” and every gene on “rna_seq_v2_mrna” (a patient's samples averaged); per gene, over the patients with both: n, Σx, Σy, Σxy, Σx², Σy², and the same six over each value's average rank among those patients (ties share the mean of their ranks). Sums only.",
+            "scope": "all patients · brca_tcga_pan_can_atlas_2018",
+            "rowsIn": 1084,
+            "rowsOut": 20338
+          },
+          {
+            "kind": "statistic",
+            "title": "Pearson's r and Spearman's ρ per gene",
+            "detail": "r from the sums: (nΣxy − ΣxΣy) / √((nΣx² − (Σx)²)(nΣy² − (Σy)²)); ρ is the same formula on the rank sums. Each p from t = r√((n − 2)/(1 − r²)) on n − 2 df, as scipy's pearsonr and spearmanr compute it. ESR1 against itself is left out. A gene with fewer than 10 patients, or no spread, is not tested.",
+            "params": [
+              [
+                "tested",
+                "20,057"
+              ],
+              [
+                "not tested",
+                "280"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg over ρ",
+            "detail": "q-values over every gene tested, from ρ's p."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "pearsonFromMoments",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Pearson's r from sums matches scipy 1.18.1 pearsonr (test/moments-golden.test.ts); ρ is the same formula on average ranks, which is how scipy's spearmanr defines it — checked against spearmanr on raw values for TCGA breast cancer ESR1 (FOXA1 ρ 0.7478, PSAT1 −0.6732). Benjamini–Hochberg matches statsmodels multipletests (test/tables-golden.test.ts).",
+        "check": {
+          "correlates:tested": {
+            "v": 20057
+          },
+          "correlates:CCDC170:rho": {
+            "v": 0.7917,
+            "abs": 0.000050000001000000005
+          },
+          "correlates:CCDC170:rho_p": {
+            "v": 2.2249983400385912e-233,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:CCDC170:q": {
+            "v": 4.4626791706154024e-229,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:CCDC170:r": {
+            "v": 0.6675,
+            "abs": 0.000050000001000000005
+          },
+          "correlates:CCDC170:p": {
+            "v": 1.86618036574955e-140,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:DCPH1:rho": {
+            "v": 0.7841,
+            "abs": 0.000050000001000000005
+          },
+          "correlates:DCPH1:rho_p": {
+            "v": 5.089149809464745e-226,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:DCPH1:q": {
+            "v": 5.103653886421719e-222,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:DCPH1:r": {
+            "v": 0.5652,
+            "abs": 0.000050000001000000005
+          },
+          "correlates:DCPH1:p": {
+            "v": 2.3489389556771624e-92,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:THSD4:rho": {
+            "v": 0.7776,
+            "abs": 0.000050000001000000005
+          },
+          "correlates:THSD4:rho_p": {
+            "v": 6.808597091162043e-220,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:THSD4:q": {
+            "v": 4.55200106191457e-216,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:THSD4:r": {
+            "v": 0.6439,
+            "abs": 0.000050000001000000005
+          },
+          "correlates:THSD4:p": {
+            "v": 1.0524911370004154e-127,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:FOXA1:rho": {
+            "v": 0.7478,
+            "abs": 0.000050000001000000005
+          },
+          "correlates:FOXA1:rho_p": {
+            "v": 2.5074740593460398e-194,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:FOXA1:q": {
+            "v": 1.257310180207588e-190,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "correlates:FOXA1:r": {
+            "v": 0.686,
+            "abs": 0.000050000001000000005
+          },
+          "…": {
+            "more": 381,
+            "sha256": "af5ded2c86034ab3"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/correlates.sql b/test/snapshots/correlates.sql
new file mode 100644
index 00000000..be011cc6
--- /dev/null
+++ b/test/snapshots/correlates.sql
@@ -0,0 +1,43 @@
+-- cohort all patients
+-- brca_tcga_pan_can_atlas_2018
+SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
+
+-- view 0 correlates: correlates:moments · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+q AS (
+  SELECT replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid, ga.hugo_gene_symbol AS gene, avg(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL)) AS v
+  FROM snapshot_db.genetic_alteration_derived ga
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+  WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna' AND ga.hugo_gene_symbol = 'ESR1'
+    AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND isFinite(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))
+    AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid, gene
+),
+o AS (
+  SELECT replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid, ga.hugo_gene_symbol AS gene, avg(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL)) AS v
+  FROM snapshot_db.genetic_alteration_derived ga
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+  WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna'
+    AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND isFinite(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))
+    AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid, gene
+),
+paired AS (SELECT o.gene AS gene, q.v AS x, o.v AS y FROM o INNER JOIN q ON q.pid = o.pid),
+ranked AS (
+  SELECT gene, x, y,
+         rank() OVER (PARTITION BY gene ORDER BY x) + (count() OVER (PARTITION BY gene, x) - 1) / 2 AS rx,
+         rank() OVER (PARTITION BY gene ORDER BY y) + (count() OVER (PARTITION BY gene, y) - 1) / 2 AS ry
+  FROM paired
+)
+SELECT gene, count() AS n, sum(x) AS sx, sum(y) AS sy, sum(x * y) AS sxy, sum(x * x) AS sxx, sum(y * y) AS syy,
+       sum(rx) AS rsx, sum(ry) AS rsy, sum(rx * ry) AS rsxy, sum(rx * rx) AS rsxx, sum(ry * ry) AS rsyy
+FROM ranked GROUP BY gene
+
+-- view 0 correlates: cytobands · brca_tcga_pan_can_atlas_2018
+SELECT g.hugo_gene_symbol AS gene, any(rgg.cytoband) AS cytoband FROM snapshot_db.gene g
+JOIN snapshot_db.reference_genome_gene rgg ON rgg.entrez_gene_id = g.entrez_gene_id
+WHERE rgg.reference_genome_id = (SELECT reference_genome_id FROM snapshot_db.cancer_study WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018')
+  AND g.hugo_gene_symbol IN ('CCDC170','DCPH1','THSD4','FOXA1','TBC1D9','RABEP1','SLC39A6','GATA3','CA12','CT62','MYB','PARD6B','AGR3','RERG','RUNDC1','ARSG','XBP1','C5AR2','ACADSB','AFF3','NUDT12','CACNA2D2','ANKRA2','WWP1','RETREG1','ZNF552','TLE3','DNAL1','SLC22A5','GFRA1','DCTN4','GATA3-AS1','FAM234B','ERBB4','SLC16A6','APH1B','MINDY1','ZNF396','DACH1','RALGPS2','APBB2','CYP2B7P','IGF1R','SPEF2','MRTFB','TCEAL1','TSPAN13','BCL2','GIN1','CLSTN2','SCUBE2','BTRC','CELSR1','RGS22','TMEM144','LONRF2','MAPT','ANXA9','TMBIM6','CCDC125','CST9','FNIP1','KDM4B','TMPRSS6','TAPT1','SERPINA11','FRS2','ELP2','ABAT','TTC8','PCBD2','CMBL','NAT1','WDR19','SLC46A1','FSIP1','ZNF587','QNG1','PLA2G12A','SKP1','MLPH','DNAJC12','KCTD6','ETFBKMT','LRBA','REPS2','TPSG1','TMC4','NDFIP1','RMND1','SYTL4','MACIR','SAMD15','BBS1','SEPSECS','TMEM161B','EXOC6','FBXL5','PREX1','RAB30','HAPLN3','YBX1','SMCO4','PSAT1','ACOT9','PPP1R14C','PADI2','NCK2','UBE2E3','BCL11A','CCDC71L','MSANTD3','NXN','GLIPR2','MTHFD1L','USB1','MSL3','VGLL1','CSRP2','KCNN4','RARRES1','TTC7A','B3GNT5','FSCN1','CCDC82','NT5DC2','ENO1','CPNE2','MID1','YBX3','ITM2C','PRKX','CDC42EP1','ARHGEF4','ARL4C','PALS2','FZD9','SRD5A1','MRAS','FOXC1','EPHB6','STAC','KRT16','RGMA','FAM171A1','SLC43A3','RASAL1','RBM38','CLCN4','TUBB6','NCS1','A2ML1','RSU1','PLOD1','SLC15A1','RAP2B','MFGE8','MCF2L2','UPP1','CIB2','ST8SIA1','CXCL1','ACE2','WNT6','RNF145','IFRD1','PFKP','ART3','PTK7','EN1','CHST2','UNC13D','DSC2','KCNG1','CDCA7','ATP6V1C2','NFIL3','CDH3','FMNL2','GSTP1','CHST3','LMO4','ESYT3','INTS6L','JOSD1','INAVA','MICALL1','TTLL4','APBA2','TTYH3','BTG3','ANKS6','IL12RB2','RIN3','SCPEP1','SH2D2A','SRSF12','PDSS1','FABP7','GAL')
+GROUP BY gene
diff --git a/test/snapshots/correlates.svg b/test/snapshots/correlates.svg
new file mode 100644
index 00000000..8b72e95b
--- /dev/null
+++ b/test/snapshots/correlates.svg
@@ -0,0 +1,2 @@
+<!-- view 0 correlates -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 550" width="100%" role="img" aria-label="Co-expression — ESR1 against every gene — all patients" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Co-expression — ESR1 against every gene — all patients</title><rect width="960" height="550" fill="#ffffff"/><text x="8" y="14" font-size="11" fill="#5b6b76">20,057 genes against ESR1 · 15,572 at q &lt; 0.05 · Spearman ρ</text><line x1="62" y1="212" x2="299" y2="212" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="215.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="62" y1="169.8" x2="299" y2="169.8" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="173.3" font-size="10" fill="#5b6b76" text-anchor="end">500</text><line x1="62" y1="127.5" x2="299" y2="127.5" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="131" font-size="10" fill="#5b6b76" text-anchor="end">1,000</text><line x1="62" y1="85.3" x2="299" y2="85.3" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="88.8" font-size="10" fill="#5b6b76" text-anchor="end">1,500</text><line x1="62" y1="43" x2="299" y2="43" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="46.5" font-size="10" fill="#5b6b76" text-anchor="end">2,000</text><text x="16.8" y="127.5" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 16.8 127.5)">genes</text><rect x="97.9" y="211.6" width="5.3" height="0.4" fill="#E69F00" opacity="0.8"><title>ρ −0.70 to −0.65: 5 genes</title></rect><rect x="103.8" y="210.1" width="5.3" height="1.9" fill="#E69F00" opacity="0.8"><title>ρ −0.65 to −0.60: 23 genes</title></rect><rect x="109.7" y="206.4" width="5.3" height="5.6" fill="#E69F00" opacity="0.8"><title>ρ −0.60 to −0.55: 66 genes</title></rect><rect x="115.6" y="199.6" width="5.3" height="12.4" fill="#E69F00" opacity="0.8"><title>ρ −0.55 to −0.50: 147 genes</title></rect><rect x="121.6" y="188.3" width="5.3" height="23.7" fill="#E69F00" opacity="0.8"><title>ρ −0.50 to −0.45: 281 genes</title></rect><rect x="127.5" y="175.9" width="5.3" height="36.1" fill="#E69F00" opacity="0.8"><title>ρ −0.45 to −0.40: 427 genes</title></rect><rect x="133.4" y="158.1" width="5.3" height="53.9" fill="#E69F00" opacity="0.8"><title>ρ −0.40 to −0.35: 638 genes</title></rect><rect x="139.3" y="145.6" width="5.3" height="66.4" fill="#E69F00" opacity="0.8"><title>ρ −0.35 to −0.30: 786 genes</title></rect><rect x="145.3" y="119.6" width="5.3" height="92.4" fill="#E69F00" opacity="0.8"><title>ρ −0.30 to −0.25: 1,094 genes</title></rect><rect x="151.2" y="106.1" width="5.3" height="105.9" fill="#E69F00" opacity="0.8"><title>ρ −0.25 to −0.20: 1,253 genes</title></rect><rect x="157.1" y="93.6" width="5.3" height="118.4" fill="#E69F00" opacity="0.8"><title>ρ −0.20 to −0.15: 1,401 genes</title></rect><rect x="163" y="89.6" width="5.3" height="122.4" fill="#E69F00" opacity="0.8"><title>ρ −0.15 to −0.10: 1,448 genes</title></rect><rect x="169" y="63.1" width="5.3" height="148.9" fill="#E69F00" opacity="0.8"><title>ρ −0.10 to −0.05: 1,762 genes</title></rect><rect x="174.9" y="47.8" width="5.3" height="164.2" fill="#E69F00" opacity="0.8"><title>ρ −0.05 to 0.00: 1,943 genes</title></rect><rect x="180.8" y="64.8" width="5.3" height="147.2" fill="#0072B2" opacity="0.8"><title>ρ 0.00 to 0.05: 1,742 genes</title></rect><rect x="186.7" y="102.7" width="5.3" height="109.3" fill="#0072B2" opacity="0.8"><title>ρ 0.05 to 0.10: 1,293 genes</title></rect><rect x="192.7" y="114.4" width="5.3" height="97.6" fill="#0072B2" opacity="0.8"><title>ρ 0.10 to 0.15: 1,155 genes</title></rect><rect x="198.6" y="132.2" width="5.3" height="79.8" fill="#0072B2" opacity="0.8"><title>ρ 0.15 to 0.20: 944 genes</title></rect><rect x="204.5" y="142" width="5.3" height="70" fill="#0072B2" opacity="0.8"><title>ρ 0.20 to 0.25: 828 genes</title></rect><rect x="210.4" y="153.9" width="5.3" height="58.1" fill="#0072B2" opacity="0.8"><title>ρ 0.25 to 0.30: 687 genes</title></rect><rect x="216.4" y="157.3" width="5.3" height="54.7" fill="#0072B2" opacity="0.8"><title>ρ 0.30 to 0.35: 647 genes</title></rect><rect x="222.3" y="171.3" width="5.3" height="40.7" fill="#0072B2" opacity="0.8"><title>ρ 0.35 to 0.40: 482 genes</title></rect><rect x="228.2" y="180.1" width="5.3" height="31.9" fill="#0072B2" opacity="0.8"><title>ρ 0.40 to 0.45: 378 genes</title></rect><rect x="234.1" y="188.8" width="5.3" height="23.2" fill="#0072B2" opacity="0.8"><title>ρ 0.45 to 0.50: 275 genes</title></rect><rect x="240.1" y="196.5" width="5.3" height="15.5" fill="#0072B2" opacity="0.8"><title>ρ 0.50 to 0.55: 183 genes</title></rect><rect x="246" y="203.4" width="5.3" height="8.6" fill="#0072B2" opacity="0.8"><title>ρ 0.55 to 0.60: 102 genes</title></rect><rect x="251.9" y="208.5" width="5.3" height="3.5" fill="#0072B2" opacity="0.8"><title>ρ 0.60 to 0.65: 41 genes</title></rect><rect x="257.8" y="210.5" width="5.3" height="1.5" fill="#0072B2" opacity="0.8"><title>ρ 0.65 to 0.70: 18 genes</title></rect><rect x="263.8" y="211.6" width="5.3" height="0.4" fill="#0072B2" opacity="0.8"><title>ρ 0.70 to 0.75: 5 genes</title></rect><rect x="269.7" y="211.7" width="5.3" height="0.3" fill="#0072B2" opacity="0.8"><title>ρ 0.75 to 0.80: 3 genes</title></rect><line x1="180.5" y1="43" x2="180.5" y2="212" stroke="#5b6b76" stroke-width="0.75"/><line x1="62" y1="212" x2="62" y2="215" stroke="#5b6b76" stroke-width="1"/><text x="51.6" y="228" font-size="10" fill="#5b6b76">−1.0</text><line x1="121.3" y1="212" x2="121.3" y2="215" stroke="#5b6b76" stroke-width="1"/><text x="110.9" y="228" font-size="10" fill="#5b6b76">−0.5</text><line x1="180.5" y1="212" x2="180.5" y2="215" stroke="#5b6b76" stroke-width="1"/><text x="173.1" y="228" font-size="10" fill="#5b6b76">0.0</text><line x1="239.8" y1="212" x2="239.8" y2="215" stroke="#5b6b76" stroke-width="1"/><text x="232.4" y="228" font-size="10" fill="#5b6b76">0.5</text><line x1="299" y1="212" x2="299" y2="215" stroke="#5b6b76" stroke-width="1"/><text x="291.6" y="228" font-size="10" fill="#5b6b76">1.0</text><line x1="62" y1="212" x2="299" y2="212" stroke="#5b6b76" stroke-width="1"/><text x="180.5" y="247" font-size="11" fill="#16232b" text-anchor="middle">Spearman ρ with ESR1</text><rect x="335" y="37" width="10" height="10" rx="2" fill="#0072B2"/><text x="350" y="46" font-size="11" fill="#16232b" font-weight="600">rise with ESR1</text><text x="546.6" y="46" font-size="10" fill="#5b6b76" text-anchor="end">ρ</text><text x="577.3" y="46" font-size="10" fill="#5b6b76" text-anchor="end">r</text><text x="629.5" y="46" font-size="10" fill="#5b6b76" text-anchor="end">q</text><line x1="335" y1="50" x2="629.5" y2="50" stroke="#dde4e8" stroke-width="1"/><text x="335" y="63.9" font-size="11" fill="#16232b" font-weight="600">CCDC170</text><rect x="401.2" y="55.5" width="84.4" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="63.5" font-size="10" fill="#5b6b76" text-anchor="end">0.79</text><text x="577.3" y="63.5" font-size="10" fill="#5b6b76" text-anchor="end">0.67</text><text x="629.5" y="63.5" font-size="10" fill="#5b6b76" text-anchor="end">4.5e-229</text><text x="335" y="79.9" font-size="11" fill="#16232b" font-weight="600">DCPH1</text><rect x="401.2" y="71.5" width="83.6" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="79.5" font-size="10" fill="#5b6b76" text-anchor="end">0.78</text><text x="577.3" y="79.5" font-size="10" fill="#5b6b76" text-anchor="end">0.57</text><text x="629.5" y="79.5" font-size="10" fill="#5b6b76" text-anchor="end">5.1e-222</text><text x="335" y="95.9" font-size="11" fill="#16232b" font-weight="600">THSD4</text><rect x="401.2" y="87.5" width="82.9" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="95.5" font-size="10" fill="#5b6b76" text-anchor="end">0.78</text><text x="577.3" y="95.5" font-size="10" fill="#5b6b76" text-anchor="end">0.64</text><text x="629.5" y="95.5" font-size="10" fill="#5b6b76" text-anchor="end">4.6e-216</text><text x="335" y="111.9" font-size="11" fill="#16232b" font-weight="600">FOXA1</text><rect x="401.2" y="103.5" width="79.7" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="111.5" font-size="10" fill="#5b6b76" text-anchor="end">0.75</text><text x="577.3" y="111.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="629.5" y="111.5" font-size="10" fill="#5b6b76" text-anchor="end">1.3e-190</text><text x="335" y="127.9" font-size="11" fill="#16232b" font-weight="600">TBC1D9</text><rect x="401.2" y="119.5" width="79.6" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="127.5" font-size="10" fill="#5b6b76" text-anchor="end">0.75</text><text x="577.3" y="127.5" font-size="10" fill="#5b6b76" text-anchor="end">0.55</text><text x="629.5" y="127.5" font-size="10" fill="#5b6b76" text-anchor="end">2.5e-189</text><text x="335" y="143.9" font-size="11" fill="#16232b" font-weight="600">RABEP1</text><rect x="401.2" y="135.5" width="77.7" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="143.5" font-size="10" fill="#5b6b76" text-anchor="end">0.73</text><text x="577.3" y="143.5" font-size="10" fill="#5b6b76" text-anchor="end">0.62</text><text x="629.5" y="143.5" font-size="10" fill="#5b6b76" text-anchor="end">5.9e-176</text><text x="335" y="159.9" font-size="11" fill="#16232b" font-weight="600">SLC39A6</text><rect x="401.2" y="151.5" width="77" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="159.5" font-size="10" fill="#5b6b76" text-anchor="end">0.72</text><text x="577.3" y="159.5" font-size="10" fill="#5b6b76" text-anchor="end">0.45</text><text x="629.5" y="159.5" font-size="10" fill="#5b6b76" text-anchor="end">2.7e-171</text><text x="335" y="175.9" font-size="11" fill="#16232b" font-weight="600">GATA3</text><rect x="401.2" y="167.5" width="76.8" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="175.5" font-size="10" fill="#5b6b76" text-anchor="end">0.72</text><text x="577.3" y="175.5" font-size="10" fill="#5b6b76" text-anchor="end">0.56</text><text x="629.5" y="175.5" font-size="10" fill="#5b6b76" text-anchor="end">3.4e-170</text><text x="335" y="191.9" font-size="11" fill="#16232b" font-weight="600">CA12</text><rect x="401.2" y="183.5" width="74" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="191.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="577.3" y="191.5" font-size="10" fill="#5b6b76" text-anchor="end">0.52</text><text x="629.5" y="191.5" font-size="10" fill="#5b6b76" text-anchor="end">5e-153</text><text x="335" y="207.9" font-size="11" fill="#16232b" font-weight="600">CT62</text><rect x="401.2" y="199.5" width="74" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="207.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="577.3" y="207.5" font-size="10" fill="#5b6b76" text-anchor="end">0.43</text><text x="629.5" y="207.5" font-size="10" fill="#5b6b76" text-anchor="end">7.3e-153</text><text x="335" y="223.9" font-size="11" fill="#16232b" font-weight="600">MYB</text><rect x="401.2" y="215.5" width="74" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="223.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="577.3" y="223.5" font-size="10" fill="#5b6b76" text-anchor="end">0.57</text><text x="629.5" y="223.5" font-size="10" fill="#5b6b76" text-anchor="end">1.1e-152</text><text x="335" y="239.9" font-size="11" fill="#16232b" font-weight="600">PARD6B</text><rect x="401.2" y="231.5" width="73.8" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="239.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="577.3" y="239.5" font-size="10" fill="#5b6b76" text-anchor="end">0.42</text><text x="629.5" y="239.5" font-size="10" fill="#5b6b76" text-anchor="end">7e-152</text><text x="335" y="255.9" font-size="11" fill="#16232b" font-weight="600">AGR3</text><rect x="401.2" y="247.5" width="73.8" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="255.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="577.3" y="255.5" font-size="10" fill="#5b6b76" text-anchor="end">0.45</text><text x="629.5" y="255.5" font-size="10" fill="#5b6b76" text-anchor="end">1.1e-151</text><text x="335" y="271.9" font-size="11" fill="#16232b" font-weight="600">RERG</text><rect x="401.2" y="263.5" width="73.2" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="271.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="577.3" y="271.5" font-size="10" fill="#5b6b76" text-anchor="end">0.45</text><text x="629.5" y="271.5" font-size="10" fill="#5b6b76" text-anchor="end">1.7e-148</text><text x="335" y="287.9" font-size="11" fill="#16232b" font-weight="600">RUNDC1</text><rect x="401.2" y="279.5" width="73.2" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="287.5" font-size="10" fill="#5b6b76" text-anchor="end">0.69</text><text x="577.3" y="287.5" font-size="10" fill="#5b6b76" text-anchor="end">0.54</text><text x="629.5" y="287.5" font-size="10" fill="#5b6b76" text-anchor="end">2.6e-148</text><text x="335" y="303.9" font-size="11" fill="#16232b" font-weight="600">ARSG</text><rect x="401.2" y="295.5" width="72.9" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="303.5" font-size="10" fill="#5b6b76" text-anchor="end">0.68</text><text x="577.3" y="303.5" font-size="10" fill="#5b6b76" text-anchor="end">0.49</text><text x="629.5" y="303.5" font-size="10" fill="#5b6b76" text-anchor="end">1.5e-146</text><text x="335" y="319.9" font-size="11" fill="#16232b" font-weight="600">XBP1</text><rect x="401.2" y="311.5" width="72.3" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="319.5" font-size="10" fill="#5b6b76" text-anchor="end">0.68</text><text x="577.3" y="319.5" font-size="10" fill="#5b6b76" text-anchor="end">0.57</text><text x="629.5" y="319.5" font-size="10" fill="#5b6b76" text-anchor="end">2.3e-143</text><text x="335" y="335.9" font-size="11" fill="#16232b" font-weight="600">C5AR2</text><rect x="401.2" y="327.5" width="71.9" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="335.5" font-size="10" fill="#5b6b76" text-anchor="end">0.67</text><text x="577.3" y="335.5" font-size="10" fill="#5b6b76" text-anchor="end">0.45</text><text x="629.5" y="335.5" font-size="10" fill="#5b6b76" text-anchor="end">1.4e-141</text><text x="335" y="351.9" font-size="11" fill="#16232b" font-weight="600">ACADSB</text><rect x="401.2" y="343.5" width="71.4" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="351.5" font-size="10" fill="#5b6b76" text-anchor="end">0.67</text><text x="577.3" y="351.5" font-size="10" fill="#5b6b76" text-anchor="end">0.46</text><text x="629.5" y="351.5" font-size="10" fill="#5b6b76" text-anchor="end">1.6e-138</text><text x="335" y="367.9" font-size="11" fill="#16232b" font-weight="600">AFF3</text><rect x="401.2" y="359.5" width="70.7" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="367.5" font-size="10" fill="#5b6b76" text-anchor="end">0.66</text><text x="577.3" y="367.5" font-size="10" fill="#5b6b76" text-anchor="end">0.51</text><text x="629.5" y="367.5" font-size="10" fill="#5b6b76" text-anchor="end">3.7e-135</text><text x="335" y="383.9" font-size="11" fill="#16232b" font-weight="600">NUDT12</text><rect x="401.2" y="375.5" width="70.5" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="383.5" font-size="10" fill="#5b6b76" text-anchor="end">0.66</text><text x="577.3" y="383.5" font-size="10" fill="#5b6b76" text-anchor="end">0.55</text><text x="629.5" y="383.5" font-size="10" fill="#5b6b76" text-anchor="end">5e-134</text><text x="335" y="399.9" font-size="11" fill="#16232b" font-weight="600">CACNA2D2</text><rect x="401.2" y="391.5" width="70.2" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="399.5" font-size="10" fill="#5b6b76" text-anchor="end">0.66</text><text x="577.3" y="399.5" font-size="10" fill="#5b6b76" text-anchor="end">0.41</text><text x="629.5" y="399.5" font-size="10" fill="#5b6b76" text-anchor="end">1.5e-132</text><text x="335" y="415.9" font-size="11" fill="#16232b" font-weight="600">ANKRA2</text><rect x="401.2" y="407.5" width="69.9" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="415.5" font-size="10" fill="#5b6b76" text-anchor="end">0.66</text><text x="577.3" y="415.5" font-size="10" fill="#5b6b76" text-anchor="end">0.54</text><text x="629.5" y="415.5" font-size="10" fill="#5b6b76" text-anchor="end">4.5e-131</text><text x="335" y="431.9" font-size="11" fill="#16232b" font-weight="600">WWP1</text><rect x="401.2" y="423.5" width="69.9" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="431.5" font-size="10" fill="#5b6b76" text-anchor="end">0.66</text><text x="577.3" y="431.5" font-size="10" fill="#5b6b76" text-anchor="end">0.41</text><text x="629.5" y="431.5" font-size="10" fill="#5b6b76" text-anchor="end">8.5e-131</text><text x="335" y="447.9" font-size="11" fill="#16232b" font-weight="600">RETREG1</text><rect x="401.2" y="439.5" width="69.7" height="9" fill="#0072B2" opacity="0.85"/><text x="546.6" y="447.5" font-size="10" fill="#5b6b76" text-anchor="end">0.65</text><text x="577.3" y="447.5" font-size="10" fill="#5b6b76" text-anchor="end">0.46</text><text x="629.5" y="447.5" font-size="10" fill="#5b6b76" text-anchor="end">6.3e-130</text><rect x="657.5" y="37" width="10" height="10" rx="2" fill="#E69F00"/><text x="672.5" y="46" font-size="11" fill="#16232b" font-weight="600">fall as ESR1 rises</text><text x="863.1" y="46" font-size="10" fill="#5b6b76" text-anchor="end">ρ</text><text x="899.8" y="46" font-size="10" fill="#5b6b76" text-anchor="end">r</text><text x="952" y="46" font-size="10" fill="#5b6b76" text-anchor="end">q</text><line x1="657.5" y1="50" x2="952" y2="50" stroke="#dde4e8" stroke-width="1"/><text x="657.5" y="63.9" font-size="11" fill="#16232b" font-weight="600">HAPLN3</text><rect x="721.5" y="55.5" width="65.8" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="63.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.68</text><text x="899.8" y="63.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.37</text><text x="952" y="63.5" font-size="10" fill="#5b6b76" text-anchor="end">2.3e-144</text><text x="657.5" y="79.9" font-size="11" fill="#16232b" font-weight="600">YBX1</text><rect x="721.5" y="71.5" width="65.7" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="79.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.68</text><text x="899.8" y="79.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.42</text><text x="952" y="79.5" font-size="10" fill="#5b6b76" text-anchor="end">8.1e-144</text><text x="657.5" y="95.9" font-size="11" fill="#16232b" font-weight="600">SMCO4</text><rect x="721.5" y="87.5" width="65.4" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="95.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.68</text><text x="899.8" y="95.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.46</text><text x="952" y="95.5" font-size="10" fill="#5b6b76" text-anchor="end">4.7e-142</text><text x="657.5" y="111.9" font-size="11" fill="#16232b" font-weight="600">PSAT1</text><rect x="721.5" y="103.5" width="65.2" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="111.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.67</text><text x="899.8" y="111.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.36</text><text x="952" y="111.5" font-size="10" fill="#5b6b76" text-anchor="end">9e-141</text><text x="657.5" y="127.9" font-size="11" fill="#16232b" font-weight="600">ACOT9</text><rect x="721.5" y="119.5" width="63.1" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="127.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.65</text><text x="899.8" y="127.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.47</text><text x="952" y="127.5" font-size="10" fill="#5b6b76" text-anchor="end">9e-129</text><text x="657.5" y="143.9" font-size="11" fill="#16232b" font-weight="600">PPP1R14C</text><rect x="721.5" y="135.5" width="62" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="143.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.64</text><text x="899.8" y="143.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.30</text><text x="952" y="143.5" font-size="10" fill="#5b6b76" text-anchor="end">5.9e-123</text><text x="657.5" y="159.9" font-size="11" fill="#16232b" font-weight="600">PADI2</text><rect x="721.5" y="151.5" width="61.8" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="159.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.64</text><text x="899.8" y="159.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.39</text><text x="952" y="159.5" font-size="10" fill="#5b6b76" text-anchor="end">6.6e-122</text><text x="657.5" y="175.9" font-size="11" fill="#16232b" font-weight="600">NCK2</text><rect x="721.5" y="167.5" width="61.5" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="175.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.63</text><text x="899.8" y="175.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.45</text><text x="952" y="175.5" font-size="10" fill="#5b6b76" text-anchor="end">1.8e-120</text><text x="657.5" y="191.9" font-size="11" fill="#16232b" font-weight="600">UBE2E3</text><rect x="721.5" y="183.5" width="61" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="191.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.63</text><text x="899.8" y="191.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.50</text><text x="952" y="191.5" font-size="10" fill="#5b6b76" text-anchor="end">3.6e-118</text><text x="657.5" y="207.9" font-size="11" fill="#16232b" font-weight="600">BCL11A</text><rect x="721.5" y="199.5" width="61" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="207.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.63</text><text x="899.8" y="207.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.36</text><text x="952" y="207.5" font-size="10" fill="#5b6b76" text-anchor="end">6e-118</text><text x="657.5" y="223.9" font-size="11" fill="#16232b" font-weight="600">CCDC71L</text><rect x="721.5" y="215.5" width="61" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="223.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.63</text><text x="899.8" y="223.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.42</text><text x="952" y="223.5" font-size="10" fill="#5b6b76" text-anchor="end">6e-118</text><text x="657.5" y="239.9" font-size="11" fill="#16232b" font-weight="600">MSANTD3</text><rect x="721.5" y="231.5" width="60.6" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="239.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.63</text><text x="899.8" y="239.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.47</text><text x="952" y="239.5" font-size="10" fill="#5b6b76" text-anchor="end">3.2e-116</text><text x="657.5" y="255.9" font-size="11" fill="#16232b" font-weight="600">NXN</text><rect x="721.5" y="247.5" width="60.4" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="255.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.62</text><text x="899.8" y="255.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.44</text><text x="952" y="255.5" font-size="10" fill="#5b6b76" text-anchor="end">3.4e-115</text><text x="657.5" y="271.9" font-size="11" fill="#16232b" font-weight="600">GLIPR2</text><rect x="721.5" y="263.5" width="60.3" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="271.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.62</text><text x="899.8" y="271.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.39</text><text x="952" y="271.5" font-size="10" fill="#5b6b76" text-anchor="end">7.6e-115</text><text x="657.5" y="287.9" font-size="11" fill="#16232b" font-weight="600">MTHFD1L</text><rect x="721.5" y="279.5" width="60" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="287.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.62</text><text x="899.8" y="287.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.40</text><text x="952" y="287.5" font-size="10" fill="#5b6b76" text-anchor="end">4.3e-113</text><text x="657.5" y="303.9" font-size="11" fill="#16232b" font-weight="600">USB1</text><rect x="721.5" y="295.5" width="59.9" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="303.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.62</text><text x="899.8" y="303.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.45</text><text x="952" y="303.5" font-size="10" fill="#5b6b76" text-anchor="end">6.7e-113</text><text x="657.5" y="319.9" font-size="11" fill="#16232b" font-weight="600">MSL3</text><rect x="721.5" y="311.5" width="59.8" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="319.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.62</text><text x="899.8" y="319.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.47</text><text x="952" y="319.5" font-size="10" fill="#5b6b76" text-anchor="end">2.6e-112</text><text x="657.5" y="335.9" font-size="11" fill="#16232b" font-weight="600">VGLL1</text><rect x="721.5" y="327.5" width="59.4" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="335.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.61</text><text x="899.8" y="335.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.21</text><text x="952" y="335.5" font-size="10" fill="#5b6b76" text-anchor="end">1.7e-110</text><text x="657.5" y="351.9" font-size="11" fill="#16232b" font-weight="600">CSRP2</text><rect x="721.5" y="343.5" width="59" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="351.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.61</text><text x="899.8" y="351.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.34</text><text x="952" y="351.5" font-size="10" fill="#5b6b76" text-anchor="end">2.2e-108</text><text x="657.5" y="367.9" font-size="11" fill="#16232b" font-weight="600">KCNN4</text><rect x="721.5" y="359.5" width="59" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="367.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.61</text><text x="899.8" y="367.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.33</text><text x="952" y="367.5" font-size="10" fill="#5b6b76" text-anchor="end">2.7e-108</text><text x="657.5" y="383.9" font-size="11" fill="#16232b" font-weight="600">RARRES1</text><rect x="721.5" y="375.5" width="58.9" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="383.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.61</text><text x="899.8" y="383.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.20</text><text x="952" y="383.5" font-size="10" fill="#5b6b76" text-anchor="end">7.4e-108</text><text x="657.5" y="399.9" font-size="11" fill="#16232b" font-weight="600">TTC7A</text><rect x="721.5" y="391.5" width="58.8" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="399.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.61</text><text x="899.8" y="399.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.43</text><text x="952" y="399.5" font-size="10" fill="#5b6b76" text-anchor="end">2.1e-107</text><text x="657.5" y="415.9" font-size="11" fill="#16232b" font-weight="600">B3GNT5</text><rect x="721.5" y="407.5" width="58.7" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="415.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.61</text><text x="899.8" y="415.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.40</text><text x="952" y="415.5" font-size="10" fill="#5b6b76" text-anchor="end">4.1e-107</text><text x="657.5" y="431.9" font-size="11" fill="#16232b" font-weight="600">FSCN1</text><rect x="721.5" y="423.5" width="58.5" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="431.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.60</text><text x="899.8" y="431.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.32</text><text x="952" y="431.5" font-size="10" fill="#5b6b76" text-anchor="end">1.9e-106</text><text x="657.5" y="447.9" font-size="11" fill="#16232b" font-weight="600">CCDC82</text><rect x="721.5" y="439.5" width="58.4" height="9" fill="#E69F00" opacity="0.85"/><text x="863.1" y="447.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.60</text><text x="899.8" y="447.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.45</text><text x="952" y="447.5" font-size="10" fill="#5b6b76" text-anchor="end">6.6e-106</text><text x="335" y="470" font-size="10" fill="#5b6b76">+150 more listed in the result and the MCP text; every one of the 20,057 genes is in the histogram</text><text x="8" y="490" font-size="10" fill="#5b6b76">ESR1 on “rna_seq_v2_mrna” against every gene on “rna_seq_v2_mrna”, over the patients in “all patients” with both values (a patient's samples averaged, as the</text><text x="8" y="504" font-size="10" fill="#5b6b76">heatmap takes them). Ranked by Spearman's ρ, as the portal ranks, with Pearson's r beside it; q is Benjamini–Hochberg over ρ's p across all 20,057 genes tested. A</text><text x="8" y="518" font-size="10" fill="#5b6b76">correlation is not a regulatory link: genes that rise together across a cohort often both track a third thing — a subtype, the tumour's purity, proliferation. 280 genes were</text><text x="8" y="532" font-size="10" fill="#5b6b76">not tested: fewer than 10 patients with both values, or no spread.</text></svg>
diff --git a/test/snapshots/correlates.txt b/test/snapshots/correlates.txt
new file mode 100644
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--- /dev/null
+++ b/test/snapshots/correlates.txt
@@ -0,0 +1,216 @@
+Analysis over brca_tcga_pan_can_atlas_2018 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- all patients: 1,084 patients — all patients in the study
+
+Genes co-expressed with ESR1 (rna_seq_v2_mrna) in all patients; Spearman ρ, BH q over 20,057 genes; 15,572 at q<0.05:
+ most positively correlated:
+  CCDC170: ρ 0.7917 (q=4.5e-229), r 0.6675, n=1082
+  DCPH1: ρ 0.7841 (q=5.1e-222), r 0.5652, n=1082
+  THSD4: ρ 0.7776 (q=4.6e-216), r 0.6439, n=1082
+  FOXA1: ρ 0.7478 (q=1.3e-190), r 0.686, n=1082
+  TBC1D9: ρ 0.746 (q=2.5e-189), r 0.5514, n=1082
+  RABEP1: ρ 0.7283 (q=5.9e-176), r 0.6183, n=1082
+  SLC39A6: ρ 0.7217 (q=2.7e-171), r 0.45, n=1082
+  GATA3: ρ 0.7201 (q=3.4e-170), r 0.5603, n=1082
+  CA12: ρ 0.6942 (q=5.0e-153), r 0.516, n=1082
+  CT62: ρ 0.6938 (q=7.3e-153), r 0.43, n=1082
+  MYB: ρ 0.6935 (q=1.1e-152), r 0.5738, n=1082
+  PARD6B: ρ 0.6921 (q=7.0e-152), r 0.4221, n=1082
+  AGR3: ρ 0.6917 (q=1.1e-151), r 0.4529, n=1082
+  RERG: ρ 0.6865 (q=1.7e-148), r 0.4489, n=1082
+  RUNDC1: ρ 0.6862 (q=2.6e-148), r 0.5386, n=1082
+  ARSG: ρ 0.6832 (q=1.5e-146), r 0.4884, n=1082
+  XBP1: ρ 0.6777 (q=2.3e-143), r 0.5701, n=1082
+  C5AR2: ρ 0.6746 (q=1.4e-141), r 0.4487, n=1082
+  ACADSB: ρ 0.6692 (q=1.6e-138), r 0.4641, n=1082
+  AFF3: ρ 0.6632 (q=3.7e-135), r 0.508, n=1082
+  NUDT12: ρ 0.6611 (q=5.0e-134), r 0.551, n=1082
+  CACNA2D2: ρ 0.6584 (q=1.5e-132), r 0.4085, n=1082
+  ANKRA2: ρ 0.6557 (q=4.5e-131), r 0.536, n=1082
+  WWP1: ρ 0.6551 (q=8.5e-131), r 0.4141, n=1082
+  RETREG1: ρ 0.6535 (q=6.3e-130), r 0.4608, n=1082
+  ZNF552: ρ 0.6509 (q=1.4e-128), r 0.4146, n=1082
+  TLE3: ρ 0.6496 (q=6.2e-128), r 0.5597, n=1082
+  DNAL1: ρ 0.6476 (q=7.0e-127), r 0.5518, n=1082
+  SLC22A5: ρ 0.6456 (q=7.3e-126), r 0.4794, n=1082
+  GFRA1: ρ 0.6442 (q=4.0e-125), r 0.4822, n=1082
+  DCTN4: ρ 0.6399 (q=5.9e-123), r 0.5774, n=1082
+  GATA3-AS1: ρ 0.6396 (q=8.3e-123), r 0.3017, n=1082
+  FAM234B: ρ 0.6393 (q=1.1e-122), r 0.2777, n=1082
+  ERBB4: ρ 0.6361 (q=4.8e-121), r 0.4517, n=1082
+  SLC16A6: ρ 0.6349 (q=1.8e-120), r 0.2774, n=1082
+  APH1B: ρ 0.634 (q=4.5e-120), r 0.528, n=1082
+  MINDY1: ρ 0.6339 (q=5.4e-120), r 0.473, n=1082
+  ZNF396: ρ 0.6337 (q=6.3e-120), r 0.4807, n=1082
+  DACH1: ρ 0.6314 (q=8.4e-119), r 0.4958, n=1082
+  RALGPS2: ρ 0.6273 (q=7.8e-117), r 0.4589, n=1082
+  APBB2: ρ 0.6252 (q=8.3e-116), r 0.4555, n=1082
+  CYP2B7P: ρ 0.6248 (q=1.3e-115), r 0.3341, n=1082
+  IGF1R: ρ 0.6218 (q=3.3e-114), r 0.1326, n=1082
+  SPEF2: ρ 0.6204 (q=1.5e-113), r 0.4583, n=1082
+  MRTFB: ρ 0.6178 (q=2.4e-112), r 0.4556, n=1082
+  TCEAL1: ρ 0.6171 (q=4.6e-112), r 0.3587, n=1082
+  TSPAN13: ρ 0.6171 (q=4.6e-112), r 0.4134, n=1082
+  BCL2: ρ 0.6165 (q=9.2e-112), r 0.4689, n=1082
+  GIN1: ρ 0.6165 (q=8.8e-112), r 0.5233, n=1082
+  CLSTN2: ρ 0.616 (q=1.5e-111), r 0.3511, n=1082
+  SCUBE2: ρ 0.616 (q=1.5e-111), r 0.3649, n=1082
+  BTRC: ρ 0.6154 (q=2.6e-111), r 0.4736, n=1082
+  CELSR1: ρ 0.6146 (q=6.2e-111), r 0.4595, n=1082
+  RGS22: ρ 0.613 (q=3.4e-110), r 0.3173, n=1082
+  TMEM144: ρ 0.6111 (q=2.5e-109), r 0.5146, n=1082
+  LONRF2: ρ 0.6106 (q=4.3e-109), r 0.37, n=1082
+  MAPT: ρ 0.6106 (q=4.3e-109), r 0.4031, n=1082
+  ANXA9: ρ 0.609 (q=2.3e-108), r 0.3709, n=1082
+  TMBIM6: ρ 0.6082 (q=4.6e-108), r 0.4915, n=1082
+  CCDC125: ρ 0.6068 (q=2.0e-107), r 0.5104, n=1082
+  CST9: ρ 0.6057 (q=6.0e-107), r 0.2638, n=1082
+  FNIP1: ρ 0.6046 (q=1.9e-106), r 0.4927, n=1082
+  KDM4B: ρ 0.604 (q=3.5e-106), r 0.4094, n=1082
+  TMPRSS6: ρ 0.603 (q=9.7e-106), r 0.3263, n=1082
+  TAPT1: ρ 0.6025 (q=1.5e-105), r 0.4741, n=1082
+  SERPINA11: ρ 0.6006 (q=9.9e-105), r 0.3158, n=1082
+  FRS2: ρ 0.6004 (q=1.2e-104), r 0.2337, n=1082
+  ELP2: ρ 0.599 (q=5.2e-104), r 0.4214, n=1082
+  ABAT: ρ 0.5987 (q=6.9e-104), r 0.3787, n=1082
+  TTC8: ρ 0.5981 (q=1.2e-103), r 0.4767, n=1082
+  PCBD2: ρ 0.5972 (q=3.0e-103), r 0.4669, n=1082
+  CMBL: ρ 0.5965 (q=5.9e-103), r 0.4366, n=1082
+  NAT1: ρ 0.5965 (q=5.9e-103), r 0.2747, n=1082
+  WDR19: ρ 0.5958 (q=1.1e-102), r 0.4586, n=1082
+  SLC46A1: ρ 0.595 (q=2.4e-102), r 0.4347, n=1082
+  FSIP1: ρ 0.5938 (q=8.1e-102), r 0.3055, n=1082
+  ZNF587: ρ 0.5934 (q=1.2e-101), r 0.3584, n=1082
+  QNG1: ρ 0.5933 (q=1.3e-101), r 0.4653, n=1082
+  PLA2G12A: ρ 0.5925 (q=2.8e-101), r 0.5422, n=1082
+  SKP1: ρ 0.5923 (q=3.3e-101), r 0.48, n=1082
+  MLPH: ρ 0.5912 (q=1.0e-100), r 0.3597, n=1082
+  DNAJC12: ρ 0.5896 (q=4.8e-100), r 0.312, n=1082
+  KCTD6: ρ 0.5885 (q=1.3e-99), r 0.3478, n=1082
+  ETFBKMT: ρ 0.5878 (q=2.7e-99), r 0.4548, n=1082
+  LRBA: ρ 0.5874 (q=4.0e-99), r 0.5031, n=1082
+  REPS2: ρ 0.5839 (q=1.1e-97), r 0.3902, n=1082
+  TPSG1: ρ 0.5839 (q=1.1e-97), r 0.2078, n=1082
+  TMC4: ρ 0.5836 (q=1.5e-97), r 0.4209, n=1082
+  NDFIP1: ρ 0.5826 (q=3.7e-97), r 0.4907, n=1082
+  RMND1: ρ 0.5819 (q=7.4e-97), r 0.4186, n=1082
+  SYTL4: ρ 0.5818 (q=7.4e-97), r 0.3617, n=1082
+  MACIR: ρ 0.5816 (q=9.5e-97), r 0.3356, n=1082
+  SAMD15: ρ 0.5815 (q=1.0e-96), r 0.3968, n=1082
+  BBS1: ρ 0.5787 (q=1.4e-95), r 0.4129, n=1082
+  SEPSECS: ρ 0.5787 (q=1.4e-95), r 0.467, n=1082
+  TMEM161B: ρ 0.5779 (q=3.0e-95), r 0.4767, n=1082
+  EXOC6: ρ 0.5776 (q=3.7e-95), r 0.4399, n=1082
+  FBXL5: ρ 0.577 (q=6.4e-95), r 0.4875, n=1082
+  PREX1: ρ 0.5769 (q=7.4e-95), r 0.3452, n=1082
+  RAB30: ρ 0.5761 (q=1.5e-94), r 0.4224, n=1082
+ most negatively correlated:
+  HAPLN3: ρ -0.6795 (q=2.3e-144), r -0.3676, n=1082
+  YBX1: ρ -0.6785 (q=8.1e-144), r -0.4242, n=1082
+  SMCO4: ρ -0.6755 (q=4.7e-142), r -0.4643, n=1082
+  PSAT1: ρ -0.6732 (q=9.0e-141), r -0.3561, n=1082
+  ACOT9: ρ -0.6513 (q=9.0e-129), r -0.4689, n=1082
+  PPP1R14C: ρ -0.6399 (q=5.9e-123), r -0.301, n=1082
+  PADI2: ρ -0.6378 (q=6.6e-122), r -0.3904, n=1082
+  NCK2: ρ -0.6349 (q=1.8e-120), r -0.4534, n=1082
+  UBE2E3: ρ -0.6301 (q=3.6e-118), r -0.5035, n=1082
+  BCL11A: ρ -0.6297 (q=6.0e-118), r -0.3608, n=1082
+  CCDC71L: ρ -0.6296 (q=6.0e-118), r -0.4197, n=1082
+  MSANTD3: ρ -0.6261 (q=3.2e-116), r -0.4676, n=1082
+  NXN: ρ -0.6239 (q=3.4e-115), r -0.4446, n=1082
+  GLIPR2: ρ -0.6231 (q=7.6e-115), r -0.387, n=1082
+  MTHFD1L: ρ -0.6194 (q=4.3e-113), r -0.404, n=1082
+  USB1: ρ -0.619 (q=6.7e-113), r -0.4453, n=1082
+  MSL3: ρ -0.6177 (q=2.6e-112), r -0.4725, n=1082
+  VGLL1: ρ -0.6137 (q=1.7e-110), r -0.2137, n=1082
+  CSRP2: ρ -0.609 (q=2.2e-108), r -0.3422, n=1082
+  KCNN4: ρ -0.6088 (q=2.7e-108), r -0.3303, n=1082
+  RARRES1: ρ -0.6078 (q=7.4e-108), r -0.2045, n=1082
+  TTC7A: ρ -0.6068 (q=2.1e-107), r -0.4252, n=1082
+  B3GNT5: ρ -0.6061 (q=4.1e-107), r -0.3966, n=1082
+  FSCN1: ρ -0.6046 (q=1.9e-106), r -0.3211, n=1082
+  CCDC82: ρ -0.6033 (q=6.6e-106), r -0.4511, n=1082
+  NT5DC2: ρ -0.6024 (q=1.7e-105), r -0.388, n=1082
+  ENO1: ρ -0.6024 (q=1.7e-105), r -0.4196, n=1082
+  CPNE2: ρ -0.6017 (q=3.2e-105), r -0.4652, n=1082
+  MID1: ρ -0.5989 (q=5.8e-104), r -0.3967, n=1082
+  YBX3: ρ -0.5986 (q=7.5e-104), r -0.3926, n=1082
+  ITM2C: ρ -0.5974 (q=2.3e-103), r -0.3885, n=1082
+  PRKX: ρ -0.597 (q=3.4e-103), r -0.402, n=1082
+  CDC42EP1: ρ -0.5966 (q=5.2e-103), r -0.4207, n=1082
+  ARHGEF4: ρ -0.5954 (q=1.6e-102), r -0.3243, n=1082
+  ARL4C: ρ -0.5945 (q=3.9e-102), r -0.4274, n=1082
+  PALS2: ρ -0.5944 (q=4.6e-102), r -0.3824, n=1082
+  FZD9: ρ -0.5921 (q=3.9e-101), r -0.2214, n=1082
+  SRD5A1: ρ -0.5902 (q=2.6e-100), r -0.2853, n=1082
+  MRAS: ρ -0.5849 (q=4.3e-98), r -0.3858, n=1082
+  FOXC1: ρ -0.5849 (q=4.2e-98), r -0.3386, n=1082
+  EPHB6: ρ -0.5821 (q=6.1e-97), r -0.2295, n=1082
+  STAC: ρ -0.5814 (q=1.1e-96), r -0.2815, n=1082
+  KRT16: ρ -0.5809 (q=1.8e-96), r -0.1838, n=1082
+  RGMA: ρ -0.5798 (q=4.8e-96), r -0.394, n=1082
+  FAM171A1: ρ -0.5786 (q=1.6e-95), r -0.3924, n=1082
+  SLC43A3: ρ -0.5785 (q=1.7e-95), r -0.1708, n=1082
+  RASAL1: ρ -0.5783 (q=1.9e-95), r -0.3489, n=1082
+  RBM38: ρ -0.5776 (q=3.7e-95), r -0.3661, n=1082
+  CLCN4: ρ -0.5776 (q=3.6e-95), r -0.3326, n=1082
+  TUBB6: ρ -0.5766 (q=9.6e-95), r -0.4434, n=1082
+  NCS1: ρ -0.5757 (q=2.1e-94), r -0.4021, n=1082
+  A2ML1: ρ -0.5742 (q=8.5e-94), r -0.1998, n=1082
+  RSU1: ρ -0.5733 (q=1.9e-93), r -0.4027, n=1082
+  PLOD1: ρ -0.5731 (q=2.1e-93), r -0.3407, n=1082
+  SLC15A1: ρ -0.5723 (q=4.7e-93), r -0.1519, n=1082
+  RAP2B: ρ -0.5721 (q=5.6e-93), r -0.4518, n=1082
+  MFGE8: ρ -0.5697 (q=4.7e-92), r -0.2526, n=1082
+  MCF2L2: ρ -0.5696 (q=5.3e-92), r -0.2589, n=1082
+  UPP1: ρ -0.5694 (q=6.0e-92), r -0.3009, n=1082
+  CIB2: ρ -0.569 (q=8.9e-92), r -0.3322, n=1082
+  ST8SIA1: ρ -0.5688 (q=1.1e-91), r -0.37, n=1082
+  CXCL1: ρ -0.568 (q=2.1e-91), r -0.1851, n=1082
+  ACE2: ρ -0.568 (q=2.0e-91), r -0.0985, n=1082
+  WNT6: ρ -0.566 (q=1.3e-90), r -0.2561, n=1082
+  RNF145: ρ -0.5656 (q=1.8e-90), r -0.3563, n=1082
+  IFRD1: ρ -0.5638 (q=8.4e-90), r -0.3738, n=1082
+  PFKP: ρ -0.562 (q=4.3e-89), r -0.3957, n=1082
+  ART3: ρ -0.5615 (q=6.7e-89), r -0.1669, n=1082
+  PTK7: ρ -0.5608 (q=1.2e-88), r -0.4226, n=1082
+  EN1: ρ -0.5597 (q=3.0e-88), r -0.2881, n=1082
+  CHST2: ρ -0.559 (q=5.5e-88), r -0.2951, n=1082
+  UNC13D: ρ -0.5584 (q=9.2e-88), r -0.3432, n=1082
+  DSC2: ρ -0.558 (q=1.3e-87), r -0.3279, n=1082
+  KCNG1: ρ -0.5566 (q=4.6e-87), r -0.1918, n=1082
+  CDCA7: ρ -0.5559 (q=7.9e-87), r -0.363, n=1082
+  ATP6V1C2: ρ -0.5559 (q=7.8e-87), r -0.2332, n=1082
+  NFIL3: ρ -0.5555 (q=1.1e-86), r -0.4152, n=1082
+  CDH3: ρ -0.5553 (q=1.4e-86), r -0.388, n=1082
+  FMNL2: ρ -0.555 (q=1.7e-86), r -0.4073, n=1082
+  GSTP1: ρ -0.5541 (q=3.7e-86), r -0.3257, n=1082
+  CHST3: ρ -0.5541 (q=3.6e-86), r -0.3816, n=1082
+  LMO4: ρ -0.554 (q=4.1e-86), r -0.3318, n=1082
+  ESYT3: ρ -0.5535 (q=6.3e-86), r -0.3241, n=1082
+  INTS6L: ρ -0.553 (q=9.1e-86), r -0.3954, n=1082
+  JOSD1: ρ -0.5527 (q=1.2e-85), r -0.4366, n=1082
+  INAVA: ρ -0.5524 (q=1.5e-85), r -0.3143, n=1082
+  MICALL1: ρ -0.5523 (q=1.7e-85), r -0.3806, n=1082
+  TTLL4: ρ -0.5519 (q=2.3e-85), r -0.3634, n=1082
+  APBA2: ρ -0.5515 (q=3.3e-85), r -0.3666, n=1082
+  TTYH3: ρ -0.5512 (q=4.2e-85), r -0.4035, n=1082
+  BTG3: ρ -0.551 (q=4.9e-85), r -0.3397, n=1082
+  ANKS6: ρ -0.5508 (q=5.6e-85), r -0.3766, n=1082
+  IL12RB2: ρ -0.5505 (q=7.4e-85), r -0.2098, n=1082
+  RIN3: ρ -0.55 (q=1.1e-84), r -0.4432, n=1082
+  SCPEP1: ρ -0.548 (q=5.8e-84), r -0.3666, n=1082
+  SH2D2A: ρ -0.5467 (q=1.8e-83), r -0.3763, n=1082
+  SRSF12: ρ -0.5464 (q=2.3e-83), r -0.3222, n=1082
+  PDSS1: ρ -0.5456 (q=4.2e-83), r -0.3721, n=1082
+  FABP7: ρ -0.5454 (q=5.2e-83), r -0.084, n=1082
+  GAL: ρ -0.5445 (q=1.1e-82), r -0.1888, n=1082
+  CAVEAT (tell the user): ESR1 on “rna_seq_v2_mrna” against every gene on “rna_seq_v2_mrna”, over the patients in “all patients” with both values (a patient's samples averaged, as the heatmap takes them). Ranked by Spearman's ρ, as the portal ranks, with Pearson's r beside it; q is Benjamini–Hochberg over ρ's p across all 20,057 genes tested. A correlation is not a regulatory link: genes that rise together across a cohort often both track a third thing — a subtype, the tumour's purity, proliferation. 280 genes were not tested: fewer than 10 patients with both values, or no spread.
+  METHOD:
+    - Sum every gene's values and ranks against ESR1's [all patients · brca_tcga_pan_can_atlas_2018] (1,084 → 20,338 rows): The correlates:moments query above. Per patient, ESR1 on “rna_seq_v2_mrna” and every gene on “rna_seq_v2_mrna” (a patient's samples averaged); per gene, over the patients with both: n, Σx, Σy, Σxy, Σx², Σy², and the same six over each value's average rank among those patients (ties share the mean of their ranks). Sums only.
+    - Pearson's r and Spearman's ρ per gene: r from the sums: (nΣxy − ΣxΣy) / √((nΣx² − (Σx)²)(nΣy² − (Σy)²)); ρ is the same formula on the rank sums. Each p from t = r√((n − 2)/(1 − r²)) on n − 2 df, as scipy's pearsonr and spearmanr compute it. ESR1 against itself is left out. A gene with fewer than 10 patients, or no spread, is not tested.
+        tested: 20,057
+        not tested: 280
+    - Benjamini–Hochberg over ρ: q-values over every gene tested, from ρ's p.
+    Pearson's r from sums matches scipy 1.18.1 pearsonr (test/moments-golden.test.ts); ρ is the same formula on average ranks, which is how scipy's spearmanr defines it — checked against spearmanr on raw values for TCGA breast cancer ESR1 (FOXA1 ρ 0.7478, PSAT1 −0.6732). Benjamini–Hochberg matches statsmodels multipletests (test/tables-golden.test.ts).
diff --git a/test/snapshots/course.json b/test/snapshots/course.json
new file mode 100644
index 00000000..e12b69db
--- /dev/null
+++ b/test/snapshots/course.json
@@ -0,0 +1,782 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "TP53 mut": {
+      "count": 13105,
+      "text": "OQL[TP53: MUT]",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "TP53 wt": {
+      "count": 11594,
+      "text": "NOT OQL[TP53:] AND profiled for TP53 MUTATION",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "course",
+      "cohorts": [
+        "TP53 mut",
+        "TP53 wt"
+      ],
+      "studies": [
+        "msk_chord_2024"
+      ],
+      "course": [
+        {
+          "cohort": "TP53 mut",
+          "eventType": "Treatment",
+          "variants": [
+            "Treatment"
+          ],
+          "isInterval": true,
+          "points": [
+            {
+              "t": -12,
+              "atRisk": 2411,
+              "n": 0,
+              "pct": 0
+            },
+            {
+              "t": -9,
+              "atRisk": 2757,
+              "n": 0,
+              "pct": 0
+            },
+            {
+              "t": -6,
+              "atRisk": 3527,
+              "n": 0,
+              "pct": 0
+            },
+            {
+              "t": -3,
+              "atRisk": 12392,
+              "n": 0,
+              "pct": 0
+            },
+            {
+              "t": 0,
+              "atRisk": 12738,
+              "n": 12738,
+              "pct": 100
+            },
+            {
+              "t": 3,
+              "atRisk": 12222,
+              "n": 9368,
+              "pct": 76.6
+            },
+            {
+              "t": 6,
+              "atRisk": 11605,
+              "n": 7122,
+              "pct": 61.4
+            },
+            {
+              "t": 9,
+              "atRisk": 10910,
+              "n": 5792,
+              "pct": 53.1
+            },
+            {
+              "t": 12,
+              "atRisk": 10100,
+              "n": 5149,
+              "pct": 51
+            },
+            {
+              "t": 15,
+              "atRisk": 9355,
+              "n": 4587,
+              "pct": 49
+            },
+            {
+              "t": 18,
+              "atRisk": 8656,
+              "n": 4071,
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+            },
+            {
+              "t": 21,
+              "atRisk": 7990,
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+              "pct": 46
+            },
+            {
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+            {
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+            },
+            {
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+              "pct": 43.3
+            },
+            {
+              "t": 33,
+              "atRisk": 5832,
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+              "pct": 42.7
+            },
+            {
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+              "atRisk": 5364,
+              "n": 2263,
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+            {
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+              "n": 2137,
+              "pct": 42.6
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+            {
+              "t": 42,
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+              "n": 1947,
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+            {
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+            },
+            {
+              "…": "5 more",
+              "sha256": "f2706579d3f82710"
+            }
+          ]
+        },
+        {
+          "cohort": "TP53 mut",
+          "eventType": "Surgery",
+          "variants": [
+            "Surgery"
+          ],
+          "isInterval": false,
+          "points": [
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+              "pct": 1.7
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+            {
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+            },
+            {
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+              "n": 1128,
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+            {
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+            {
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+            },
+            {
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+              "atRisk": 4686,
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+            },
+            {
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+              "n": 399,
+              "pct": 9.2
+            },
+            {
+              "…": "5 more",
+              "sha256": "e8c841d738387602"
+            }
+          ]
+        },
+        {
+          "cohort": "TP53 wt",
+          "eventType": "Treatment",
+          "variants": [
+            "Treatment"
+          ],
+          "isInterval": true,
+          "points": [
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+              "n": 0,
+              "pct": 0
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+            {
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+            },
+            {
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+            },
+            {
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+            },
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+            },
+            {
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+              "n": 2754,
+              "pct": 48.3
+            },
+            {
+              "…": "5 more",
+              "sha256": "c19851a088b9d5c8"
+            }
+          ]
+        },
+        {
+          "cohort": "TP53 wt",
+          "eventType": "Surgery",
+          "variants": [
+            "Surgery"
+          ],
+          "isInterval": false,
+          "points": [
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+              "n": 105,
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+            },
+            {
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+            },
+            {
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+            },
+            {
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+              "n": 978,
+              "pct": 9.7
+            },
+            {
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+            },
+            {
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+              "n": 711,
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+            },
+            {
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+              "pct": 7.2
+            },
+            {
+              "t": 21,
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+              "n": 637,
+              "pct": 7.6
+            },
+            {
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+              "pct": 7.7
+            },
+            {
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+              "pct": 6.4
+            },
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+            },
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+            },
+            {
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+              "pct": 5.9
+            },
+            {
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+              "n": 363,
+              "pct": 6.1
+            },
+            {
+              "t": 45,
+              "atRisk": 5696,
+              "n": 368,
+              "pct": 6.5
+            },
+            {
+              "…": "5 more",
+              "sha256": "8b83331b53b62db9"
+            }
+          ]
+        }
+      ],
+      "courseAxis": {
+        "binMonths": 3,
+        "from": -12,
+        "to": 60
+      },
+      "eventOptions": [
+        {
+          "canonical": "sample acquisition",
+          "label": "Sample acquisition",
+          "patients": 13105,
+          "perPatient": 1,
+          "hasDurations": false
+        },
+        {
+          "canonical": "sequencing",
+          "label": "Sequencing",
+          "patients": 13105,
+          "perPatient": 1,
+          "hasDurations": false
+        },
+        {
+          "canonical": "diagnosis",
+          "label": "Diagnosis",
+          "patients": 13104,
+          "perPatient": 60.8,
+          "hasDurations": false
+        },
+        {
+          "canonical": "treatment",
+          "label": "Treatment",
+          "patients": 12738,
+          "perPatient": 7.7,
+          "hasDurations": true
+        },
+        {
+          "canonical": "surgery",
+          "label": "Surgery",
+          "patients": 11249,
+          "perPatient": 3.5,
+          "hasDurations": false
+        },
+        {
+          "canonical": "lab_test",
+          "label": "Lab_Test",
+          "patients": 8996,
+          "perPatient": 24.8,
+          "hasDurations": false
+        },
+        {
+          "canonical": "pathology",
+          "label": "Pathology",
+          "patients": 6687,
+          "perPatient": 1.3,
+          "hasDurations": false
+        }
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Aligned on first Treatment — time 0 is that event, so every patient is lined up on the same clinical landmark. Patients with no such event are absent from every bin. THE DENOMINATOR IS NOT THE WHOLE COHORT: each bin is over the patients still under observation in it, from their first recorded event to their last contact (the later of their last recorded event and their OS follow-up where the study declares one). Without that, a curve would fall simply because follow-up ran out. Follow-up ending at the last recorded event biases the tail upward — by construction a patient has an event on their last observed day. Bins are half-open [t, t+3mo). The figure stops each line where fewer than 10 patients remain observable; the numbers past that point are in the result but not drawn. Event types carrying real durations are drawn as a state the patient is IN during the bin; the rest as an event that HAPPENED in it. That is read off the data, not the event's name.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients per time bin",
+            "detail": "The timelineSpan and eventCourse queries above. Per 3-month bin: the cohort's patients observable in it, and of those the ones in each event's state (or with the event in it). Counts only.",
+            "scope": "TP53 mut · msk_chord_2024",
+            "rowsIn": 13105
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients per time bin",
+            "detail": "The timelineSpan and eventCourse queries above. Per 3-month bin: the cohort's patients observable in it, and of those the ones in each event's state (or with the event in it). Counts only.",
+            "scope": "TP53 wt · msk_chord_2024",
+            "rowsIn": 11594
+          },
+          {
+            "kind": "transform",
+            "title": "Match event types",
+            "detail": "Event types, keys and values match ignoring case and surrounding whitespace; nothing else is merged.",
+            "params": [
+              [
+                "Treatment",
+                "has durations: a state the patient is in during a bin"
+              ],
+              [
+                "Surgery",
+                "no durations: an event that happened in a bin"
+              ]
+            ]
+          },
+          {
+            "kind": "transform",
+            "title": "Align on first Treatment",
+            "detail": "Day 0 is each patient's first Treatment; a patient without one is in no bin. A patient is observable from their first recorded event to their last contact — the later of their last recorded event and their OS follow-up (OS_MONTHS × 30.4375 days, assuming it starts at the timeline's day 0).",
+            "params": [
+              [
+                "TP53 mut",
+                "13,105 in the cohort → 12,738 with the anchor event → 12,738 observable in the window"
+              ],
+              [
+                "TP53 wt",
+                "11,594 in the cohort → 11,044 with the anchor event → 11,044 observable in the window"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Share per bin",
+            "detail": "For each bin from month -12 to 63 (half-open [t, t+3)): patients in the state ÷ patients observable, as a percentage. The figure stops each line where fewer than 10 patients remain observable."
+          }
+        ],
+        "implementation": {
+          "functions": [],
+          "file": "lib/views/course.ts",
+          "commit": null
+        },
+        "check": {
+          "course:TP53 mut:Treatment:-4:pct": {
+            "v": 0,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:-4:at_risk": {
+            "v": 2411,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:-3:pct": {
+            "v": 0,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:-3:at_risk": {
+            "v": 2757,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:-2:pct": {
+            "v": 0,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:-2:at_risk": {
+            "v": 3527,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:-1:pct": {
+            "v": 0,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:-1:at_risk": {
+            "v": 12392,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:0:pct": {
+            "v": 100,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:0:at_risk": {
+            "v": 12738,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:1:pct": {
+            "v": 76.6,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:1:at_risk": {
+            "v": 12222,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:2:pct": {
+            "v": 61.4,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:2:at_risk": {
+            "v": 11605,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:3:pct": {
+            "v": 53.1,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:3:at_risk": {
+            "v": 10910,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:4:pct": {
+            "v": 51,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:4:at_risk": {
+            "v": 10100,
+            "abs": 0
+          },
+          "course:TP53 mut:Treatment:5:pct": {
+            "v": 49,
+            "abs": 0.050000001
+          },
+          "course:TP53 mut:Treatment:5:at_risk": {
+            "v": 9355,
+            "abs": 0
+          },
+          "…": {
+            "more": 180,
+            "sha256": "504ee14ebe1d1b66"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/course.sql b/test/snapshots/course.sql
new file mode 100644
index 00000000..84a7d600
--- /dev/null
+++ b/test/snapshots/course.sql
@@ -0,0 +1,187 @@
+-- cohort TP53 mut
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort TP53 wt
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))
+
+-- view 0 course: eventCourse:@Treatment/3mo · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+    AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+  GROUP BY pid
+),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), NULL) * 30.4375 AS os_day
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient'
+    AND attribute_name = 'OS_MONTHS'
+  GROUP BY pid
+),
+span AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS entry,
+         max(coalesce(ce.stop_date, ce.start_date)) AS last_ev
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+obs AS (
+  SELECT sp.pid AS pid, toFloat64(sp.entry) AS entry,
+         greatest(toFloat64(sp.last_ev), coalesce(os.os_day, toFloat64(sp.last_ev))) AS last_day
+  FROM span sp LEFT JOIN os ON os.pid = sp.pid
+),
+denom AS (
+  SELECT arrayJoin(range(
+    toInt32(greatest(floor((o.entry - a.day) / 91.3125), -4)),
+    toInt32(least(floor((o.last_day - a.day) / 91.3125), 20)) + 1)) AS bin, o.pid AS pid, '' AS et
+  FROM obs o JOIN anch a ON a.pid = o.pid
+),
+numer AS (
+  SELECT arrayJoin(range(
+    toInt32(greatest(floor((ce.start_date - a.day) / 91.3125), -4)),
+    toInt32(least(floor((coalesce(ce.stop_date, ce.start_date) - a.day) / 91.3125), 20)) + 1)) AS bin,
+         p.stable_id AS pid, lowerUTF8(trimBoth(ce.event_type)) AS et
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN anch a ON a.pid = p.stable_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+    AND lowerUTF8(trimBoth(ce.event_type)) IN (lowerUTF8(trimBoth('treatment')), lowerUTF8(trimBoth('surgery')))
+)
+SELECT bin, et, n FROM (
+  SELECT toInt32(bin) AS bin, et, uniqExact(pid) AS n
+  FROM (SELECT bin, pid, et FROM denom UNION ALL SELECT bin, pid, et FROM numer)
+  GROUP BY bin, et
+  UNION ALL SELECT toInt32(0) AS bin, '#anchored' AS et, uniqExact(pid) AS n FROM anch
+  UNION ALL SELECT toInt32(0) AS bin, '#observed' AS et, uniqExact(pid) AS n FROM denom
+) ORDER BY bin, et
+
+-- view 0 course: eventCourse:@Treatment/3mo · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+    AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+  GROUP BY pid
+),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), NULL) * 30.4375 AS os_day
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient'
+    AND attribute_name = 'OS_MONTHS'
+  GROUP BY pid
+),
+span AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS entry,
+         max(coalesce(ce.stop_date, ce.start_date)) AS last_ev
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+obs AS (
+  SELECT sp.pid AS pid, toFloat64(sp.entry) AS entry,
+         greatest(toFloat64(sp.last_ev), coalesce(os.os_day, toFloat64(sp.last_ev))) AS last_day
+  FROM span sp LEFT JOIN os ON os.pid = sp.pid
+),
+denom AS (
+  SELECT arrayJoin(range(
+    toInt32(greatest(floor((o.entry - a.day) / 91.3125), -4)),
+    toInt32(least(floor((o.last_day - a.day) / 91.3125), 20)) + 1)) AS bin, o.pid AS pid, '' AS et
+  FROM obs o JOIN anch a ON a.pid = o.pid
+),
+numer AS (
+  SELECT arrayJoin(range(
+    toInt32(greatest(floor((ce.start_date - a.day) / 91.3125), -4)),
+    toInt32(least(floor((coalesce(ce.stop_date, ce.start_date) - a.day) / 91.3125), 20)) + 1)) AS bin,
+         p.stable_id AS pid, lowerUTF8(trimBoth(ce.event_type)) AS et
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN anch a ON a.pid = p.stable_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+    AND lowerUTF8(trimBoth(ce.event_type)) IN (lowerUTF8(trimBoth('treatment')), lowerUTF8(trimBoth('surgery')))
+)
+SELECT bin, et, n FROM (
+  SELECT toInt32(bin) AS bin, et, uniqExact(pid) AS n
+  FROM (SELECT bin, pid, et FROM denom UNION ALL SELECT bin, pid, et FROM numer)
+  GROUP BY bin, et
+  UNION ALL SELECT toInt32(0) AS bin, '#anchored' AS et, uniqExact(pid) AS n FROM anch
+  UNION ALL SELECT toInt32(0) AS bin, '#observed' AS et, uniqExact(pid) AS n FROM denom
+) ORDER BY bin, et
+
+-- view 0 course: timelineSpan · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))
+SELECT ce.event_type AS et,
+       uniqExact(p.stable_id) AS pts,
+       count() AS events,
+       uniqExact(ce.start_date) AS days,
+       min(ce.start_date) AS first_day,
+       max(ce.start_date) AS last_day,
+       countIf(ce.stop_date IS NOT NULL AND ce.stop_date != ce.start_date) AS interval_events
+FROM snapshot_db.clinical_event ce
+JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+  AND p.stable_id IN (SELECT pid FROM cohort)
+GROUP BY et ORDER BY pts DESC
diff --git a/test/snapshots/course.svg b/test/snapshots/course.svg
new file mode 100644
index 00000000..47593817
--- /dev/null
+++ b/test/snapshots/course.svg
@@ -0,0 +1,2 @@
+<!-- view 0 course -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 497" width="100%" role="img" aria-label="Clinical course — Treatment, Surgery — TP53 mut, TP53 wt" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Clinical course — Treatment, Surgery — TP53 mut, TP53 wt</title><rect width="720" height="497" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Treatment</text><text x="69.2" y="16" font-size="11" fill="#5b6b76"> — % in this state</text><line x1="46" y1="28" x2="46" y2="128" stroke="#dde4e8" stroke-width="0.75"/><line x1="155.7" y1="28" x2="155.7" y2="128" stroke="#dde4e8" stroke-width="0.75"/><line x1="265.3" y1="28" x2="265.3" y2="128" stroke="#dde4e8" stroke-width="0.75"/><line x1="375" y1="28" x2="375" y2="128" stroke="#dde4e8" stroke-width="0.75"/><line x1="484.7" y1="28" x2="484.7" y2="128" stroke="#dde4e8" stroke-width="0.75"/><line x1="594.3" y1="28" x2="594.3" y2="128" stroke="#dde4e8" stroke-width="0.75"/><line x1="704" y1="28" x2="704" y2="128" stroke="#dde4e8" stroke-width="0.75"/><line x1="46" y1="128" x2="704" y2="128" stroke="#dde4e8" stroke-width="0.75"/><text x="40" y="131.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="46" y1="78" x2="704" y2="78" stroke="#dde4e8" stroke-width="0.75"/><text x="40" y="81.5" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><line x1="46" y1="28" x2="704" y2="28" stroke="#dde4e8" stroke-width="0.75"/><text x="40" y="31.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><line x1="155.7" y1="28" x2="155.7" y2="128" stroke="#5b6b76" stroke-width="1" stroke-dasharray="3 3"/><path d="M46 128L73.4 128L100.8 128L128.3 128L155.7 28L183.1 51.4L210.5 66.6L237.9 74.9L265.3 77L292.8 79L320.2 81L347.6 82L375 82.5L402.4 83.6L429.8 84.7L457.3 85.3L484.7 85.8L512.1 85.4L539.5 86.5L566.9 86.3L594.3 87.1L621.8 87.4L649.2 88.1L676.6 87.9L704 88.3" fill="none" stroke="#0072B2" stroke-width="2" stroke-linejoin="round"/><path d="M46 128L73.4 128L100.8 128L128.3 128L155.7 28L183.1 57.1L210.5 67.4L237.9 72.3L265.3 74.5L292.8 75.9L320.2 77L347.6 77.4L375 78.5L402.4 78.5L429.8 79.3L457.3 79.4L484.7 80L512.1 79.8L539.5 79.7L566.9 79.7L594.3 78.9L621.8 78.7L649.2 78L676.6 77.6L704 78.2" fill="none" stroke="#E69F00" stroke-width="2" stroke-linejoin="round"/><text x="8" y="150" font-size="12" fill="#16232b" font-weight="600">Surgery</text><text x="54.1" y="150" font-size="11" fill="#5b6b76"> — % with an event</text><line x1="46" y1="162" x2="46" y2="262" stroke="#dde4e8" stroke-width="0.75"/><line x1="155.7" y1="162" x2="155.7" y2="262" stroke="#dde4e8" stroke-width="0.75"/><line x1="265.3" y1="162" x2="265.3" y2="262" stroke="#dde4e8" stroke-width="0.75"/><line x1="375" y1="162" x2="375" y2="262" stroke="#dde4e8" stroke-width="0.75"/><line x1="484.7" y1="162" x2="484.7" y2="262" stroke="#dde4e8" stroke-width="0.75"/><line x1="594.3" y1="162" x2="594.3" y2="262" stroke="#dde4e8" stroke-width="0.75"/><line x1="704" y1="162" x2="704" y2="262" stroke="#dde4e8" stroke-width="0.75"/><line x1="46" y1="262" x2="704" y2="262" stroke="#dde4e8" stroke-width="0.75"/><text x="40" y="265.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="46" y1="212" x2="704" y2="212" stroke="#dde4e8" stroke-width="0.75"/><text x="40" y="215.5" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><line x1="46" y1="162" x2="704" y2="162" stroke="#dde4e8" stroke-width="0.75"/><text x="40" y="165.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><line x1="155.7" y1="162" x2="155.7" y2="262" stroke="#5b6b76" stroke-width="1" stroke-dasharray="3 3"/><path d="M46 260.3L73.4 259.2L100.8 258.7L128.3 230.9L155.7 232.8L183.1 241.8L210.5 245.3L237.9 246.7L265.3 248.3L292.8 249.9L320.2 249.6L347.6 251.1L375 250.9L402.4 251.7L429.8 252.2L457.3 252.2L484.7 253L512.1 253.4L539.5 252.2L566.9 252.8L594.3 253.7L621.8 252.4L649.2 254L676.6 253.8L704 253.9" fill="none" stroke="#0072B2" stroke-width="2" stroke-linejoin="round"/><path d="M46 258.2L73.4 258.4L100.8 256.6L128.3 228.2L155.7 230.9L183.1 246.2L210.5 251.2L237.9 252.3L265.3 253.3L292.8 254.3L320.2 254.8L347.6 254.4L375 254.3L402.4 255.6L429.8 255.6L457.3 255.5L484.7 255.3L512.1 256.1L539.5 255.9L566.9 255.5L594.3 255.6L621.8 255.7L649.2 256L676.6 255.7L704 255.5" fill="none" stroke="#E69F00" stroke-width="2" stroke-linejoin="round"/><line x1="46" y1="262" x2="46" y2="265" stroke="#5b6b76" stroke-width="1"/><text x="37" y="278" font-size="10" fill="#5b6b76">−12</text><line x1="155.7" y1="262" x2="155.7" y2="265" stroke="#5b6b76" stroke-width="1"/><text x="152.7" y="278" font-size="10" fill="#5b6b76">0</text><line x1="265.3" y1="262" x2="265.3" y2="265" stroke="#5b6b76" stroke-width="1"/><text x="259.3" y="278" font-size="10" fill="#5b6b76">12</text><line x1="375" y1="262" x2="375" y2="265" stroke="#5b6b76" stroke-width="1"/><text x="369" y="278" font-size="10" fill="#5b6b76">24</text><line x1="484.7" y1="262" x2="484.7" y2="265" stroke="#5b6b76" stroke-width="1"/><text x="478.7" y="278" font-size="10" fill="#5b6b76">36</text><line x1="594.3" y1="262" x2="594.3" y2="265" stroke="#5b6b76" stroke-width="1"/><text x="588.3" y="278" font-size="10" fill="#5b6b76">48</text><line x1="704" y1="262" x2="704" y2="265" stroke="#5b6b76" stroke-width="1"/><text x="698" y="278" font-size="10" fill="#5b6b76">60</text><line x1="46" y1="262" x2="704" y2="262" stroke="#5b6b76" stroke-width="1"/><text x="375" y="297" font-size="11" fill="#16232b" text-anchor="middle">Months from the anchor event</text><text x="8" y="317" font-size="10" fill="#5b6b76">patients still observable</text><line x1="8" y1="329" x2="26" y2="329" stroke="#0072B2" stroke-width="2"/><text x="46" y="332.5" font-size="10" fill="#5b6b76" text-anchor="middle">2,411</text><text x="155.7" y="332.5" font-size="10" fill="#5b6b76" text-anchor="middle">12,738</text><text x="265.3" y="332.5" font-size="10" fill="#5b6b76" text-anchor="middle">10,100</text><text x="375" y="332.5" font-size="10" fill="#5b6b76" text-anchor="middle">7,392</text><text x="484.7" y="332.5" font-size="10" fill="#5b6b76" text-anchor="middle">5,364</text><text x="594.3" y="332.5" font-size="10" fill="#5b6b76" text-anchor="middle">4,005</text><text x="702.6" y="332.5" font-size="10" fill="#5b6b76" text-anchor="middle">2,887</text><line x1="8" y1="345" x2="26" y2="345" stroke="#E69F00" stroke-width="2"/><text x="46" y="348.5" font-size="10" fill="#5b6b76" text-anchor="middle">2,781</text><text x="155.7" y="348.5" font-size="10" fill="#5b6b76" text-anchor="middle">11,044</text><text x="265.3" y="348.5" font-size="10" fill="#5b6b76" text-anchor="middle">9,629</text><text x="375" y="348.5" font-size="10" fill="#5b6b76" text-anchor="middle">8,010</text><text x="484.7" y="348.5" font-size="10" fill="#5b6b76" text-anchor="middle">6,568</text><text x="594.3" y="348.5" font-size="10" fill="#5b6b76" text-anchor="middle">5,372</text><text x="702.6" y="348.5" font-size="10" fill="#5b6b76" text-anchor="middle">4,205</text><line x1="8" y1="369" x2="26" y2="369" stroke="#0072B2" stroke-width="2"/><text x="31" y="372.9" font-size="11" fill="#16232b">TP53 mut</text><line x1="95.46600000000001" y1="369" x2="113.46600000000001" y2="369" stroke="#E69F00" stroke-width="2"/><text x="118.5" y="372.9" font-size="11" fill="#16232b">TP53 wt</text><text x="8" y="395" font-size="10" fill="#5b6b76">Aligned on first Treatment — time 0 is that event, so every patient is lined up on the same clinical landmark. Patients with no such event are absent from</text><text x="8" y="409" font-size="10" fill="#5b6b76">every bin. THE DENOMINATOR IS NOT THE WHOLE COHORT: each bin is over the patients still under observation in it, from their first recorded event to their</text><text x="8" y="423" font-size="10" fill="#5b6b76">last contact (the later of their last recorded event and their OS follow-up where the study declares one). Without that, a curve would fall simply because</text><text x="8" y="437" font-size="10" fill="#5b6b76">follow-up ran out. Follow-up ending at the last recorded event biases the tail upward — by construction a patient has an event on their last observed day.</text><text x="8" y="451" font-size="10" fill="#5b6b76">Bins are half-open [t, t+3mo). The figure stops each line where fewer than 10 patients remain observable; the numbers past that point are in the result but</text><text x="8" y="465" font-size="10" fill="#5b6b76">not drawn. Event types carrying real durations are drawn as a state the patient is IN during the bin; the rest as an event that HAPPENED in it. That is read off</text><text x="8" y="479" font-size="10" fill="#5b6b76">the data, not the event's name.</text></svg>
diff --git a/test/snapshots/course.txt b/test/snapshots/course.txt
new file mode 100644
index 00000000..67f64060
--- /dev/null
+++ b/test/snapshots/course.txt
@@ -0,0 +1,22 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- TP53 mut: 13,105 patients — OQL[TP53: MUT]
+- TP53 wt: 11,594 patients — NOT OQL[TP53:] AND profiled for TP53 MUTATION
+
+Clinical course (3-month bins, -12 to +60mo relative to the anchor; DENOMINATOR IS THE PATIENTS STILL OBSERVABLE IN EACH BIN, not the cohort):
+  TP53 mut / Treatment [a state patients are IN]: 0mo 100% (n=12,738), 12mo 51% (n=10,100), 24mo 45.5% (n=7,392), 60mo 39.7% (n=2,887)
+  TP53 mut / Surgery [an event that HAPPENED]: 0mo 29.2% (n=12,738), 12mo 13.7% (n=10,100), 24mo 11.1% (n=7,392), 60mo 8.1% (n=2,887)
+  TP53 wt / Treatment [a state patients are IN]: 0mo 100% (n=11,044), 12mo 53.5% (n=9,629), 24mo 49.5% (n=8,010), 60mo 49.8% (n=4,205)
+  TP53 wt / Surgery [an event that HAPPENED]: 0mo 31.1% (n=11,044), 12mo 8.7% (n=9,629), 24mo 7.7% (n=8,010), 60mo 6.5% (n=4,205)
+  CAVEAT (tell the user): Aligned on first Treatment — time 0 is that event, so every patient is lined up on the same clinical landmark. Patients with no such event are absent from every bin. THE DENOMINATOR IS NOT THE WHOLE COHORT: each bin is over the patients still under observation in it, from their first recorded event to their last contact (the later of their last recorded event and their OS follow-up where the study declares one). Without that, a curve would fall simply because follow-up ran out. Follow-up ending at the last recorded event biases the tail upward — by construction a patient has an event on their last observed day. Bins are half-open [t, t+3mo). The figure stops each line where fewer than 10 patients remain observable; the numbers past that point are in the result but not drawn. Event types carrying real durations are drawn as a state the patient is IN during the bin; the rest as an event that HAPPENED in it. That is read off the data, not the event's name.
+  METHOD:
+    - Count patients per time bin [TP53 mut · msk_chord_2024]: The timelineSpan and eventCourse queries above. Per 3-month bin: the cohort's patients observable in it, and of those the ones in each event's state (or with the event in it). Counts only.
+    - Count patients per time bin [TP53 wt · msk_chord_2024]: The timelineSpan and eventCourse queries above. Per 3-month bin: the cohort's patients observable in it, and of those the ones in each event's state (or with the event in it). Counts only.
+    - Match event types: Event types, keys and values match ignoring case and surrounding whitespace; nothing else is merged.
+        Treatment: has durations: a state the patient is in during a bin
+        Surgery: no durations: an event that happened in a bin
+    - Align on first Treatment: Day 0 is each patient's first Treatment; a patient without one is in no bin. A patient is observable from their first recorded event to their last contact — the later of their last recorded event and their OS follow-up (OS_MONTHS × 30.4375 days, assuming it starts at the timeline's day 0).
+        TP53 mut: 13,105 in the cohort → 12,738 with the anchor event → 12,738 observable in the window
+        TP53 wt: 11,594 in the cohort → 11,044 with the anchor event → 11,044 observable in the window
+    - Share per bin: For each bin from month -12 to 63 (half-open [t, t+3)): patients in the state ÷ patients observable, as a percentage. The figure stops each line where fewer than 10 patients remain observable.
diff --git a/test/snapshots/cox.json b/test/snapshots/cox.json
new file mode 100644
index 00000000..511b3abf
--- /dev/null
+++ b/test/snapshots/cox.json
@@ -0,0 +1,138 @@
+{
+  "study": "crc_apc_impact_2020",
+  "cohorts": {
+    "all patients": {
+      "count": 471,
+      "text": "all patients in the study",
+      "studies": [
+        "crc_apc_impact_2020"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "cox",
+      "cohorts": [
+        "all patients"
+      ],
+      "endpoint": "OS",
+      "cox": {
+        "coefficients": [
+          {
+            "label": "TP53/KRAS/PIK3CA (any) mutation status: Mutated",
+            "reference": "Not mutated",
+            "level": "Mutated",
+            "axisIndex": 0,
+            "beta": -0.3285283537241268,
+            "se": 0.21751119615856146,
+            "hr": 0.7199825137508375,
+            "ciLow": 0.47008466395358267,
+            "ciHigh": 1.1027265083426774,
+            "p": 0.1309420416160914
+          }
+        ],
+        "n": 471,
+        "events": 311
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "crc_apc_impact_2020",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Cox proportional-hazards regression (Efron ties) on 471 patients, 311 events, 1 term. Computed from real per-patient rows within each local study — not pooled aggregate counts across untrusted remote peers, unlike KM/log-rank.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Fetch one row per patient",
+            "detail": "The coxRows:OS query above. Patients with a OS_MONTHS value and a value for every covariate are kept. Event: OS_STATUS starting with “1” (e.g. “1:DECEASED”); any other value, including a missing one, is censored. Every cohort patient had a row.",
+            "scope": "crc_apc_impact_2020",
+            "rowsIn": 471,
+            "rowsOut": 471
+          },
+          {
+            "kind": "transform",
+            "title": "Code the covariates",
+            "detail": "Each numeric covariate is one term. Each categorical covariate becomes one 0/1 term per level, measured against a reference level that gets no term.",
+            "params": [
+              [
+                "TP53/KRAS/PIK3CA (any) mutation status: Mutated",
+                "1 if c0 = “Mutated”, else 0 · 440 of 471 patients · reference “Not mutated”: the fixed default for a mutated-vs-not axis"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Fit a Cox proportional-hazards model",
+            "detail": "Maximum partial likelihood with Efron's method for tied event times, by Newton–Raphson with step-halving.",
+            "params": [
+              [
+                "patients / events",
+                "471 / 311"
+              ],
+              [
+                "terms",
+                "1"
+              ],
+              [
+                "baseline hazard",
+                "one, shared by every patient"
+              ],
+              [
+                "standard errors",
+                "from the observed information at the estimate"
+              ],
+              [
+                "95% interval",
+                "exp(β ± 1.959964·SE), Wald"
+              ],
+              [
+                "p-value",
+                "Wald χ² on 1 df, (β/SE)²"
+              ],
+              [
+                "fit",
+                "converged in 5 iterations"
+              ],
+              [
+                "log partial likelihood",
+                "-1635.6078"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "coxRegression"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Matches lifelines 0.30.3 CoxPHFitter (Efron ties) to a relative 1e-6 on coefficients, SEs, p-values and intervals (test/regression-golden.test.ts).",
+        "check": {
+          "cox::TP53/KRAS/PIK3CA (any) mutation status: Mutated:beta": {
+            "v": -0.3285283537241268,
+            "rel": 0.0001,
+            "abs": 0.000001
+          },
+          "cox::TP53/KRAS/PIK3CA (any) mutation status: Mutated:se": {
+            "v": 0.21751119615856146,
+            "rel": 0.0001
+          },
+          "cox::TP53/KRAS/PIK3CA (any) mutation status: Mutated:p": {
+            "v": 0.1309420416160914,
+            "rel": 0.001,
+            "abs": 1e-12
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/cox.sql b/test/snapshots/cox.sql
new file mode 100644
index 00000000..e80dc7b0
--- /dev/null
+++ b/test/snapshots/cox.sql
@@ -0,0 +1,100 @@
+-- cohort all patients
+-- crc_apc_impact_2020
+SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'
+
+-- view 0 cox: coxNotProfiled:OS · crc_apc_impact_2020
+SELECT countIf(c0 = 'Not profiled') AS n FROM (
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), NULL) AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+c0 AS (
+SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))) AND alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'KRAS')))) AND alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'PIK3CA'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS alt_case, if(countIf(g.variant_type = 'mutation') > 0, 'Mutated', 'Not mutated') AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND hugo_gene_symbol IN ('TP53','KRAS','PIK3CA')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'crc_apc_impact_2020' AND replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case)
+)
+SELECT os.months AS m, if(os.status LIKE '1%', 1, 0) AS ev, c0.v AS c0
+FROM os
+  INNER JOIN c0 ON c0.id = os.pid
+WHERE os.months IS NOT NULL
+)
+
+-- view 0 cox: coxRows:OS · crc_apc_impact_2020
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), NULL) AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+c0 AS (
+SELECT id, v FROM (
+SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))) AND alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'KRAS')))) AND alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'PIK3CA'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS alt_case, if(countIf(g.variant_type = 'mutation') > 0, 'Mutated', 'Not mutated') AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND hugo_gene_symbol IN ('TP53','KRAS','PIK3CA')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'crc_apc_impact_2020' AND replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case)
+) WHERE v != 'Not profiled'
+)
+SELECT os.months AS m, if(os.status LIKE '1%', 1, 0) AS ev, c0.v AS c0
+FROM os
+  INNER JOIN c0 ON c0.id = os.pid
+WHERE os.months IS NOT NULL
diff --git a/test/snapshots/cox.svg b/test/snapshots/cox.svg
new file mode 100644
index 00000000..c45d80bc
--- /dev/null
+++ b/test/snapshots/cox.svg
@@ -0,0 +1,2 @@
+<!-- view 0 cox -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 760 137" width="100%" role="img" aria-label="Cox regression — hazard ratios" style="max-width:760px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Cox regression — hazard ratios</title><rect width="760" height="137" fill="#ffffff"/><text x="630.8" y="14" font-size="10" fill="#5b6b76">HR [95% CI]</text><text x="752" y="14" font-size="10" fill="#5b6b76" text-anchor="end">p</text><rect x="4" y="24" width="752" height="22" fill="#dde4e8" opacity="0.35"/><text x="8" y="38.9" font-size="11" fill="#16232b"><title>TP53/KRAS/PIK3CA (any) mutation status: Mutated vs Not mutated</title>TP53/KRAS/PIK3CA (any) mutation status: Mutate…</text><line x1="314" y1="35" x2="586.4" y2="35" stroke="#0072B2" stroke-width="2"/><rect x="446.2" y="31" width="8" height="8" fill="#0072B2"/><text x="630.8" y="38.9" font-size="11" fill="#16232b">0.72 [0.47–1.10]</text><text x="752" y="38.9" font-size="11" fill="#5b6b76" text-anchor="end">0.13</text><line x1="555.1" y1="20" x2="555.1" y2="46" stroke="#5b6b76" stroke-width="1" stroke-dasharray="3 3"/><text x="555.1" y="14" font-size="10" fill="#5b6b76" text-anchor="middle">HR = 1</text><line x1="392" y1="50" x2="392" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="384.6" y="66" font-size="10" fill="#5b6b76">0.6</text><line x1="483.9" y1="50" x2="483.9" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="476.5" y="66" font-size="10" fill="#5b6b76">0.8</text><line x1="555.1" y1="50" x2="555.1" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="552.1" y="66" font-size="10" fill="#5b6b76">1</text><line x1="613.4" y1="50" x2="613.4" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="606" y="66" font-size="10" fill="#5b6b76">1.2</text><line x1="280.40000000000003" y1="50" x2="616.794" y2="50" stroke="#5b6b76" stroke-width="1"/><text x="448.6" y="85" font-size="11" fill="#16232b" text-anchor="middle">HR (log scale) — left of 1 is lower, right is higher</text><text x="8" y="105" font-size="10" fill="#5b6b76">Cox proportional-hazards regression (Efron ties) on 471 patients, 311 events, 1 term. Computed from real per-patient rows within each local study — not pooled</text><text x="8" y="119" font-size="10" fill="#5b6b76">aggregate counts across untrusted remote peers, unlike KM/log-rank.</text></svg>
diff --git a/test/snapshots/cox.txt b/test/snapshots/cox.txt
new file mode 100644
index 00000000..be0dd212
--- /dev/null
+++ b/test/snapshots/cox.txt
@@ -0,0 +1,22 @@
+Analysis over crc_apc_impact_2020 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- all patients: 471 patients — all patients in the study
+
+Cox regression (OS, n=471, 311 events):
+  TP53/KRAS/PIK3CA (any) mutation status: Mutated vs Not mutated: HR 0.72 (95% CI 0.47-1.10) p=0.1309
+  CAVEAT (tell the user): Cox proportional-hazards regression (Efron ties) on 471 patients, 311 events, 1 term. Computed from real per-patient rows within each local study — not pooled aggregate counts across untrusted remote peers, unlike KM/log-rank.
+  METHOD:
+    - Fetch one row per patient [crc_apc_impact_2020] (471 → 471 rows): The coxRows:OS query above. Patients with a OS_MONTHS value and a value for every covariate are kept. Event: OS_STATUS starting with “1” (e.g. “1:DECEASED”); any other value, including a missing one, is censored. Every cohort patient had a row.
+    - Code the covariates: Each numeric covariate is one term. Each categorical covariate becomes one 0/1 term per level, measured against a reference level that gets no term.
+        TP53/KRAS/PIK3CA (any) mutation status: Mutated: 1 if c0 = “Mutated”, else 0 · 440 of 471 patients · reference “Not mutated”: the fixed default for a mutated-vs-not axis
+    - Fit a Cox proportional-hazards model: Maximum partial likelihood with Efron's method for tied event times, by Newton–Raphson with step-halving.
+        patients / events: 471 / 311
+        terms: 1
+        baseline hazard: one, shared by every patient
+        standard errors: from the observed information at the estimate
+        95% interval: exp(β ± 1.959964·SE), Wald
+        p-value: Wald χ² on 1 df, (β/SE)²
+        fit: converged in 5 iterations
+        log partial likelihood: -1635.6078
+    Matches lifelines 0.30.3 CoxPHFitter (Efron ties) to a relative 1e-6 on coefficients, SEs, p-values and intervals (test/regression-golden.test.ts).
diff --git a/test/snapshots/diffexpr.json b/test/snapshots/diffexpr.json
new file mode 100644
index 00000000..aaf97a52
--- /dev/null
+++ b/test/snapshots/diffexpr.json
@@ -0,0 +1,2865 @@
+{
+  "study": "brca_tcga_pan_can_atlas_2018",
+  "cohorts": {
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+      "count": 171,
+      "text": "SUBTYPE in [BRCA_Basal]",
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ]
+    },
+    "Luminal A": {
+      "count": 499,
+      "text": "SUBTYPE in [BRCA_LumA]",
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "diffexpr",
+      "cohorts": [
+        "Basal-like",
+        "Luminal A"
+      ],
+      "diffexpr": {
+        "profileType": "rna_seq_v2_mrna_median_all_sample_Zscores",
+        "log2": false,
+        "effectLabel": "difference in mean z-score",
+        "groups": [
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+          "Luminal A"
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+                "SERPINE1",
+                "CAV1",
+                "PPARG",
+                "F3",
+                "ATP2B1",
+                "BCKDHB",
+                "PRKCE",
+                "TJP1",
+                "MGMT",
+                {
+                  "…": "53 more",
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+                {
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+                  "sha256": "6492e32f94cde5c7"
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+              ],
+              "curveAt": [
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+                {
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+                {
+                  "…": "124 more",
+                  "sha256": "c1be36ad83bd7cdf"
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+              ],
+              "q": 0.010964912280701754
+            },
+            {
+              "…": "30 more",
+              "sha256": "a1150be8c9c8746a"
+            }
+          ],
+          "skipped": []
+        }
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "brca_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Welch's t-test per gene on rna_seq_v2_mrna_median_all_sample_Zscores as stored, Basal-like minus Luminal A, each patient contributing the mean of their samples; computed from three running totals per gene and cohort, so it is exact across sites. 20,006 genes tested, 14 left out (fewer than 3 patients with a value in a cohort, or no variance); q is Benjamini–Hochberg across every gene tested. The 1500 most significant genes are drawn as points, the rest as density. Z-scores are standardised within each study, so a difference is in study-specific units. A t-test on thousands of patients finds tiny differences significant — read the effect, not only the q. Enrichment is preranked GSEA on the unrounded t (Subramanian et al. 2005, weight 1), significance from 1000 random sets of the same size drawn from the same ranking, seeded by the set's name so a shared link reproduces it; p = (b+1)/(m+1) over the same-sign nulls, q is Benjamini–Hochberg across the sets tested (not GSEA's own NES-based FDR). Gene-set permutation ignores gene–gene correlation, so it is anti-conservative next to phenotype permutation, which needs patient-level values. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Sum every gene's values",
+            "detail": "The diffexpr:moments query above, on “rna_seq_v2_mrna_median_all_sample_Zscores” as stored. Per gene, over the cohort's patients with a value (a patient with several samples contributes their average): n, Σv and Σv². Every gene in the profile, no individual value.",
+            "scope": "Basal-like · brca_tcga_pan_can_atlas_2018",
+            "rowsIn": 171
+          },
+          {
+            "kind": "fetch",
+            "title": "Sum every gene's values",
+            "detail": "The diffexpr:moments query above, on “rna_seq_v2_mrna_median_all_sample_Zscores” as stored. Per gene, over the cohort's patients with a value (a patient with several samples contributes their average): n, Σv and Σv². Every gene in the profile, no individual value.",
+            "scope": "Luminal A · brca_tcga_pan_can_atlas_2018",
+            "rowsIn": 499
+          },
+          {
+            "kind": "transform",
+            "title": "Leave out genes that cannot be tested",
+            "detail": "A gene with fewer than 3 patients with a value in either cohort, or with no variance (Var A + Var B ≤ 1e-9 · (1 + mean A² + mean B²) — judged to a tolerance, since a constant gene's variance from the sums is rounding noise).",
+            "rowsIn": 20020,
+            "rowsOut": 20006
+          },
+          {
+            "kind": "statistic",
+            "title": "Welch's t-test per gene",
+            "detail": "Mean A − mean B (Basal-like minus Luminal A) with each cohort's variance (Σv² − (Σv)²/n)/(n − 1); unequal-variance t, Welch–Satterthwaite df, two-sided. With thousands of patients a tiny difference is significant: read the effect too.",
+            "params": [
+              [
+                "genes tested",
+                "20,006"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across genes",
+            "detail": "q-values over all 20,006 genes tested."
+          },
+          {
+            "kind": "statistic",
+            "title": "Preranked gene-set enrichment (GSEA)",
+            "detail": "Genes ranked by the unrounded t, Basal-like over Luminal A first. Enrichment score as in Subramanian et al. 2005 with weight 1 (each member's step is |t| / Σ|t| of the members). Significance from 1000 random sets of the same size drawn from the same ranking, from a generator seeded by the set's name — so a shared link gives the same p — with p = (b + 1)/(m + 1) over the m null scores of the same sign and NES = ES / mean of those. Gene-set permutation ignores correlation between genes, so it is anti-conservative next to phenotype permutation. A set with fewer than 10 ranked members is not tested. q is Benjamini–Hochberg across the sets tested.",
+            "params": [
+              [
+                "sets tested",
+                "50"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "welchT",
+            "benjaminiHochberg",
+            "preRankedGsea"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Welch's t from moments matches scipy 1.18.1 ttest_ind(equal_var=False) and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/moments-golden.test.ts, test/tables-golden.test.ts); enrichment scores match gseapy 1.3.1 prerank, and NES and p match the script's port of our seeded null exactly (test/gsea-golden.test.ts).",
+        "check": {
+          "diffexpr:tested": {
+            "v": 20006,
+            "abs": 0
+          },
+          "diffexpr:TTC36:p": {
+            "v": 8.091447449946383e-220,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:TTC36:q": {
+            "v": 1.6187749768362735e-215,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:FSIP1:p": {
+            "v": 2.3035779855199614e-204,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:FSIP1:q": {
+            "v": 2.3042690589156173e-200,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:SRARP:p": {
+            "v": 2.5912634421936607e-200,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:SRARP:q": {
+            "v": 1.7280272141508793e-196,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:DRAIC:p": {
+            "v": 5.510248672884023e-190,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:DRAIC:q": {
+            "v": 2.755950873742944e-186,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:NAT1:p": {
+            "v": 2.7043279892748184e-182,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:NAT1:q": {
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+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:MAPT-IT1:p": {
+            "v": 2.0129532020700296e-175,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:MAPT-IT1:q": {
+            "v": 6.711856960102169e-172,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:CT62:p": {
+            "v": 6.399560110459782e-157,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:CT62:q": {
+            "v": 1.8289942795694058e-153,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:SLC7A13:p": {
+            "v": 3.892473730008788e-156,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:SLC7A13:q": {
+            "v": 9.734103680319477e-153,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:CYP2B7P:p": {
+            "v": 6.3502571936527e-150,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:CYP2B7P:q": {
+            "v": 1.4115916157357323e-146,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "diffexpr:TFF1:p": {
+            "v": 5.061562842349789e-149,
+            "rel": 0.000001,
+            "abs": 1e-300
+          },
+          "…": {
+            "more": 3131,
+            "sha256": "baa736b72024a2de"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/diffexpr.sql b/test/snapshots/diffexpr.sql
new file mode 100644
index 00000000..e7f23c4d
--- /dev/null
+++ b/test/snapshots/diffexpr.sql
@@ -0,0 +1,52 @@
+-- cohort Basal-like
+-- brca_tcga_pan_can_atlas_2018
+(SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND type = 'patient'
+    AND attribute_name = 'SUBTYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('BRCA_Basal'))))
+
+-- cohort Luminal A
+-- brca_tcga_pan_can_atlas_2018
+(SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND type = 'patient'
+    AND attribute_name = 'SUBTYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('BRCA_LumA'))))
+
+-- view 0 diffexpr: diffexpr:genesets · brca_tcga_pan_can_atlas_2018
+SELECT gs.external_id AS set_name, groupUniqArray(g.hugo_gene_symbol) AS genes
+FROM snapshot_db.geneset gs
+JOIN snapshot_db.geneset_gene gg ON gg.geneset_id = gs.id
+JOIN snapshot_db.gene g ON g.entrez_gene_id = gg.entrez_gene_id
+WHERE startsWith(gs.external_id, 'HALLMARK_') GROUP BY set_name ORDER BY set_name
+
+-- view 0 diffexpr: diffexpr:moments:Basal-like · brca_tcga_pan_can_atlas_2018
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND type = 'patient'
+    AND attribute_name = 'SUBTYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('BRCA_Basal'))))),
+vals AS (
+  SELECT replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid, ga.hugo_gene_symbol AS gene, avg(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL)) AS v
+  FROM snapshot_db.genetic_alteration_derived ga
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+  WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores'
+    AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND isFinite(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))
+    AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid, gene
+)
+SELECT gene, count() AS n, sum(v) AS sx, sum(v * v) AS sxx FROM vals GROUP BY gene
+
+-- view 0 diffexpr: diffexpr:moments:Luminal A · brca_tcga_pan_can_atlas_2018
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND type = 'patient'
+    AND attribute_name = 'SUBTYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('BRCA_LumA'))))),
+vals AS (
+  SELECT replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid, ga.hugo_gene_symbol AS gene, avg(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL)) AS v
+  FROM snapshot_db.genetic_alteration_derived ga
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+  WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores'
+    AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND isFinite(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))
+    AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid, gene
+)
+SELECT gene, count() AS n, sum(v) AS sx, sum(v * v) AS sxx FROM vals GROUP BY gene
diff --git a/test/snapshots/diffexpr.svg b/test/snapshots/diffexpr.svg
new file mode 100644
index 00000000..f22f1562
--- /dev/null
+++ b/test/snapshots/diffexpr.svg
@@ -0,0 +1,2 @@
+<!-- view 0 diffexpr -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 757" width="100%" role="img" aria-label="Differential expression — rna_seq_v2_mrna_median_all_sample_Zscores — Basal-like vs Luminal A" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Differential expression — rna_seq_v2_mrna_median_all_sample_Zscores — Basal-like vs Luminal A</title><rect width="960" height="757" fill="#ffffff"/><text x="8" y="13" font-size="10" fill="#5b6b76">20,006 genes, q &lt; 0.05:</text><circle cx="130.13" cy="9.5" r="4" fill="#0072B2"/><text x="140.1" y="13" font-size="10" fill="#16232b">7,563 higher in Basal-like</text><circle cx="276.18" cy="9.5" r="4" fill="#E69F00"/><text x="286.2" y="13" font-size="10" fill="#16232b">7,698 higher in Luminal A</text><circle cx="130.13" cy="26.5" r="4" fill="#9aa5ad"/><text x="140.1" y="30" font-size="10" fill="#16232b">not significant</text><rect x="51" y="50" width="409" height="336" fill="none" 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text-anchor="end">250</text><text x="14.8" y="218" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 14.8 218)">−log₁₀ p</text><line x1="95.9" y1="50" x2="95.9" y2="386" stroke="#dde4e8" stroke-width="0.75"/><line x1="95.9" y1="386" x2="95.9" y2="389" stroke="#5b6b76" stroke-width="1"/><text x="89.9" y="402" font-size="10" fill="#5b6b76">−4</text><line x1="175.7" y1="50" x2="175.7" y2="386" stroke="#dde4e8" stroke-width="0.75"/><line x1="175.7" y1="386" x2="175.7" y2="389" stroke="#5b6b76" stroke-width="1"/><text x="169.7" y="402" font-size="10" fill="#5b6b76">−2</text><line x1="255.5" y1="50" x2="255.5" y2="386" stroke="#dde4e8" stroke-width="0.75"/><line x1="255.5" y1="386" x2="255.5" y2="389" stroke="#5b6b76" stroke-width="1"/><text x="252.5" y="402" font-size="10" fill="#5b6b76">0</text><line x1="335.3" y1="50" x2="335.3" y2="386" stroke="#dde4e8" stroke-width="0.75"/><line x1="335.3" y1="386" x2="335.3" y2="389" stroke="#5b6b76" stroke-width="1"/><text 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6.7e-172</title></circle><circle cx="157.9" cy="177.3" r="2" fill="#E69F00" opacity="0.8"><title>CT62: −2.45 · q = 1.8e-153</title></circle><circle cx="179" cy="178.3" r="2" fill="#E69F00" opacity="0.8"><title>SLC7A13: −1.92 · q = 9.7e-153</title></circle><circle cx="184.7" cy="186.4" r="2" fill="#E69F00" opacity="0.8"><title>CYP2B7P: −1.78 · q = 1.4e-146</title></circle><circle cx="166.1" cy="187.6" r="2" fill="#E69F00" opacity="0.8"><title>TFF1: −2.24 · q = 1e-145</title></circle><circle cx="184.3" cy="190.5" r="2" fill="#E69F00" opacity="0.8"><title>NEK10: −1.78 · q = 1.5e-143</title></circle><circle cx="164.9" cy="192.9" r="2" fill="#E69F00" opacity="0.8"><title>ABCC8: −2.27 · q = 9.8e-142</title></circle><circle cx="186.2" cy="193.8" r="2" fill="#E69F00" opacity="0.8"><title>MAPT-AS1: −1.74 · q = 4.3e-141</title></circle><circle cx="165.3" cy="194.1" r="2" fill="#E69F00" opacity="0.8"><title>ANKRD30A: −2.26 · q = 7.2e-141</title></circle><circle cx="324.3" cy="196.2" r="2" fill="#0072B2" opacity="0.8"><title>BCL11A: 1.72 · q = 2.7e-139</title></circle><circle cx="191.7" cy="197.4" r="2" fill="#E69F00" opacity="0.8"><title>TPSG1: −1.6 · q = 2.1e-138</title></circle><circle cx="181.3" cy="199.9" r="2" fill="#E69F00" opacity="0.8"><title>PGR: −1.86 · q = 1.5e-136</title></circle><circle cx="328" cy="200.1" r="2" fill="#0072B2" opacity="0.8"><title>CDCA7: 1.82 · q = 2.2e-136</title></circle><circle cx="173.6" cy="202.1" r="2" fill="#E69F00" opacity="0.8"><title>SERPINA11: −2.05 · q = 6.8e-135</title></circle><circle cx="188.8" cy="202.8" r="2" fill="#E69F00" opacity="0.8"><title>SLC39A6: −1.67 · q = 2.1e-134</title></circle><circle cx="192.1" cy="203.7" r="2" fill="#E69F00" opacity="0.8"><title>TUBA3E: −1.59 · q = 1e-133</title></circle><circle cx="186.4" cy="206" r="2" fill="#E69F00" opacity="0.8"><title>NXNL2: −1.73 · q = 5.5e-132</title></circle><circle cx="190.8" cy="206.3" r="2" fill="#E69F00" opacity="0.8"><title>TMPRSS6: −1.62 · q = 9e-132</title></circle><circle cx="331.5" cy="208.3" r="2" fill="#0072B2" opacity="0.8"><title>CDC20: 1.9 · q = 3.2e-130</title></circle><circle cx="181.4" cy="211.3" r="2" fill="#E69F00" opacity="0.8"><title>RABEP1: −1.86 · q = 5.8e-128</title></circle><circle cx="152.6" cy="212.5" r="2" fill="#E69F00" opacity="0.8"><title>AGR3: −2.58 · q = 4.4e-127</title></circle><circle cx="190.8" cy="213.3" r="2" fill="#E69F00" opacity="0.8"><title>GRPR: −1.62 · q = 1.8e-126</title></circle><circle cx="195.8" cy="213.7" r="2" fill="#E69F00" opacity="0.8"><title>TUBA3D: −1.5 · q = 3.4e-126</title></circle><circle cx="185.2" cy="214.5" r="2" fill="#E69F00" opacity="0.8"><title>MCCC2: −1.76 · q = 1.4e-125</title></circle><circle cx="184.2" cy="214.6" r="2" fill="#E69F00" opacity="0.8"><title>CFAP57: −1.79 · q = 1.7e-125</title></circle><circle cx="194.1" cy="215.7" r="2" fill="#E69F00" opacity="0.8"><title>DCPH1: −1.54 · q = 1.2e-124</title></circle><circle cx="188.2" cy="215.9" r="2" fill="#E69F00" opacity="0.8"><title>TCEAL1: −1.69 · q = 1.6e-124</title></circle><circle cx="183.5" cy="218.4" r="2" fill="#E69F00" opacity="0.8"><title>SYT9: −1.81 · q = 1.3e-122</title></circle><circle cx="172.9" cy="219.9" r="2" fill="#E69F00" opacity="0.8"><title>GP2: −2.07 · q = 1.8e-121</title></circle><circle cx="173.4" cy="220" r="2" fill="#E69F00" opacity="0.8"><title>ZNF552: −2.06 · q = 1.8e-121</title></circle><circle cx="168.5" cy="220.6" r="2" fill="#E69F00" opacity="0.8"><title>TBC1D9: −2.18 · q = 5.3e-121</title></circle><circle cx="185.5" cy="220.6" r="2" fill="#E69F00" opacity="0.8"><title>CFAP61: −1.76 · q = 5.3e-121</title></circle><circle cx="327.2" cy="221.8" r="2" fill="#0072B2" opacity="0.8"><title>KIF2C: 1.8 · q = 4.7e-120</title></circle><circle cx="329.6" cy="223" r="2" fill="#0072B2" opacity="0.8"><title>CENPA: 1.86 · q = 3.2e-119</title></circle><circle cx="181.6" cy="223.9" r="2" fill="#E69F00" opacity="0.8"><title>KDM4B: −1.85 · q = 1.8e-118</title></circle><circle cx="176.1" cy="225.5" r="2" fill="#E69F00" opacity="0.8"><title>SCUBE2: −1.99 · q = 2.9e-117</title></circle><circle cx="187.7" cy="226.2" r="2" fill="#E69F00" opacity="0.8"><title>PGLYRP2: −1.7 · q = 9.8e-117</title></circle><circle cx="186.9" cy="227.1" r="2" fill="#E69F00" opacity="0.8"><title>RGS22: −1.72 · q = 4.8e-116</title></circle><circle cx="172" cy="227.5" r="2" fill="#E69F00" opacity="0.8"><title>CCDC170: −2.09 · q = 8.9e-116</title></circle><circle cx="327.3" cy="228.8" r="2" fill="#0072B2" opacity="0.8"><title>AURKB: 1.8 · q = 8.8e-115</title></circle><circle cx="323.7" cy="229" r="2" fill="#0072B2" opacity="0.8"><title>ORC6: 1.71 · q = 1.2e-114</title></circle><circle cx="324.1" cy="229.1" r="2" fill="#0072B2" opacity="0.8"><title>PLK1: 1.72 · q = 1.4e-114</title></circle><circle cx="183" cy="229.2" r="2" fill="#E69F00" opacity="0.8"><title>F7: −1.82 · q = 1.7e-114</title></circle><circle cx="174.6" cy="230.1" r="2" fill="#E69F00" opacity="0.8"><title>ANXA9: −2.03 · q = 8e-114</title></circle><circle cx="190.3" cy="231.8" r="2" fill="#E69F00" opacity="0.8"><title>IGFALS: −1.63 · q = 1.6e-112</title></circle><circle cx="167.2" cy="231.9" r="2" fill="#E69F00" opacity="0.8"><title>XBP1: −2.21 · q = 1.7e-112</title></circle><circle cx="188.9" cy="231.9" r="2" fill="#E69F00" opacity="0.8"><title>HPX: −1.67 · q = 1.7e-112</title></circle><circle cx="331.6" cy="232.1" r="2" fill="#0072B2" opacity="0.8"><title>PSAT1: 1.91 · q = 2.4e-112</title></circle><circle cx="195.3" cy="233.6" r="2" fill="#E69F00" opacity="0.8"><title>APOF: −1.51 · q = 3.5e-111</title></circle><circle cx="182.3" cy="233.9" r="2" fill="#E69F00" opacity="0.8"><title>CACNA2D2: −1.84 · q = 5.6e-111</title></circle><circle cx="178.6" cy="234.7" r="2" fill="#E69F00" opacity="0.8"><title>ATOSA: −1.93 · q = 2.2e-110</title></circle><circle cx="197.9" cy="234.8" r="2" fill="#E69F00" opacity="0.8"><title>PLAC9P1: −1.44 · q = 2.7e-110</title></circle><circle cx="176" cy="234.8" r="2" fill="#E69F00" opacity="0.8"><title>ABAT: −1.99 · q = 2.7e-110</title></circle><circle cx="191.2" cy="235.1" r="2" fill="#E69F00" opacity="0.8"><title>EVL: −1.61 · q = 4.6e-110</title></circle><circle cx="191.9" cy="235.6" r="2" fill="#E69F00" opacity="0.8"><title>PLEKHD1: −1.59 · q = 1e-109</title></circle><circle cx="174.1" cy="236" r="2" fill="#E69F00" opacity="0.8"><title>C5AR2: −2.04 · q = 1.9e-109</title></circle><circle cx="190.3" cy="236.2" r="2" fill="#E69F00" opacity="0.8"><title>TPRG1: −1.63 · q = 2.8e-109</title></circle><circle cx="321.7" cy="237.1" r="2" fill="#0072B2" opacity="0.8"><title>HJURP: 1.66 · q = 1.4e-108</title></circle><circle cx="187.2" cy="237.6" r="2" fill="#E69F00" opacity="0.8"><title>AKR7A3: −1.71 · q = 3.4e-108</title></circle><circle cx="199.9" cy="238" r="2" fill="#E69F00" opacity="0.8"><title>CST9: −1.39 · q = 7.1e-108</title></circle><circle cx="182.5" cy="238.4" r="2" fill="#E69F00" opacity="0.8"><title>RUNDC1: −1.83 · q = 1.4e-107</title></circle><circle cx="195.4" cy="239.1" r="2" fill="#E69F00" opacity="0.8"><title>LRRC31: −1.51 · q = 4.9e-107</title></circle><circle cx="335.5" cy="239.3" r="2" fill="#0072B2" opacity="0.8"><title>FOXC1: 2 · q = 6.1e-107</title></circle><circle cx="328.6" cy="240.1" r="2" fill="#0072B2" opacity="0.8"><title>PIMREG: 1.83 · q = 2.8e-106</title></circle><circle cx="331.7" cy="240.4" r="2" fill="#0072B2" opacity="0.8"><title>CDCA8: 1.91 · q = 4.6e-106</title></circle><circle cx="197.6" cy="240.8" r="2" fill="#E69F00" opacity="0.8"><title>CIB3: −1.45 · q = 8.3e-106</title></circle><circle cx="329.4" cy="241.1" r="2" fill="#0072B2" opacity="0.8"><title>CENPN: 1.85 · q = 1.5e-105</title></circle><circle cx="186.8" cy="241.5" r="2" fill="#E69F00" opacity="0.8"><title>SLC16A6: −1.72 · q = 2.9e-105</title></circle><circle cx="323.8" cy="241.5" r="2" fill="#0072B2" opacity="0.8"><title>CEP55: 1.71 · q = 3e-105</title></circle><circle cx="322.7" cy="241.8" r="2" fill="#0072B2" opacity="0.8"><title>TPX2: 1.68 · q = 4.9e-105</title></circle><circle cx="198.1" cy="242.1" r="2" fill="#E69F00" opacity="0.8"><title>KRT37: −1.44 · q = 7.5e-105</title></circle><circle cx="318.5" cy="242.3" r="2" fill="#0072B2" opacity="0.8"><title>CDC45: 1.58 · q = 1.1e-104</title></circle><circle cx="321.1" cy="242.9" r="2" fill="#0072B2" opacity="0.8"><title>PALS2: 1.64 · q = 3.4e-104</title></circle><circle cx="318.7" cy="243.9" r="2" fill="#0072B2" opacity="0.8"><title>MYBL2: 1.58 · q = 1.8e-103</title></circle><circle cx="184.3" cy="243.9" r="2" fill="#E69F00" opacity="0.8"><title>SPEF1: −1.78 · q = 1.8e-103</title></circle><circle cx="326.1" cy="244.6" r="2" fill="#0072B2" opacity="0.8"><title>TTK: 1.77 · q = 6.7e-103</title></circle><circle cx="320.5" cy="245.2" r="2" fill="#0072B2" opacity="0.8"><title>KIF18B: 1.63 · q = 1.7e-102</title></circle><circle cx="171.1" cy="245.3" r="2" fill="#E69F00" opacity="0.8"><title>ESR1: −2.11 · q = 2e-102</title></circle><circle cx="320.8" cy="245.3" r="2" fill="#0072B2" opacity="0.8"><title>CDCA5: 1.64 · q = 2.1e-102</title></circle><circle cx="168.9" cy="245.6" r="2" fill="#E69F00" opacity="0.8"><title>CA12: −2.17 · q = 3.6e-102</title></circle><circle cx="186.7" cy="245.8" r="2" fill="#E69F00" opacity="0.8"><title>CHAD: −1.72 · q = 5.1e-102</title></circle><circle cx="329.7" cy="245.9" r="2" fill="#0072B2" opacity="0.8"><title>B3GNT5: 1.86 · q = 6.2e-102</title></circle><circle cx="179" cy="246.4" r="2" fill="#E69F00" opacity="0.8"><title>CYP4Z2P: −1.92 · q = 1.4e-101</title></circle><circle cx="184.4" cy="246.7" r="2" fill="#E69F00" opacity="0.8"><title>ANKRD30B: −1.78 · q = 2.3e-101</title></circle><circle cx="186.2" cy="247.3" r="2" fill="#E69F00" opacity="0.8"><title>SLC4A8: −1.74 · q = 6.6e-101</title></circle><circle cx="321.9" cy="247.4" r="2" fill="#0072B2" opacity="0.8"><title>CCNB2: 1.66 · q = 7.2e-101</title></circle><circle cx="320.4" cy="247.4" r="2" fill="#0072B2" opacity="0.8"><title>BIRC5: 1.63 · q = 7.7e-101</title></circle><circle cx="324.4" cy="247.5" r="2" fill="#0072B2" opacity="0.8"><title>RAD54L: 1.73 · q = 9.3e-101</title></circle><circle cx="168.2" cy="247.7" r="2" fill="#E69F00" opacity="0.8"><title>TFF3: −2.19 · q = 1.3e-100</title></circle><circle cx="177.6" cy="247.9" r="2" fill="#E69F00" opacity="0.8"><title>CCDC125: −1.95 · q = 1.8e-100</title></circle><circle cx="185.4" cy="248.1" r="2" fill="#E69F00" opacity="0.8"><title>EFCC1: −1.76 · q = 2.5e-100</title></circle><circle cx="323.2" cy="248.3" r="2" fill="#0072B2" opacity="0.8"><title>MELK: 1.7 · q = 3.3e-100</title></circle><circle cx="178.3" cy="248.3" r="2" fill="#E69F00" opacity="0.8"><title>SYTL5: −1.94 · q = 3.6e-100</title></circle><circle cx="181.2" cy="248.4" r="2" fill="#E69F00" opacity="0.8"><title>LRRC46: −1.86 · q = 4.5e-100</title></circle><circle cx="191.5" cy="249.1" r="2" fill="#E69F00" opacity="0.8"><title>MAG: −1.6 · q = 1.3e-99</title></circle><circle cx="325.5" cy="249.4" r="2" fill="#0072B2" opacity="0.8"><title>TICRR: 1.75 · q = 2.6e-99</title></circle><circle cx="184.2" cy="249.7" r="2" fill="#E69F00" opacity="0.8"><title>NEK5: −1.79 · q = 4e-99</title></circle><circle cx="184.8" cy="250.1" r="2" fill="#E69F00" opacity="0.8"><title>EFCAB12: −1.77 · q = 8.1e-99</title></circle><circle cx="201.9" cy="250.7" r="2" fill="#E69F00" opacity="0.8"><title>SLC26A3: −1.34 · q = 2.4e-98</title></circle><circle cx="320.5" cy="251.3" r="2" fill="#0072B2" opacity="0.8"><title>DEPDC1: 1.63 · q = 6.1e-98</title></circle><circle cx="325.3" cy="252" r="2" fill="#0072B2" opacity="0.8"><title>FAM72B: 1.75 · q = 2.3e-97</title></circle><circle cx="200.8" cy="252.2" r="2" fill="#E69F00" opacity="0.8"><title>PDZK1: −1.37 · q = 2.9e-97</title></circle><circle cx="326.7" cy="252.4" r="2" fill="#0072B2" opacity="0.8"><title>CDC25A: 1.79 · q = 4.3e-97</title></circle><circle cx="181" cy="252.7" r="2" fill="#E69F00" opacity="0.8"><title>DNAJC12: −1.87 · q = 7.9e-97</title></circle><circle cx="199.6" cy="253" r="2" fill="#E69F00" opacity="0.8"><title>KCTD6: −1.4 · q = 1.2e-96</title></circle><circle cx="320.4" cy="253" r="2" fill="#0072B2" opacity="0.8"><title>FAM83D: 1.63 · q = 1.2e-96</title></circle><circle cx="321.3" cy="253.2" r="2" fill="#0072B2" opacity="0.8"><title>BUB1: 1.65 · q = 1.9e-96</title></circle><circle cx="321.7" cy="253.3" r="2" fill="#0072B2" opacity="0.8"><title>SKA1: 1.66 · q = 1.9e-96</title></circle><circle cx="323.9" cy="253.4" r="2" fill="#0072B2" opacity="0.8"><title>MCM10: 1.71 · q = 2.3e-96</title></circle><circle cx="195.1" cy="253.6" r="2" fill="#E69F00" opacity="0.8"><title>SPATA4: −1.51 · q = 3.5e-96</title></circle><circle cx="322.1" cy="253.9" r="2" fill="#0072B2" opacity="0.8"><title>KIFC1: 1.67 · q = 5.4e-96</title></circle><circle cx="197.7" cy="253.9" r="2" fill="#E69F00" opacity="0.8"><title>THPO: −1.45 · q = 5.9e-96</title></circle><circle cx="321.4" cy="254.3" r="2" fill="#0072B2" opacity="0.8"><title>CCNA2: 1.65 · q = 1.3e-95</title></circle><circle cx="172.4" cy="254.6" r="2" fill="#E69F00" opacity="0.8"><title>SLC7A8: −2.08 · q = 1.9e-95</title></circle><circle cx="326.5" cy="254.7" r="2" fill="#0072B2" opacity="0.8"><title>ORC1: 1.78 · q = 2.3e-95</title></circle><circle cx="199.5" cy="254.9" r="2" fill="#E69F00" opacity="0.8"><title>SNTN: −1.4 · q = 3.4e-95</title></circle><circle cx="195.2" cy="255" r="2" fill="#E69F00" opacity="0.8"><title>ATP1A4: −1.51 · q = 4.2e-95</title></circle><circle cx="326.1" cy="255.1" r="2" fill="#0072B2" opacity="0.8"><title>PRKX: 1.77 · q = 4.9e-95</title></circle><circle cx="323.4" cy="255.2" r="2" fill="#0072B2" opacity="0.8"><title>NDC80: 1.7 · q = 5e-95</title></circle><circle cx="315.5" cy="255.3" r="2" fill="#0072B2" opacity="0.8"><title>DLGAP5: 1.5 · q = 6.8e-95</title></circle><circle cx="185.7" cy="255.4" r="2" fill="#E69F00" opacity="0.8"><title>TRIM3: −1.75 · q = 7.8e-95</title></circle><circle cx="188.2" cy="255.4" r="2" fill="#E69F00" opacity="0.8"><title>FYB2: −1.69 · q = 7.9e-95</title></circle><circle cx="204.1" cy="255.9" r="2" fill="#E69F00" opacity="0.8"><title>PHGR1: −1.29 · q = 1.8e-94</title></circle><circle cx="306.5" cy="255.9" r="2" fill="#0072B2" opacity="0.8"><title>UBE2E3: 1.28 · q = 1.8e-94</title></circle><circle cx="185.9" cy="256" r="2" fill="#E69F00" opacity="0.8"><title>RARA: −1.74 · q = 2.1e-94</title></circle><circle cx="190.9" cy="256.2" r="2" fill="#E69F00" opacity="0.8"><title>ELOVL5: −1.62 · q = 3e-94</title></circle><circle cx="181.2" cy="256.3" r="2" fill="#E69F00" opacity="0.8"><title>BBOF1: −1.86 · q = 3.5e-94</title></circle><circle cx="187.6" cy="256.5" r="2" fill="#E69F00" opacity="0.8"><title>DEFB132: −1.7 · q = 5.3e-94</title></circle><circle cx="182" cy="256.6" r="2" fill="#E69F00" opacity="0.8"><title>ACADSB: −1.84 · q = 6.3e-94</title></circle><circle cx="172" cy="256.7" r="2" fill="#E69F00" opacity="0.8"><title>DNALI1: −2.09 · q = 7.5e-94</title></circle><circle cx="185.7" cy="257.7" r="2" fill="#E69F00" opacity="0.8"><title>ERGIC1: −1.75 · q = 4.4e-93</title></circle><circle cx="178.2" cy="257.9" r="2" fill="#E69F00" opacity="0.8"><title>BCAS1: −1.94 · q = 5.8e-93</title></circle><circle cx="322.7" cy="258" r="2" fill="#0072B2" opacity="0.8"><title>MTFR2: 1.69 · q = 6.3e-93</title></circle><circle cx="176.7" cy="258.2" r="2" fill="#E69F00" opacity="0.8"><title>MAPT: −1.98 · q = 9.4e-93</title></circle><circle cx="172.8" cy="258.3" r="2" fill="#E69F00" opacity="0.8"><title>DYNLRB2: −2.07 · q = 1.1e-92</title></circle><circle cx="198.3" cy="258.4" r="2" fill="#E69F00" opacity="0.8"><title>ROPN1L: −1.43 · q = 1.3e-92</title></circle><circle cx="185.6" cy="258.5" r="2" fill="#E69F00" opacity="0.8"><title>ATP7B: −1.75 · q = 1.7e-92</title></circle><circle cx="193.3" cy="258.8" r="2" fill="#E69F00" opacity="0.8"><title>SUSD3: −1.56 · q = 2.6e-92</title></circle><circle cx="325.4" cy="258.9" r="2" fill="#0072B2" opacity="0.8"><title>ZNF695: 1.75 · q = 3e-92</title></circle><circle cx="190.7" cy="259.4" r="2" fill="#E69F00" opacity="0.8"><title>STARD10: −1.62 · q = 7.3e-92</title></circle><circle cx="318.8" cy="259.4" r="2" fill="#0072B2" opacity="0.8"><title>EXO1: 1.59 · q = 8e-92</title></circle><circle cx="172.8" cy="259.4" r="2" fill="#E69F00" opacity="0.8"><title>CAPN8: −2.07 · q = 8.1e-92</title></circle><circle cx="171" cy="259.6" r="2" fill="#E69F00" opacity="0.8"><title>THSD4: −2.12 · q = 1.2e-91</title></circle><circle cx="318.2" cy="259.7" r="2" fill="#0072B2" opacity="0.8"><title>UBE2C: 1.57 · q = 1.3e-91</title></circle><circle cx="170.8" cy="259.9" r="2" fill="#E69F00" opacity="0.8"><title>DEGS2: −2.12 · q = 1.7e-91</title></circle><circle cx="316.9" cy="260" r="2" fill="#0072B2" opacity="0.8"><title>NCAPH: 1.54 · q = 2.2e-91</title></circle><circle cx="201" cy="260.2" r="2" fill="#E69F00" opacity="0.8"><title>IL20: −1.37 · q = 2.9e-91</title></circle><circle cx="334.1" cy="260.2" r="2" fill="#0072B2" opacity="0.8"><title>CTPS1: 1.97 · q = 3e-91</title></circle><circle cx="202.2" cy="260.8" r="2" fill="#E69F00" opacity="0.8"><title>STC2: −1.33 · q = 9e-91</title></circle><circle cx="179.4" cy="261.1" r="2" fill="#E69F00" opacity="0.8"><title>DACH1: −1.91 · q = 1.6e-90</title></circle><circle cx="197" cy="261.4" r="2" fill="#E69F00" opacity="0.8"><title>LINC00908: −1.47 · q = 2.4e-90</title></circle><circle cx="318.2" cy="261.7" r="2" fill="#0072B2" opacity="0.8"><title>NUF2: 1.57 · q = 4.5e-90</title></circle><circle cx="176.8" cy="262.1" r="2" fill="#E69F00" opacity="0.8"><title>SLC22A5: −1.97 · q = 8e-90</title></circle><circle cx="173.5" cy="262.2" r="2" fill="#E69F00" opacity="0.8"><title>NOSTRIN: −2.05 · q = 9.2e-90</title></circle><circle cx="188.5" cy="262.2" r="2" fill="#E69F00" opacity="0.8"><title>STK32B: −1.68 · q = 9.2e-90</title></circle><circle cx="316.7" cy="262.4" r="2" fill="#0072B2" opacity="0.8"><title>NCAPG: 1.53 · q = 1.3e-89</title></circle><circle cx="166.4" cy="262.7" r="2" fill="#E69F00" opacity="0.8"><title>ERBB4: −2.23 · q = 2.2e-89</title></circle><circle cx="176.2" cy="262.8" r="2" fill="#E69F00" opacity="0.8"><title>ZMYND10: −1.99 · q = 2.7e-89</title></circle><circle cx="206.3" cy="262.8" r="2" fill="#E69F00" opacity="0.8"><title>KCNJ3: −1.23 · q = 2.9e-89</title></circle><circle cx="327.9" cy="263.2" r="2" fill="#0072B2" opacity="0.8"><title>MTHFD1L: 1.81 · q = 5.7e-89</title></circle><circle cx="321.1" cy="263.6" r="2" fill="#0072B2" opacity="0.8"><title>GTSE1: 1.64 · q = 1.1e-88</title></circle><circle cx="325.5" cy="263.9" r="2" fill="#0072B2" opacity="0.8"><title>CDCA3: 1.75 · q = 1.8e-88</title></circle><circle cx="184.7" cy="264" r="2" fill="#E69F00" opacity="0.8"><title>DRC3: −1.77 · q = 2.2e-88</title></circle><circle cx="335.9" cy="264.3" r="2" fill="#0072B2" opacity="0.8"><title>CCNE1: 2.02 · q = 3.6e-88</title></circle><circle cx="314.1" cy="264.6" r="2" fill="#0072B2" opacity="0.8"><title>KIF20A: 1.47 · q = 6.8e-88</title></circle><circle cx="325.9" cy="265.1" r="2" fill="#0072B2" opacity="0.8"><title>FANCA: 1.76 · q = 1.6e-87</title></circle><circle cx="313.8" cy="265.2" r="2" fill="#0072B2" opacity="0.8"><title>AURKA: 1.46 · q = 1.7e-87</title></circle><circle cx="185.7" cy="265.2" r="2" fill="#E69F00" opacity="0.8"><title>SYTL2: −1.75 · q = 1.7e-87</title></circle><circle cx="176.8" cy="265.4" r="2" fill="#E69F00" opacity="0.8"><title>ARSG: −1.97 · q = 2.5e-87</title></circle><circle cx="184.5" cy="265.6" r="2" fill="#E69F00" opacity="0.8"><title>MAGED2: −1.78 · q = 3.9e-87</title></circle><circle cx="192.4" cy="265.9" r="2" fill="#E69F00" opacity="0.8"><title>FAM234B: −1.58 · q = 6e-87</title></circle><circle cx="187.3" cy="266.2" r="2" fill="#E69F00" opacity="0.8"><title>ABCC11: −1.71 · q = 1e-86</title></circle><circle cx="323.7" cy="266.2" r="2" fill="#0072B2" opacity="0.8"><title>HASPIN: 1.71 · q = 1.1e-86</title></circle><circle cx="204.9" cy="266.8" r="2" fill="#E69F00" opacity="0.8"><title>MS4A8: −1.27 · q = 3.2e-86</title></circle><circle cx="330" cy="267" r="2" fill="#0072B2" opacity="0.8"><title>CDCA2: 1.87 · q = 3.9e-86</title></circle><circle cx="178.6" cy="267.1" r="2" fill="#E69F00" opacity="0.8"><title>INPP5J: −1.93 · q = 4.7e-86</title></circle><circle cx="198.8" cy="267.1" r="2" fill="#E69F00" opacity="0.8"><title>PLA2G10: −1.42 · q = 4.9e-86</title></circle><circle cx="185.5" cy="267.1" r="2" fill="#E69F00" opacity="0.8"><title>CFAP69: −1.76 · q = 5e-86</title></circle><circle cx="327.8" cy="267.2" r="2" fill="#0072B2" opacity="0.8"><title>FOXM1: 1.81 · q = 5.4e-86</title></circle><circle cx="181.2" cy="267.4" r="2" fill="#E69F00" opacity="0.8"><title>ANKRA2: −1.86 · q = 8.1e-86</title></circle><circle cx="195.7" cy="267.4" r="2" fill="#E69F00" opacity="0.8"><title>LINC02913: −1.5 · q = 8.2e-86</title></circle><circle cx="202.8" cy="267.4" r="2" fill="#E69F00" opacity="0.8"><title>SH3BGRL: −1.32 · q = 8.2e-86</title></circle><circle cx="168.1" cy="267.5" r="2" fill="#E69F00" opacity="0.8"><title>PRR15: −2.19 · q = 9.7e-86</title></circle><circle cx="184" cy="267.6" r="2" fill="#E69F00" opacity="0.8"><title>LRTOMT: −1.79 · q = 1.1e-85</title></circle><circle cx="200.1" cy="267.7" r="2" fill="#E69F00" opacity="0.8"><title>TMEM26: −1.39 · q = 1.4e-85</title></circle><circle cx="180.1" cy="267.9" r="2" fill="#E69F00" opacity="0.8"><title>GATA3-AS1: −1.89 · q = 2e-85</title></circle><circle cx="323.5" cy="268.2" r="2" fill="#0072B2" opacity="0.8"><title>DSCC1: 1.7 · q = 3.5e-85</title></circle><circle cx="189.2" cy="268.5" r="2" fill="#E69F00" opacity="0.8"><title>BMERB1: −1.66 · q = 5.1e-85</title></circle><circle cx="172.7" cy="268.5" r="2" fill="#E69F00" opacity="0.8"><title>FAM47E: −2.08 · q = 5.8e-85</title></circle><circle cx="186.2" cy="268.5" r="2" fill="#E69F00" opacity="0.8"><title>GREB1: −1.74 · q = 5.9e-85</title></circle><circle cx="176.6" cy="268.6" r="2" fill="#E69F00" opacity="0.8"><title>KIAA0825: −1.98 · q = 6.3e-85</title></circle><circle cx="202.6" cy="269" r="2" fill="#E69F00" opacity="0.8"><title>IGDCC3: −1.33 · q = 1.4e-84</title></circle><circle cx="311.9" cy="269.3" r="2" fill="#0072B2" opacity="0.8"><title>KIF4A: 1.41 · q = 2.3e-84</title></circle><circle cx="181.1" cy="269.4" r="2" fill="#E69F00" opacity="0.8"><title>CLSTN2: −1.86 · q = 2.6e-84</title></circle><circle cx="207.9" cy="269.4" r="2" fill="#E69F00" opacity="0.8"><title>CXADRP3: −1.19 · q = 2.9e-84</title></circle><circle cx="318" cy="269.6" r="2" fill="#0072B2" opacity="0.8"><title>CBX2: 1.57 · q = 3.4e-84</title></circle><circle cx="183.3" cy="269.8" r="2" fill="#E69F00" opacity="0.8"><title>WFS1: −1.81 · q = 4.9e-84</title></circle><circle cx="192.4" cy="269.9" r="2" fill="#E69F00" opacity="0.8"><title>SLC7A2: −1.58 · q = 6e-84</title></circle><circle cx="199.8" cy="270" r="2" fill="#E69F00" opacity="0.8"><title>UGT2B15: −1.4 · q = 7.8e-84</title></circle><circle cx="178.2" cy="270.1" r="2" fill="#E69F00" opacity="0.8"><title>TTC8: −1.94 · q = 9.5e-84</title></circle><circle cx="192.3" cy="270.4" r="2" fill="#E69F00" opacity="0.8"><title>CFAP46: −1.58 · q = 1.6e-83</title></circle><circle cx="313.3" cy="270.5" r="2" fill="#0072B2" opacity="0.8"><title>SGO1: 1.45 · q = 1.7e-83</title></circle><circle cx="200.8" cy="270.7" r="2" fill="#E69F00" opacity="0.8"><title>IQUB: −1.37 · q = 2.3e-83</title></circle><circle cx="312" cy="270.7" r="2" fill="#0072B2" opacity="0.8"><title>RAD51: 1.42 · q = 2.5e-83</title></circle><circle cx="171.4" cy="271.1" r="2" fill="#E69F00" opacity="0.8"><title>AR: −2.11 · q = 5.4e-83</title></circle><circle cx="198.2" cy="271.2" r="2" fill="#E69F00" opacity="0.8"><title>HMGCS2: −1.44 · q = 6e-83</title></circle><circle cx="197.7" cy="271.2" r="2" fill="#E69F00" opacity="0.8"><title>GPC1-AS1: −1.45 · q = 6.1e-83</title></circle><circle cx="314.8" cy="271.3" r="2" fill="#0072B2" opacity="0.8"><title>KIF14: 1.49 · q = 6.6e-83</title></circle><circle cx="318.2" cy="271.3" r="2" fill="#0072B2" opacity="0.8"><title>SKA3: 1.57 · q = 7.4e-83</title></circle><circle cx="333.6" cy="271.5" r="2" fill="#0072B2" opacity="0.8"><title>YBX1: 1.96 · q = 9.6e-83</title></circle><circle cx="194.9" cy="271.5" r="2" fill="#E69F00" opacity="0.8"><title>KCNH1: −1.52 · q = 1.1e-82</title></circle><circle cx="326.2" cy="271.8" r="2" fill="#0072B2" opacity="0.8"><title>LRP8: 1.77 · q = 1.7e-82</title></circle><circle cx="161.6" cy="271.8" r="2" fill="#E69F00" opacity="0.8"><title>MLPH: −2.35 · q = 1.8e-82</title></circle><circle cx="179.6" cy="271.9" r="2" fill="#E69F00" opacity="0.8"><title>TSPAN1: −1.9 · q = 1.9e-82</title></circle><circle cx="320" cy="272" r="2" fill="#0072B2" opacity="0.8"><title>ANLN: 1.62 · q = 2.5e-82</title></circle><circle cx="195.6" cy="272.2" r="2" fill="#E69F00" opacity="0.8"><title>WWP1: −1.5 · q = 3.1e-82</title></circle><circle cx="199.7" cy="272.5" r="2" fill="#E69F00" opacity="0.8"><title>KCNE4: −1.4 · q = 6.1e-82</title></circle><circle cx="183.5" cy="272.7" r="2" fill="#E69F00" opacity="0.8"><title>CFAP184: −1.8 · q = 7.8e-82</title></circle><circle cx="191" cy="273.2" r="2" fill="#E69F00" opacity="0.8"><title>CHRD: −1.62 · q = 2e-81</title></circle><circle cx="311.2" cy="273.6" r="2" fill="#0072B2" opacity="0.8"><title>TROAP: 1.4 · q = 3.7e-81</title></circle><circle cx="323.5" cy="273.7" r="2" fill="#0072B2" opacity="0.8"><title>SLC7A5: 1.7 · q = 4.6e-81</title></circle><circle cx="167" cy="273.9" r="2" fill="#E69F00" opacity="0.8"><title>AGR2: −2.22 · q = 6.9e-81</title></circle><circle cx="335.6" cy="273.9" r="2" fill="#0072B2" opacity="0.8"><title>IFRD1: 2.01 · q = 6.9e-81</title></circle><circle cx="159.6" cy="274" r="2" fill="#E69F00" opacity="0.8"><title>FOXA1: −2.4 · q = 7.3e-81</title></circle><circle cx="316.6" cy="274.2" r="2" fill="#0072B2" opacity="0.8"><title>RASAL1: 1.53 · q = 1e-80</title></circle><circle cx="202.6" cy="274.2" r="2" fill="#E69F00" opacity="0.8"><title>UMOD: −1.33 · q = 1.2e-80</title></circle><circle cx="198.2" cy="274.4" r="2" fill="#E69F00" opacity="0.8"><title>KRT8P41: −1.44 · q = 1.7e-80</title></circle><circle cx="313.4" cy="274.5" r="2" fill="#0072B2" opacity="0.8"><title>SMCO4: 1.45 · q = 1.7e-80</title></circle><circle cx="201.1" cy="274.6" r="2" fill="#E69F00" opacity="0.8"><title>BPIFB2: −1.36 · q = 2.3e-80</title></circle><circle cx="178.8" cy="274.7" r="2" fill="#E69F00" opacity="0.8"><title>SMIM14: −1.92 · q = 2.6e-80</title></circle><circle cx="188.7" cy="274.8" r="2" fill="#E69F00" opacity="0.8"><title>PCP2: −1.67 · q = 3e-80</title></circle><circle cx="186" cy="275.4" r="2" fill="#E69F00" opacity="0.8"><title>LRIG1: −1.74 · q = 8.2e-80</title></circle><circle cx="314.4" cy="275.7" r="2" fill="#0072B2" opacity="0.8"><title>ASPM: 1.48 · q = 1.6e-79</title></circle><circle cx="179.1" cy="275.8" r="2" fill="#E69F00" opacity="0.8"><title>DNAI7: −1.92 · q = 1.7e-79</title></circle><circle cx="183.1" cy="275.8" r="2" fill="#E69F00" opacity="0.8"><title>BORCS7: −1.82 · q = 1.9e-79</title></circle><circle cx="181.9" cy="276" r="2" fill="#E69F00" opacity="0.8"><title>DNAH7: −1.84 · q = 2.3e-79</title></circle><circle cx="166.3" cy="276.1" r="2" fill="#E69F00" opacity="0.8"><title>SLC44A4: −2.23 · q = 3.1e-79</title></circle><circle cx="319.1" cy="276.1" r="2" fill="#0072B2" opacity="0.8"><title>BTG3: 1.59 · q = 3.2e-79</title></circle><circle cx="179.5" cy="276.2" r="2" fill="#E69F00" opacity="0.8"><title>AFF3: −1.9 · q = 3.4e-79</title></circle><circle cx="187.5" cy="276.2" r="2" fill="#E69F00" opacity="0.8"><title>DNAAF1: −1.7 · q = 3.7e-79</title></circle><circle cx="185.6" cy="276.4" r="2" fill="#E69F00" opacity="0.8"><title>TMBIM6: −1.75 · q = 4.8e-79</title></circle><circle cx="185.4" cy="276.5" r="2" fill="#E69F00" opacity="0.8"><title>KCNK15: −1.76 · q = 5.7e-79</title></circle><circle cx="179.9" cy="276.5" r="2" fill="#E69F00" opacity="0.8"><title>SUOX: −1.89 · q = 6.3e-79</title></circle><circle cx="318" cy="276.7" r="2" fill="#0072B2" opacity="0.8"><title>RGMA: 1.57 · q = 7.6e-79</title></circle><circle cx="314.6" cy="276.8" r="2" fill="#0072B2" opacity="0.8"><title>PM20D2: 1.48 · q = 9.9e-79</title></circle><circle cx="326" cy="276.8" r="2" fill="#0072B2" opacity="0.8"><title>PPP1R14C: 1.77 · q = 1.1e-78</title></circle><circle cx="171.8" cy="276.9" r="2" fill="#E69F00" opacity="0.8"><title>KCNJ11: −2.1 · q = 1.2e-78</title></circle><circle cx="313" cy="277" r="2" fill="#0072B2" opacity="0.8"><title>SHCBP1: 1.44 · q = 1.4e-78</title></circle><circle cx="185.3" cy="277" r="2" fill="#E69F00" opacity="0.8"><title>SAMD15: −1.76 · q = 1.5e-78</title></circle><circle cx="196.5" cy="277.5" r="2" fill="#E69F00" opacity="0.8"><title>HECTD2-AS1: −1.48 · q = 3.6e-78</title></circle><circle cx="192.3" cy="278" r="2" fill="#E69F00" opacity="0.8"><title>ACOX2: −1.58 · q = 7.6e-78</title></circle><circle cx="182.3" cy="278.5" r="2" fill="#E69F00" opacity="0.8"><title>TMBIM4: −1.83 · q = 2.1e-77</title></circle><circle cx="204.6" cy="278.6" r="2" fill="#E69F00" opacity="0.8"><title>FRMPD2: −1.27 · q = 2.3e-77</title></circle><circle cx="322.3" cy="278.9" r="2" fill="#0072B2" opacity="0.8"><title>AUNIP: 1.67 · q = 3.7e-77</title></circle><circle cx="198.5" cy="279" r="2" fill="#E69F00" opacity="0.8"><title>PCAT18: −1.43 · q = 4.9e-77</title></circle><circle cx="190.9" cy="279.4" r="2" fill="#E69F00" opacity="0.8"><title>FMO5: −1.62 · q = 9.1e-77</title></circle><circle cx="184" cy="279.4" r="2" fill="#E69F00" opacity="0.8"><title>PTPRT: −1.79 · q = 9.1e-77</title></circle><circle cx="184.7" cy="279.6" r="2" fill="#E69F00" opacity="0.8"><title>APH1B: −1.78 · q = 1.3e-76</title></circle><circle cx="184.9" cy="279.6" r="2" fill="#E69F00" opacity="0.8"><title>BBS1: −1.77 · q = 1.3e-76</title></circle><circle cx="197.9" cy="279.6" r="2" fill="#E69F00" opacity="0.8"><title>FGD3: −1.44 · q = 1.4e-76</title></circle><circle cx="320.4" cy="279.7" r="2" fill="#0072B2" opacity="0.8"><title>CENPO: 1.63 · q = 1.6e-76</title></circle><circle cx="176.1" cy="279.7" r="2" fill="#E69F00" opacity="0.8"><title>INPP4B: −1.99 · q = 1.7e-76</title></circle><circle cx="334.5" cy="279.8" r="2" fill="#0072B2" opacity="0.8"><title>ROPN1: 1.98 · q = 1.7e-76</title></circle><circle cx="199.4" cy="279.8" r="2" fill="#E69F00" opacity="0.8"><title>TMEM232: −1.41 · q = 1.8e-76</title></circle><circle cx="334" cy="279.8" r="2" fill="#0072B2" opacity="0.8"><title>CENPW: 1.97 · q = 1.8e-76</title></circle><circle cx="322.3" cy="279.9" r="2" fill="#0072B2" opacity="0.8"><title>LBR: 1.67 · q = 2.2e-76</title></circle><circle cx="184.7" cy="280" r="2" fill="#E69F00" opacity="0.8"><title>DNAL1: −1.78 · q = 2.5e-76</title></circle><circle cx="326.4" cy="280" r="2" fill="#0072B2" opacity="0.8"><title>CHEK1: 1.78 · q = 2.8e-76</title></circle><circle cx="332.3" cy="280.2" r="2" fill="#0072B2" opacity="0.8"><title>SRSF12: 1.92 · q = 3.8e-76</title></circle><circle cx="185.3" cy="280.4" r="2" fill="#E69F00" opacity="0.8"><title>ACOT4: −1.76 · q = 4.9e-76</title></circle><circle cx="324.7" cy="280.5" r="2" fill="#0072B2" opacity="0.8"><title>KCNK5: 1.73 · q = 6.2e-76</title></circle><circle cx="320.2" cy="280.7" r="2" fill="#0072B2" opacity="0.8"><title>PHGDH: 1.62 · q = 8.5e-76</title></circle><circle cx="183.8" cy="280.8" r="2" fill="#E69F00" opacity="0.8"><title>CYP4B1: −1.8 · q = 1.1e-75</title></circle><circle cx="190.1" cy="281.2" r="2" fill="#E69F00" opacity="0.8"><title>RAI2: −1.64 · q = 2e-75</title></circle><circle cx="188.5" cy="281.2" r="2" fill="#E69F00" opacity="0.8"><title>PREX1: −1.68 · q = 2e-75</title></circle><circle cx="202.7" cy="281.4" r="2" fill="#E69F00" opacity="0.8"><title>UGDH: −1.32 · q = 3.1e-75</title></circle><circle cx="180.7" cy="281.4" r="2" fill="#E69F00" opacity="0.8"><title>AK8: −1.87 · q = 3.1e-75</title></circle><circle cx="193.2" cy="281.5" r="2" fill="#E69F00" opacity="0.8"><title>SLC40A1: −1.56 · q = 3.6e-75</title></circle><circle cx="307" cy="281.6" r="2" fill="#0072B2" opacity="0.8"><title>GSTP1: 1.29 · q = 3.9e-75</title></circle><circle cx="182.7" cy="281.7" r="2" fill="#E69F00" opacity="0.8"><title>APBB2: −1.83 · q = 5.3e-75</title></circle><circle cx="311.4" cy="281.8" r="2" fill="#0072B2" opacity="0.8"><title>OIP5: 1.4 · q = 6.1e-75</title></circle><circle cx="176.6" cy="282" r="2" fill="#E69F00" opacity="0.8"><title>RHOB: −1.98 · q = 8.2e-75</title></circle><circle cx="185.8" cy="282.1" r="2" fill="#E69F00" opacity="0.8"><title>REEP5: −1.75 · q = 1e-74</title></circle><circle cx="202.3" cy="282.1" r="2" fill="#E69F00" opacity="0.8"><title>DRC2: −1.33 · q = 1e-74</title></circle><circle cx="190" cy="282.2" r="2" fill="#E69F00" opacity="0.8"><title>WDR93: −1.64 · q = 1.2e-74</title></circle><circle cx="325.1" cy="282.3" r="2" fill="#0072B2" opacity="0.8"><title>ESYT3: 1.75 · q = 1.4e-74</title></circle><circle cx="203.1" cy="282.4" r="2" fill="#E69F00" opacity="0.8"><title>AGTR1: −1.31 · q = 1.6e-74</title></circle><circle cx="324.9" cy="282.4" r="2" fill="#0072B2" opacity="0.8"><title>NUP93: 1.74 · q = 1.7e-74</title></circle><circle cx="190.6" cy="282.4" r="2" fill="#E69F00" opacity="0.8"><title>MOAP1: −1.63 · q = 1.8e-74</title></circle><circle cx="313.3" cy="282.5" r="2" fill="#0072B2" opacity="0.8"><title>CCDC82: 1.45 · q = 2e-74</title></circle><circle cx="184.2" cy="282.6" r="2" fill="#E69F00" opacity="0.8"><title>GFRA1: −1.79 · q = 2.4e-74</title></circle><circle cx="188.8" cy="282.6" r="2" fill="#E69F00" opacity="0.8"><title>DNAAF8: −1.67 · q = 2.5e-74</title></circle><circle cx="187.1" cy="282.7" r="2" fill="#E69F00" opacity="0.8"><title>ATP8B1: −1.72 · q = 2.7e-74</title></circle><circle cx="202.1" cy="282.7" r="2" fill="#E69F00" opacity="0.8"><title>SOWAHA: −1.34 · q = 2.8e-74</title></circle><circle cx="194.4" cy="282.9" r="2" fill="#E69F00" opacity="0.8"><title>NCAM2: −1.53 · q = 3.7e-74</title></circle><circle cx="189.5" cy="283" r="2" fill="#E69F00" opacity="0.8"><title>TSPAN13: −1.65 · q = 4.6e-74</title></circle><circle cx="192.1" cy="283" r="2" fill="#E69F00" opacity="0.8"><title>DNAAF19: −1.59 · q = 4.8e-74</title></circle><circle cx="186.5" cy="283" r="2" fill="#E69F00" opacity="0.8"><title>ZNF703: −1.73 · q = 4.8e-74</title></circle><circle cx="202.9" cy="283" r="2" fill="#E69F00" opacity="0.8"><title>SPMIP5: −1.32 · q = 4.9e-74</title></circle><circle cx="321.8" cy="283.3" r="2" fill="#0072B2" opacity="0.8"><title>TRIP13: 1.66 · q = 7.8e-74</title></circle><circle cx="323.7" cy="283.3" r="2" fill="#0072B2" opacity="0.8"><title>FAM171A1: 1.71 · q = 8.2e-74</title></circle><circle cx="181.8" cy="283.3" r="2" fill="#E69F00" opacity="0.8"><title>PIERCE1: −1.85 · q = 8.4e-74</title></circle><circle cx="192.8" cy="283.4" r="2" fill="#E69F00" opacity="0.8"><title>KIF13B: −1.57 · q = 8.7e-74</title></circle><circle cx="317.5" cy="283.5" r="2" fill="#0072B2" opacity="0.8"><title>CDT1: 1.55 · q = 1e-73</title></circle><circle cx="186" cy="283.5" r="2" fill="#E69F00" opacity="0.8"><title>GASK1B: −1.74 · q = 1.1e-73</title></circle><circle cx="312.2" cy="283.6" r="2" fill="#0072B2" opacity="0.8"><title>POLQ: 1.42 · q = 1.3e-73</title></circle><circle cx="199.6" cy="283.7" r="2" fill="#E69F00" opacity="0.8"><title>RAB30: −1.4 · q = 1.4e-73</title></circle><circle cx="187.5" cy="283.7" r="2" fill="#E69F00" opacity="0.8"><title>ZNF205-AS1: −1.7 · q = 1.6e-73</title></circle><circle cx="185.4" cy="283.9" r="2" fill="#E69F00" opacity="0.8"><title>CIRBP: −1.76 · q = 2.1e-73</title></circle><circle cx="204.8" cy="284" r="2" fill="#E69F00" opacity="0.8"><title>EMX1: −1.27 · q = 2.6e-73</title></circle><circle cx="205.6" cy="284" r="2" fill="#E69F00" opacity="0.8"><title>CASD1: −1.25 · q = 2.8e-73</title></circle><circle cx="312.1" cy="284.1" r="2" fill="#0072B2" opacity="0.8"><title>KPNA2: 1.42 · q = 3.1e-73</title></circle><circle cx="321.7" cy="284.3" r="2" fill="#0072B2" opacity="0.8"><title>CMC2: 1.66 · q = 4.2e-73</title></circle><circle cx="177" cy="284.3" r="2" fill="#E69F00" opacity="0.8"><title>UBXN10: −1.97 · q = 4.2e-73</title></circle><circle cx="191.8" cy="284.3" r="2" fill="#E69F00" opacity="0.8"><title>SCN7A: −1.6 · q = 4.7e-73</title></circle><circle cx="309.8" cy="284.4" r="2" fill="#0072B2" opacity="0.8"><title>PLCH1: 1.36 · q = 5.3e-73</title></circle><circle cx="185.8" cy="284.6" r="2" fill="#E69F00" opacity="0.8"><title>ADAMTS15: −1.75 · q = 7.7e-73</title></circle><circle cx="313.1" cy="284.7" r="2" fill="#0072B2" opacity="0.8"><title>KIF15: 1.44 · q = 9.3e-73</title></circle><circle cx="202" cy="284.8" r="2" fill="#E69F00" opacity="0.8"><title>AKR7L: −1.34 · q = 9.7e-73</title></circle><circle cx="174.5" cy="285" r="2" fill="#E69F00" opacity="0.8"><title>SIDT1: −2.03 · q = 1.5e-72</title></circle><circle cx="188.5" cy="285" r="2" fill="#E69F00" opacity="0.8"><title>TMEM144: −1.68 · q = 1.6e-72</title></circle><circle cx="180.4" cy="285.1" r="2" fill="#E69F00" opacity="0.8"><title>BBS4: −1.88 · q = 1.8e-72</title></circle><circle cx="323.9" cy="285.2" r="2" fill="#0072B2" opacity="0.8"><title>PVR: 1.71 · q = 2.2e-72</title></circle><circle cx="201.3" cy="285.2" r="2" fill="#E69F00" opacity="0.8"><title>REPS2: −1.36 · q = 2.2e-72</title></circle><circle cx="192" cy="285.3" r="2" fill="#E69F00" opacity="0.8"><title>KLHDC7A: −1.59 · q = 2.3e-72</title></circle><circle cx="331.6" cy="285.6" r="2" fill="#0072B2" opacity="0.8"><title>CHODL: 1.91 · q = 3.9e-72</title></circle><circle cx="311.2" cy="285.8" r="2" fill="#0072B2" opacity="0.8"><title>BUB1B: 1.4 · q = 6e-72</title></circle><circle cx="321.7" cy="286" r="2" fill="#0072B2" opacity="0.8"><title>STIL: 1.66 · q = 8.8e-72</title></circle><circle cx="189.3" cy="286.4" r="2" fill="#E69F00" opacity="0.8"><title>PRRT3: −1.66 · q = 1.7e-71</title></circle><circle cx="316.1" cy="286.4" r="2" fill="#0072B2" opacity="0.8"><title>PTTG1: 1.52 · q = 1.8e-71</title></circle><circle cx="191.4" cy="286.5" r="2" fill="#E69F00" opacity="0.8"><title>KIF16B: −1.61 · q = 2.2e-71</title></circle><circle cx="318.8" cy="286.6" r="2" fill="#0072B2" opacity="0.8"><title>DSC2: 1.59 · q = 2.3e-71</title></circle><circle cx="193" cy="286.8" r="2" fill="#E69F00" opacity="0.8"><title>BTG2: −1.57 · q = 3.6e-71</title></circle><circle cx="313" cy="286.8" r="2" fill="#0072B2" opacity="0.8"><title>KIF18A: 1.44 · q = 3.6e-71</title></circle><circle cx="315.2" cy="286.8" r="2" fill="#0072B2" opacity="0.8"><title>MND1: 1.5 · q = 3.7e-71</title></circle><circle cx="321.7" cy="287.1" r="2" fill="#0072B2" opacity="0.8"><title>PIF1: 1.66 · q = 5.5e-71</title></circle><circle cx="313.2" cy="287.4" r="2" fill="#0072B2" opacity="0.8"><title>CIP2A: 1.45 · q = 9.2e-71</title></circle><circle cx="308.9" cy="287.7" r="2" fill="#0072B2" opacity="0.8"><title>GGH: 1.34 · q = 1.6e-70</title></circle><circle cx="330" cy="287.7" r="2" fill="#0072B2" opacity="0.8"><title>SFT2D2: 1.87 · q = 1.8e-70</title></circle><circle cx="187.2" cy="287.8" r="2" fill="#E69F00" opacity="0.8"><title>CELSR1: −1.71 · q = 2e-70</title></circle><circle cx="317" cy="287.9" r="2" fill="#0072B2" opacity="0.8"><title>CHRNA5: 1.54 · q = 2.5e-70</title></circle><circle cx="312.6" cy="288" r="2" fill="#0072B2" opacity="0.8"><title>MKI67: 1.43 · q = 2.8e-70</title></circle><circle cx="184.4" cy="288.2" r="2" fill="#E69F00" opacity="0.8"><title>SYTL4: −1.78 · q = 4.2e-70</title></circle><circle cx="317.8" cy="288.3" r="2" fill="#0072B2" opacity="0.8"><title>ABTB2: 1.56 · q = 4.6e-70</title></circle><circle cx="196.7" cy="288.4" r="2" fill="#E69F00" opacity="0.8"><title>GIPR: −1.47 · q = 5.5e-70</title></circle><circle cx="184.5" cy="288.5" r="2" fill="#E69F00" opacity="0.8"><title>FAM174A: −1.78 · q = 6.5e-70</title></circle><circle cx="191" cy="288.6" r="2" fill="#E69F00" opacity="0.8"><title>CRIP1: −1.62 · q = 7.4e-70</title></circle><circle cx="312.8" cy="288.6" r="2" fill="#0072B2" opacity="0.8"><title>INAVA: 1.44 · q = 8.3e-70</title></circle><circle cx="312.1" cy="288.7" r="2" fill="#0072B2" opacity="0.8"><title>DEPDC1B: 1.42 · q = 9.8e-70</title></circle><circle cx="323.4" cy="288.8" r="2" fill="#0072B2" opacity="0.8"><title>STMN1: 1.7 · q = 1e-69</title></circle><circle cx="194.3" cy="288.8" r="2" fill="#E69F00" opacity="0.8"><title>BAIAP3: −1.53 · q = 1.1e-69</title></circle><circle cx="189.5" cy="288.8" r="2" fill="#E69F00" opacity="0.8"><title>HEXIM1: −1.65 · q = 1.2e-69</title></circle><circle cx="185" cy="288.8" r="2" fill="#E69F00" opacity="0.8"><title>ABLIM3: −1.77 · q = 1.2e-69</title></circle><circle cx="202.2" cy="288.9" r="2" fill="#E69F00" opacity="0.8"><title>PARD6B: −1.34 · q = 1.2e-69</title></circle><circle cx="318.7" cy="288.9" r="2" fill="#0072B2" opacity="0.8"><title>CEBPB: 1.58 · q = 1.3e-69</title></circle><circle cx="193.4" cy="289" r="2" fill="#E69F00" opacity="0.8"><title>GALNT7: −1.56 · q = 1.6e-69</title></circle><circle cx="337.4" cy="289.1" r="2" fill="#0072B2" opacity="0.8"><title>FZD9: 2.05 · q = 1.9e-69</title></circle><circle cx="192.6" cy="289.2" r="2" fill="#E69F00" opacity="0.8"><title>FAM161B: −1.58 · q = 2e-69</title></circle><circle cx="191.8" cy="289.2" r="2" fill="#E69F00" opacity="0.8"><title>CCNG2: −1.6 · q = 2.2e-69</title></circle><circle cx="197.8" cy="289.2" r="2" fill="#E69F00" opacity="0.8"><title>ASAH1: −1.45 · q = 2.3e-69</title></circle><circle cx="204.8" cy="289.4" r="2" fill="#E69F00" opacity="0.8"><title>FAM81B: −1.27 · q = 3.3e-69</title></circle><circle cx="198.6" cy="289.4" r="2" fill="#E69F00" opacity="0.8"><title>CCDC74A: −1.43 · q = 3.3e-69</title></circle><circle cx="199.8" cy="289.5" r="2" fill="#E69F00" opacity="0.8"><title>NHLRC4: −1.4 · q = 3.4e-69</title></circle><circle cx="185.5" cy="289.5" r="2" fill="#E69F00" opacity="0.8"><title>RNF103: −1.75 · q = 4e-69</title></circle><circle cx="170.2" cy="289.7" r="2" fill="#E69F00" opacity="0.8"><title>GATA3: −2.14 · q = 4.9e-69</title></circle><circle cx="318.3" cy="289.7" r="2" fill="#0072B2" opacity="0.8"><title>RCOR2: 1.57 · q = 5.2e-69</title></circle><circle cx="201.8" cy="289.7" r="2" fill="#E69F00" opacity="0.8"><title>ACSM1: −1.35 · q = 5.4e-69</title></circle><circle cx="323.5" cy="289.8" r="2" fill="#0072B2" opacity="0.8"><title>LDHB: 1.7 · q = 5.8e-69</title></circle><circle cx="188.1" cy="290" r="2" fill="#E69F00" opacity="0.8"><title>DALRD3: −1.69 · q = 8.1e-69</title></circle><circle cx="187.2" cy="290.4" r="2" fill="#E69F00" opacity="0.8"><title>ANKRD42: −1.71 · q = 1.7e-68</title></circle><circle cx="324.7" cy="290.5" r="2" fill="#0072B2" opacity="0.8"><title>CKS1B: 1.73 · q = 2.1e-68</title></circle><circle cx="309.3" cy="290.5" r="2" fill="#0072B2" opacity="0.8"><title>ZNF462: 1.35 · q = 2.1e-68</title></circle><circle cx="315.2" cy="290.6" r="2" fill="#0072B2" opacity="0.8"><title>KIF23: 1.5 · q = 2.4e-68</title></circle><circle cx="214.1" cy="290.6" r="2" fill="#E69F00" opacity="0.8"><title>SERPINA6: −1.04 · q = 2.5e-68</title></circle><circle cx="323.4" cy="290.6" r="2" fill="#0072B2" opacity="0.8"><title>HPDL: 1.7 · q = 2.7e-68</title></circle><circle cx="306.4" cy="290.7" r="2" fill="#0072B2" opacity="0.8"><title>NFIL3: 1.28 · q = 3.1e-68</title></circle><circle cx="189.2" cy="290.8" r="2" fill="#E69F00" opacity="0.8"><title>KLHDC2: −1.66 · q = 3.7e-68</title></circle><circle cx="200.5" cy="290.9" r="2" fill="#E69F00" opacity="0.8"><title>CNTD1: −1.38 · q = 4e-68</title></circle><circle cx="187.8" cy="290.9" r="2" fill="#E69F00" opacity="0.8"><title>LCMT2: −1.7 · q = 4.2e-68</title></circle><circle cx="334.9" cy="290.9" r="2" fill="#0072B2" opacity="0.8"><title>TTLL4: 1.99 · q = 4.4e-68</title></circle><circle cx="322" cy="291.2" r="2" fill="#0072B2" opacity="0.8"><title>TUBB: 1.67 · q = 7.5e-68</title></circle><circle cx="327.8" cy="291.3" r="2" fill="#0072B2" opacity="0.8"><title>YEATS2: 1.81 · q = 7.8e-68</title></circle><circle cx="323.3" cy="291.3" r="2" fill="#0072B2" opacity="0.8"><title>AMD1: 1.7 · q = 8.8e-68</title></circle><circle cx="313.3" cy="291.5" r="2" fill="#0072B2" opacity="0.8"><title>CENPF: 1.45 · q = 1.2e-67</title></circle><circle cx="313.2" cy="291.5" r="2" fill="#0072B2" opacity="0.8"><title>FAM72A: 1.45 · q = 1.3e-67</title></circle><circle cx="320.9" cy="291.6" r="2" fill="#0072B2" opacity="0.8"><title>USB1: 1.64 · q = 1.5e-67</title></circle><circle cx="212.8" cy="291.7" r="2" fill="#E69F00" opacity="0.8"><title>LDLRAD1: −1.07 · q = 1.7e-67</title></circle><circle cx="214" cy="291.9" r="2" fill="#E69F00" opacity="0.8"><title>ABCC13: −1.04 · q = 2.2e-67</title></circle><circle cx="194.8" cy="291.9" r="2" fill="#E69F00" opacity="0.8"><title>TCEAL3: −1.52 · q = 2.2e-67</title></circle><circle cx="212" cy="291.9" r="2" fill="#E69F00" opacity="0.8"><title>CYP4F8: −1.09 · q = 2.3e-67</title></circle><circle cx="311.1" cy="291.9" r="2" fill="#0072B2" opacity="0.8"><title>PLAAT1: 1.39 · q = 2.5e-67</title></circle><circle cx="314.9" cy="292.2" r="2" fill="#0072B2" opacity="0.8"><title>MAP3K21: 1.49 · q = 4e-67</title></circle><circle cx="320.6" cy="292.2" r="2" fill="#0072B2" opacity="0.8"><title>CLCN4: 1.63 · q = 4.3e-67</title></circle><circle cx="322.4" cy="292.4" r="2" fill="#0072B2" opacity="0.8"><title>MSANTD3: 1.68 · q = 5.4e-67</title></circle><circle cx="332.1" cy="292.4" r="2" fill="#0072B2" opacity="0.8"><title>PDSS1: 1.92 · q = 6.2e-67</title></circle><circle cx="202.7" cy="292.5" r="2" fill="#E69F00" opacity="0.8"><title>TCEAL4: −1.32 · q = 6.2e-67</title></circle><circle cx="310.6" cy="292.5" r="2" fill="#0072B2" opacity="0.8"><title>E2F1: 1.38 · q = 6.3e-67</title></circle><circle cx="187.5" cy="292.5" r="2" fill="#E69F00" opacity="0.8"><title>KIAA0232: −1.7 · q = 7.3e-67</title></circle><circle cx="208.3" cy="292.6" r="2" fill="#E69F00" opacity="0.8"><title>CPB1: −1.18 · q = 8.1e-67</title></circle><circle cx="188" cy="292.7" r="2" fill="#E69F00" opacity="0.8"><title>CPEB2: −1.69 · q = 9.4e-67</title></circle><circle cx="191.4" cy="292.8" r="2" fill="#E69F00" opacity="0.8"><title>FUT8: −1.61 · q = 1.1e-66</title></circle><circle cx="175.5" cy="292.9" r="2" fill="#E69F00" opacity="0.8"><title>FBP1: −2 · q = 1.4e-66</title></circle><circle cx="198.6" cy="292.9" r="2" fill="#E69F00" opacity="0.8"><title>BTRC: −1.43 · q = 1.4e-66</title></circle><circle cx="187.6" cy="293.1" r="2" fill="#E69F00" opacity="0.8"><title>TIGD6: −1.7 · q = 2.1e-66</title></circle><circle cx="309.9" cy="293.3" r="2" fill="#0072B2" opacity="0.8"><title>CENPE: 1.36 · q = 2.5e-66</title></circle><circle cx="318.6" cy="293.4" r="2" fill="#0072B2" opacity="0.8"><title>BLM: 1.58 · q = 3e-66</title></circle><circle cx="205.5" cy="293.4" r="2" fill="#E69F00" opacity="0.8"><title>CACNA1H: −1.25 · q = 3.4e-66</title></circle><circle cx="335.9" cy="293.5" r="2" fill="#0072B2" opacity="0.8"><title>A2ML1: 2.02 · q = 3.6e-66</title></circle><circle cx="190.7" cy="293.5" r="2" fill="#E69F00" opacity="0.8"><title>TP53INP1: −1.62 · q = 3.9e-66</title></circle><circle cx="315.6" cy="293.6" r="2" fill="#0072B2" opacity="0.8"><title>DIAPH3: 1.51 · q = 4.7e-66</title></circle><circle cx="324.3" cy="293.6" r="2" fill="#0072B2" opacity="0.8"><title>MCM7: 1.72 · q = 4.7e-66</title></circle><circle cx="201.6" cy="293.8" r="2" fill="#E69F00" opacity="0.8"><title>KRT222: −1.35 · q = 6.2e-66</title></circle><circle cx="196.7" cy="293.8" r="2" fill="#E69F00" opacity="0.8"><title>GALNT6: −1.47 · q = 6.5e-66</title></circle><circle cx="333" cy="293.9" r="2" fill="#0072B2" opacity="0.8"><title>VGLL1: 1.94 · q = 7.4e-66</title></circle><circle cx="194" cy="294.1" r="2" fill="#E69F00" opacity="0.8"><title>SEC16A: −1.54 · q = 1.1e-65</title></circle><circle cx="204.6" cy="294.1" r="2" fill="#E69F00" opacity="0.8"><title>NR2E3: −1.28 · q = 1.2e-65</title></circle><circle cx="313.4" cy="294.2" r="2" fill="#0072B2" opacity="0.8"><title>BEND3: 1.45 · q = 1.2e-65</title></circle><circle cx="194.5" cy="294.2" r="2" fill="#E69F00" opacity="0.8"><title>KCND3: −1.53 · q = 1.3e-65</title></circle><circle cx="188.7" cy="294.2" r="2" fill="#E69F00" opacity="0.8"><title>RNASE4: −1.67 · q = 1.4e-65</title></circle><circle cx="189.6" cy="294.2" r="2" fill="#E69F00" opacity="0.8"><title>SKP1: −1.65 · q = 1.4e-65</title></circle><circle cx="201.4" cy="294.3" r="2" fill="#E69F00" opacity="0.8"><title>HEPACAM2: −1.36 · q = 1.5e-65</title></circle><circle cx="194.3" cy="294.4" r="2" fill="#E69F00" opacity="0.8"><title>MEGF9: −1.53 · q = 1.7e-65</title></circle><circle cx="319.4" cy="294.4" r="2" fill="#0072B2" opacity="0.8"><title>DBF4: 1.6 · q = 1.7e-65</title></circle><circle cx="208.7" cy="294.4" r="2" fill="#E69F00" opacity="0.8"><title>SEC14L2: −1.17 · q = 1.8e-65</title></circle><circle cx="212.7" cy="294.5" r="2" fill="#E69F00" opacity="0.8"><title>SIAH2: −1.07 · q = 2.1e-65</title></circle><circle cx="318.8" cy="294.6" r="2" fill="#0072B2" opacity="0.8"><title>CHEK2: 1.59 · q = 2.4e-65</title></circle><circle cx="188.5" cy="294.6" r="2" fill="#E69F00" opacity="0.8"><title>MAP3K12: −1.68 · q = 2.6e-65</title></circle><circle cx="195.5" cy="294.6" r="2" fill="#E69F00" opacity="0.8"><title>CCDC183: −1.5 · q = 2.7e-65</title></circle><circle cx="191.2" cy="294.7" r="2" fill="#E69F00" opacity="0.8"><title>TLE3: −1.61 · q = 3.2e-65</title></circle><circle cx="307.8" cy="294.8" r="2" fill="#0072B2" opacity="0.8"><title>CDK1: 1.31 · q = 3.4e-65</title></circle><circle cx="185.7" cy="294.8" r="2" fill="#E69F00" opacity="0.8"><title>RSPH1: −1.75 · q = 3.6e-65</title></circle><circle cx="323.9" cy="294.9" r="2" fill="#0072B2" opacity="0.8"><title>ENO1: 1.71 · q = 4.3e-65</title></circle><circle cx="318.7" cy="295" r="2" fill="#0072B2" opacity="0.8"><title>RBM38: 1.58 · q = 4.8e-65</title></circle><circle cx="322" cy="295" r="2" fill="#0072B2" opacity="0.8"><title>ANP32E: 1.67 · q = 4.9e-65</title></circle><circle cx="204.8" cy="295.1" r="2" fill="#E69F00" opacity="0.8"><title>GNMT: −1.27 · q = 5.8e-65</title></circle><circle cx="211" cy="295.1" r="2" fill="#E69F00" opacity="0.8"><title>SLC30A8: −1.11 · q = 5.8e-65</title></circle><circle cx="215.7" cy="295.1" r="2" fill="#E69F00" opacity="0.8"><title>OTOR: −0.998 · q = 5.8e-65</title></circle><circle cx="190.4" cy="295.1" r="2" fill="#E69F00" opacity="0.8"><title>CROT: −1.63 · q = 6e-65</title></circle><circle cx="218" cy="295.1" r="2" fill="#E69F00" opacity="0.8"><title>CST5: −0.94 · q = 6e-65</title></circle><circle cx="193" cy="295.1" r="2" fill="#E69F00" opacity="0.8"><title>SPOPL: −1.57 · q = 6.1e-65</title></circle><circle cx="199.6" cy="295.3" r="2" fill="#E69F00" opacity="0.8"><title>KAT6B: −1.4 · q = 7.8e-65</title></circle><circle cx="321.8" cy="295.3" r="2" fill="#0072B2" opacity="0.8"><title>EZH2: 1.66 · q = 8.6e-65</title></circle><circle cx="197.1" cy="295.3" r="2" fill="#E69F00" opacity="0.8"><title>CYBRD1: −1.46 · q = 8.8e-65</title></circle><circle cx="312.1" cy="295.3" r="2" fill="#0072B2" opacity="0.8"><title>IQGAP3: 1.42 · q = 8.9e-65</title></circle><circle cx="214" cy="295.4" r="2" fill="#E69F00" opacity="0.8"><title>TMC3: −1.04 · q = 1e-64</title></circle><circle cx="308.5" cy="295.5" r="2" fill="#0072B2" opacity="0.8"><title>SH2D2A: 1.33 · q = 1.1e-64</title></circle><circle cx="178.1" cy="295.6" r="2" fill="#E69F00" opacity="0.8"><title>REEP6: −1.94 · q = 1.3e-64</title></circle><circle cx="324.9" cy="295.6" r="2" fill="#0072B2" opacity="0.8"><title>HMGA1: 1.74 · q = 1.4e-64</title></circle><circle cx="189" cy="295.6" r="2" fill="#E69F00" opacity="0.8"><title>KLHDC1: −1.67 · q = 1.4e-64</title></circle><circle cx="327" cy="295.6" r="2" fill="#0072B2" opacity="0.8"><title>AMER1: 1.79 · q = 1.4e-64</title></circle><circle cx="190" cy="295.6" r="2" fill="#E69F00" opacity="0.8"><title>ASTN2: −1.64 · q = 1.5e-64</title></circle><circle cx="327.5" cy="295.7" r="2" fill="#0072B2" opacity="0.8"><title>UCK2: 1.81 · q = 1.6e-64</title></circle><circle cx="312.7" cy="295.8" r="2" fill="#0072B2" opacity="0.8"><title>MAD2L1: 1.43 · q = 1.9e-64</title></circle><circle cx="204.1" cy="296" r="2" fill="#E69F00" opacity="0.8"><title>DHRS2: −1.29 · q = 2.7e-64</title></circle><circle cx="320.2" cy="296.1" r="2" fill="#0072B2" opacity="0.8"><title>NCK2: 1.62 · q = 3.2e-64</title></circle><circle cx="188.4" cy="296.2" r="2" fill="#E69F00" opacity="0.8"><title>GIN1: −1.68 · q = 4.2e-64</title></circle><circle cx="216.6" cy="296.3" r="2" fill="#E69F00" opacity="0.8"><title>TRH: −0.974 · q = 4.8e-64</title></circle><circle cx="325.1" cy="296.3" r="2" fill="#0072B2" opacity="0.8"><title>FANCE: 1.74 · q = 5.1e-64</title></circle><circle cx="332.7" cy="296.4" r="2" fill="#0072B2" opacity="0.8"><title>SUV39H2: 1.94 · q = 6.2e-64</title></circle><circle cx="210.4" cy="296.5" r="2" fill="#E69F00" opacity="0.8"><title>CYP4A11: −1.13 · q = 7.3e-64</title></circle><circle cx="186.7" cy="296.5" r="2" fill="#E69F00" opacity="0.8"><title>FBXO36: −1.72 · q = 7.3e-64</title></circle><circle cx="203.9" cy="296.6" r="2" fill="#E69F00" opacity="0.8"><title>PLCD4: −1.29 · q = 7.9e-64</title></circle><circle cx="320.8" cy="296.6" r="2" fill="#0072B2" opacity="0.8"><title>IMPA2: 1.64 · q = 7.9e-64</title></circle><circle cx="300.6" cy="296.6" r="2" fill="#0072B2" opacity="0.8"><title>HENMT1: 1.13 · q = 8.1e-64</title></circle><circle cx="190.8" cy="296.7" r="2" fill="#E69F00" opacity="0.8"><title>PCBD2: −1.62 · q = 9.7e-64</title></circle><circle cx="198.7" cy="296.8" r="2" fill="#E69F00" opacity="0.8"><title>NOVA1: −1.42 · q = 1.1e-63</title></circle><circle cx="200.1" cy="296.8" r="2" fill="#E69F00" opacity="0.8"><title>PKIB: −1.39 · q = 1.2e-63</title></circle><circle cx="187" cy="296.8" r="2" fill="#E69F00" opacity="0.8"><title>EXOC6: −1.72 · q = 1.2e-63</title></circle><circle cx="190.4" cy="296.9" r="2" fill="#E69F00" opacity="0.8"><title>CRY2: −1.63 · q = 1.3e-63</title></circle><circle cx="215.3" cy="297" r="2" fill="#E69F00" opacity="0.8"><title>PYY: −1.01 · q = 1.6e-63</title></circle><circle cx="184.4" cy="297" r="2" fill="#E69F00" opacity="0.8"><title>PCSK4: −1.78 · q = 1.6e-63</title></circle><circle cx="210.5" cy="297.1" r="2" fill="#E69F00" opacity="0.8"><title>QDPR: −1.13 · q = 1.8e-63</title></circle><circle cx="317.8" cy="297.2" r="2" fill="#0072B2" opacity="0.8"><title>PSRC1: 1.56 · q = 2.1e-63</title></circle><circle cx="309.9" cy="297.3" r="2" fill="#0072B2" opacity="0.8"><title>MID1: 1.36 · q = 2.8e-63</title></circle><circle cx="194.1" cy="297.4" r="2" fill="#E69F00" opacity="0.8"><title>FNIP1: −1.54 · q = 3.2e-63</title></circle><circle cx="329.1" cy="297.4" r="2" fill="#0072B2" opacity="0.8"><title>SRPK1: 1.84 · q = 3.3e-63</title></circle><circle cx="194.2" cy="297.5" r="2" fill="#E69F00" opacity="0.8"><title>IFT140: −1.54 · q = 4e-63</title></circle><circle cx="306.1" cy="297.5" r="2" fill="#0072B2" opacity="0.8"><title>E2F8: 1.27 · q = 4e-63</title></circle><circle cx="318.3" cy="297.6" r="2" fill="#0072B2" opacity="0.8"><title>TUBA1C: 1.57 · q = 4.6e-63</title></circle><circle cx="203.4" cy="297.6" r="2" fill="#E69F00" opacity="0.8"><title>FRS2: −1.31 · q = 4.8e-63</title></circle><circle cx="326.7" cy="297.7" r="2" fill="#0072B2" opacity="0.8"><title>CDK2AP1: 1.78 · q = 5e-63</title></circle><circle cx="309.2" cy="297.7" r="2" fill="#0072B2" opacity="0.8"><title>TACC3: 1.35 · q = 5.3e-63</title></circle><circle cx="218" cy="297.7" r="2" fill="#E69F00" opacity="0.8"><title>NLRP8: −0.94 · q = 5.7e-63</title></circle><circle cx="203.4" cy="298" r="2" fill="#E69F00" opacity="0.8"><title>SCGB1D2: −1.31 · q = 9.2e-63</title></circle><circle cx="211.5" cy="298.1" r="2" fill="#E69F00" opacity="0.8"><title>SHROOM1: −1.1 · q = 1e-62</title></circle><circle cx="312" cy="298.1" r="2" fill="#0072B2" opacity="0.8"><title>ST8SIA1: 1.42 · q = 1.1e-62</title></circle><circle cx="320.6" cy="298.2" r="2" fill="#0072B2" opacity="0.8"><title>FANCB: 1.63 · q = 1.2e-62</title></circle><circle cx="329.2" cy="298.2" r="2" fill="#0072B2" opacity="0.8"><title>MICALL1: 1.85 · q = 1.3e-62</title></circle><circle cx="208.7" cy="298.2" r="2" fill="#E69F00" opacity="0.8"><title>AMBP: −1.17 · q = 1.3e-62</title></circle><circle cx="211.8" cy="298.2" r="2" fill="#E69F00" opacity="0.8"><title>ELP2: −1.1 · q = 1.3e-62</title></circle><circle cx="320.5" cy="298.5" r="2" fill="#0072B2" opacity="0.8"><title>E2F2: 1.63 · q = 2.1e-62</title></circle><circle cx="185.1" cy="298.5" r="2" fill="#E69F00" opacity="0.8"><title>P4HTM: −1.76 · q = 2.2e-62</title></circle><circle cx="194.2" cy="298.6" r="2" fill="#E69F00" opacity="0.8"><title>BTD: −1.54 · q = 2.4e-62</title></circle><circle cx="199.7" cy="298.7" r="2" fill="#E69F00" opacity="0.8"><title>GUSBP2: −1.4 · q = 2.9e-62</title></circle><circle cx="308.5" cy="298.7" r="2" fill="#0072B2" opacity="0.8"><title>NEIL3: 1.33 · q = 3.2e-62</title></circle><circle cx="191.2" cy="298.7" r="2" fill="#E69F00" opacity="0.8"><title>CFAP43: −1.61 · q = 3.3e-62</title></circle><circle cx="311.8" cy="298.8" r="2" fill="#0072B2" opacity="0.8"><title>PRC1: 1.41 · q = 3.5e-62</title></circle><circle cx="199.9" cy="298.8" r="2" fill="#E69F00" opacity="0.8"><title>DCDC1: −1.39 · q = 3.7e-62</title></circle><circle cx="187.6" cy="298.8" r="2" fill="#E69F00" opacity="0.8"><title>FBXL5: −1.7 · q = 3.8e-62</title></circle><circle cx="314.4" cy="298.9" r="2" fill="#0072B2" opacity="0.8"><title>KCNN4: 1.48 · q = 4.3e-62</title></circle><circle cx="326.7" cy="298.9" r="2" fill="#0072B2" opacity="0.8"><title>IL12RB2: 1.79 · q = 4.5e-62</title></circle><circle cx="165.5" cy="299" r="2" fill="#E69F00" opacity="0.8"><title>SPDEF: −2.26 · q = 4.9e-62</title></circle><circle cx="318" cy="299" r="2" fill="#0072B2" opacity="0.8"><title>CCDC150: 1.57 · q = 5e-62</title></circle><circle cx="217.9" cy="299" r="2" fill="#E69F00" opacity="0.8"><title>ACMSD: −0.942 · q = 5.3e-62</title></circle><circle cx="310.2" cy="299" r="2" fill="#0072B2" opacity="0.8"><title>GINS1: 1.37 · q = 5.6e-62</title></circle><circle cx="188.9" cy="299.1" r="2" fill="#E69F00" opacity="0.8"><title>GALNT10: −1.67 · q = 6.3e-62</title></circle><circle cx="189.1" cy="299.3" r="2" fill="#E69F00" opacity="0.8"><title>RPH3AL: −1.66 · q = 8e-62</title></circle><circle cx="323.5" cy="299.4" r="2" fill="#0072B2" opacity="0.8"><title>E2F3: 1.71 · q = 1e-61</title></circle><circle cx="316.4" cy="299.5" r="2" fill="#0072B2" opacity="0.8"><title>PTDSS1: 1.53 · q = 1.2e-61</title></circle><circle cx="318.6" cy="299.5" r="2" fill="#0072B2" opacity="0.8"><title>NT5DC2: 1.58 · q = 1.3e-61</title></circle><circle cx="201.4" cy="299.5" r="2" fill="#E69F00" opacity="0.8"><title>GNA14: −1.36 · q = 1.3e-61</title></circle><circle cx="319.8" cy="299.5" r="2" fill="#0072B2" opacity="0.8"><title>ODC1: 1.61 · q = 1.3e-61</title></circle><circle cx="200.9" cy="299.6" r="2" fill="#E69F00" opacity="0.8"><title>ZBTB16: −1.37 · q = 1.5e-61</title></circle><circle cx="320.3" cy="299.7" r="2" fill="#0072B2" opacity="0.8"><title>PSMG1: 1.62 · q = 1.6e-61</title></circle><circle cx="177" cy="299.7" r="2" fill="#E69F00" opacity="0.8"><title>GPR160: −1.97 · q = 1.7e-61</title></circle><circle cx="199.8" cy="299.7" r="2" fill="#E69F00" opacity="0.8"><title>SLC49A3: −1.39 · q = 1.8e-61</title></circle><circle cx="320.5" cy="299.7" r="2" fill="#0072B2" opacity="0.8"><title>PDE7A: 1.63 · q = 1.8e-61</title></circle><circle cx="191.5" cy="299.9" r="2" fill="#E69F00" opacity="0.8"><title>SYT17: −1.61 · q = 2.4e-61</title></circle><circle cx="197.3" cy="299.9" r="2" fill="#E69F00" opacity="0.8"><title>TTC23L: −1.46 · q = 2.6e-61</title></circle><circle cx="193.2" cy="300.1" r="2" fill="#E69F00" opacity="0.8"><title>MRTFB: −1.56 · q = 3.4e-61</title></circle><circle cx="309.1" cy="300.1" r="2" fill="#0072B2" opacity="0.8"><title>CKAP2L: 1.34 · q = 3.8e-61</title></circle><circle cx="188.3" cy="300.2" r="2" fill="#E69F00" opacity="0.8"><title>WDR19: −1.69 · q = 4.1e-61</title></circle><circle cx="309.7" cy="300.2" r="2" fill="#0072B2" opacity="0.8"><title>ARHGAP11A: 1.36 · q = 4.1e-61</title></circle><circle cx="194.1" cy="300.2" r="2" fill="#E69F00" opacity="0.8"><title>PIP: −1.54 · q = 4.3e-61</title></circle><circle cx="329.6" cy="300.4" r="2" fill="#0072B2" opacity="0.8"><title>KCMF1: 1.86 · q = 5.4e-61</title></circle><circle cx="183.1" cy="300.4" r="2" fill="#E69F00" opacity="0.8"><title>RND1: −1.82 · q = 5.4e-61</title></circle><circle cx="304.9" cy="300.4" r="2" fill="#0072B2" opacity="0.8"><title>JRKL: 1.24 · q = 5.4e-61</title></circle><circle cx="308.9" cy="300.4" r="2" fill="#0072B2" opacity="0.8"><title>UBE2T: 1.34 · q = 5.7e-61</title></circle><circle cx="186.5" cy="300.4" r="2" fill="#E69F00" opacity="0.8"><title>FAM241A: −1.73 · q = 5.7e-61</title></circle><circle cx="186.8" cy="300.4" r="2" fill="#E69F00" opacity="0.8"><title>REEP1: −1.72 · q = 6.1e-61</title></circle><circle cx="187.3" cy="300.5" r="2" fill="#E69F00" opacity="0.8"><title>DNAH5: −1.71 · q = 6.9e-61</title></circle><circle cx="211.3" cy="300.5" r="2" fill="#E69F00" opacity="0.8"><title>INSYN2A: −1.11 · q = 7.3e-61</title></circle><circle cx="197.6" cy="300.6" r="2" fill="#E69F00" opacity="0.8"><title>RET: −1.45 · q = 7.9e-61</title></circle><circle cx="188" cy="300.8" r="2" fill="#E69F00" opacity="0.8"><title>DNAAF3: −1.69 · q = 1.2e-60</title></circle><circle cx="195.2" cy="300.8" r="2" fill="#E69F00" opacity="0.8"><title>DCTN4: −1.51 · q = 1.3e-60</title></circle><circle cx="217" cy="300.9" r="2" fill="#E69F00" opacity="0.8"><title>CCDC83: −0.965 · q = 1.4e-60</title></circle><circle cx="191.1" cy="300.9" r="2" fill="#E69F00" opacity="0.8"><title>MS4A2: −1.61 · q = 1.4e-60</title></circle><circle cx="184.8" cy="300.9" r="2" fill="#E69F00" opacity="0.8"><title>BCAM: −1.77 · q = 1.4e-60</title></circle><circle cx="197.8" cy="301" r="2" fill="#E69F00" opacity="0.8"><title>ZNF385A: −1.45 · q = 1.5e-60</title></circle><circle cx="319.7" cy="301" r="2" fill="#0072B2" opacity="0.8"><title>COLGALT1: 1.61 · q = 1.7e-60</title></circle><circle cx="319.8" cy="301" r="2" fill="#0072B2" opacity="0.8"><title>HAPLN3: 1.61 · q = 1.7e-60</title></circle><circle cx="194.1" cy="301.1" r="2" fill="#E69F00" opacity="0.8"><title>GLUL: −1.54 · q = 1.9e-60</title></circle><circle cx="194.9" cy="301.1" r="2" fill="#E69F00" opacity="0.8"><title>SLC39A9: −1.52 · q = 2.1e-60</title></circle><circle cx="209.8" cy="301.2" r="2" fill="#E69F00" opacity="0.8"><title>CAPSL: −1.15 · q = 2.1e-60</title></circle><circle cx="187.9" cy="301.2" r="2" fill="#E69F00" opacity="0.8"><title>ETFBKMT: −1.69 · q = 2.3e-60</title></circle><circle cx="207.6" cy="301.3" r="2" fill="#E69F00" opacity="0.8"><title>CACNA1F: −1.2 · q = 2.7e-60</title></circle><circle cx="307.8" cy="301.3" r="2" fill="#0072B2" opacity="0.8"><title>CCNB1: 1.31 · q = 2.8e-60</title></circle><circle cx="321.6" cy="301.4" r="2" fill="#0072B2" opacity="0.8"><title>RAD51AP1: 1.66 · q = 3e-60</title></circle><circle cx="195" cy="301.4" r="2" fill="#E69F00" opacity="0.8"><title>PTGER3: −1.52 · q = 3.1e-60</title></circle><circle cx="188.6" cy="301.4" r="2" fill="#E69F00" opacity="0.8"><title>TNS2: −1.68 · q = 3.4e-60</title></circle><circle cx="318" cy="301.4" r="2" fill="#0072B2" opacity="0.8"><title>CFAP20: 1.57 · q = 3.4e-60</title></circle><circle cx="197.1" cy="301.5" r="2" fill="#E69F00" opacity="0.8"><title>GLIPR1L2: −1.46 · q = 3.8e-60</title></circle><circle cx="186.8" cy="301.6" r="2" fill="#E69F00" opacity="0.8"><title>GPD1L: −1.72 · q = 4.5e-60</title></circle><circle cx="195.2" cy="301.6" r="2" fill="#E69F00" opacity="0.8"><title>GRIK3: −1.51 · q = 4.6e-60</title></circle><circle cx="318.4" cy="301.7" r="2" fill="#0072B2" opacity="0.8"><title>MCM2: 1.58 · q = 5.3e-60</title></circle><circle cx="213" cy="301.7" r="2" fill="#E69F00" opacity="0.8"><title>TUBA3C: −1.07 · q = 5.3e-60</title></circle><circle cx="184.3" cy="302" r="2" fill="#E69F00" opacity="0.8"><title>SPEF2: −1.78 · q = 8.9e-60</title></circle><circle cx="322.6" cy="302" r="2" fill="#0072B2" opacity="0.8"><title>GMPS: 1.68 · q = 9.4e-60</title></circle><circle cx="318.5" cy="302" r="2" fill="#0072B2" opacity="0.8"><title>PRIM2: 1.58 · q = 9.7e-60</title></circle><circle cx="184.1" cy="302" r="2" fill="#E69F00" opacity="0.8"><title>IVD: −1.79 · q = 9.7e-60</title></circle><circle cx="196" cy="302.1" r="2" fill="#E69F00" opacity="0.8"><title>TSPAN31: −1.49 · q = 1e-59</title></circle><circle cx="194.7" cy="302.1" r="2" fill="#E69F00" opacity="0.8"><title>LRBA: −1.52 · q = 1.1e-59</title></circle><circle cx="200.2" cy="302.1" r="2" fill="#E69F00" opacity="0.8"><title>CEACAM5: −1.39 · q = 1.1e-59</title></circle><circle cx="307.5" cy="302.1" r="2" fill="#0072B2" opacity="0.8"><title>CENPI: 1.3 · q = 1.1e-59</title></circle><circle cx="199.8" cy="302.4" r="2" fill="#E69F00" opacity="0.8"><title>GSTM3: −1.4 · q = 1.8e-59</title></circle><circle cx="309" cy="302.4" r="2" fill="#0072B2" opacity="0.8"><title>KIF11: 1.34 · q = 1.8e-59</title></circle><circle cx="186.1" cy="302.5" r="2" fill="#E69F00" opacity="0.8"><title>ZBTB42: −1.74 · q = 2.1e-59</title></circle><circle cx="317.9" cy="302.5" r="2" fill="#0072B2" opacity="0.8"><title>TTC7A: 1.56 · q = 2.1e-59</title></circle><circle cx="197.7" cy="302.5" r="2" fill="#E69F00" opacity="0.8"><title>CCDC74B: −1.45 · q = 2.2e-59</title></circle><circle cx="193.5" cy="302.5" r="2" fill="#E69F00" opacity="0.8"><title>CPLX1: −1.55 · q = 2.3e-59</title></circle><circle cx="187.9" cy="302.5" r="2" fill="#E69F00" opacity="0.8"><title>CACNA1D: −1.69 · q = 2.3e-59</title></circle><circle cx="183.3" cy="302.6" r="2" fill="#E69F00" opacity="0.8"><title>CXXC5: −1.81 · q = 2.4e-59</title></circle><circle cx="196.6" cy="302.6" r="2" fill="#E69F00" opacity="0.8"><title>NUMA1: −1.48 · q = 2.7e-59</title></circle><circle cx="191" cy="302.6" r="2" fill="#E69F00" opacity="0.8"><title>ZFYVE16: −1.62 · q = 2.8e-59</title></circle><circle cx="197.4" cy="302.7" r="2" fill="#E69F00" opacity="0.8"><title>KCTD3: −1.46 · q = 2.8e-59</title></circle><circle cx="326" cy="302.7" r="2" fill="#0072B2" opacity="0.8"><title>C9ORF40: 1.77 · q = 2.9e-59</title></circle><circle cx="195" cy="302.7" r="2" fill="#E69F00" opacity="0.8"><title>CST2: −1.52 · q = 3e-59</title></circle><circle cx="183.1" cy="302.8" r="2" fill="#E69F00" opacity="0.8"><title>EFCAB6: −1.81 · q = 3.7e-59</title></circle><circle cx="319" cy="302.8" r="2" fill="#0072B2" opacity="0.8"><title>KARS1: 1.59 · q = 3.8e-59</title></circle><circle cx="193.1" cy="302.9" r="2" fill="#E69F00" opacity="0.8"><title>CFAP90: −1.56 · q = 4.1e-59</title></circle><circle cx="194.1" cy="302.9" r="2" fill="#E69F00" opacity="0.8"><title>RAPGEF3: −1.54 · q = 4.2e-59</title></circle><circle cx="313.5" cy="303" r="2" fill="#0072B2" opacity="0.8"><title>SLC16A10: 1.45 · q = 4.9e-59</title></circle><circle cx="195.3" cy="303" r="2" fill="#E69F00" opacity="0.8"><title>KIF12: −1.51 · q = 5e-59</title></circle><circle cx="195" cy="303" r="2" fill="#E69F00" opacity="0.8"><title>ZBTB4: −1.52 · q = 5e-59</title></circle><circle cx="215.9" cy="303" r="2" fill="#E69F00" opacity="0.8"><title>LINC00593: −0.991 · q = 5.1e-59</title></circle><circle cx="313.2" cy="303" r="2" fill="#0072B2" opacity="0.8"><title>ARHGEF39: 1.45 · q = 5.1e-59</title></circle><circle cx="202.8" cy="303" r="2" fill="#E69F00" opacity="0.8"><title>ADCY1: −1.32 · q = 5.2e-59</title></circle><circle cx="214.3" cy="303" r="2" fill="#E69F00" opacity="0.8"><title>AOX2P: −1.03 · q = 5.4e-59</title></circle><circle cx="321.8" cy="303" r="2" fill="#0072B2" opacity="0.8"><title>ECE2: 1.66 · q = 5.6e-59</title></circle><circle cx="212.9" cy="303.1" r="2" fill="#E69F00" opacity="0.8"><title>PEBP4: −1.07 · q = 5.7e-59</title></circle><circle cx="192.7" cy="303.1" r="2" fill="#E69F00" opacity="0.8"><title>ITPR1: −1.57 · q = 6e-59</title></circle><circle cx="297.2" cy="303.1" r="2" fill="#0072B2" opacity="0.8"><title>CDC6: 1.05 · q = 6.1e-59</title></circle><circle cx="318.7" cy="303.2" r="2" fill="#0072B2" opacity="0.8"><title>AGO2: 1.58 · q = 7.8e-59</title></circle><circle cx="199.5" cy="303.3" r="2" fill="#E69F00" opacity="0.8"><title>CACNG1: −1.4 · q = 8e-59</title></circle><circle cx="187.4" cy="303.3" r="2" fill="#E69F00" opacity="0.8"><title>TADA2B: −1.71 · q = 9e-59</title></circle><circle cx="198.6" cy="303.3" r="2" fill="#E69F00" opacity="0.8"><title>TOM1L2: −1.43 · q = 9.1e-59</title></circle><circle cx="202.9" cy="303.3" r="2" fill="#E69F00" opacity="0.8"><title>LEO1: −1.32 · q = 9.1e-59</title></circle><circle cx="199.2" cy="303.4" r="2" fill="#E69F00" opacity="0.8"><title>KIF5C: −1.41 · q = 9.6e-59</title></circle><circle cx="193.9" cy="303.4" r="2" fill="#E69F00" opacity="0.8"><title>DTWD2: −1.54 · q = 9.8e-59</title></circle><circle cx="196.7" cy="303.4" r="2" fill="#E69F00" opacity="0.8"><title>NDFIP1: −1.47 · q = 9.9e-59</title></circle><circle cx="184.7" cy="303.4" r="2" fill="#E69F00" opacity="0.8"><title>TMEM25: −1.77 · q = 1e-58</title></circle><circle cx="195.2" cy="303.4" r="2" fill="#E69F00" opacity="0.8"><title>ANKMY1: −1.51 · q = 1e-58</title></circle><circle cx="211.5" cy="303.5" r="2" fill="#E69F00" opacity="0.8"><title>GRIK1-AS1: −1.1 · q = 1.2e-58</title></circle><circle cx="320.7" cy="303.5" r="2" fill="#0072B2" opacity="0.8"><title>DONSON: 1.63 · q = 1.2e-58</title></circle><circle cx="199.5" cy="303.5" r="2" fill="#E69F00" opacity="0.8"><title>CYB5D1: −1.4 · q = 1.3e-58</title></circle><circle cx="194" cy="303.6" r="2" fill="#E69F00" opacity="0.8"><title>CYB5D2: −1.54 · q = 1.4e-58</title></circle><circle cx="326.4" cy="303.6" r="2" fill="#0072B2" opacity="0.8"><title>UGT8: 1.78 · q = 1.4e-58</title></circle><circle cx="197.6" cy="303.6" r="2" fill="#E69F00" opacity="0.8"><title>LFNG: −1.45 · q = 1.4e-58</title></circle><circle cx="212.6" cy="303.6" r="2" fill="#E69F00" opacity="0.8"><title>STH: −1.08 · q = 1.5e-58</title></circle><circle cx="200.4" cy="303.7" r="2" fill="#E69F00" opacity="0.8"><title>FUT8-AS1: −1.38 · q = 1.7e-58</title></circle><circle cx="216.3" cy="303.9" r="2" fill="#E69F00" opacity="0.8"><title>PVALB: −0.982 · q = 2.3e-58</title></circle><circle cx="303.6" cy="303.9" r="2" fill="#0072B2" opacity="0.8"><title>ADGRG6: 1.21 · q = 2.5e-58</title></circle><circle cx="191.3" cy="304" r="2" fill="#E69F00" opacity="0.8"><title>WBP1L: −1.61 · q = 2.9e-58</title></circle><circle cx="192.4" cy="304" r="2" fill="#E69F00" opacity="0.8"><title>CYP21A2: −1.58 · q = 3.1e-58</title></circle><circle cx="313.2" cy="304.1" r="2" fill="#0072B2" opacity="0.8"><title>GABRP: 1.45 · q = 3.4e-58</title></circle><circle cx="195.4" cy="304.1" r="2" fill="#E69F00" opacity="0.8"><title>LZTFL1: −1.51 · q = 3.5e-58</title></circle><circle cx="197.7" cy="304.2" r="2" fill="#E69F00" opacity="0.8"><title>GUSBP3: −1.45 · q = 4e-58</title></circle><circle cx="178.3" cy="304.2" r="2" fill="#E69F00" opacity="0.8"><title>C9ORF152: −1.93 · q = 4.2e-58</title></circle><circle cx="194.7" cy="304.2" r="2" fill="#E69F00" opacity="0.8"><title>COMMD10: −1.52 · q = 4.2e-58</title></circle><circle cx="318.8" cy="304.3" r="2" fill="#0072B2" opacity="0.8"><title>ULBP3: 1.59 · q = 4.9e-58</title></circle><circle cx="193.3" cy="304.3" r="2" fill="#E69F00" opacity="0.8"><title>MINDY1: −1.56 · q = 5e-58</title></circle><circle cx="199.9" cy="304.4" r="2" fill="#E69F00" opacity="0.8"><title>SCGB2A2: −1.39 · q = 6e-58</title></circle><circle cx="319.7" cy="304.5" r="2" fill="#0072B2" opacity="0.8"><title>MCM6: 1.61 · q = 6.4e-58</title></circle><circle cx="190.6" cy="304.5" r="2" fill="#E69F00" opacity="0.8"><title>CREB3L4: −1.63 · q = 6.5e-58</title></circle><circle cx="196.3" cy="304.5" r="2" fill="#E69F00" opacity="0.8"><title>DNMBP: −1.48 · q = 6.5e-58</title></circle><circle cx="326.3" cy="304.5" r="2" fill="#0072B2" opacity="0.8"><title>DKC1: 1.77 · q = 7e-58</title></circle><circle cx="193.5" cy="304.6" r="2" fill="#E69F00" opacity="0.8"><title>SYNJ2BP: −1.55 · q = 8.3e-58</title></circle><circle cx="304.5" cy="304.6" r="2" fill="#0072B2" opacity="0.8"><title>SPC25: 1.23 · q = 8.7e-58</title></circle><circle cx="319.2" cy="304.7" r="2" fill="#0072B2" opacity="0.8"><title>SLC6A11: 1.6 · q = 9e-58</title></circle><circle cx="186" cy="304.7" r="2" fill="#E69F00" opacity="0.8"><title>TMC5: −1.74 · q = 9.8e-58</title></circle><circle cx="320.1" cy="304.7" r="2" fill="#0072B2" opacity="0.8"><title>UQCRHL: 1.62 · q = 9.9e-58</title></circle><circle cx="207.4" cy="304.7" r="2" fill="#E69F00" opacity="0.8"><title>LIN7A: −1.21 · q = 1e-57</title></circle><circle cx="324" cy="304.7" r="2" fill="#0072B2" opacity="0.8"><title>DUSP9: 1.72 · q = 1e-57</title></circle><circle cx="314.6" cy="304.7" r="2" fill="#0072B2" opacity="0.8"><title>CENPL: 1.48 · q = 1e-57</title></circle><circle cx="304.4" cy="304.8" r="2" fill="#0072B2" opacity="0.8"><title>CDKN3: 1.22 · q = 1.1e-57</title></circle><circle cx="197.2" cy="304.8" r="2" fill="#E69F00" opacity="0.8"><title>HNRNPH2: −1.46 · q = 1.1e-57</title></circle><circle cx="194.5" cy="304.8" r="2" fill="#E69F00" opacity="0.8"><title>TAPT1: −1.53 · q = 1.1e-57</title></circle><circle cx="302.7" cy="304.9" r="2" fill="#0072B2" opacity="0.8"><title>ESPL1: 1.18 · q = 1.3e-57</title></circle><circle cx="220.1" cy="305" r="2" fill="#E69F00" opacity="0.8"><title>CDC20B: −0.887 · q = 1.6e-57</title></circle><circle cx="192.5" cy="305" r="2" fill="#E69F00" opacity="0.8"><title>EAPP: −1.58 · q = 1.7e-57</title></circle><circle cx="202.3" cy="305" r="2" fill="#E69F00" opacity="0.8"><title>RAB6C: −1.33 · q = 1.7e-57</title></circle><circle cx="208.6" cy="305.1" r="2" fill="#E69F00" opacity="0.8"><title>C17ORF58: −1.18 · q = 1.9e-57</title></circle><circle cx="188.7" cy="305.1" r="2" fill="#E69F00" opacity="0.8"><title>FAM114A2: −1.67 · q = 2.1e-57</title></circle><circle cx="322.5" cy="305.2" r="2" fill="#0072B2" opacity="0.8"><title>GPSM2: 1.68 · q = 2.1e-57</title></circle><circle cx="312" cy="305.2" r="2" fill="#0072B2" opacity="0.8"><title>CDIN1: 1.42 · q = 2.2e-57</title></circle><circle cx="194.7" cy="305.2" r="2" fill="#E69F00" opacity="0.8"><title>TMEM45B: −1.52 · q = 2.2e-57</title></circle><circle cx="328.2" cy="305.2" r="2" fill="#0072B2" opacity="0.8"><title>FAM136A: 1.82 · q = 2.2e-57</title></circle><circle cx="313.3" cy="305.2" r="2" fill="#0072B2" opacity="0.8"><title>KNSTRN: 1.45 · q = 2.5e-57</title></circle><circle cx="324.3" cy="305.3" r="2" fill="#0072B2" opacity="0.8"><title>PSMB2: 1.72 · q = 2.5e-57</title></circle><circle cx="210.9" cy="305.3" r="2" fill="#E69F00" opacity="0.8"><title>FGF10: −1.12 · q = 2.8e-57</title></circle><circle cx="190.2" cy="305.4" r="2" fill="#E69F00" opacity="0.8"><title>CAPN9: −1.64 · q = 3e-57</title></circle><circle cx="320" cy="305.4" r="2" fill="#0072B2" opacity="0.8"><title>MED30: 1.62 · q = 3e-57</title></circle><circle cx="323.7" cy="305.4" r="2" fill="#0072B2" opacity="0.8"><title>RIPK2: 1.71 · q = 3.2e-57</title></circle><circle cx="190.4" cy="305.4" r="2" fill="#E69F00" opacity="0.8"><title>ZNF442: −1.63 · q = 3.4e-57</title></circle><circle cx="196.9" cy="305.4" r="2" fill="#E69F00" opacity="0.8"><title>CFAP70: −1.47 · q = 3.4e-57</title></circle><circle cx="197.7" cy="305.6" r="2" fill="#E69F00" opacity="0.8"><title>POLI: −1.45 · q = 4.3e-57</title></circle><circle cx="200" cy="305.6" r="2" fill="#E69F00" opacity="0.8"><title>ZNF587: −1.39 · q = 4.4e-57</title></circle><circle cx="315.1" cy="305.6" r="2" fill="#0072B2" opacity="0.8"><title>L3MBTL4: 1.49 · q = 4.4e-57</title></circle><circle cx="202.1" cy="305.6" r="2" fill="#E69F00" opacity="0.8"><title>CYP4Z1: −1.34 · q = 4.5e-57</title></circle><circle cx="201.5" cy="305.6" r="2" fill="#E69F00" opacity="0.8"><title>NUDT4: −1.35 · q = 4.5e-57</title></circle><circle cx="319.2" cy="305.6" r="2" fill="#0072B2" opacity="0.8"><title>ZIC1: 1.6 · q = 4.5e-57</title></circle><circle cx="313.8" cy="305.6" r="2" fill="#0072B2" opacity="0.8"><title>FMNL2: 1.46 · q = 4.6e-57</title></circle><circle cx="192.9" cy="305.6" r="2" fill="#E69F00" opacity="0.8"><title>WNK4: −1.57 · q = 4.6e-57</title></circle><circle cx="321.5" cy="305.7" r="2" fill="#0072B2" opacity="0.8"><title>NIP7: 1.65 · q = 5.5e-57</title></circle><circle cx="195.2" cy="305.9" r="2" fill="#E69F00" opacity="0.8"><title>LRRC26: −1.51 · q = 8.1e-57</title></circle><circle cx="202.4" cy="306" r="2" fill="#E69F00" opacity="0.8"><title>SLC19A2: −1.33 · q = 9.3e-57</title></circle><circle cx="199.1" cy="306.1" r="2" fill="#E69F00" opacity="0.8"><title>NBR1: −1.41 · q = 1.1e-56</title></circle><circle cx="210.6" cy="306.1" r="2" fill="#E69F00" opacity="0.8"><title>SEZ6L: −1.13 · q = 1.1e-56</title></circle><circle cx="188.7" cy="306.2" r="2" fill="#E69F00" opacity="0.8"><title>TGFB3: −1.68 · q = 1.3e-56</title></circle><circle cx="310.3" cy="306.2" r="2" fill="#0072B2" opacity="0.8"><title>CDH3: 1.37 · q = 1.4e-56</title></circle><circle cx="218.5" cy="306.3" r="2" fill="#E69F00" opacity="0.8"><title>MCCD1: −0.926 · q = 1.5e-56</title></circle><circle cx="196.6" cy="306.3" r="2" fill="#E69F00" opacity="0.8"><title>POLN: −1.48 · q = 1.5e-56</title></circle><circle cx="318.5" cy="306.3" r="2" fill="#0072B2" opacity="0.8"><title>RHBDF2: 1.58 · q = 1.6e-56</title></circle><circle cx="182.3" cy="306.4" r="2" fill="#E69F00" opacity="0.8"><title>HID1: −1.83 · q = 1.9e-56</title></circle><circle cx="323.5" cy="306.5" r="2" fill="#0072B2" opacity="0.8"><title>MEMO1: 1.7 · q = 2e-56</title></circle><circle cx="210" cy="306.5" r="2" fill="#E69F00" opacity="0.8"><title>SYT1: −1.14 · q = 2.1e-56</title></circle><circle cx="193.4" cy="306.5" r="2" fill="#E69F00" opacity="0.8"><title>PGGT1B: −1.56 · q = 2.2e-56</title></circle><circle cx="186" cy="306.5" r="2" fill="#E69F00" opacity="0.8"><title>VAV3: −1.74 · q = 2.4e-56</title></circle><circle cx="308.9" cy="306.6" r="2" fill="#0072B2" opacity="0.8"><title>CHAC1: 1.34 · q = 2.6e-56</title></circle><circle cx="200.3" cy="306.6" r="2" fill="#E69F00" opacity="0.8"><title>TCEAL6: −1.38 · q = 2.7e-56</title></circle><circle cx="191.2" cy="306.7" r="2" fill="#E69F00" opacity="0.8"><title>SPG11: −1.61 · q = 2.9e-56</title></circle><circle cx="319.6" cy="306.8" r="2" fill="#0072B2" opacity="0.8"><title>MSL3: 1.61 · q = 3.4e-56</title></circle><circle cx="192.1" cy="306.8" r="2" fill="#E69F00" opacity="0.8"><title>CCNH: −1.59 · q = 3.6e-56</title></circle><circle cx="209.3" cy="306.8" r="2" fill="#E69F00" opacity="0.8"><title>RP1: −1.16 · q = 3.8e-56</title></circle><circle cx="193.7" cy="306.9" r="2" fill="#E69F00" opacity="0.8"><title>ADCY9: −1.55 · q = 4.3e-56</title></circle><circle cx="320.4" cy="307" r="2" fill="#0072B2" opacity="0.8"><title>STAC: 1.63 · q = 5e-56</title></circle><circle cx="311" cy="307" r="2" fill="#0072B2" opacity="0.8"><title>TMEM65: 1.39 · q = 5e-56</title></circle><circle cx="324.6" cy="307.1" r="2" fill="#0072B2" opacity="0.8"><title>STK38: 1.73 · q = 5.7e-56</title></circle><circle cx="200.6" cy="307.1" r="2" fill="#E69F00" opacity="0.8"><title>NPAS1: −1.38 · q = 5.9e-56</title></circle><circle cx="192.9" cy="307.1" r="2" fill="#E69F00" opacity="0.8"><title>RAB5B: −1.57 · q = 6.3e-56</title></circle><circle cx="190.4" cy="307.1" r="2" fill="#E69F00" opacity="0.8"><title>CCDC30: −1.63 · q = 6.4e-56</title></circle><circle cx="321.3" cy="307.1" r="2" fill="#0072B2" opacity="0.8"><title>RANBP1: 1.65 · q = 6.5e-56</title></circle><circle cx="307" cy="307.2" r="2" fill="#0072B2" opacity="0.8"><title>SLC9A6: 1.29 · q = 6.8e-56</title></circle><circle cx="194.3" cy="307.2" r="2" fill="#E69F00" opacity="0.8"><title>EEIG1: −1.53 · q = 6.9e-56</title></circle><circle cx="323.9" cy="307.2" r="2" fill="#0072B2" opacity="0.8"><title>TBX19: 1.71 · q = 7.4e-56</title></circle><circle cx="321.6" cy="307.2" r="2" fill="#0072B2" opacity="0.8"><title>POP1: 1.66 · q = 7.7e-56</title></circle><circle cx="186.2" cy="307.2" r="2" fill="#E69F00" opacity="0.8"><title>KIAA0040: −1.74 · q = 7.8e-56</title></circle><circle cx="191.4" cy="307.3" r="2" fill="#E69F00" opacity="0.8"><title>BCL2: −1.61 · q = 9.1e-56</title></circle><circle cx="200.3" cy="307.3" r="2" fill="#E69F00" opacity="0.8"><title>UGCG: −1.38 · q = 9.2e-56</title></circle><circle cx="190.8" cy="307.4" r="2" fill="#E69F00" opacity="0.8"><title>MACIR: −1.62 · q = 1.1e-55</title></circle><circle cx="194.3" cy="307.4" r="2" fill="#E69F00" opacity="0.8"><title>CISH: −1.53 · q = 1.1e-55</title></circle><circle cx="319.7" cy="307.5" r="2" fill="#0072B2" opacity="0.8"><title>KRT16: 1.61 · q = 1.2e-55</title></circle><circle cx="322.6" cy="307.6" r="2" fill="#0072B2" opacity="0.8"><title>HDGF: 1.68 · q = 1.4e-55</title></circle><circle cx="320.5" cy="307.6" r="2" fill="#0072B2" opacity="0.8"><title>UTP4: 1.63 · q = 1.4e-55</title></circle><circle cx="315.4" cy="307.6" r="2" fill="#0072B2" opacity="0.8"><title>CDC7: 1.5 · q = 1.4e-55</title></circle><circle cx="197.2" cy="307.7" r="2" fill="#E69F00" opacity="0.8"><title>SLC46A1: −1.46 · q = 1.8e-55</title></circle><circle cx="323.5" cy="307.7" r="2" fill="#0072B2" opacity="0.8"><title>CTSV: 1.71 · q = 1.9e-55</title></circle><circle cx="219.8" cy="307.8" r="2" fill="#E69F00" opacity="0.8"><title>KCNC2: −0.895 · q = 2e-55</title></circle><circle cx="314.2" cy="307.8" r="2" fill="#0072B2" opacity="0.8"><title>COA7: 1.47 · q = 2.1e-55</title></circle><circle cx="187.5" cy="307.9" r="2" fill="#E69F00" opacity="0.8"><title>C4A: −1.71 · q = 2.5e-55</title></circle><circle cx="192.2" cy="307.9" r="2" fill="#E69F00" opacity="0.8"><title>FYCO1: −1.59 · q = 2.6e-55</title></circle><circle cx="193" cy="308.1" r="2" fill="#E69F00" opacity="0.8"><title>COQ7: −1.57 · q = 3.3e-55</title></circle><circle cx="323" cy="308.1" r="2" fill="#0072B2" opacity="0.8"><title>RNASEH1: 1.69 · q = 3.3e-55</title></circle><circle cx="306.9" cy="308.1" r="2" fill="#0072B2" opacity="0.8"><title>ERCC6L: 1.29 · q = 3.6e-55</title></circle><circle cx="308.4" cy="308.1" r="2" fill="#0072B2" opacity="0.8"><title>PELI1: 1.33 · q = 3.7e-55</title></circle><circle cx="204.2" cy="308.1" r="2" fill="#E69F00" opacity="0.8"><title>SLC27A2: −1.29 · q = 3.7e-55</title></circle><circle cx="310.5" cy="308.3" r="2" fill="#0072B2" opacity="0.8"><title>FEN1: 1.38 · q = 4.8e-55</title></circle><circle cx="199.9" cy="308.3" r="2" fill="#E69F00" opacity="0.8"><title>LINC00160: −1.39 · q = 4.9e-55</title></circle><circle cx="190.1" cy="308.4" r="2" fill="#E69F00" opacity="0.8"><title>DELE1: −1.64 · q = 5.5e-55</title></circle><circle cx="309" cy="308.4" r="2" fill="#0072B2" opacity="0.8"><title>PRAME: 1.34 · q = 5.7e-55</title></circle><circle cx="193.4" cy="308.4" r="2" fill="#E69F00" opacity="0.8"><title>IFT88: −1.56 · q = 6.2e-55</title></circle><circle cx="307.4" cy="308.4" r="2" fill="#0072B2" opacity="0.8"><title>YBX3: 1.3 · q = 6.3e-55</title></circle><circle cx="301.7" cy="308.5" r="2" fill="#0072B2" opacity="0.8"><title>SFRP1: 1.16 · q = 6.6e-55</title></circle><circle cx="191.7" cy="308.6" r="2" fill="#E69F00" opacity="0.8"><title>FBXL17: −1.6 · q = 7.6e-55</title></circle><circle cx="314.4" cy="308.6" r="2" fill="#0072B2" opacity="0.8"><title>GINS4: 1.48 · q = 8e-55</title></circle><circle cx="331.7" cy="308.7" r="2" fill="#0072B2" opacity="0.8"><title>LEMD1: 1.91 · q = 1.1e-54</title></circle><circle cx="200.2" cy="308.9" r="2" fill="#E69F00" opacity="0.8"><title>HACD3: −1.39 · q = 1.4e-54</title></circle><circle cx="315.9" cy="308.9" r="2" fill="#0072B2" opacity="0.8"><title>CLCN5: 1.51 · q = 1.4e-54</title></circle><circle cx="188.1" cy="308.9" r="2" fill="#E69F00" opacity="0.8"><title>SYBU: −1.69 · q = 1.5e-54</title></circle><circle cx="177.4" cy="309" r="2" fill="#E69F00" opacity="0.8"><title>CMBL: −1.96 · q = 1.6e-54</title></circle><circle cx="194.9" cy="309" r="2" fill="#E69F00" opacity="0.8"><title>RIBC1: −1.52 · q = 1.7e-54</title></circle><circle cx="206.9" cy="309" r="2" fill="#E69F00" opacity="0.8"><title>ASB16: −1.22 · q = 1.7e-54</title></circle><circle cx="324.6" cy="309" r="2" fill="#0072B2" opacity="0.8"><title>ARL9: 1.73 · q = 1.7e-54</title></circle><circle cx="194.2" cy="309" r="2" fill="#E69F00" opacity="0.8"><title>BTF3: −1.54 · q = 1.7e-54</title></circle><circle cx="334.5" cy="309" r="2" fill="#0072B2" opacity="0.8"><title>ART3: 1.98 · q = 1.8e-54</title></circle><circle cx="193" cy="309.1" r="2" fill="#E69F00" opacity="0.8"><title>BBS5: −1.57 · q = 2e-54</title></circle><circle cx="303.7" cy="309.1" r="2" fill="#0072B2" opacity="0.8"><title>NUSAP1: 1.21 · q = 2.1e-54</title></circle><circle cx="189.4" cy="309.2" r="2" fill="#E69F00" opacity="0.8"><title>TSPAN15: −1.66 · q = 2.3e-54</title></circle><circle cx="315.8" cy="309.2" r="2" fill="#0072B2" opacity="0.8"><title>CHAC2: 1.51 · q = 2.4e-54</title></circle><circle cx="323.9" cy="309.2" r="2" fill="#0072B2" opacity="0.8"><title>PAK1IP1: 1.71 · q = 2.4e-54</title></circle><circle cx="194.6" cy="309.3" r="2" fill="#E69F00" opacity="0.8"><title>PEX11A: −1.53 · q = 2.6e-54</title></circle><circle cx="315.9" cy="309.3" r="2" fill="#0072B2" opacity="0.8"><title>CCDC18: 1.51 · q = 2.6e-54</title></circle><circle cx="212.5" cy="309.3" r="2" fill="#E69F00" opacity="0.8"><title>FER1L5: −1.08 · q = 2.7e-54</title></circle><circle cx="311.8" cy="309.3" r="2" fill="#0072B2" opacity="0.8"><title>MTBP: 1.41 · q = 2.9e-54</title></circle><circle cx="324.8" cy="309.3" r="2" fill="#0072B2" opacity="0.8"><title>RFC4: 1.74 · q = 2.9e-54</title></circle><circle cx="195.2" cy="309.4" r="2" fill="#E69F00" opacity="0.8"><title>EPB41L5: −1.51 · q = 3.1e-54</title></circle><circle cx="322.2" cy="309.4" r="2" fill="#0072B2" opacity="0.8"><title>GJB3: 1.67 · q = 3.1e-54</title></circle><circle cx="300.1" cy="309.4" r="2" fill="#0072B2" opacity="0.8"><title>SPAG5: 1.12 · q = 3.3e-54</title></circle><circle cx="198.3" cy="309.4" r="2" fill="#E69F00" opacity="0.8"><title>NXPH3: −1.43 · q = 3.6e-54</title></circle><circle cx="310" cy="309.5" r="2" fill="#0072B2" opacity="0.8"><title>PSMD7: 1.37 · q = 3.6e-54</title></circle><circle cx="209.4" cy="309.5" r="2" fill="#E69F00" opacity="0.8"><title>CFAP157: −1.16 · q = 3.8e-54</title></circle><circle cx="315.8" cy="309.5" r="2" fill="#0072B2" opacity="0.8"><title>TYMS: 1.51 · q = 3.8e-54</title></circle><circle cx="315.7" cy="309.5" r="2" fill="#0072B2" opacity="0.8"><title>TSPAN33: 1.51 · q = 4e-54</title></circle><circle cx="204.8" cy="309.6" r="2" fill="#E69F00" opacity="0.8"><title>SLC18A2: −1.27 · q = 4.6e-54</title></circle><circle cx="319" cy="309.6" r="2" fill="#0072B2" opacity="0.8"><title>MCM5: 1.59 · q = 4.6e-54</title></circle><circle cx="315.5" cy="309.6" r="2" fill="#0072B2" opacity="0.8"><title>MFSD2B: 1.5 · q = 4.7e-54</title></circle><circle cx="314.8" cy="309.6" r="2" fill="#0072B2" opacity="0.8"><title>SF3B3: 1.49 · q = 4.7e-54</title></circle><circle cx="194.9" cy="309.6" r="2" fill="#E69F00" opacity="0.8"><title>CYB5R1: −1.52 · q = 4.9e-54</title></circle><circle cx="315.6" cy="309.6" r="2" fill="#0072B2" opacity="0.8"><title>FAM72D: 1.51 · q = 5e-54</title></circle><circle cx="200" cy="309.7" r="2" fill="#E69F00" opacity="0.8"><title>PEX12: −1.39 · q = 5.2e-54</title></circle><circle cx="194.9" cy="309.7" r="2" fill="#E69F00" opacity="0.8"><title>IGSF9B: −1.52 · q = 5.5e-54</title></circle><circle cx="190.6" cy="309.7" r="2" fill="#E69F00" opacity="0.8"><title>NPNT: −1.63 · q = 5.8e-54</title></circle><circle cx="193.7" cy="309.7" r="2" fill="#E69F00" opacity="0.8"><title>TPCN1: −1.55 · q = 5.9e-54</title></circle><circle cx="301.3" cy="309.8" r="2" fill="#0072B2" opacity="0.8"><title>NEK2: 1.15 · q = 7e-54</title></circle><circle cx="199.5" cy="309.9" r="2" fill="#E69F00" opacity="0.8"><title>PLIN5: −1.4 · q = 7.3e-54</title></circle><circle cx="192.7" cy="309.9" r="2" fill="#E69F00" opacity="0.8"><title>CERT1: −1.57 · q = 7.5e-54</title></circle><circle cx="312.5" cy="309.9" r="2" fill="#0072B2" opacity="0.8"><title>GPT2: 1.43 · q = 8.2e-54</title></circle><circle cx="190.5" cy="310" r="2" fill="#E69F00" opacity="0.8"><title>KRT18: −1.63 · q = 8.5e-54</title></circle><circle cx="183.6" cy="310" r="2" fill="#E69F00" opacity="0.8"><title>NUDT12: −1.8 · q = 9.3e-54</title></circle><circle cx="309.6" cy="310" r="2" fill="#0072B2" opacity="0.8"><title>TCF7L1: 1.36 · q = 9.4e-54</title></circle><circle cx="186.6" cy="310.1" r="2" fill="#E69F00" opacity="0.8"><title>HCAR1: −1.73 · q = 1e-53</title></circle><circle cx="190.5" cy="310.1" r="2" fill="#E69F00" opacity="0.8"><title>CPA3: −1.63 · q = 1.1e-53</title></circle><circle cx="321.7" cy="310.1" r="2" fill="#0072B2" opacity="0.8"><title>KCNG1: 1.66 · q = 1.1e-53</title></circle><circle cx="198.2" cy="310.1" r="2" fill="#E69F00" opacity="0.8"><title>ARFIP2: −1.44 · q = 1.1e-53</title></circle><circle cx="196.4" cy="310.1" r="2" fill="#E69F00" opacity="0.8"><title>TMEM62: −1.48 · q = 1.1e-53</title></circle><circle cx="319.7" cy="310.1" r="2" fill="#0072B2" opacity="0.8"><title>TCF19: 1.61 · q = 1.1e-53</title></circle><circle cx="313.1" cy="310.1" r="2" fill="#0072B2" opacity="0.8"><title>MEX3A: 1.44 · q = 1.2e-53</title></circle><circle cx="199" cy="310.2" r="2" fill="#E69F00" opacity="0.8"><title>SPATA6L: −1.42 · q = 1.2e-53</title></circle><circle cx="191.8" cy="310.2" r="2" fill="#E69F00" opacity="0.8"><title>CFAP210: −1.6 · q = 1.2e-53</title></circle><circle cx="331.1" cy="310.2" r="2" fill="#0072B2" opacity="0.8"><title>GABBR2: 1.89 · q = 1.2e-53</title></circle><circle cx="199.4" cy="310.2" r="2" fill="#E69F00" opacity="0.8"><title>POC5: −1.4 · q = 1.2e-53</title></circle><circle cx="191.8" cy="310.2" r="2" fill="#E69F00" opacity="0.8"><title>FAAH: −1.6 · q = 1.3e-53</title></circle><circle cx="322.7" cy="310.2" r="2" fill="#0072B2" opacity="0.8"><title>RBM28: 1.69 · q = 1.3e-53</title></circle><circle cx="313.3" cy="310.2" r="2" fill="#0072B2" opacity="0.8"><title>GPR19: 1.45 · q = 1.3e-53</title></circle><circle cx="317.6" cy="310.2" r="2" fill="#0072B2" opacity="0.8"><title>JOSD1: 1.56 · q = 1.4e-53</title></circle><circle cx="193.9" cy="310.3" r="2" fill="#E69F00" opacity="0.8"><title>DAZAP2: −1.54 · q = 1.5e-53</title></circle><circle cx="328.5" cy="310.3" r="2" fill="#0072B2" opacity="0.8"><title>UBXN2A: 1.83 · q = 1.5e-53</title></circle><circle cx="316.4" cy="310.3" r="2" fill="#0072B2" opacity="0.8"><title>SRD5A1: 1.53 · q = 1.6e-53</title></circle><circle cx="325.2" cy="310.4" r="2" fill="#0072B2" opacity="0.8"><title>DEK: 1.75 · q = 1.8e-53</title></circle><circle cx="310" cy="310.4" r="2" fill="#0072B2" opacity="0.8"><title>TBC1D1: 1.37 · q = 1.9e-53</title></circle><circle cx="323.2" cy="310.5" r="2" fill="#0072B2" opacity="0.8"><title>GAL: 1.7 · q = 2e-53</title></circle><circle cx="197.5" cy="310.5" r="2" fill="#E69F00" opacity="0.8"><title>C3ORF18: −1.45 · q = 2.1e-53</title></circle><circle cx="309.9" cy="310.5" r="2" fill="#0072B2" opacity="0.8"><title>NHSL1: 1.36 · q = 2.1e-53</title></circle><circle cx="192.4" cy="310.5" r="2" fill="#E69F00" opacity="0.8"><title>CYB5A: −1.58 · q = 2.1e-53</title></circle><circle cx="199.3" cy="310.5" r="2" fill="#E69F00" opacity="0.8"><title>RETREG1: −1.41 · q = 2.2e-53</title></circle><circle cx="185.2" cy="310.6" r="2" fill="#E69F00" opacity="0.8"><title>LONRF2: −1.76 · q = 2.4e-53</title></circle><circle cx="193.2" cy="310.6" r="2" fill="#E69F00" opacity="0.8"><title>RALGAPA1: −1.56 · q = 2.5e-53</title></circle><circle cx="203" cy="310.6" r="2" fill="#E69F00" opacity="0.8"><title>LYPD6: −1.32 · q = 2.6e-53</title></circle><circle cx="192.8" cy="310.6" r="2" fill="#E69F00" opacity="0.8"><title>NEK11: −1.57 · q = 2.6e-53</title></circle><circle cx="313.8" cy="310.6" r="2" fill="#0072B2" opacity="0.8"><title>NCS1: 1.46 · q = 2.7e-53</title></circle><circle cx="185.4" cy="310.6" r="2" fill="#E69F00" opacity="0.8"><title>HHAT: −1.76 · q = 2.7e-53</title></circle><circle cx="202.8" cy="310.7" r="2" fill="#E69F00" opacity="0.8"><title>PLEKHF2: −1.32 · q = 2.8e-53</title></circle><circle cx="193.7" cy="310.7" r="2" fill="#E69F00" opacity="0.8"><title>FAM120AOS: −1.55 · q = 3e-53</title></circle><circle cx="182.9" cy="310.7" r="2" fill="#E69F00" opacity="0.8"><title>RALGPS2: −1.82 · q = 3.3e-53</title></circle><circle cx="326.8" cy="310.8" r="2" fill="#0072B2" opacity="0.8"><title>ROPN1B: 1.79 · q = 3.5e-53</title></circle><circle cx="196.4" cy="310.8" r="2" fill="#E69F00" opacity="0.8"><title>TRIM23: −1.48 · q = 3.7e-53</title></circle><circle cx="188.6" cy="310.8" r="2" fill="#E69F00" opacity="0.8"><title>MAN2B2: −1.68 · q = 3.9e-53</title></circle><circle cx="320.9" cy="310.8" r="2" fill="#0072B2" opacity="0.8"><title>UQCRH: 1.64 · q = 3.9e-53</title></circle><circle cx="309.9" cy="310.9" r="2" fill="#0072B2" opacity="0.8"><title>GINS3: 1.36 · q = 4.1e-53</title></circle><circle cx="315.1" cy="310.9" r="2" fill="#0072B2" opacity="0.8"><title>FERMT1: 1.49 · q = 4.5e-53</title></circle><circle cx="317.7" cy="311" r="2" fill="#0072B2" opacity="0.8"><title>SNRPA1: 1.56 · q = 5e-53</title></circle><circle cx="218" cy="311" r="2" fill="#E69F00" opacity="0.8"><title>LINC03042: −0.941 · q = 5.3e-53</title></circle><circle cx="304.4" cy="311.1" r="2" fill="#0072B2" opacity="0.8"><title>LMNB1: 1.23 · q = 5.7e-53</title></circle><circle cx="192.4" cy="311.1" r="2" fill="#E69F00" opacity="0.8"><title>ZNF396: −1.58 · q = 6.1e-53</title></circle><circle cx="207.1" cy="311.1" r="2" fill="#E69F00" opacity="0.8"><title>ERICH3: −1.21 · q = 6.4e-53</title></circle><circle cx="323.3" cy="311.1" r="2" fill="#0072B2" opacity="0.8"><title>TEX10: 1.7 · q = 6.4e-53</title></circle><circle cx="198" cy="311.1" r="2" fill="#E69F00" opacity="0.8"><title>PRKAB1: −1.44 · q = 6.5e-53</title></circle><circle cx="197.2" cy="311.2" r="2" fill="#E69F00" opacity="0.8"><title>KATNIP: −1.46 · q = 7e-53</title></circle><circle cx="194.4" cy="311.2" r="2" fill="#E69F00" opacity="0.8"><title>CCDC159: −1.53 · q = 7e-53</title></circle><circle cx="314.8" cy="311.2" r="2" fill="#0072B2" opacity="0.8"><title>LSM2: 1.49 · q = 7.1e-53</title></circle><circle cx="310.9" cy="311.2" r="2" fill="#0072B2" opacity="0.8"><title>NUDCD1: 1.39 · q = 7.3e-53</title></circle><circle cx="196.3" cy="311.2" r="2" fill="#E69F00" opacity="0.8"><title>FANK1: −1.48 · q = 7.3e-53</title></circle><circle cx="307.9" cy="311.2" r="2" fill="#0072B2" opacity="0.8"><title>PPARA: 1.31 · q = 7.8e-53</title></circle><circle cx="315.2" cy="311.3" r="2" fill="#0072B2" opacity="0.8"><title>EMC8: 1.5 · q = 8.4e-53</title></circle><circle cx="325.5" cy="311.3" r="2" fill="#0072B2" opacity="0.8"><title>KCNQ4: 1.75 · q = 8.8e-53</title></circle><circle cx="325.2" cy="311.3" r="2" fill="#0072B2" opacity="0.8"><title>XPO5: 1.75 · q = 9e-53</title></circle><circle cx="203.9" cy="311.3" r="2" fill="#E69F00" opacity="0.8"><title>SCN2B: −1.29 · q = 9e-53</title></circle><circle cx="323.3" cy="311.3" r="2" fill="#0072B2" opacity="0.8"><title>SERBP1: 1.7 · q = 9.1e-53</title></circle><circle cx="170" cy="311.3" r="2" fill="#E69F00" opacity="0.8"><title>CAPN13: −2.14 · q = 9.2e-53</title></circle><circle cx="196.9" cy="311.4" r="2" fill="#E69F00" opacity="0.8"><title>ARHGEF38: −1.47 · q = 1.1e-52</title></circle><circle cx="190.7" cy="311.5" r="2" fill="#E69F00" opacity="0.8"><title>FRY: −1.62 · q = 1.1e-52</title></circle><circle cx="195.5" cy="311.5" r="2" fill="#E69F00" opacity="0.8"><title>IQCH: −1.5 · q = 1.2e-52</title></circle><circle cx="200.4" cy="311.5" r="2" fill="#E69F00" opacity="0.8"><title>MAP3K1: −1.38 · q = 1.3e-52</title></circle><circle cx="215.8" cy="311.6" r="2" fill="#E69F00" opacity="0.8"><title>MRPS30: −0.996 · q = 1.4e-52</title></circle><circle cx="200.4" cy="311.6" r="2" fill="#E69F00" opacity="0.8"><title>ACER2: −1.38 · q = 1.4e-52</title></circle><circle cx="209" cy="311.6" r="2" fill="#E69F00" opacity="0.8"><title>RIIAD1: −1.17 · q = 1.5e-52</title></circle><circle cx="223.9" cy="311.6" r="2" fill="#E69F00" opacity="0.8"><title>CST9L: −0.793 · q = 1.5e-52</title></circle><circle cx="189.6" cy="311.7" r="2" fill="#E69F00" opacity="0.8"><title>LRRC17: −1.65 · q = 1.6e-52</title></circle><circle cx="321" cy="311.7" r="2" fill="#0072B2" opacity="0.8"><title>ZDHHC18: 1.64 · q = 1.6e-52</title></circle><circle cx="199.2" cy="311.7" r="2" fill="#E69F00" opacity="0.8"><title>CEP126: −1.41 · q = 1.7e-52</title></circle><circle cx="206.1" cy="311.8" r="2" fill="#E69F00" opacity="0.8"><title>AMFR: −1.24 · q = 1.9e-52</title></circle><circle cx="195.6" cy="311.8" r="2" fill="#E69F00" opacity="0.8"><title>SLC2A10: −1.5 · q = 2e-52</title></circle><circle cx="195" cy="311.8" r="2" fill="#E69F00" opacity="0.8"><title>TOGARAM1: −1.52 · q = 2e-52</title></circle><circle cx="205.7" cy="311.8" r="2" fill="#E69F00" opacity="0.8"><title>ATG16L1: −1.25 · q = 2e-52</title></circle><circle cx="312.6" cy="311.8" r="2" fill="#0072B2" opacity="0.8"><title>AKR1E2: 1.43 · q = 2.1e-52</title></circle><circle cx="314.2" cy="311.9" r="2" fill="#0072B2" opacity="0.8"><title>TAF4B: 1.47 · q = 2.5e-52</title></circle><circle cx="320.5" cy="311.9" r="2" fill="#0072B2" opacity="0.8"><title>SLC43A3: 1.63 · q = 2.6e-52</title></circle><circle cx="185.3" cy="311.9" r="2" fill="#E69F00" opacity="0.8"><title>TTC39A: −1.76 · q = 2.6e-52</title></circle><circle cx="199.3" cy="312.1" r="2" fill="#E69F00" opacity="0.8"><title>JKAMP: −1.41 · q = 3.2e-52</title></circle><circle cx="193.1" cy="312.1" r="2" fill="#E69F00" opacity="0.8"><title>MREG: −1.56 · q = 3.6e-52</title></circle><circle cx="195.9" cy="312.2" r="2" fill="#E69F00" opacity="0.8"><title>PPP1R3C: −1.49 · q = 3.8e-52</title></circle><circle cx="324.3" cy="312.2" r="2" fill="#0072B2" opacity="0.8"><title>PDIA6: 1.72 · q = 3.8e-52</title></circle><circle cx="196.7" cy="312.2" r="2" fill="#E69F00" opacity="0.8"><title>TPRN: −1.47 · q = 3.9e-52</title></circle><circle cx="323.3" cy="312.2" r="2" fill="#0072B2" opacity="0.8"><title>FAF1: 1.7 · q = 4.2e-52</title></circle><circle cx="317" cy="312.2" r="2" fill="#0072B2" opacity="0.8"><title>ASNS: 1.54 · q = 4.3e-52</title></circle><circle cx="199.8" cy="312.2" r="2" fill="#E69F00" opacity="0.8"><title>ATP1A2: −1.4 · q = 4.4e-52</title></circle><circle cx="202.2" cy="312.3" r="2" fill="#E69F00" opacity="0.8"><title>NTN4: −1.34 · q = 4.7e-52</title></circle><circle cx="323.6" cy="312.3" r="2" fill="#0072B2" opacity="0.8"><title>RNF8: 1.71 · q = 5e-52</title></circle><circle cx="318.8" cy="312.3" r="2" fill="#0072B2" opacity="0.8"><title>GTPBP8: 1.59 · q = 5e-52</title></circle><circle cx="212.6" cy="312.3" r="2" fill="#E69F00" opacity="0.8"><title>LINC02995: −1.08 · q = 5.1e-52</title></circle><circle cx="313.8" cy="312.4" r="2" fill="#0072B2" opacity="0.8"><title>DESI2: 1.46 · q = 5.3e-52</title></circle><circle cx="199.2" cy="312.4" r="2" fill="#E69F00" opacity="0.8"><title>YPEL2: −1.41 · q = 5.4e-52</title></circle><circle cx="325.4" cy="312.4" r="2" fill="#0072B2" opacity="0.8"><title>ATL2: 1.75 · q = 5.6e-52</title></circle><circle cx="321.7" cy="312.4" r="2" fill="#0072B2" opacity="0.8"><title>NDUFAF4: 1.66 · q = 5.6e-52</title></circle><circle cx="196.7" cy="312.4" r="2" fill="#E69F00" opacity="0.8"><title>GDF15: −1.47 · q = 5.6e-52</title></circle><circle cx="209.9" cy="312.4" r="2" fill="#E69F00" opacity="0.8"><title>CYP2B6: −1.14 · q = 5.7e-52</title></circle><circle cx="312.2" cy="312.4" r="2" fill="#0072B2" opacity="0.8"><title>HAUS8: 1.42 · q = 6.1e-52</title></circle><circle cx="196.6" cy="312.5" r="2" fill="#E69F00" opacity="0.8"><title>CCDC191: −1.48 · q = 6.4e-52</title></circle><circle cx="315" cy="312.5" r="2" fill="#0072B2" opacity="0.8"><title>ULBP1: 1.49 · q = 6.9e-52</title></circle><circle cx="207.3" cy="312.5" r="2" fill="#E69F00" opacity="0.8"><title>DUBR: −1.21 · q = 7.2e-52</title></circle><circle cx="193.6" cy="312.6" r="2" fill="#E69F00" opacity="0.8"><title>PHOSPHO2: −1.55 · q = 7.7e-52</title></circle><circle cx="314.3" cy="312.6" r="2" fill="#0072B2" opacity="0.8"><title>NFE2L3: 1.47 · q = 7.9e-52</title></circle><circle cx="321.3" cy="312.6" r="2" fill="#0072B2" opacity="0.8"><title>SKP2: 1.65 · q = 8.1e-52</title></circle><circle cx="205" cy="312.7" r="2" fill="#E69F00" opacity="0.8"><title>CYP4F22: −1.27 · q = 9.9e-52</title></circle><circle cx="193.3" cy="312.8" r="2" fill="#E69F00" opacity="0.8"><title>ADIRF: −1.56 · q = 1.1e-51</title></circle><circle cx="324.6" cy="312.8" r="2" fill="#0072B2" opacity="0.8"><title>ATP6V1C2: 1.73 · q = 1.2e-51</title></circle><circle cx="311" cy="312.9" r="2" fill="#0072B2" opacity="0.8"><title>MTMR2: 1.39 · q = 1.3e-51</title></circle><circle cx="198.3" cy="312.9" r="2" fill="#E69F00" opacity="0.8"><title>SPTBN4: −1.43 · q = 1.3e-51</title></circle><circle cx="310.5" cy="312.9" r="2" fill="#0072B2" opacity="0.8"><title>CKS2: 1.38 · q = 1.3e-51</title></circle><circle cx="203.9" cy="312.9" r="2" fill="#E69F00" opacity="0.8"><title>GCSAML: −1.29 · q = 1.4e-51</title></circle><circle cx="312.2" cy="312.9" r="2" fill="#0072B2" opacity="0.8"><title>BAK1: 1.42 · q = 1.4e-51</title></circle><circle cx="312.3" cy="312.9" r="2" fill="#0072B2" opacity="0.8"><title>ALYREF: 1.42 · q = 1.5e-51</title></circle><circle cx="313.8" cy="313" r="2" fill="#0072B2" opacity="0.8"><title>ANKS6: 1.46 · q = 1.5e-51</title></circle><circle cx="194.7" cy="313" r="2" fill="#E69F00" opacity="0.8"><title>ATP6AP1L: −1.52 · q = 1.5e-51</title></circle><circle cx="311.6" cy="313" r="2" fill="#0072B2" opacity="0.8"><title>SLC25A19: 1.41 · q = 1.6e-51</title></circle><circle cx="190.5" cy="313" r="2" fill="#E69F00" opacity="0.8"><title>SEMA3F: −1.63 · q = 1.6e-51</title></circle><circle cx="191.8" cy="313" r="2" fill="#E69F00" opacity="0.8"><title>APPL2: −1.6 · q = 1.7e-51</title></circle><circle cx="196.5" cy="313.1" r="2" fill="#E69F00" opacity="0.8"><title>LRG1: −1.48 · q = 2e-51</title></circle><circle cx="204.1" cy="313.2" r="2" fill="#E69F00" opacity="0.8"><title>RIMS4: −1.29 · q = 2.1e-51</title></circle><circle cx="187.6" cy="313.2" r="2" fill="#E69F00" opacity="0.8"><title>NEK9: −1.7 · q = 2.2e-51</title></circle><circle cx="199.1" cy="313.2" r="2" fill="#E69F00" opacity="0.8"><title>BCDIN3D: −1.41 · q = 2.2e-51</title></circle><circle cx="188.2" cy="313.2" r="2" fill="#E69F00" opacity="0.8"><title>CRACDL: −1.69 · q = 2.2e-51</title></circle><circle cx="186.9" cy="313.2" r="2" fill="#E69F00" opacity="0.8"><title>GAMT: −1.72 · q = 2.4e-51</title></circle><circle cx="327" cy="313.3" r="2" fill="#0072B2" opacity="0.8"><title>RIOK1: 1.79 · q = 2.5e-51</title></circle><circle cx="205.1" cy="313.3" r="2" fill="#E69F00" opacity="0.8"><title>RD3: −1.26 · q = 2.5e-51</title></circle><circle cx="188" cy="313.3" r="2" fill="#E69F00" opacity="0.8"><title>ENPP1: −1.69 · q = 2.7e-51</title></circle><circle cx="315.3" cy="313.3" r="2" fill="#0072B2" opacity="0.8"><title>LMO4: 1.5 · q = 2.7e-51</title></circle><circle cx="195.2" cy="313.4" r="2" fill="#E69F00" opacity="0.8"><title>DRC10: −1.51 · q = 3.5e-51</title></circle><circle cx="180.9" cy="313.5" r="2" fill="#E69F00" opacity="0.8"><title>MYB: −1.87 · q = 3.6e-51</title></circle><circle cx="310.7" cy="313.5" r="2" fill="#0072B2" opacity="0.8"><title>UBE2S: 1.38 · q = 3.7e-51</title></circle><circle cx="314.3" cy="313.5" r="2" fill="#0072B2" opacity="0.8"><title>PDK1: 1.47 · q = 3.9e-51</title></circle><circle cx="314.6" cy="313.5" r="2" fill="#0072B2" opacity="0.8"><title>NT5C2: 1.48 · q = 4e-51</title></circle><circle cx="211" cy="313.5" r="2" fill="#E69F00" opacity="0.8"><title>ENTPD5: −1.12 · q = 4.1e-51</title></circle><circle cx="201.6" cy="313.5" r="2" fill="#E69F00" opacity="0.8"><title>ZCCHC10: −1.35 · q = 4.1e-51</title></circle><circle cx="315.5" cy="313.6" r="2" fill="#0072B2" opacity="0.8"><title>CHAF1B: 1.5 · q = 4.4e-51</title></circle><circle cx="319.1" cy="313.6" r="2" fill="#0072B2" opacity="0.8"><title>TIGD2: 1.59 · q = 4.6e-51</title></circle><circle cx="184.5" cy="313.7" r="2" fill="#E69F00" opacity="0.8"><title>LIMA1: −1.78 · q = 4.9e-51</title></circle><circle cx="321.9" cy="313.7" r="2" fill="#0072B2" opacity="0.8"><title>NASP: 1.66 · q = 5e-51</title></circle><circle cx="326.4" cy="313.7" r="2" fill="#0072B2" opacity="0.8"><title>PUM3: 1.78 · q = 5.1e-51</title></circle><circle cx="196.5" cy="313.7" r="2" fill="#E69F00" opacity="0.8"><title>SPATA20: −1.48 · q = 5.5e-51</title></circle><circle cx="324.1" cy="313.8" r="2" fill="#0072B2" opacity="0.8"><title>RPIA: 1.72 · q = 5.9e-51</title></circle><circle cx="189.4" cy="313.8" r="2" fill="#E69F00" opacity="0.8"><title>MYRIP: −1.66 · q = 6.2e-51</title></circle><circle cx="199.5" cy="313.8" r="2" fill="#E69F00" opacity="0.8"><title>CD46: −1.4 · q = 6.9e-51</title></circle><circle cx="314.3" cy="313.8" r="2" fill="#0072B2" opacity="0.8"><title>CDC25B: 1.47 · q = 6.9e-51</title></circle><circle cx="205.7" cy="313.9" r="2" fill="#E69F00" opacity="0.8"><title>ELOVL2: −1.25 · q = 7.1e-51</title></circle><circle cx="197.2" cy="313.9" r="2" fill="#E69F00" opacity="0.8"><title>FBXO15: −1.46 · q = 7.9e-51</title></circle><circle cx="317.7" cy="313.9" r="2" fill="#0072B2" opacity="0.8"><title>DNAJB11: 1.56 · q = 8.1e-51</title></circle><circle cx="210.6" cy="314" r="2" fill="#E69F00" opacity="0.8"><title>ZNF92: −1.12 · q = 8.4e-51</title></circle><circle cx="328.1" cy="314" r="2" fill="#0072B2" opacity="0.8"><title>GTPBP4: 1.82 · q = 9.1e-51</title></circle><circle cx="332.2" cy="314" r="2" fill="#0072B2" opacity="0.8"><title>POU5F1: 1.92 · q = 9.1e-51</title></circle><circle cx="210.7" cy="314.1" r="2" fill="#E69F00" opacity="0.8"><title>TTLL9: −1.12 · q = 9.9e-51</title></circle><circle cx="317.2" cy="314.1" r="2" fill="#0072B2" opacity="0.8"><title>TIMM8A: 1.55 · q = 1e-50</title></circle><circle cx="332" cy="314.1" r="2" fill="#0072B2" opacity="0.8"><title>LINC02872: 1.92 · q = 1e-50</title></circle><circle cx="210.8" cy="314.1" r="2" fill="#E69F00" opacity="0.8"><title>CASQ1: −1.12 · q = 1.1e-50</title></circle><circle cx="194.2" cy="314.1" r="2" fill="#E69F00" opacity="0.8"><title>SEPSECS: −1.54 · q = 1.2e-50</title></circle><circle cx="198.8" cy="314.2" r="2" fill="#E69F00" opacity="0.8"><title>ASB8: −1.42 · q = 1.2e-50</title></circle><circle cx="197.3" cy="314.2" r="2" fill="#E69F00" opacity="0.8"><title>NME5: −1.46 · q = 1.2e-50</title></circle><circle cx="321.4" cy="314.2" r="2" fill="#0072B2" opacity="0.8"><title>PDCD5: 1.65 · q = 1.3e-50</title></circle><circle cx="196.7" cy="314.3" r="2" fill="#E69F00" opacity="0.8"><title>PHYHD1: −1.47 · q = 1.4e-50</title></circle><circle cx="325.2" cy="314.3" r="2" fill="#0072B2" opacity="0.8"><title>TMCC2: 1.75 · q = 1.4e-50</title></circle><circle cx="329.2" cy="314.3" r="2" fill="#0072B2" opacity="0.8"><title>HORMAD1: 1.85 · q = 1.5e-50</title></circle><circle cx="203.3" cy="314.3" r="2" fill="#E69F00" opacity="0.8"><title>COQ6: −1.31 · q = 1.5e-50</title></circle><circle cx="302.4" cy="314.3" r="2" fill="#0072B2" opacity="0.8"><title>RAD54B: 1.18 · q = 1.6e-50</title></circle><circle cx="196.4" cy="314.3" r="2" fill="#E69F00" opacity="0.8"><title>ADAMTSL5: −1.48 · q = 1.6e-50</title></circle><circle cx="193.2" cy="314.4" r="2" fill="#E69F00" opacity="0.8"><title>IGFBP4: −1.56 · q = 1.7e-50</title></circle><circle cx="303.2" cy="314.4" r="2" fill="#0072B2" opacity="0.8"><title>TBC1D31: 1.2 · q = 1.8e-50</title></circle><circle cx="182.9" cy="314.4" r="2" fill="#E69F00" opacity="0.8"><title>TMEM63C: −1.82 · q = 1.9e-50</title></circle><circle cx="321.5" cy="314.5" r="2" fill="#0072B2" opacity="0.8"><title>RRP1B: 1.65 · q = 2e-50</title></circle><circle cx="199" cy="314.5" r="2" fill="#E69F00" opacity="0.8"><title>CCDC24: −1.42 · q = 2.2e-50</title></circle><circle cx="302.5" cy="314.5" r="2" fill="#0072B2" opacity="0.8"><title>PBK: 1.18 · q = 2.3e-50</title></circle><circle cx="201.9" cy="314.5" r="2" fill="#E69F00" opacity="0.8"><title>AGBL2: −1.34 · q = 2.3e-50</title></circle><circle cx="190.3" cy="314.6" r="2" fill="#E69F00" opacity="0.8"><title>RAB27B: −1.64 · q = 2.4e-50</title></circle><circle cx="297" cy="314.6" r="2" fill="#0072B2" opacity="0.8"><title>CDC25C: 1.04 · q = 2.5e-50</title></circle><circle cx="190.2" cy="314.6" r="2" fill="#E69F00" opacity="0.8"><title>CCDC40: −1.64 · q = 2.6e-50</title></circle><circle cx="322.2" cy="314.6" r="2" fill="#0072B2" opacity="0.8"><title>LYAR: 1.67 · q = 2.7e-50</title></circle><circle cx="200.1" cy="314.7" r="2" fill="#E69F00" opacity="0.8"><title>ZNF814: −1.39 · q = 2.8e-50</title></circle><circle cx="194.7" cy="314.7" r="2" fill="#E69F00" opacity="0.8"><title>SSH3: −1.52 · q = 3e-50</title></circle><circle cx="315.1" cy="314.7" r="2" fill="#0072B2" opacity="0.8"><title>ELF5: 1.49 · q = 3e-50</title></circle><circle cx="193.3" cy="314.8" r="2" fill="#E69F00" opacity="0.8"><title>CGRRF1: −1.56 · q = 3.8e-50</title></circle><circle cx="201" cy="314.9" r="2" fill="#E69F00" opacity="0.8"><title>BECN1: −1.37 · q = 4.2e-50</title></circle><circle cx="200.2" cy="314.9" r="2" fill="#E69F00" opacity="0.8"><title>PIH1D2: −1.38 · q = 4.4e-50</title></circle><circle cx="194.6" cy="314.9" r="2" fill="#E69F00" opacity="0.8"><title>NUBPL: −1.53 · q = 4.5e-50</title></circle><circle cx="219.3" cy="315" r="2" fill="#E69F00" opacity="0.8"><title>CYP2A6: −0.908 · q = 4.7e-50</title></circle><circle cx="308.7" cy="315" r="2" fill="#0072B2" opacity="0.8"><title>DDIAS: 1.33 · q = 4.8e-50</title></circle><circle cx="315.4" cy="315" r="2" fill="#0072B2" opacity="0.8"><title>B3GNT4: 1.5 · q = 5.1e-50</title></circle><circle cx="307" cy="315" r="2" fill="#0072B2" opacity="0.8"><title>PARPBP: 1.29 · q = 5.4e-50</title></circle><circle cx="196.9" cy="315.1" r="2" fill="#E69F00" opacity="0.8"><title>RGS11: −1.47 · q = 5.8e-50</title></circle><circle cx="316.5" cy="315.1" r="2" fill="#0072B2" opacity="0.8"><title>SMC1B: 1.53 · q = 5.9e-50</title></circle><circle cx="199.4" cy="315.1" r="2" fill="#E69F00" opacity="0.8"><title>C1QTNF7: −1.41 · q = 6.1e-50</title></circle><circle cx="212.2" cy="315.2" r="2" fill="#E69F00" opacity="0.8"><title>PTPRN2: −1.08 · q = 6.8e-50</title></circle><circle cx="215.1" cy="315.2" r="2" fill="#E69F00" opacity="0.8"><title>HTR7P1: −1.01 · q = 7.1e-50</title></circle><circle cx="312.8" cy="315.2" r="2" fill="#0072B2" opacity="0.8"><title>RAP2B: 1.43 · q = 7.8e-50</title></circle><circle cx="207.3" cy="315.4" r="2" fill="#E69F00" opacity="0.8"><title>GRIK4: −1.21 · q = 9.5e-50</title></circle><circle cx="192.8" cy="315.4" r="2" fill="#E69F00" opacity="0.8"><title>IGBP1: −1.57 · q = 1e-49</title></circle><circle cx="309.1" cy="315.4" r="2" fill="#0072B2" opacity="0.8"><title>NUS1: 1.34 · q = 1.1e-49</title></circle><circle cx="305.8" cy="315.5" r="2" fill="#0072B2" opacity="0.8"><title>PLIN2: 1.26 · q = 1.2e-49</title></circle><circle cx="189.5" cy="315.5" r="2" fill="#E69F00" opacity="0.8"><title>FOXP1: −1.65 · q = 1.3e-49</title></circle><circle cx="323.8" cy="315.5" r="2" fill="#0072B2" opacity="0.8"><title>GPR156: 1.71 · q = 1.3e-49</title></circle><circle cx="306.6" cy="315.5" r="2" fill="#0072B2" opacity="0.8"><title>HROB: 1.28 · q = 1.3e-49</title></circle><circle cx="201.5" cy="315.6" r="2" fill="#E69F00" opacity="0.8"><title>PAX2: −1.35 · q = 1.4e-49</title></circle><circle cx="309.1" cy="315.6" r="2" fill="#0072B2" opacity="0.8"><title>MYO10: 1.34 · q = 1.4e-49</title></circle><circle cx="303.4" cy="315.6" r="2" fill="#0072B2" opacity="0.8"><title>HMMR: 1.2 · q = 1.4e-49</title></circle><circle cx="199.8" cy="315.6" r="2" fill="#E69F00" opacity="0.8"><title>SREK1: −1.4 · q = 1.5e-49</title></circle><circle cx="326.8" cy="315.6" r="2" fill="#0072B2" opacity="0.8"><title>DDN: 1.79 · q = 1.6e-49</title></circle><circle cx="199.3" cy="315.7" r="2" fill="#E69F00" opacity="0.8"><title>LRP2BP: −1.41 · q = 1.7e-49</title></circle><circle cx="197.3" cy="315.7" r="2" fill="#E69F00" opacity="0.8"><title>ZNF446: −1.46 · q = 1.8e-49</title></circle><circle cx="198.7" cy="315.8" r="2" fill="#E69F00" opacity="0.8"><title>SLC26A1: −1.42 · q = 1.9e-49</title></circle><circle cx="203" cy="315.8" r="2" fill="#E69F00" opacity="0.8"><title>DUSP4: −1.32 · q = 1.9e-49</title></circle><circle cx="180.1" cy="315.8" r="2" fill="#E69F00" opacity="0.8"><title>FAM174B: −1.89 · q = 1.9e-49</title></circle><circle cx="306.2" cy="315.8" r="2" fill="#0072B2" opacity="0.8"><title>YBX3P1: 1.27 · q = 1.9e-49</title></circle><circle cx="207.2" cy="315.8" r="2" fill="#E69F00" opacity="0.8"><title>HOXB2: −1.21 · q = 1.9e-49</title></circle><circle cx="308" cy="315.8" r="2" fill="#0072B2" opacity="0.8"><title>KIF4B: 1.32 · q = 1.9e-49</title></circle><circle cx="202.7" cy="315.8" r="2" fill="#E69F00" opacity="0.8"><title>HRC: −1.32 · q = 2e-49</title></circle><circle cx="310.5" cy="315.8" r="2" fill="#0072B2" opacity="0.8"><title>MCM4: 1.38 · q = 2.2e-49</title></circle><circle cx="304.1" cy="315.8" r="2" fill="#0072B2" opacity="0.8"><title>PROM1: 1.22 · q = 2.2e-49</title></circle><circle cx="198.5" cy="315.9" r="2" fill="#E69F00" opacity="0.8"><title>IFT46: −1.43 · q = 2.4e-49</title></circle><circle cx="321.8" cy="315.9" r="2" fill="#0072B2" opacity="0.8"><title>ANKLE1: 1.66 · q = 2.5e-49</title></circle><circle cx="214.6" cy="315.9" r="2" fill="#E69F00" opacity="0.8"><title>SERHL2: −1.02 · q = 2.6e-49</title></circle><circle cx="299.4" cy="316" r="2" fill="#0072B2" opacity="0.8"><title>LAPTM4B: 1.1 · q = 3e-49</title></circle><circle cx="188" cy="316.1" r="2" fill="#E69F00" opacity="0.8"><title>MSX2: −1.69 · q = 3.2e-49</title></circle><circle cx="225.6" cy="316.1" r="2" fill="#E69F00" opacity="0.8"><title>CCDC196: −0.748 · q = 3.4e-49</title></circle><circle cx="207.3" cy="316.1" r="2" fill="#E69F00" opacity="0.8"><title>SPOP: −1.21 · q = 3.4e-49</title></circle><circle cx="324.4" cy="316.1" r="2" fill="#0072B2" opacity="0.8"><title>GPN1: 1.73 · q = 3.5e-49</title></circle><circle cx="193.2" cy="316.1" r="2" fill="#E69F00" opacity="0.8"><title>CMYA5: −1.56 · q = 3.5e-49</title></circle><circle cx="328.2" cy="316.2" r="2" fill="#0072B2" opacity="0.8"><title>NCAPD2: 1.82 · q = 3.9e-49</title></circle><circle cx="200.9" cy="316.2" r="2" fill="#E69F00" opacity="0.8"><title>CERS6: −1.37 · q = 3.9e-49</title></circle><circle cx="303.2" cy="316.2" r="2" fill="#0072B2" opacity="0.8"><title>SLC35G1: 1.2 · q = 4.1e-49</title></circle><circle cx="213.4" cy="316.2" r="2" fill="#E69F00" opacity="0.8"><title>BMPR1B: −1.06 · q = 4.2e-49</title></circle><circle cx="320.3" cy="316.2" r="2" fill="#0072B2" opacity="0.8"><title>TAF5: 1.63 · q = 4.3e-49</title></circle><circle cx="310.4" cy="316.2" r="2" fill="#0072B2" opacity="0.8"><title>PIM1: 1.38 · q = 4.3e-49</title></circle><circle cx="322.6" cy="316.3" r="2" fill="#0072B2" opacity="0.8"><title>LPIN1: 1.68 · q = 4.7e-49</title></circle><circle cx="206.5" cy="316.3" r="2" fill="#E69F00" opacity="0.8"><title>NHERF1: −1.23 · q = 5.2e-49</title></circle><circle cx="208.2" cy="316.3" r="2" fill="#E69F00" opacity="0.8"><title>HDC: −1.19 · q = 5.2e-49</title></circle><circle cx="314.8" cy="316.4" r="2" fill="#0072B2" opacity="0.8"><title>MARCO: 1.49 · q = 5.3e-49</title></circle><circle cx="207" cy="316.4" r="2" fill="#E69F00" opacity="0.8"><title>SPTLC2: −1.22 · q = 5.4e-49</title></circle><circle cx="305.3" cy="316.4" r="2" fill="#0072B2" opacity="0.8"><title>CYP39A1: 1.25 · q = 5.5e-49</title></circle><circle cx="195.4" cy="316.4" r="2" fill="#E69F00" opacity="0.8"><title>MEIS3P1: −1.51 · q = 5.6e-49</title></circle><circle cx="195.3" cy="316.5" r="2" fill="#E69F00" opacity="0.8"><title>TBX3: −1.51 · q = 6.7e-49</title></circle><circle cx="308.3" cy="316.5" r="2" fill="#0072B2" opacity="0.8"><title>CSNK2A2: 1.32 · q = 6.7e-49</title></circle><circle cx="311.8" cy="316.5" r="2" fill="#0072B2" opacity="0.8"><title>SH3BP1: 1.41 · q = 7.4e-49</title></circle><circle cx="213" cy="316.6" r="2" fill="#E69F00" opacity="0.8"><title>CLIC6: −1.06 · q = 7.6e-49</title></circle><circle cx="205.3" cy="316.6" r="2" fill="#E69F00" opacity="0.8"><title>NKAIN1: −1.26 · q = 7.6e-49</title></circle><circle cx="200.9" cy="316.6" r="2" fill="#E69F00" opacity="0.8"><title>FAH: −1.37 · q = 7.7e-49</title></circle><circle cx="199.4" cy="316.6" r="2" fill="#E69F00" opacity="0.8"><title>DUSP5: −1.41 · q = 8e-49</title></circle><circle cx="201.2" cy="316.6" r="2" fill="#E69F00" opacity="0.8"><title>SERPINA5: −1.36 · q = 8.7e-49</title></circle><circle cx="312.5" cy="316.6" r="2" fill="#0072B2" opacity="0.8"><title>DNMT3B: 1.43 · q = 8.7e-49</title></circle><circle cx="315.8" cy="316.7" r="2" fill="#0072B2" opacity="0.8"><title>NPM2: 1.51 · q = 1e-48</title></circle><circle cx="214.1" cy="316.8" r="2" fill="#E69F00" opacity="0.8"><title>CNGA3: −1.04 · q = 1.1e-48</title></circle><circle cx="322.2" cy="316.8" r="2" fill="#0072B2" opacity="0.8"><title>ILF2: 1.67 · q = 1.1e-48</title></circle><circle cx="316.1" cy="316.8" r="2" fill="#0072B2" opacity="0.8"><title>MRAS: 1.52 · q = 1.2e-48</title></circle><circle cx="316.9" cy="316.9" r="2" fill="#0072B2" opacity="0.8"><title>LMNB2: 1.54 · q = 1.3e-48</title></circle><circle cx="313.8" cy="316.9" r="2" fill="#0072B2" opacity="0.8"><title>INCENP: 1.46 · q = 1.5e-48</title></circle><circle cx="307.6" cy="317" r="2" fill="#0072B2" opacity="0.8"><title>CBFB: 1.31 · q = 1.5e-48</title></circle><circle cx="203.1" cy="317" r="2" fill="#E69F00" opacity="0.8"><title>PLAT: −1.31 · q = 1.7e-48</title></circle><circle cx="198.6" cy="317.1" r="2" fill="#E69F00" opacity="0.8"><title>ZFYVE1: −1.42 · q = 1.8e-48</title></circle><circle cx="194.3" cy="317.1" r="2" fill="#E69F00" opacity="0.8"><title>FAM151B: −1.53 · q = 1.9e-48</title></circle><circle cx="309.2" cy="317.2" r="2" fill="#0072B2" opacity="0.8"><title>NCOA7: 1.35 · q = 2.2e-48</title></circle><circle cx="219.7" cy="317.2" r="2" fill="#E69F00" opacity="0.8"><title>RMND1: −0.898 · q = 2.2e-48</title></circle><circle cx="192" cy="317.2" r="2" fill="#E69F00" opacity="0.8"><title>SPRYD3: −1.59 · q = 2.4e-48</title></circle><circle cx="314.1" cy="317.3" r="2" fill="#0072B2" opacity="0.8"><title>PRDX4: 1.47 · q = 2.9e-48</title></circle><circle cx="222.6" cy="317.4" r="2" fill="#E69F00" opacity="0.8"><title>CFAP77: −0.825 · q = 3e-48</title></circle><circle cx="202.5" cy="317.4" r="2" fill="#E69F00" opacity="0.8"><title>SMPD3: −1.33 · q = 3.5e-48</title></circle><circle cx="305.2" cy="317.6" r="2" fill="#0072B2" opacity="0.8"><title>DCAF13: 1.25 · q = 4.4e-48</title></circle><circle cx="203.2" cy="317.6" r="2" fill="#E69F00" opacity="0.8"><title>ARHGAP32: −1.31 · q = 4.7e-48</title></circle><circle cx="306.7" cy="317.6" r="2" fill="#0072B2" opacity="0.8"><title>CIB2: 1.28 · q = 4.9e-48</title></circle><circle cx="212" cy="317.6" r="2" fill="#E69F00" opacity="0.8"><title>CST4: −1.09 · q = 5e-48</title></circle><circle cx="314.7" cy="317.7" r="2" fill="#0072B2" opacity="0.8"><title>SOX11: 1.48 · q = 5.8e-48</title></circle><circle cx="317.4" cy="317.8" r="2" fill="#0072B2" opacity="0.8"><title>YARS1: 1.55 · q = 6.4e-48</title></circle><circle cx="209.3" cy="317.8" r="2" fill="#E69F00" opacity="0.8"><title>ZNF652: −1.16 · q = 6.4e-48</title></circle><circle cx="200.4" cy="317.8" r="2" fill="#E69F00" opacity="0.8"><title>PIGQ: −1.38 · q = 6.8e-48</title></circle><circle cx="319.8" cy="317.9" r="2" fill="#0072B2" opacity="0.8"><title>MAGOH: 1.61 · q = 7.2e-48</title></circle><circle cx="197.9" cy="317.9" r="2" fill="#E69F00" opacity="0.8"><title>TEX56P: −1.44 · q = 7.5e-48</title></circle><circle cx="326" cy="317.9" r="2" fill="#0072B2" opacity="0.8"><title>EN1: 1.77 · q = 7.5e-48</title></circle><circle cx="197.7" cy="317.9" r="2" fill="#E69F00" opacity="0.8"><title>ANKHD1-EIF4EBP3: −1.45 · q = 8.2e-48</title></circle><circle cx="200.4" cy="317.9" r="2" fill="#E69F00" opacity="0.8"><title>ST8SIA6: −1.38 · q = 8.2e-48</title></circle><circle cx="202" cy="318" r="2" fill="#E69F00" opacity="0.8"><title>ACOT2: −1.34 · q = 9.1e-48</title></circle><circle cx="198.1" cy="318" r="2" fill="#E69F00" opacity="0.8"><title>SEMA3B: −1.44 · q = 9.4e-48</title></circle><circle cx="319.5" cy="318" r="2" fill="#0072B2" opacity="0.8"><title>LINC00839: 1.6 · q = 9.8e-48</title></circle><circle cx="192.7" cy="318.1" r="2" fill="#E69F00" opacity="0.8"><title>OSCP1: −1.57 · q = 1e-47</title></circle><circle cx="296.8" cy="318.1" r="2" fill="#0072B2" opacity="0.8"><title>SOX9: 1.04 · q = 1.1e-47</title></circle><circle cx="199.1" cy="318.1" r="2" fill="#E69F00" opacity="0.8"><title>CLBA1: −1.41 · q = 1.1e-47</title></circle><circle cx="306.5" cy="318.1" r="2" fill="#0072B2" opacity="0.8"><title>KLF5: 1.28 · q = 1.1e-47</title></circle><circle cx="316.2" cy="318.1" r="2" fill="#0072B2" opacity="0.8"><title>RSU1: 1.52 · q = 1.2e-47</title></circle><circle cx="310" cy="318.1" r="2" fill="#0072B2" opacity="0.8"><title>PFKP: 1.37 · q = 1.2e-47</title></circle><circle cx="192.3" cy="318.2" r="2" fill="#E69F00" opacity="0.8"><title>TCTN1: −1.59 · q = 1.3e-47</title></circle><circle cx="305.6" cy="318.2" r="2" fill="#0072B2" opacity="0.8"><title>LAD1: 1.25 · q = 1.4e-47</title></circle><circle cx="198.7" cy="318.3" r="2" fill="#E69F00" opacity="0.8"><title>LCA5L: −1.42 · q = 1.4e-47</title></circle><circle cx="316.7" cy="318.3" r="2" fill="#0072B2" opacity="0.8"><title>WDR77: 1.53 · q = 1.5e-47</title></circle><circle cx="302.7" cy="318.3" r="2" fill="#0072B2" opacity="0.8"><title>PRNP: 1.18 · q = 1.5e-47</title></circle><circle cx="310.1" cy="318.3" r="2" fill="#0072B2" opacity="0.8"><title>RCC1: 1.37 · q = 1.5e-47</title></circle><circle cx="194.5" cy="318.3" r="2" fill="#E69F00" opacity="0.8"><title>SALL2: −1.53 · q = 1.5e-47</title></circle><circle cx="218.2" cy="318.3" r="2" fill="#E69F00" opacity="0.8"><title>CATIP: −0.935 · q = 1.6e-47</title></circle><circle cx="195.2" cy="318.3" r="2" fill="#E69F00" opacity="0.8"><title>TPSAB1: −1.51 · q = 1.7e-47</title></circle><circle cx="310" cy="318.3" r="2" fill="#0072B2" opacity="0.8"><title>GMNN: 1.37 · q = 1.7e-47</title></circle><circle cx="200.8" cy="318.4" r="2" fill="#E69F00" opacity="0.8"><title>CDK17: −1.37 · q = 1.8e-47</title></circle><circle cx="188.5" cy="318.5" r="2" fill="#E69F00" opacity="0.8"><title>RBM47: −1.68 · q = 2e-47</title></circle><circle cx="201.1" cy="318.5" r="2" fill="#E69F00" opacity="0.8"><title>RABL3: −1.36 · q = 2.1e-47</title></circle><circle cx="199.1" cy="318.5" r="2" fill="#E69F00" opacity="0.8"><title>GHDC: −1.41 · q = 2.2e-47</title></circle><circle cx="201.9" cy="318.5" r="2" fill="#E69F00" opacity="0.8"><title>ACBD4: −1.34 · q = 2.2e-47</title></circle><circle cx="197.1" cy="318.5" r="2" fill="#E69F00" opacity="0.8"><title>OCEL1: −1.46 · q = 2.3e-47</title></circle><circle cx="203.2" cy="318.5" r="2" fill="#E69F00" opacity="0.8"><title>INSYN1: −1.31 · q = 2.3e-47</title></circle><circle cx="312.3" cy="318.6" r="2" fill="#0072B2" opacity="0.8"><title>CSRP2: 1.42 · q = 2.5e-47</title></circle><circle cx="315.6" cy="318.6" r="2" fill="#0072B2" opacity="0.8"><title>EML4: 1.51 · q = 2.5e-47</title></circle><circle cx="328.1" cy="318.6" r="2" fill="#0072B2" opacity="0.8"><title>CDC123: 1.82 · q = 2.7e-47</title></circle><circle cx="196.5" cy="318.6" r="2" fill="#E69F00" opacity="0.8"><title>TPSB2: −1.48 · q = 2.7e-47</title></circle><circle cx="206.3" cy="318.6" r="2" fill="#E69F00" opacity="0.8"><title>PRSS23: −1.23 · q = 2.7e-47</title></circle><circle cx="192.1" cy="318.6" r="2" fill="#E69F00" opacity="0.8"><title>GMPR2: −1.59 · q = 2.7e-47</title></circle><circle cx="316.7" cy="318.6" r="2" fill="#0072B2" opacity="0.8"><title>PSME4: 1.53 · q = 2.8e-47</title></circle><circle cx="307.3" cy="318.6" r="2" fill="#0072B2" opacity="0.8"><title>MTERF3: 1.3 · q = 2.8e-47</title></circle><circle cx="304" cy="318.7" r="2" fill="#0072B2" opacity="0.8"><title>SPC24: 1.21 · q = 2.9e-47</title></circle><circle cx="197.3" cy="318.8" r="2" fill="#E69F00" opacity="0.8"><title>CSNK1A1: −1.46 · q = 3.5e-47</title></circle><circle cx="314.3" cy="318.8" r="2" fill="#0072B2" opacity="0.8"><title>GART: 1.47 · q = 3.5e-47</title></circle><circle cx="312.2" cy="318.8" r="2" fill="#0072B2" opacity="0.8"><title>CCDC71L: 1.42 · q = 4e-47</title></circle><circle cx="318.6" cy="318.9" r="2" fill="#0072B2" opacity="0.8"><title>FSCN1: 1.58 · q = 4.1e-47</title></circle><circle cx="306.3" cy="318.9" r="2" fill="#0072B2" opacity="0.8"><title>WDR62: 1.27 · q = 4.5e-47</title></circle><circle cx="180.9" cy="319" r="2" fill="#E69F00" opacity="0.8"><title>LMX1B: −1.87 · q = 5.2e-47</title></circle><circle cx="301.7" cy="319" r="2" fill="#0072B2" opacity="0.8"><title>FAM135A: 1.16 · q = 5.3e-47</title></circle><circle cx="314.7" cy="319.1" r="2" fill="#0072B2" opacity="0.8"><title>TMEM39B: 1.48 · q = 6.5e-47</title></circle><circle cx="309.3" cy="319.1" r="2" fill="#0072B2" opacity="0.8"><title>TRIM2: 1.35 · q = 6.6e-47</title></circle><circle cx="318.4" cy="319.1" r="2" fill="#0072B2" opacity="0.8"><title>PTPN2: 1.58 · q = 6.6e-47</title></circle><circle cx="304.9" cy="319.1" r="2" fill="#0072B2" opacity="0.8"><title>TYRO3: 1.24 · q = 6.8e-47</title></circle><circle cx="202.5" cy="319.2" r="2" fill="#E69F00" opacity="0.8"><title>CSAD: −1.33 · q = 7.1e-47</title></circle><circle cx="208.1" cy="319.2" r="2" fill="#E69F00" opacity="0.8"><title>EOLA1: −1.19 · q = 7.2e-47</title></circle><circle cx="309.1" cy="319.2" r="2" fill="#0072B2" opacity="0.8"><title>MMS22L: 1.34 · q = 7.5e-47</title></circle><circle cx="311.5" cy="319.3" r="2" fill="#0072B2" opacity="0.8"><title>SNRPD1: 1.4 · q = 8.2e-47</title></circle><circle cx="296" cy="319.3" r="2" fill="#0072B2" opacity="0.8"><title>TLE1: 1.01 · q = 8.3e-47</title></circle><circle cx="200.9" cy="319.3" r="2" fill="#E69F00" opacity="0.8"><title>ALDH6A1: −1.37 · q = 8.8e-47</title></circle><circle cx="322.2" cy="319.3" r="2" fill="#0072B2" opacity="0.8"><title>NUDT5: 1.67 · q = 9e-47</title></circle><circle cx="328.4" cy="319.3" r="2" fill="#0072B2" opacity="0.8"><title>AMY1A: 1.83 · q = 9e-47</title></circle><circle cx="307.3" cy="319.4" r="2" fill="#0072B2" opacity="0.8"><title>GPM6B: 1.3 · q = 9.6e-47</title></circle><circle cx="310.7" cy="319.4" r="2" fill="#0072B2" opacity="0.8"><title>RHBDL2: 1.38 · q = 9.8e-47</title></circle><circle cx="295.4" cy="319.4" r="2" fill="#0072B2" opacity="0.8"><title>TOP2A: 1 · q = 9.9e-47</title></circle><circle cx="197" cy="319.4" r="2" fill="#E69F00" opacity="0.8"><title>RMND5B: −1.47 · q = 1e-46</title></circle><circle cx="196.2" cy="319.4" r="2" fill="#E69F00" opacity="0.8"><title>CBLN4: −1.49 · q = 1e-46</title></circle><circle cx="198.9" cy="319.4" r="2" fill="#E69F00" opacity="0.8"><title>CHN2: −1.42 · q = 1.1e-46</title></circle><circle cx="307.7" cy="319.4" r="2" fill="#0072B2" opacity="0.8"><title>RASGEF1B: 1.31 · q = 1.1e-46</title></circle><circle cx="200.8" cy="319.4" r="2" fill="#E69F00" opacity="0.8"><title>PDGFD: −1.37 · q = 1.1e-46</title></circle><circle cx="326.5" cy="319.4" r="2" fill="#0072B2" opacity="0.8"><title>RBM17: 1.78 · q = 1.1e-46</title></circle><circle cx="318" cy="319.5" r="2" fill="#0072B2" opacity="0.8"><title>SMIM45: 1.57 · q = 1.2e-46</title></circle><circle cx="316.1" cy="319.5" r="2" fill="#0072B2" opacity="0.8"><title>MAPK4: 1.52 · q = 1.3e-46</title></circle><circle cx="304.1" cy="319.5" r="2" fill="#0072B2" opacity="0.8"><title>PLK4: 1.22 · q = 1.3e-46</title></circle><circle cx="302.6" cy="319.6" r="2" fill="#0072B2" opacity="0.8"><title>INTS6L: 1.18 · q = 1.4e-46</title></circle><circle cx="318.4" cy="319.6" r="2" fill="#0072B2" opacity="0.8"><title>DPH2: 1.58 · q = 1.5e-46</title></circle><circle cx="308.2" cy="319.7" r="2" fill="#0072B2" opacity="0.8"><title>TLCD3A: 1.32 · q = 1.7e-46</title></circle><circle cx="187.6" cy="319.7" r="2" fill="#E69F00" opacity="0.8"><title>PNPLA4: −1.7 · q = 1.7e-46</title></circle><circle cx="198" cy="319.7" r="2" fill="#E69F00" opacity="0.8"><title>KIAA0319L: −1.44 · q = 1.7e-46</title></circle><circle cx="304.1" cy="319.7" r="2" fill="#0072B2" opacity="0.8"><title>PRKD3: 1.22 · q = 1.7e-46</title></circle><circle cx="196.3" cy="319.7" r="2" fill="#E69F00" opacity="0.8"><title>ADGRB2: −1.48 · q = 1.7e-46</title></circle><circle cx="312" cy="319.7" r="2" fill="#0072B2" opacity="0.8"><title>NARF: 1.42 · q = 1.8e-46</title></circle><circle cx="202.9" cy="319.7" r="2" fill="#E69F00" opacity="0.8"><title>ACOT1: −1.32 · q = 1.8e-46</title></circle><circle cx="310.6" cy="319.8" r="2" fill="#0072B2" opacity="0.8"><title>SLC16A1: 1.38 · q = 1.9e-46</title></circle><circle cx="202.7" cy="319.8" r="2" fill="#E69F00" opacity="0.8"><title>GATA2: −1.32 · q = 2e-46</title></circle><circle cx="225.6" cy="319.8" r="2" fill="#E69F00" opacity="0.8"><title>GHRH: −0.75 · q = 2e-46</title></circle><circle cx="312" cy="319.8" r="2" fill="#0072B2" opacity="0.8"><title>SPDL1: 1.42 · q = 2e-46</title></circle><circle cx="308.5" cy="319.8" r="2" fill="#0072B2" opacity="0.8"><title>APBA2: 1.33 · q = 2.2e-46</title></circle><circle cx="296.7" cy="319.8" r="2" fill="#0072B2" opacity="0.8"><title>APLF: 1.03 · q = 2.2e-46</title></circle><circle cx="191.5" cy="319.8" r="2" fill="#E69F00" opacity="0.8"><title>BHLHE40: −1.6 · q = 2.2e-46</title></circle><circle cx="309.4" cy="319.8" r="2" fill="#0072B2" opacity="0.8"><title>QSOX2: 1.35 · q = 2.2e-46</title></circle><circle cx="308.8" cy="319.9" r="2" fill="#0072B2" opacity="0.8"><title>LAMP3: 1.34 · q = 2.4e-46</title></circle><circle cx="220.1" cy="319.9" r="2" fill="#E69F00" opacity="0.8"><title>CLDN25: −0.887 · q = 2.6e-46</title></circle><circle cx="200.1" cy="319.9" r="2" fill="#E69F00" opacity="0.8"><title>GLS2: −1.39 · q = 2.6e-46</title></circle><circle cx="306" cy="320" r="2" fill="#0072B2" opacity="0.8"><title>CHST3: 1.26 · q = 2.7e-46</title></circle><circle cx="315.3" cy="320" r="2" fill="#0072B2" opacity="0.8"><title>PHF5A: 1.5 · q = 2.8e-46</title></circle><circle cx="202.7" cy="320" r="2" fill="#E69F00" opacity="0.8"><title>CSNK1G3: −1.32 · q = 2.8e-46</title></circle><circle cx="199.7" cy="320" r="2" fill="#E69F00" opacity="0.8"><title>CCNDBP1: −1.4 · q = 2.9e-46</title></circle><circle cx="199.2" cy="320" r="2" fill="#E69F00" opacity="0.8"><title>RAB11FIP3: −1.41 · q = 3e-46</title></circle><circle cx="194.1" cy="320.1" r="2" fill="#E69F00" opacity="0.8"><title>LAMP5: −1.54 · q = 3.2e-46</title></circle><circle cx="316.6" cy="320.1" r="2" fill="#0072B2" opacity="0.8"><title>MRPL37: 1.53 · q = 3.7e-46</title></circle><circle cx="198.4" cy="320.2" r="2" fill="#E69F00" opacity="0.8"><title>CLGN: −1.43 · q = 3.9e-46</title></circle><circle cx="321.6" cy="320.2" r="2" fill="#0072B2" opacity="0.8"><title>BYSL: 1.66 · q = 4.4e-46</title></circle><circle cx="316.8" cy="320.3" r="2" fill="#0072B2" opacity="0.8"><title>HDAC2: 1.54 · q = 4.5e-46</title></circle><circle cx="199.4" cy="320.3" r="2" fill="#E69F00" opacity="0.8"><title>LINC00922: −1.41 · q = 4.7e-46</title></circle><circle cx="314.4" cy="320.3" r="2" fill="#0072B2" opacity="0.8"><title>TBC1D22B: 1.48 · q = 4.9e-46</title></circle><circle cx="326.9" cy="320.3" r="2" fill="#0072B2" opacity="0.8"><title>C1QL4: 1.79 · q = 5e-46</title></circle><circle cx="203.4" cy="320.3" r="2" fill="#E69F00" opacity="0.8"><title>TRIM66: −1.31 · q = 5e-46</title></circle><circle cx="302.3" cy="320.4" r="2" fill="#0072B2" opacity="0.8"><title>ASF1B: 1.17 · q = 5.6e-46</title></circle><circle cx="202.1" cy="320.4" r="2" fill="#E69F00" opacity="0.8"><title>AAGAB: −1.34 · q = 5.6e-46</title></circle><circle cx="305.1" cy="320.4" r="2" fill="#0072B2" opacity="0.8"><title>TONSL: 1.24 · q = 5.6e-46</title></circle><circle cx="206.3" cy="320.4" r="2" fill="#E69F00" opacity="0.8"><title>ATRIP: −1.23 · q = 5.8e-46</title></circle><circle cx="196.8" cy="320.4" r="2" fill="#E69F00" opacity="0.8"><title>RAB26: −1.47 · q = 5.8e-46</title></circle><circle cx="306.2" cy="320.4" r="2" fill="#0072B2" opacity="0.8"><title>CMTM7: 1.27 · q = 6e-46</title></circle><circle cx="198.7" cy="320.4" r="2" fill="#E69F00" opacity="0.8"><title>AGGF1: −1.42 · q = 6e-46</title></circle><circle cx="200.6" cy="320.4" r="2" fill="#E69F00" opacity="0.8"><title>CIPC: −1.38 · q = 6.1e-46</title></circle><circle cx="185.5" cy="320.4" r="2" fill="#E69F00" opacity="0.8"><title>SCNN1A: −1.75 · q = 6.2e-46</title></circle><circle cx="310.1" cy="320.4" r="2" fill="#0072B2" opacity="0.8"><title>LIN9: 1.37 · q = 6.3e-46</title></circle><circle cx="202.3" cy="320.5" r="2" fill="#E69F00" opacity="0.8"><title>CTC1: −1.33 · q = 6.6e-46</title></circle><circle cx="318.3" cy="320.5" r="2" fill="#0072B2" opacity="0.8"><title>FAM131C: 1.57 · q = 7e-46</title></circle><circle cx="209.3" cy="320.5" r="2" fill="#E69F00" opacity="0.8"><title>TAC1: −1.16 · q = 7e-46</title></circle><circle cx="190.6" cy="320.5" r="2" fill="#E69F00" opacity="0.8"><title>PGPEP1: −1.63 · q = 7.4e-46</title></circle><circle cx="197.2" cy="320.6" r="2" fill="#E69F00" opacity="0.8"><title>USP30: −1.46 · q = 7.6e-46</title></circle><circle cx="313.2" cy="320.6" r="2" fill="#0072B2" opacity="0.8"><title>BORA: 1.45 · q = 7.7e-46</title></circle><circle cx="197.8" cy="320.6" r="2" fill="#E69F00" opacity="0.8"><title>CYB561D2: −1.45 · q = 8e-46</title></circle><circle cx="192.6" cy="320.6" r="2" fill="#E69F00" opacity="0.8"><title>GLI3: −1.58 · q = 8.1e-46</title></circle><circle cx="323.7" cy="320.6" r="2" fill="#0072B2" opacity="0.8"><title>SEPHS1: 1.71 · q = 8.1e-46</title></circle><circle cx="204.4" cy="320.6" r="2" fill="#E69F00" opacity="0.8"><title>G6PC3: −1.28 · q = 8.4e-46</title></circle><circle cx="197.9" cy="320.7" r="2" fill="#E69F00" opacity="0.8"><title>SLC24A1: −1.44 · q = 9.6e-46</title></circle><circle cx="192.1" cy="320.8" r="2" fill="#E69F00" opacity="0.8"><title>ZNF467: −1.59 · q = 1.1e-45</title></circle><circle cx="204.6" cy="320.8" r="2" fill="#E69F00" opacity="0.8"><title>TMEM135: −1.28 · q = 1.2e-45</title></circle><circle cx="311.8" cy="320.8" r="2" fill="#0072B2" opacity="0.8"><title>PPP1CB: 1.41 · q = 1.2e-45</title></circle><circle cx="196.2" cy="320.9" r="2" fill="#E69F00" opacity="0.8"><title>SNED1: −1.49 · q = 1.3e-45</title></circle><circle cx="203.8" cy="320.9" r="2" fill="#E69F00" opacity="0.8"><title>EWSAT1: −1.3 · q = 1.3e-45</title></circle><circle cx="198.6" cy="320.9" r="2" fill="#E69F00" opacity="0.8"><title>KERA: −1.43 · q = 1.4e-45</title></circle><circle cx="315.1" cy="320.9" r="2" fill="#0072B2" opacity="0.8"><title>CA9: 1.49 · q = 1.4e-45</title></circle><circle cx="322" cy="320.9" r="2" fill="#0072B2" opacity="0.8"><title>TMEM171: 1.67 · q = 1.4e-45</title></circle><circle cx="208.1" cy="320.9" r="2" fill="#E69F00" opacity="0.8"><title>LINC03124: −1.19 · q = 1.5e-45</title></circle><circle cx="319.8" cy="320.9" r="2" fill="#0072B2" opacity="0.8"><title>WNT6: 1.61 · q = 1.5e-45</title></circle><circle cx="203.5" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>HEMK1: −1.3 · q = 1.5e-45</title></circle><circle cx="201.2" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>UBL3: −1.36 · q = 1.6e-45</title></circle><circle cx="201.9" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>IL6ST: −1.34 · q = 1.6e-45</title></circle><circle cx="206.1" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>CRAT: −1.24 · q = 1.6e-45</title></circle><circle cx="309.6" cy="321" r="2" fill="#0072B2" opacity="0.8"><title>TNFRSF21: 1.36 · q = 1.6e-45</title></circle><circle cx="201.7" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>PTGR2: −1.35 · q = 1.7e-45</title></circle><circle cx="315.2" cy="321" r="2" fill="#0072B2" opacity="0.8"><title>USP6NL: 1.5 · q = 1.7e-45</title></circle><circle cx="195.2" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>ORAI3: −1.51 · q = 1.7e-45</title></circle><circle cx="194.9" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>IGIP: −1.52 · q = 1.7e-45</title></circle><circle cx="209.5" cy="321" r="2" fill="#E69F00" opacity="0.8"><title>CABP4: −1.15 · q = 1.8e-45</title></circle><circle cx="296.5" cy="321.1" r="2" fill="#0072B2" opacity="0.8"><title>HMGB3: 1.03 · q = 1.8e-45</title></circle><circle cx="213.6" cy="321.1" r="2" fill="#E69F00" opacity="0.8"><title>DPY19L2P4: −1.05 · q = 1.9e-45</title></circle><circle cx="204.3" cy="321.1" r="2" fill="#E69F00" opacity="0.8"><title>RAD17: −1.28 · q = 1.9e-45</title></circle><circle cx="197.1" cy="321.1" r="2" fill="#E69F00" opacity="0.8"><title>MAST4: −1.46 · q = 2e-45</title></circle><circle cx="311.1" cy="321.1" r="2" fill="#0072B2" opacity="0.8"><title>ADSL: 1.39 · q = 2.1e-45</title></circle><circle cx="329.4" cy="321.2" r="2" fill="#0072B2" opacity="0.8"><title>SLC26A9: 1.85 · q = 2.4e-45</title></circle><circle cx="302.4" cy="321.2" r="2" fill="#0072B2" opacity="0.8"><title>GPRIN2: 1.18 · q = 2.5e-45</title></circle><circle cx="197.8" cy="321.3" r="2" fill="#E69F00" opacity="0.8"><title>TMEM87B: −1.45 · q = 2.7e-45</title></circle><circle cx="310.8" cy="321.3" r="2" fill="#0072B2" opacity="0.8"><title>FBXO5: 1.39 · q = 3e-45</title></circle><circle cx="197.6" cy="321.4" r="2" fill="#E69F00" opacity="0.8"><title>ULK4: −1.45 · q = 3.2e-45</title></circle><circle cx="209.4" cy="321.4" r="2" fill="#E69F00" opacity="0.8"><title>DNAH9: −1.15 · q = 3.3e-45</title></circle><circle cx="188.3" cy="321.5" r="2" fill="#E69F00" opacity="0.8"><title>TSC22D3: −1.68 · q = 4e-45</title></circle><circle cx="224" cy="321.5" r="2" fill="#E69F00" opacity="0.8"><title>VSTM2A: −0.79 · q = 4e-45</title></circle><circle cx="202.1" cy="321.5" r="2" fill="#E69F00" opacity="0.8"><title>LGALS8: −1.34 · q = 4e-45</title></circle><circle cx="202.2" cy="321.5" r="2" fill="#E69F00" opacity="0.8"><title>SCAMP1: −1.34 · q = 4.1e-45</title></circle><circle cx="311.5" cy="321.5" r="2" fill="#0072B2" opacity="0.8"><title>MCF2L2: 1.4 · q = 4.1e-45</title></circle><circle cx="197.7" cy="321.6" r="2" fill="#E69F00" opacity="0.8"><title>HNMT: −1.45 · q = 4.4e-45</title></circle><circle cx="221.5" cy="321.6" r="2" fill="#E69F00" opacity="0.8"><title>NXPH1: −0.851 · q = 4.4e-45</title></circle><circle cx="314.2" cy="321.6" r="2" fill="#0072B2" opacity="0.8"><title>TAF11: 1.47 · q = 4.7e-45</title></circle><circle cx="319.7" cy="321.6" r="2" fill="#0072B2" opacity="0.8"><title>PDHA1: 1.61 · q = 5.1e-45</title></circle><circle cx="321.1" cy="321.7" r="2" fill="#0072B2" opacity="0.8"><title>IGF2BP2: 1.64 · q = 5.1e-45</title></circle><circle cx="202.6" cy="321.7" r="2" fill="#E69F00" opacity="0.8"><title>IQGAP2: −1.33 · q = 5.1e-45</title></circle><circle cx="213.1" cy="321.7" r="2" fill="#E69F00" opacity="0.8"><title>RBM24: −1.06 · q = 5.5e-45</title></circle><circle cx="225.3" cy="321.7" r="2" fill="#E69F00" opacity="0.8"><title>CYP4F30P: −0.757 · q = 6e-45</title></circle><circle cx="213.5" cy="321.8" r="2" fill="#E69F00" opacity="0.8"><title>SLC14A2: −1.05 · q = 6.3e-45</title></circle><circle cx="203" cy="321.8" r="2" fill="#E69F00" opacity="0.8"><title>CYP4X1: −1.32 · q = 6.4e-45</title></circle><circle cx="192.5" cy="321.8" r="2" fill="#E69F00" opacity="0.8"><title>ZBTB7A: −1.58 · q = 6.6e-45</title></circle><circle cx="315.7" cy="321.9" r="2" fill="#0072B2" opacity="0.8"><title>MRPL15: 1.51 · q = 7.3e-45</title></circle><circle cx="304.4" cy="321.9" r="2" fill="#0072B2" opacity="0.8"><title>RNF145: 1.23 · q = 7.5e-45</title></circle><circle cx="225.8" cy="321.9" r="2" fill="#E69F00" opacity="0.8"><title>GPR139: −0.745 · q = 7.5e-45</title></circle><circle cx="316" cy="321.9" r="2" fill="#0072B2" opacity="0.8"><title>ERICH5: 1.52 · q = 7.8e-45</title></circle><circle cx="321.8" cy="322" r="2" fill="#0072B2" opacity="0.8"><title>POU4F1: 1.66 · q = 9.6e-45</title></circle><circle cx="198.5" cy="322" r="2" fill="#E69F00" opacity="0.8"><title>CEP120: −1.43 · q = 1e-44</title></circle><circle cx="315.5" cy="322" r="2" fill="#0072B2" opacity="0.8"><title>MCCC1: 1.5 · q = 1e-44</title></circle><circle cx="205.9" cy="322" r="2" fill="#E69F00" opacity="0.8"><title>KIF3A: −1.24 · q = 1e-44</title></circle><circle cx="207.7" cy="322.1" r="2" fill="#E69F00" opacity="0.8"><title>RBM5: −1.2 · q = 1e-44</title></circle><circle cx="196.3" cy="322.1" r="2" fill="#E69F00" opacity="0.8"><title>FBXL7: −1.48 · q = 1e-44</title></circle><circle cx="318.2" cy="322.1" r="2" fill="#0072B2" opacity="0.8"><title>MCM3: 1.57 · q = 1.1e-44</title></circle><circle cx="317.9" cy="322.1" r="2" fill="#0072B2" opacity="0.8"><title>DNAJC2: 1.56 · q = 1.1e-44</title></circle><circle cx="202.8" cy="322.1" r="2" fill="#E69F00" opacity="0.8"><title>COG2: −1.32 · q = 1.1e-44</title></circle><circle cx="303.8" cy="322.1" r="2" fill="#0072B2" opacity="0.8"><title>PIR: 1.21 · q = 1.1e-44</title></circle><circle cx="312.7" cy="322.1" r="2" fill="#0072B2" opacity="0.8"><title>CCT4: 1.43 · q = 1.2e-44</title></circle><circle cx="300.2" cy="322.2" r="2" fill="#0072B2" opacity="0.8"><title>GCNT2: 1.12 · q = 1.2e-44</title></circle><circle cx="206.2" cy="322.2" r="2" fill="#E69F00" opacity="0.8"><title>TOB1: −1.24 · q = 1.2e-44</title></circle><circle cx="314.4" cy="322.2" r="2" fill="#0072B2" opacity="0.8"><title>RARRES1: 1.48 · q = 1.2e-44</title></circle><circle cx="321.1" cy="322.2" r="2" fill="#0072B2" opacity="0.8"><title>ABCA13: 1.64 · q = 1.3e-44</title></circle><circle cx="310.1" cy="322.3" r="2" fill="#0072B2" opacity="0.8"><title>MSN: 1.37 · q = 1.4e-44</title></circle><circle cx="322.9" cy="322.3" r="2" fill="#0072B2" opacity="0.8"><title>RHCG: 1.69 · q = 1.5e-44</title></circle><circle cx="304.7" cy="322.4" r="2" fill="#0072B2" opacity="0.8"><title>HR: 1.23 · q = 1.7e-44</title></circle><circle cx="309.3" cy="322.4" r="2" fill="#0072B2" opacity="0.8"><title>IFNAR2: 1.35 · q = 1.8e-44</title></circle><circle cx="201.2" cy="322.4" r="2" fill="#E69F00" opacity="0.8"><title>PWWP3B: −1.36 · q = 1.8e-44</title></circle><circle cx="318.1" cy="322.4" r="2" fill="#0072B2" opacity="0.8"><title>RHEB: 1.57 · q = 1.9e-44</title></circle><circle cx="210.6" cy="322.5" r="2" fill="#E69F00" opacity="0.8"><title>PTP4A2: −1.12 · q = 2e-44</title></circle><circle cx="301.5" cy="322.5" r="2" fill="#0072B2" opacity="0.8"><title>MPZL2: 1.15 · q = 2.1e-44</title></circle><circle cx="316.2" cy="322.5" r="2" fill="#0072B2" opacity="0.8"><title>SSRP1: 1.52 · q = 2.1e-44</title></circle><circle cx="197.2" cy="322.5" r="2" fill="#E69F00" opacity="0.8"><title>GOLGA1: −1.46 · q = 2.1e-44</title></circle><circle cx="201.7" cy="322.5" r="2" fill="#E69F00" opacity="0.8"><title>COG7: −1.35 · q = 2.2e-44</title></circle><circle cx="300.8" cy="322.5" r="2" fill="#0072B2" opacity="0.8"><title>CENPM: 1.13 · q = 2.3e-44</title></circle><circle cx="192.5" cy="322.5" r="2" fill="#E69F00" opacity="0.8"><title>PRLR: −1.58 · q = 2.3e-44</title></circle><circle cx="321.2" cy="322.6" r="2" fill="#0072B2" opacity="0.8"><title>TCAM1P: 1.65 · q = 2.5e-44</title></circle><circle cx="327.7" cy="322.6" r="2" fill="#0072B2" opacity="0.8"><title>GFRA3: 1.81 · q = 2.7e-44</title></circle><circle cx="216.1" cy="322.6" r="2" fill="#E69F00" opacity="0.8"><title>FCN2: −0.988 · q = 2.8e-44</title></circle><circle cx="194.2" cy="322.7" r="2" fill="#E69F00" opacity="0.8"><title>LRRC56: −1.54 · q = 2.9e-44</title></circle><circle cx="195.9" cy="322.7" r="2" fill="#E69F00" opacity="0.8"><title>ALAD: −1.49 · q = 3e-44</title></circle><circle cx="304.2" cy="322.7" r="2" fill="#0072B2" opacity="0.8"><title>PRR11: 1.22 · q = 3.3e-44</title></circle><circle cx="193.4" cy="322.7" r="2" fill="#E69F00" opacity="0.8"><title>PRICKLE2: −1.56 · q = 3.3e-44</title></circle><circle cx="206.7" cy="322.8" r="2" fill="#E69F00" opacity="0.8"><title>IGSF22: −1.22 · q = 3.4e-44</title></circle><circle cx="297.2" cy="322.8" r="2" fill="#0072B2" opacity="0.8"><title>TRIM29: 1.05 · q = 3.6e-44</title></circle><circle cx="213.5" cy="322.9" r="2" fill="#E69F00" opacity="0.8"><title>MS4A14: −1.05 · q = 4.2e-44</title></circle><circle cx="303.4" cy="322.9" r="2" fill="#0072B2" opacity="0.8"><title>BOP1: 1.2 · q = 4.3e-44</title></circle><circle cx="205.2" cy="322.9" r="2" fill="#E69F00" opacity="0.8"><title>NECAB3: −1.26 · q = 4.3e-44</title></circle><circle cx="205.3" cy="322.9" r="2" fill="#E69F00" opacity="0.8"><title>ANKRD24: −1.26 · q = 4.4e-44</title></circle><circle cx="319.4" cy="322.9" r="2" fill="#0072B2" opacity="0.8"><title>IRAK1: 1.6 · q = 4.5e-44</title></circle><circle cx="312.6" cy="322.9" r="2" fill="#0072B2" opacity="0.8"><title>GARS1: 1.43 · q = 4.7e-44</title></circle><circle cx="315.4" cy="323" r="2" fill="#0072B2" opacity="0.8"><title>WDR43: 1.5 · q = 5.3e-44</title></circle><circle cx="208.1" cy="323" r="2" fill="#E69F00" opacity="0.8"><title>OVGP1: −1.19 · q = 5.7e-44</title></circle><circle cx="311.2" cy="323.1" r="2" fill="#0072B2" opacity="0.8"><title>DDX39A: 1.4 · q = 5.9e-44</title></circle><circle cx="296.9" cy="323.1" r="2" fill="#0072B2" opacity="0.8"><title>TUT4: 1.04 · q = 6.1e-44</title></circle><circle cx="311.7" cy="323.1" r="2" fill="#0072B2" opacity="0.8"><title>SMOC1: 1.41 · q = 6.2e-44</title></circle><circle cx="311.4" cy="323.1" r="2" fill="#0072B2" opacity="0.8"><title>RFWD3: 1.4 · q = 6.4e-44</title></circle><circle cx="222.6" cy="323.1" r="2" fill="#E69F00" opacity="0.8"><title>G6PC2: −0.824 · q = 6.5e-44</title></circle><circle cx="317.8" cy="323.1" r="2" fill="#0072B2" opacity="0.8"><title>MSH2: 1.56 · q = 6.6e-44</title></circle><circle cx="200.9" cy="323.2" r="2" fill="#E69F00" opacity="0.8"><title>ELMOD2: −1.37 · q = 7.2e-44</title></circle><circle cx="215.9" cy="323.2" r="2" fill="#E69F00" opacity="0.8"><title>GCM1: −0.993 · q = 7.4e-44</title></circle><circle cx="315.2" cy="323.2" r="2" fill="#0072B2" opacity="0.8"><title>GSDMC: 1.5 · q = 7.4e-44</title></circle><circle cx="322" cy="323.2" r="2" fill="#0072B2" opacity="0.8"><title>PLOD1: 1.67 · q = 7.7e-44</title></circle><circle cx="198.5" cy="323.2" r="2" fill="#E69F00" opacity="0.8"><title>SMOC2: −1.43 · q = 7.9e-44</title></circle><circle cx="201.3" cy="323.2" r="2" fill="#E69F00" opacity="0.8"><title>ARL3: −1.36 · q = 7.9e-44</title></circle><circle cx="200.5" cy="323.2" r="2" fill="#E69F00" opacity="0.8"><title>PJA2: −1.38 · q = 7.9e-44</title></circle><circle cx="200.3" cy="323.2" r="2" fill="#E69F00" opacity="0.8"><title>PLAAT2: −1.38 · q = 8e-44</title></circle><circle cx="294" cy="323.3" r="2" fill="#0072B2" opacity="0.8"><title>CNKSR3: 0.965 · q = 8.3e-44</title></circle><circle cx="319.8" cy="323.3" r="2" fill="#0072B2" opacity="0.8"><title>PAPSS1: 1.61 · q = 8.3e-44</title></circle><circle cx="200.9" cy="323.3" r="2" fill="#E69F00" opacity="0.8"><title>C1ORF21: −1.37 · q = 8.5e-44</title></circle><circle cx="204.6" cy="323.3" r="2" fill="#E69F00" opacity="0.8"><title>SIGLEC17P: −1.27 · q = 8.7e-44</title></circle><circle cx="298" cy="323.3" r="2" fill="#0072B2" opacity="0.8"><title>PKMYT1: 1.06 · q = 8.9e-44</title></circle><circle cx="205.4" cy="323.3" r="2" fill="#E69F00" opacity="0.8"><title>CFAP53: −1.26 · q = 9.2e-44</title></circle><circle cx="203.3" cy="323.4" r="2" fill="#E69F00" opacity="0.8"><title>BMP4: −1.31 · q = 9.8e-44</title></circle><circle cx="190.1" cy="323.4" r="2" fill="#E69F00" opacity="0.8"><title>MYO5C: −1.64 · q = 1.1e-43</title></circle><circle cx="201.3" cy="323.4" r="2" fill="#E69F00" opacity="0.8"><title>MORN1: −1.36 · q = 1.1e-43</title></circle><circle cx="310.5" cy="323.4" r="2" fill="#0072B2" opacity="0.8"><title>TBPL1: 1.38 · q = 1.1e-43</title></circle><circle cx="311.1" cy="323.5" r="2" fill="#0072B2" opacity="0.8"><title>CCT5: 1.39 · q = 1.2e-43</title></circle><circle cx="316.7" cy="323.5" r="2" fill="#0072B2" opacity="0.8"><title>GDF5: 1.53 · q = 1.3e-43</title></circle><circle cx="310.4" cy="323.5" r="2" fill="#0072B2" opacity="0.8"><title>XPNPEP1: 1.38 · q = 1.3e-43</title></circle><circle cx="199.5" cy="323.6" r="2" fill="#E69F00" opacity="0.8"><title>TMEM9B: −1.4 · q = 1.4e-43</title></circle><circle cx="307.7" cy="323.6" r="2" fill="#0072B2" opacity="0.8"><title>SLC36A4: 1.31 · q = 1.5e-43</title></circle><circle cx="321.6" cy="323.6" r="2" fill="#0072B2" opacity="0.8"><title>SLC15A1: 1.66 · q = 1.5e-43</title></circle><circle cx="320" cy="323.6" r="2" fill="#0072B2" opacity="0.8"><title>CT83: 1.62 · q = 1.5e-43</title></circle><circle cx="209" cy="323.6" r="2" fill="#E69F00" opacity="0.8"><title>CIMIP6: −1.17 · q = 1.5e-43</title></circle><circle cx="312.7" cy="323.6" r="2" fill="#0072B2" opacity="0.8"><title>RCC2: 1.43 · q = 1.6e-43</title></circle><circle cx="222.7" cy="323.6" r="2" fill="#E69F00" opacity="0.8"><title>SLC22A10: −0.823 · q = 1.6e-43</title></circle><circle cx="314.8" cy="323.7" r="2" fill="#0072B2" opacity="0.8"><title>POLR2D: 1.49 · q = 1.7e-43</title></circle><circle cx="305.2" cy="323.7" r="2" fill="#0072B2" opacity="0.8"><title>SMOX: 1.25 · q = 1.7e-43</title></circle><circle cx="318.5" cy="323.7" r="2" fill="#0072B2" opacity="0.8"><title>POLR3G: 1.58 · q = 1.8e-43</title></circle><circle cx="308" cy="323.7" r="2" fill="#0072B2" opacity="0.8"><title>CALR: 1.32 · q = 1.8e-43</title></circle><circle cx="221.3" cy="323.7" r="2" fill="#E69F00" opacity="0.8"><title>SPPL2C: −0.857 · q = 1.9e-43</title></circle><circle cx="317.1" cy="323.8" r="2" fill="#0072B2" opacity="0.8"><title>ZIC4: 1.54 · q = 2e-43</title></circle><circle cx="224.4" cy="323.8" r="2" fill="#E69F00" opacity="0.8"><title>CDHR4: −0.78 · q = 2e-43</title></circle><circle cx="206.7" cy="323.8" r="2" fill="#E69F00" opacity="0.8"><title>TTC16: −1.22 · q = 2.1e-43</title></circle><circle cx="226.2" cy="323.8" r="2" fill="#E69F00" opacity="0.8"><title>SPINK4: −0.733 · q = 2.2e-43</title></circle><circle cx="200.3" cy="323.9" r="2" fill="#E69F00" opacity="0.8"><title>NOXRED1: −1.38 · q = 2.3e-43</title></circle><circle cx="199.2" cy="323.9" r="2" fill="#E69F00" opacity="0.8"><title>CERS4: −1.41 · q = 2.4e-43</title></circle><circle cx="204" cy="323.9" r="2" fill="#E69F00" opacity="0.8"><title>NAV3: −1.29 · q = 2.5e-43</title></circle><circle cx="312.6" cy="323.9" r="2" fill="#0072B2" opacity="0.8"><title>PNO1: 1.43 · q = 2.5e-43</title></circle><circle cx="208.1" cy="323.9" r="2" fill="#E69F00" opacity="0.8"><title>LETM2: −1.19 · q = 2.6e-43</title></circle><circle cx="198.7" cy="323.9" r="2" fill="#E69F00" opacity="0.8"><title>PDE11A: −1.42 · q = 2.6e-43</title></circle><circle cx="207.1" cy="323.9" r="2" fill="#E69F00" opacity="0.8"><title>SREBF1: −1.21 · q = 2.6e-43</title></circle><circle cx="202.8" cy="323.9" r="2" fill="#E69F00" opacity="0.8"><title>MDP1: −1.32 · q = 2.7e-43</title></circle><circle cx="205.2" cy="324" r="2" fill="#E69F00" opacity="0.8"><title>DGKD: −1.26 · q = 2.9e-43</title></circle><circle cx="201.3" cy="324" r="2" fill="#E69F00" opacity="0.8"><title>WDR25: −1.36 · q = 3.1e-43</title></circle><circle cx="190.3" cy="324" r="2" fill="#E69F00" opacity="0.8"><title>VIPR1: −1.63 · q = 3.2e-43</title></circle><circle cx="209.9" cy="324" r="2" fill="#E69F00" opacity="0.8"><title>TLCD3B: −1.14 · q = 3.2e-43</title></circle><circle cx="316.8" cy="324.1" r="2" fill="#0072B2" opacity="0.8"><title>MELTF: 1.54 · q = 3.5e-43</title></circle><circle cx="199.8" cy="324.1" r="2" fill="#E69F00" opacity="0.8"><title>NRIP1: −1.4 · q = 3.6e-43</title></circle><circle cx="324.1" cy="324.1" r="2" fill="#0072B2" opacity="0.8"><title>OPRK1: 1.72 · q = 3.6e-43</title></circle><circle cx="315.1" cy="324.2" r="2" fill="#0072B2" opacity="0.8"><title>PUS7: 1.49 · q = 3.8e-43</title></circle><circle cx="206.2" cy="324.2" r="2" fill="#E69F00" opacity="0.8"><title>ALKBH1: −1.24 · q = 3.9e-43</title></circle><circle cx="201.5" cy="324.2" r="2" fill="#E69F00" opacity="0.8"><title>ZSWIM5: −1.35 · q = 3.9e-43</title></circle><circle cx="316.9" cy="324.2" r="2" fill="#0072B2" opacity="0.8"><title>NRTN: 1.54 · q = 4.1e-43</title></circle><circle cx="309" cy="324.2" r="2" fill="#0072B2" opacity="0.8"><title>TXNL4B: 1.34 · q = 4.2e-43</title></circle><circle cx="310.4" cy="324.2" r="2" fill="#0072B2" opacity="0.8"><title>DCPS: 1.38 · q = 4.2e-43</title></circle><circle cx="205" cy="324.2" r="2" fill="#E69F00" opacity="0.8"><title>CREB3L1: −1.27 · q = 4.3e-43</title></circle><circle cx="314.4" cy="324.2" r="2" fill="#0072B2" opacity="0.8"><title>CMSS1: 1.48 · q = 4.4e-43</title></circle><circle cx="202.7" cy="324.2" r="2" fill="#E69F00" opacity="0.8"><title>DNAJC14: −1.32 · q = 4.5e-43</title></circle><circle cx="308.3" cy="324.3" r="2" fill="#0072B2" opacity="0.8"><title>AIF1L: 1.32 · q = 4.6e-43</title></circle><circle cx="309.7" cy="324.3" r="2" fill="#0072B2" opacity="0.8"><title>GLIPR2: 1.36 · q = 4.6e-43</title></circle><circle cx="209.4" cy="324.3" r="2" fill="#E69F00" opacity="0.8"><title>AHI1-DT: −1.16 · q = 5.1e-43</title></circle><circle cx="331.7" cy="324.3" r="2" fill="#0072B2" opacity="0.8"><title>IGF2BP3: 1.91 · q = 5.2e-43</title></circle><circle cx="193.8" cy="324.4" r="2" fill="#E69F00" opacity="0.8"><title>VPS39: −1.55 · q = 5.4e-43</title></circle><circle cx="218.4" cy="324.4" r="2" fill="#E69F00" opacity="0.8"><title>SERPINA4: −0.929 · q = 5.7e-43</title></circle><circle cx="210.5" cy="324.4" r="2" fill="#E69F00" opacity="0.8"><title>MYOZ3: −1.13 · q = 5.8e-43</title></circle><circle cx="212.1" cy="324.4" r="2" fill="#E69F00" opacity="0.8"><title>DNAI1: −1.09 · q = 5.8e-43</title></circle><circle cx="298.9" cy="324.4" r="2" fill="#0072B2" opacity="0.8"><title>RACGAP1: 1.09 · q = 6e-43</title></circle><circle cx="201.7" cy="324.4" r="2" fill="#E69F00" opacity="0.8"><title>CFAP44: −1.35 · q = 6e-43</title></circle><circle cx="305" cy="324.4" r="2" fill="#0072B2" opacity="0.8"><title>ENY2: 1.24 · q = 6.2e-43</title></circle><circle cx="301.4" cy="324.4" r="2" fill="#0072B2" opacity="0.8"><title>ATAD2: 1.15 · q = 6.3e-43</title></circle><circle cx="315.5" cy="324.5" r="2" fill="#0072B2" opacity="0.8"><title>MRPS5: 1.5 · q = 6.7e-43</title></circle><circle cx="209.7" cy="324.5" r="2" fill="#E69F00" opacity="0.8"><title>DNMBP-AS1: −1.15 · q = 6.8e-43</title></circle><circle cx="196" cy="324.5" r="2" fill="#E69F00" opacity="0.8"><title>LRFN2: −1.49 · q = 6.8e-43</title></circle><circle cx="201.4" cy="324.5" r="2" fill="#E69F00" opacity="0.8"><title>PIGH: −1.36 · q = 7e-43</title></circle><circle cx="192.3" cy="324.5" r="2" fill="#E69F00" opacity="0.8"><title>BPIFB1: −1.58 · q = 7.2e-43</title></circle><circle cx="316" cy="324.5" r="2" fill="#0072B2" opacity="0.8"><title>PNMA3: 1.52 · q = 7.3e-43</title></circle><circle cx="203.5" cy="324.6" r="2" fill="#E69F00" opacity="0.8"><title>ZC3H10: −1.3 · q = 7.6e-43</title></circle><circle cx="310.3" cy="324.6" r="2" fill="#0072B2" opacity="0.8"><title>ABRACL: 1.37 · q = 7.9e-43</title></circle><circle cx="311.8" cy="324.6" r="2" fill="#0072B2" opacity="0.8"><title>TRIT1: 1.41 · q = 8.1e-43</title></circle><circle cx="310.9" cy="324.6" r="2" fill="#0072B2" opacity="0.8"><title>FABP5: 1.39 · q = 8.2e-43</title></circle><circle cx="195.5" cy="324.6" r="2" fill="#E69F00" opacity="0.8"><title>MATN3: −1.5 · q = 8.4e-43</title></circle><circle cx="228.8" cy="324.6" r="2" fill="#E69F00" opacity="0.8"><title>EDDM3B: −0.669 · q = 8.8e-43</title></circle><circle cx="207.9" cy="324.7" r="2" fill="#E69F00" opacity="0.8"><title>HGD: −1.19 · q = 9.2e-43</title></circle><circle cx="207.4" cy="324.7" r="2" fill="#E69F00" opacity="0.8"><title>HOGA1: −1.21 · q = 9.7e-43</title></circle><circle cx="301.8" cy="324.7" r="2" fill="#0072B2" opacity="0.8"><title>NXN: 1.16 · q = 9.8e-43</title></circle><circle cx="204.5" cy="324.7" r="2" fill="#E69F00" opacity="0.8"><title>POLK: −1.28 · q = 1e-42</title></circle><circle cx="203.7" cy="324.7" r="2" fill="#E69F00" opacity="0.8"><title>NFATC4: −1.3 · q = 1.1e-42</title></circle><circle cx="210.9" cy="324.8" r="2" fill="#E69F00" opacity="0.8"><title>SH3D21: −1.12 · q = 1.1e-42</title></circle><circle cx="310.3" cy="324.8" r="2" fill="#0072B2" opacity="0.8"><title>SYNCRIP: 1.37 · q = 1.2e-42</title></circle><circle cx="309.8" cy="324.9" r="2" fill="#0072B2" opacity="0.8"><title>SLC25A37: 1.36 · q = 1.3e-42</title></circle><circle cx="298.5" cy="324.9" r="2" fill="#0072B2" opacity="0.8"><title>EFNA5: 1.08 · q = 1.4e-42</title></circle><circle cx="307.1" cy="324.9" r="2" fill="#0072B2" opacity="0.8"><title>TRDMT1: 1.29 · q = 1.4e-42</title></circle><circle cx="306.9" cy="324.9" r="2" fill="#0072B2" opacity="0.8"><title>TGFA: 1.29 · q = 1.5e-42</title></circle><circle cx="201.8" cy="325" r="2" fill="#E69F00" opacity="0.8"><title>GOLM2: −1.35 · q = 1.5e-42</title></circle><circle cx="183.2" cy="325" r="2" fill="#E69F00" opacity="0.8"><title>RAB17: −1.81 · q = 1.6e-42</title></circle><circle cx="309.4" cy="325" r="2" fill="#0072B2" opacity="0.8"><title>TTC13: 1.35 · q = 1.7e-42</title></circle><circle cx="309.6" cy="325" r="2" fill="#0072B2" opacity="0.8"><title>SLC25A13: 1.36 · q = 1.8e-42</title></circle><circle cx="198.9" cy="325" r="2" fill="#E69F00" opacity="0.8"><title>RBKS: −1.42 · q = 1.8e-42</title></circle><circle cx="303.9" cy="325.1" r="2" fill="#0072B2" opacity="0.8"><title>CLSPN: 1.21 · q = 1.9e-42</title></circle><circle cx="215.6" cy="325.1" r="2" fill="#E69F00" opacity="0.8"><title>SLC25A48: −1 · q = 2e-42</title></circle><circle cx="206.8" cy="325.1" r="2" fill="#E69F00" opacity="0.8"><title>FEM1C: −1.22 · q = 2.1e-42</title></circle><circle cx="199.1" cy="325.1" r="2" fill="#E69F00" opacity="0.8"><title>NECTIN2: −1.41 · q = 2.1e-42</title></circle><circle cx="310" cy="325.2" r="2" fill="#0072B2" opacity="0.8"><title>SCLT1: 1.37 · q = 2.2e-42</title></circle><circle cx="206.7" cy="325.2" r="2" fill="#E69F00" opacity="0.8"><title>SUGT1P3: −1.22 · q = 2.2e-42</title></circle><circle cx="207.9" cy="325.2" r="2" fill="#E69F00" opacity="0.8"><title>MS4A7: −1.19 · q = 2.2e-42</title></circle><circle cx="207.1" cy="325.2" r="2" fill="#E69F00" opacity="0.8"><title>RETREG3: −1.21 · q = 2.4e-42</title></circle><circle cx="203.4" cy="325.3" r="2" fill="#E69F00" opacity="0.8"><title>PBX1: −1.31 · q = 2.8e-42</title></circle><circle cx="198.8" cy="325.3" r="2" fill="#E69F00" opacity="0.8"><title>RAB19: −1.42 · q = 2.8e-42</title></circle><circle cx="199.2" cy="325.3" r="2" fill="#E69F00" opacity="0.8"><title>TMEM229B: −1.41 · q = 2.8e-42</title></circle><circle cx="323.8" cy="325.3" r="2" fill="#0072B2" opacity="0.8"><title>CCDC77: 1.71 · q = 2.8e-42</title></circle><circle cx="312.2" cy="325.4" r="2" fill="#0072B2" opacity="0.8"><title>GPR161: 1.42 · q = 3.1e-42</title></circle><circle cx="300.1" cy="325.4" r="2" fill="#0072B2" opacity="0.8"><title>CIT: 1.12 · q = 3.3e-42</title></circle><circle cx="211.5" cy="325.4" r="2" fill="#E69F00" opacity="0.8"><title>PHF12: −1.1 · q = 3.6e-42</title></circle><circle cx="203.2" cy="325.5" r="2" fill="#E69F00" opacity="0.8"><title>GLUD1: −1.31 · q = 3.8e-42</title></circle><circle cx="204.9" cy="325.5" r="2" fill="#E69F00" opacity="0.8"><title>SFXN2: −1.27 · q = 3.8e-42</title></circle><circle cx="317.1" cy="325.5" r="2" fill="#0072B2" opacity="0.8"><title>CSTB: 1.54 · q = 3.9e-42</title></circle><circle cx="309.9" cy="325.5" r="2" fill="#0072B2" opacity="0.8"><title>PGM1: 1.36 · q = 4.1e-42</title></circle><circle cx="298.9" cy="325.5" r="2" fill="#0072B2" opacity="0.8"><title>C16ORF87: 1.09 · q = 4.3e-42</title></circle><circle cx="220.5" cy="325.5" r="2" fill="#E69F00" opacity="0.8"><title>UGT2B11: −0.878 · q = 4.3e-42</title></circle><circle cx="200.9" cy="325.6" r="2" fill="#E69F00" opacity="0.8"><title>CCDC160: −1.37 · q = 4.3e-42</title></circle><circle cx="319.4" cy="325.6" r="2" fill="#0072B2" opacity="0.8"><title>UTP11: 1.6 · q = 4.3e-42</title></circle><circle cx="206.1" cy="325.6" r="2" fill="#E69F00" opacity="0.8"><title>MIDEAS: −1.24 · q = 4.4e-42</title></circle><circle cx="201.3" cy="325.6" r="2" fill="#E69F00" opacity="0.8"><title>EEF1A2: −1.36 · q = 4.4e-42</title></circle><circle cx="200.1" cy="325.6" r="2" fill="#E69F00" opacity="0.8"><title>TRIM8: −1.39 · q = 4.5e-42</title></circle><circle cx="303" cy="325.6" r="2" fill="#0072B2" opacity="0.8"><title>SERPINB5: 1.19 · q = 4.6e-42</title></circle><circle cx="210.7" cy="325.6" r="2" fill="#E69F00" opacity="0.8"><title>SLC22A4: −1.12 · q = 4.6e-42</title></circle><circle cx="316.4" cy="325.6" r="2" fill="#0072B2" opacity="0.8"><title>MASTL: 1.53 · q = 4.7e-42</title></circle><circle cx="307.2" cy="325.6" r="2" fill="#0072B2" opacity="0.8"><title>SMC4: 1.3 · q = 4.8e-42</title></circle><circle cx="310.6" cy="325.7" r="2" fill="#0072B2" opacity="0.8"><title>ACE2: 1.38 · q = 5.3e-42</title></circle><circle cx="312.3" cy="325.7" r="2" fill="#0072B2" opacity="0.8"><title>C5ORF34: 1.42 · q = 5.3e-42</title></circle><circle cx="310" cy="325.7" r="2" fill="#0072B2" opacity="0.8"><title>FAM98A: 1.37 · q = 5.4e-42</title></circle><circle cx="187.7" cy="325.7" r="2" fill="#E69F00" opacity="0.8"><title>SRMS: −1.7 · q = 5.4e-42</title></circle><circle cx="220.4" cy="325.7" r="2" fill="#E69F00" opacity="0.8"><title>TAT: −0.878 · q = 5.7e-42</title></circle><circle cx="201.8" cy="325.7" r="2" fill="#E69F00" opacity="0.8"><title>STAM2: −1.35 · q = 6e-42</title></circle><circle cx="222.3" cy="325.8" r="2" fill="#E69F00" opacity="0.8"><title>SYNPO2L: −0.831 · q = 6.2e-42</title></circle><circle cx="304.8" cy="325.8" r="2" fill="#0072B2" opacity="0.8"><title>ACTR3: 1.24 · q = 6.6e-42</title></circle><circle cx="304.9" cy="325.8" r="2" fill="#0072B2" opacity="0.8"><title>CORO1C: 1.24 · q = 6.8e-42</title></circle><circle cx="307.3" cy="325.8" r="2" fill="#0072B2" opacity="0.8"><title>TMEM123: 1.3 · q = 6.8e-42</title></circle><circle cx="198.1" cy="325.8" r="2" fill="#E69F00" opacity="0.8"><title>FRAT1: −1.44 · q = 6.8e-42</title></circle><circle cx="198.1" cy="325.8" r="2" fill="#E69F00" opacity="0.8"><title>EPB41L4A: −1.44 · q = 6.9e-42</title></circle><circle cx="198.8" cy="325.8" r="2" fill="#E69F00" opacity="0.8"><title>ZCCHC24: −1.42 · q = 7.1e-42</title></circle><circle cx="195.2" cy="325.8" r="2" fill="#E69F00" opacity="0.8"><title>ABCG1: −1.51 · q = 7.1e-42</title></circle><circle cx="210.3" cy="325.9" r="2" fill="#E69F00" opacity="0.8"><title>GALNTL6: −1.13 · q = 7.3e-42</title></circle><circle cx="207.3" cy="325.9" r="2" fill="#E69F00" opacity="0.8"><title>SCUBE1: −1.21 · q = 7.5e-42</title></circle><circle cx="301.3" cy="325.9" r="2" fill="#0072B2" opacity="0.8"><title>TK1: 1.15 · q = 7.8e-42</title></circle><circle cx="305.9" cy="325.9" r="2" fill="#0072B2" opacity="0.8"><title>KRT6B: 1.26 · q = 8.3e-42</title></circle><circle cx="222" cy="326" r="2" fill="#E69F00" opacity="0.8"><title>CLEC3A: −0.84 · q = 9.6e-42</title></circle><circle cx="191.1" cy="326" r="2" fill="#E69F00" opacity="0.8"><title>NBEA: −1.61 · q = 9.6e-42</title></circle><circle cx="202.3" cy="326" r="2" fill="#E69F00" opacity="0.8"><title>SLC12A2-DT: −1.33 · q = 9.8e-42</title></circle><circle cx="200.1" cy="326.1" r="2" fill="#E69F00" opacity="0.8"><title>FAM13B: −1.39 · q = 1e-41</title></circle><circle cx="311.1" cy="326.1" r="2" fill="#0072B2" opacity="0.8"><title>PADI2: 1.39 · q = 1e-41</title></circle><circle cx="210.1" cy="326.1" r="2" fill="#E69F00" opacity="0.8"><title>PRR29: −1.14 · q = 1e-41</title></circle><circle cx="307.1" cy="326.1" r="2" fill="#0072B2" opacity="0.8"><title>C1GALT1: 1.29 · q = 1.1e-41</title></circle><circle cx="199.9" cy="326.1" r="2" fill="#E69F00" opacity="0.8"><title>TMEM205: −1.39 · q = 1.1e-41</title></circle><circle cx="303.9" cy="326.1" r="2" fill="#0072B2" opacity="0.8"><title>HLA-DOB: 1.21 · q = 1.1e-41</title></circle><circle cx="304.6" cy="326.1" r="2" fill="#0072B2" opacity="0.8"><title>PCDHB9: 1.23 · q = 1.1e-41</title></circle><circle cx="314.5" cy="326.1" r="2" fill="#0072B2" opacity="0.8"><title>ATP1B3: 1.48 · q = 1.2e-41</title></circle><circle cx="201.2" cy="326.1" r="2" fill="#E69F00" opacity="0.8"><title>EBF4: −1.36 · q = 1.2e-41</title></circle><circle cx="202.5" cy="326.2" r="2" fill="#E69F00" opacity="0.8"><title>ZNF563: −1.33 · q = 1.2e-41</title></circle><circle cx="210.5" cy="326.2" r="2" fill="#E69F00" opacity="0.8"><title>SFXN5: −1.13 · q = 1.2e-41</title></circle><circle cx="207.1" cy="326.2" r="2" fill="#E69F00" opacity="0.8"><title>SARAF: −1.21 · q = 1.3e-41</title></circle><circle cx="309.7" cy="326.2" r="2" fill="#0072B2" opacity="0.8"><title>NAE1: 1.36 · q = 1.3e-41</title></circle><circle cx="304.6" cy="326.2" r="2" fill="#0072B2" opacity="0.8"><title>MOB3B: 1.23 · q = 1.3e-41</title></circle><circle cx="201" cy="326.2" r="2" fill="#E69F00" opacity="0.8"><title>ZNF799: −1.37 · q = 1.3e-41</title></circle><circle cx="199.3" cy="326.2" r="2" fill="#E69F00" opacity="0.8"><title>CACFD1: −1.41 · q = 1.4e-41</title></circle><circle cx="314" cy="326.3" r="2" fill="#0072B2" opacity="0.8"><title>CEACAM19: 1.47 · q = 1.4e-41</title></circle><circle cx="207.4" cy="326.3" r="2" fill="#E69F00" opacity="0.8"><title>RANBP3L: −1.21 · q = 1.5e-41</title></circle><circle cx="312.5" cy="326.3" r="2" fill="#0072B2" opacity="0.8"><title>OTX1: 1.43 · q = 1.5e-41</title></circle><circle cx="211.1" cy="326.3" r="2" fill="#E69F00" opacity="0.8"><title>EVA1B: −1.11 · q = 1.5e-41</title></circle><circle cx="192.5" cy="326.3" r="2" fill="#E69F00" opacity="0.8"><title>TMC4: −1.58 · q = 1.6e-41</title></circle><circle cx="314.7" cy="326.3" r="2" fill="#0072B2" opacity="0.8"><title>FGFBP1: 1.48 · q = 1.6e-41</title></circle><circle cx="303.7" cy="326.4" r="2" fill="#0072B2" opacity="0.8"><title>NMT2: 1.21 · q = 1.7e-41</title></circle><circle cx="206" cy="326.4" r="2" fill="#E69F00" opacity="0.8"><title>TMED7: −1.24 · q = 1.7e-41</title></circle><circle cx="207.7" cy="326.4" r="2" fill="#E69F00" opacity="0.8"><title>SLC38A11: −1.2 · q = 1.8e-41</title></circle><circle cx="214.4" cy="326.4" r="2" fill="#E69F00" opacity="0.8"><title>DNAJC1: −1.03 · q = 1.8e-41</title></circle><circle cx="206.6" cy="326.4" r="2" fill="#E69F00" opacity="0.8"><title>ITPK1: −1.23 · q = 1.8e-41</title></circle><circle cx="324.6" cy="326.4" r="2" fill="#0072B2" opacity="0.8"><title>CXCL5: 1.73 · q = 1.8e-41</title></circle><circle cx="315.2" cy="326.4" r="2" fill="#0072B2" opacity="0.8"><title>TEX30: 1.5 · q = 1.9e-41</title></circle><circle cx="309.6" cy="326.4" r="2" fill="#0072B2" opacity="0.8"><title>RPF2: 1.36 · q = 2e-41</title></circle><circle cx="205.1" cy="326.4" r="2" fill="#E69F00" opacity="0.8"><title>FDXACB1: −1.26 · q = 2e-41</title></circle><circle cx="305.8" cy="326.5" r="2" fill="#0072B2" opacity="0.8"><title>PPP1R14B: 1.26 · q = 2.1e-41</title></circle><circle cx="302.8" cy="326.5" r="2" fill="#0072B2" opacity="0.8"><title>RELT: 1.18 · q = 2.1e-41</title></circle><circle cx="202.5" cy="326.5" r="2" fill="#E69F00" opacity="0.8"><title>PLA2G12A: −1.33 · q = 2.2e-41</title></circle><circle cx="312.1" cy="326.5" r="2" fill="#0072B2" opacity="0.8"><title>FABP7: 1.42 · q = 2.2e-41</title></circle><circle cx="316" cy="326.5" r="2" fill="#0072B2" opacity="0.8"><title>LANCL3: 1.52 · q = 2.3e-41</title></circle><circle cx="204.4" cy="326.5" r="2" fill="#E69F00" opacity="0.8"><title>CPEB4: −1.28 · q = 2.4e-41</title></circle><circle cx="198" cy="326.6" r="2" fill="#E69F00" opacity="0.8"><title>ACVR1B: −1.44 · q = 2.5e-41</title></circle><circle cx="311.5" cy="326.6" r="2" fill="#0072B2" opacity="0.8"><title>MKRN3: 1.4 · q = 2.5e-41</title></circle><circle cx="294.4" cy="326.6" r="2" fill="#0072B2" opacity="0.8"><title>ITGB8: 0.975 · q = 2.5e-41</title></circle><circle cx="220.2" cy="326.6" r="2" fill="#E69F00" opacity="0.8"><title>ABCC12: −0.885 · q = 2.6e-41</title></circle><circle cx="209.9" cy="326.6" r="2" fill="#E69F00" opacity="0.8"><title>ADCY5: −1.14 · q = 2.7e-41</title></circle><circle cx="212.6" cy="326.6" r="2" fill="#E69F00" opacity="0.8"><title>GRM4: −1.08 · q = 2.8e-41</title></circle><circle cx="316.5" cy="326.7" r="2" fill="#0072B2" opacity="0.8"><title>CEBPG: 1.53 · q = 2.9e-41</title></circle><circle cx="207.6" cy="326.7" r="2" fill="#E69F00" opacity="0.8"><title>IKBKB: −1.2 · q = 3.2e-41</title></circle><circle cx="300.9" cy="326.7" r="2" fill="#0072B2" opacity="0.8"><title>RECQL4: 1.14 · q = 3.2e-41</title></circle><circle cx="308.5" cy="326.7" r="2" fill="#0072B2" opacity="0.8"><title>NUTF2: 1.33 · q = 3.3e-41</title></circle><circle cx="205.4" cy="326.7" r="2" fill="#E69F00" opacity="0.8"><title>KCNMA1: −1.26 · q = 3.4e-41</title></circle><circle cx="309.7" cy="326.7" r="2" fill="#0072B2" opacity="0.8"><title>CDCA4: 1.36 · q = 3.4e-41</title></circle><circle cx="209" cy="326.8" r="2" fill="#E69F00" opacity="0.8"><title>SIGLEC15: −1.17 · q = 3.5e-41</title></circle><circle cx="202.4" cy="326.8" r="2" fill="#E69F00" opacity="0.8"><title>ENTPD8: −1.33 · q = 3.5e-41</title></circle><circle cx="304.3" cy="326.8" r="2" fill="#0072B2" opacity="0.8"><title>SGO2: 1.22 · q = 3.8e-41</title></circle><circle cx="204.9" cy="326.8" r="2" fill="#E69F00" opacity="0.8"><title>SLC24A2: −1.27 · q = 3.8e-41</title></circle><circle cx="206.5" cy="326.8" r="2" fill="#E69F00" opacity="0.8"><title>HEMK2: −1.23 · q = 3.9e-41</title></circle><circle cx="307.7" cy="326.8" r="2" fill="#0072B2" opacity="0.8"><title>AURKAP1: 1.31 · q = 4e-41</title></circle><circle cx="210.2" cy="326.9" r="2" fill="#E69F00" opacity="0.8"><title>CYP4F29P: −1.13 · q = 4.1e-41</title></circle><circle cx="205" cy="326.9" r="2" fill="#E69F00" opacity="0.8"><title>DUSP28: −1.26 · q = 4.5e-41</title></circle><circle cx="211.4" cy="326.9" r="2" fill="#E69F00" opacity="0.8"><title>SLC29A3: −1.11 · q = 4.5e-41</title></circle><circle cx="312.7" cy="326.9" r="2" fill="#0072B2" opacity="0.8"><title>PACC1: 1.43 · q = 4.6e-41</title></circle><circle cx="209.8" cy="326.9" r="2" fill="#E69F00" opacity="0.8"><title>CFAP45: −1.15 · q = 4.6e-41</title></circle><circle cx="305.8" cy="327" r="2" fill="#0072B2" opacity="0.8"><title>ADAT2: 1.26 · q = 5.1e-41</title></circle><circle cx="309.7" cy="327" r="2" fill="#0072B2" opacity="0.8"><title>LSM6: 1.36 · q = 5.3e-41</title></circle><circle cx="317" cy="327" r="2" fill="#0072B2" opacity="0.8"><title>UBASH3B: 1.54 · q = 5.3e-41</title></circle><circle cx="199.7" cy="327" r="2" fill="#E69F00" opacity="0.8"><title>AP1G2: −1.4 · q = 5.5e-41</title></circle><circle cx="205.9" cy="327" r="2" fill="#E69F00" opacity="0.8"><title>NUDT16L1: −1.24 · q = 5.7e-41</title></circle><circle cx="200.2" cy="327.1" r="2" fill="#E69F00" opacity="0.8"><title>EFHC1: −1.38 · q = 5.8e-41</title></circle><circle cx="196.7" cy="327.1" r="2" fill="#E69F00" opacity="0.8"><title>AHNAK: −1.47 · q = 6.1e-41</title></circle><circle cx="309.6" cy="327.1" r="2" fill="#0072B2" opacity="0.8"><title>NCAPG2: 1.35 · q = 6.2e-41</title></circle><circle cx="203.8" cy="327.1" r="2" fill="#E69F00" opacity="0.8"><title>SPARCL1: −1.3 · q = 6.5e-41</title></circle><circle cx="203.7" cy="327.1" r="2" fill="#E69F00" opacity="0.8"><title>LINC02447: −1.3 · q = 6.6e-41</title></circle><circle cx="204.5" cy="327.1" r="2" fill="#E69F00" opacity="0.8"><title>TKFC: −1.28 · q = 6.8e-41</title></circle><circle cx="312.5" cy="327.2" r="2" fill="#0072B2" opacity="0.8"><title>ST3GAL6: 1.43 · q = 6.8e-41</title></circle><circle cx="204.7" cy="327.2" r="2" fill="#E69F00" opacity="0.8"><title>LYPD6B: −1.27 · q = 6.8e-41</title></circle><circle cx="197.2" cy="327.2" r="2" fill="#E69F00" opacity="0.8"><title>JADE2: −1.46 · q = 7.1e-41</title></circle><circle cx="315.2" cy="327.2" r="2" fill="#0072B2" opacity="0.8"><title>SCHIP1: 1.5 · q = 7.2e-41</title></circle><circle cx="324.3" cy="196.2" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="328" cy="200.1" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="331.5" cy="208.3" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="327.2" cy="221.8" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="329.6" cy="223" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="327.3" cy="228.8" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="169.7" cy="95.3" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="176.1" cy="115.5" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="158.3" cy="120.7" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="172" cy="134.2" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="178.6" cy="144.2" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="180.2" cy="153.2" r="3.2" fill="none" stroke="#16232b" stroke-width="1"/><text x="283.1" y="198.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">BCL11A</text><line x1="331.5" y1="200.1" x2="340" y2="200.1" stroke="#5b6b76" stroke-width="0.75"/><text x="341" y="202.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">CDCA7</text><line x1="333.9" y1="210.8" x2="339.9" y2="216.7" stroke="#5b6b76" stroke-width="0.75"/><text x="340.9" y="225.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">CDC20</text><line x1="323.7" y1="221.8" x2="315.2" y2="221.8" stroke="#5b6b76" stroke-width="0.75"/><text x="286" y="224.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">KIF2C</text><line x1="332.1" y1="225.4" x2="338" y2="231.4" stroke="#5b6b76" stroke-width="0.75"/><text x="339" y="239.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">CENPA</text><line x1="324.8" y1="231.3" x2="302.1" y2="254" stroke="#5b6b76" stroke-width="0.75"/><text x="268.7" y="262.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">AURKB</text><text x="174.7" y="97.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">TTC36</text><text x="181.1" y="118" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">FSIP1</text><text x="122.2" y="123.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">SRARP</text><text x="177" y="136.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">DRAIC</text><text x="148.4" y="146.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">NAT1</text><text x="185.2" y="155.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">MAPT-IT1</text><text x="51" y="437" font-size="10" fill="#5b6b76">← higher in Luminal A</text><text x="460" y="437" font-size="10" fill="#5b6b76" text-anchor="end">higher in Basal-like →</text><text x="494" y="14" font-size="12" fill="#16232b" font-weight="600">Gene-set enrichment</text><text x="494" y="28" font-size="10" fill="#5b6b76">preranked GSEA, 1,000 permutations · bars are q &lt; 0.05 when solid</text><text x="494" y="52.9" font-size="11" fill="#16232b">e2f targets</text><rect x="756.3" y="43" width="106.3" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="52.5" font-size="10" fill="#5b6b76" text-anchor="end">3.7 · q = 0.0088</text><text x="494" y="70.9" font-size="11" fill="#16232b">g2m checkpoint</text><rect x="756.3" y="61" width="99" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="70.5" font-size="10" fill="#5b6b76" text-anchor="end">3.4 · q = 0.0088</text><text x="494" y="88.9" font-size="11" fill="#16232b">myc targets v1</text><rect x="756.3" y="79" width="93.5" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="88.5" font-size="10" fill="#5b6b76" text-anchor="end">3.2 · q = 0.0088</text><text x="494" y="106.9" font-size="11" fill="#16232b">estrogen response early</text><rect x="676.5" y="97" width="79.8" height="12" rx="2" fill="#E69F00" opacity="0.9"/><text x="960" y="106.5" font-size="10" fill="#5b6b76" text-anchor="end">−2.8 · q = 0.0088</text><text x="494" y="124.9" font-size="11" fill="#16232b">myc targets v2</text><rect x="756.3" y="115" width="75.6" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="124.5" font-size="10" fill="#5b6b76" text-anchor="end">2.6 · q = 0.0088</text><text x="494" y="142.9" font-size="11" fill="#16232b">mtorc1 signaling</text><rect x="756.3" y="133" width="71" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="142.5" font-size="10" fill="#5b6b76" text-anchor="end">2.5 · q = 0.0088</text><text x="494" y="160.9" font-size="11" fill="#16232b">mitotic spindle</text><rect x="756.3" y="151" width="66.4" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="160.5" font-size="10" fill="#5b6b76" text-anchor="end">2.3 · q = 0.0088</text><text x="494" y="178.9" font-size="11" fill="#16232b">allograft rejection</text><rect x="756.3" y="169" width="66" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="178.5" font-size="10" fill="#5b6b76" text-anchor="end">2.3 · q = 0.0088</text><text x="494" y="196.9" font-size="11" fill="#16232b">estrogen response late</text><rect x="693.7" y="187" width="62.6" height="12" rx="2" fill="#E69F00" opacity="0.9"/><text x="960" y="196.5" font-size="10" fill="#5b6b76" text-anchor="end">−2.2 · q = 0.0088</text><text x="494" y="214.9" font-size="11" fill="#16232b">interferon gamma response</text><rect x="756.3" y="205" width="58" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="214.5" font-size="10" fill="#5b6b76" text-anchor="end">2 · q = 0.0088</text><text x="494" y="232.9" font-size="11" fill="#16232b">unfolded protein response</text><rect x="756.3" y="223" width="55.3" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="232.5" font-size="10" fill="#5b6b76" text-anchor="end">1.9 · q = 0.0088</text><text x="494" y="250.9" font-size="11" fill="#16232b">glycolysis</text><rect x="756.3" y="241" width="50.5" height="12" rx="2" fill="#0072B2" opacity="0.9"/><text x="960" y="250.5" font-size="10" fill="#5b6b76" text-anchor="end">1.7 · q = 0.0088</text><line x1="756.3" y1="38" x2="756.3" y2="256" stroke="#5b6b76" stroke-width="1"/><line x1="640.6" y1="256" x2="640.6" y2="259" stroke="#5b6b76" stroke-width="1"/><text x="634.6" y="272" font-size="10" fill="#5b6b76">−4</text><line x1="698.4" y1="256" x2="698.4" y2="259" stroke="#5b6b76" stroke-width="1"/><text x="692.4" y="272" font-size="10" fill="#5b6b76">−2</text><line x1="756.3" y1="256" x2="756.3" y2="259" stroke="#5b6b76" stroke-width="1"/><text x="753.3" y="272" font-size="10" fill="#5b6b76">0</text><line x1="814.2" y1="256" x2="814.2" y2="259" stroke="#5b6b76" stroke-width="1"/><text x="811.2" y="272" font-size="10" fill="#5b6b76">2</text><line x1="872.1" y1="256" x2="872.1" y2="259" stroke="#5b6b76" stroke-width="1"/><text x="869.1" y="272" font-size="10" fill="#5b6b76">4</text><line x1="640.554" y1="256" x2="872.06" y2="256" stroke="#5b6b76" stroke-width="1"/><text x="756.3" y="291" font-size="11" fill="#16232b" text-anchor="middle">NES</text><text x="640.6" y="307" font-size="10" fill="#5b6b76">← higher in Luminal A</text><text x="872.1" y="307" font-size="10" fill="#5b6b76" text-anchor="end">higher in Basal-like →</text><text x="494" y="323" font-size="10" fill="#5b6b76">+38 more sets tested (23 of 50 at q &lt; 0.05) — every set is in the result and the MCP text</text><text x="494" y="348" font-size="11" fill="#16232b">e2f targets — ES 0.78, 132 leading-edge genes</text><line x1="548" y1="428" x2="956" y2="428" stroke="#dde4e8" stroke-width="0.75"/><text x="542" y="431.5" font-size="10" fill="#5b6b76" text-anchor="end">−0.2</text><line x1="548" y1="413.6" x2="956" y2="413.6" stroke="#dde4e8" stroke-width="0.75"/><text x="542" y="417.1" font-size="10" fill="#5b6b76" text-anchor="end">0.0</text><line x1="548" y1="399.2" x2="956" y2="399.2" stroke="#dde4e8" stroke-width="0.75"/><text x="542" y="402.7" font-size="10" fill="#5b6b76" text-anchor="end">0.2</text><line x1="548" y1="384.8" x2="956" y2="384.8" stroke="#dde4e8" stroke-width="0.75"/><text x="542" y="388.3" font-size="10" fill="#5b6b76" text-anchor="end">0.4</text><line x1="548" y1="370.4" x2="956" y2="370.4" stroke="#dde4e8" stroke-width="0.75"/><text x="542" y="373.9" font-size="10" fill="#5b6b76" text-anchor="end">0.6</text><line x1="548" y1="356" x2="956" y2="356" stroke="#dde4e8" stroke-width="0.75"/><text x="542" y="359.5" font-size="10" fill="#5b6b76" text-anchor="end">0.8</text><text x="508.8" y="392" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 508.8 392)">ES</text><line x1="548" y1="413.6" x2="956" y2="413.6" stroke="#5b6b76" stroke-width="0.75"/><path d="M548 413.6 L550 397.6 L552.1 385.9 L554.1 380 L556.2 375.6 L558.2 372.3 L560.2 370.1 L562.3 366.8 L564.3 366 L566.4 364.1 L568.4 363.8 L570.4 362.1 L572.5 361.8 L574.5 361.2 L576.6 360.3 L578.6 359.8 L580.6 359.6 L582.7 358.8 L584.7 357.8 L586.8 357.9 L588.8 357.4 L590.8 357.5 L592.9 357.9 L594.9 358 L596.9 358.2 L599 358.3 L601 358.4 L603.1 358.4 L605.1 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rna_seq_v2_mrna_median_all_sample_Zscores as stored, Basal-like minus Luminal A, each patient contributing the mean of their samples;</text><text x="8" y="669" font-size="10" fill="#5b6b76">computed from three running totals per gene and cohort, so it is exact across sites. 20,006 genes tested, 14 left out (fewer than 3 patients with a value in a cohort, or no</text><text x="8" y="683" font-size="10" fill="#5b6b76">variance); q is Benjamini–Hochberg across every gene tested. The 1500 most significant genes are drawn as points, the rest as density. Z-scores are standardised within</text><text x="8" y="697" font-size="10" fill="#5b6b76">each study, so a difference is in study-specific units. A t-test on thousands of patients finds tiny differences significant — read the effect, not only the q. Enrichment is</text><text x="8" y="711" font-size="10" fill="#5b6b76">preranked GSEA on the unrounded t (Subramanian et al. 2005, weight 1), significance from 1000 random sets of the same size drawn from the same ranking, seeded by</text><text x="8" y="725" font-size="10" fill="#5b6b76">the set's name so a shared link reproduces it; p = (b+1)/(m+1) over the same-sign nulls, q is Benjamini–Hochberg across the sets tested (not GSEA's own NES-based</text><text x="8" y="739" font-size="10" fill="#5b6b76">FDR). Gene-set permutation ignores gene–gene correlation, so it is anti-conservative next to phenotype permutation, which needs patient-level values.</text></svg>
diff --git a/test/snapshots/diffexpr.txt b/test/snapshots/diffexpr.txt
new file mode 100644
index 00000000..49717b37
--- /dev/null
+++ b/test/snapshots/diffexpr.txt
@@ -0,0 +1,33 @@
+Analysis over brca_tcga_pan_can_atlas_2018 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Basal-like: 171 patients — SUBTYPE in [BRCA_Basal]
+- Luminal A: 499 patients — SUBTYPE in [BRCA_LumA]
+
+Differential expression, Basal-like vs Luminal A on rna_seq_v2_mrna_median_all_sample_Zscores: 20,006 genes tested, 7,563 higher in Basal-like and 7,698 higher in Luminal A at q<0.05 (difference in mean z-score).
+  most significant, higher in Basal-like: BCL11A +1.72 (q=2.7e-139), CDCA7 +1.82 (q=2.2e-136), CDC20 +1.90 (q=3.2e-130), KIF2C +1.80 (q=4.7e-120), CENPA +1.86 (q=3.2e-119), AURKB +1.80 (q=8.8e-115), ORC6 +1.71 (q=1.2e-114), PLK1 +1.72 (q=1.4e-114), PSAT1 +1.91 (q=2.4e-112), HJURP +1.66 (q=1.4e-108), FOXC1 +2.00 (q=6.1e-107), PIMREG +1.83 (q=2.8e-106)
+  most significant, higher in Luminal A: TTC36 -2.15 (q=1.6e-215), FSIP1 -1.99 (q=2.3e-200), SRARP -2.44 (q=1.7e-196), DRAIC -2.09 (q=2.8e-186), NAT1 -1.93 (q=1.1e-178), MAPT-IT1 -1.89 (q=6.7e-172), CT62 -2.45 (q=1.8e-153), SLC7A13 -1.92 (q=9.7e-153), CYP2B7P -1.78 (q=1.4e-146), TFF1 -2.24 (q=1.0e-145), NEK10 -1.78 (q=1.5e-143), ABCC8 -2.27 (q=9.8e-142)
+  GSEA (Welch t, Basal-like over Luminal A; 1000 gene-set permutations):
+    HALLMARK_E2F_TARGETS: NES 3.67, q=0.0088, 200 genes, leading edge CDC20 CDCA8 KIF2C AURKB ORC6 CCNE1 PLK1 CTPS1 …
+    HALLMARK_G2M_CHECKPOINT: NES 3.42, q=0.0088, 200 genes, leading edge CDC20 CENPA KIF2C AURKB ORC6 TTK PLK1 TPX2 …
+    HALLMARK_MYC_TARGETS_V1: NES 3.23, q=0.0088, 200 genes, leading edge CDC20 CTPS1 IFRD1 CDC45 CCNA2 SRPK1 MCM7 KPNA2 …
+    HALLMARK_ESTROGEN_RESPONSE_EARLY: NES -2.76, q=0.0088, 199 genes, leading edge P2RY2 CCN5 CXCL12 MSMB BLVRB OLFM1 OLFML3 DYNLT3 …
+    HALLMARK_MYC_TARGETS_V2: NES 2.61, q=0.0088, 58 genes, leading edge PLK1 NIP7 MCM5 NDUFAF4 BYSL MCM4 WDR43 PLK4 …
+    HALLMARK_MTORC1_SIGNALING: NES 2.45, q=0.0088, 200 genes, leading edge PSAT1 PLK1 CDC25A IFRD1 BUB1 AURKA SLC7A5 PHGDH …
+    HALLMARK_MITOTIC_SPINDLE: NES 2.30, q=0.0088, 199 genes, leading edge KIF2C TTK PLK1 TPX2 CCNB2 NDC80 BIRC5 BUB1 …
+    HALLMARK_ALLOGRAFT_REJECTION: NES 2.28, q=0.0088, 200 genes, leading edge RIPK2 IFNAR2 TAP2 HLA-DOB CCL7 EGFR LYN NCK1 …
+    HALLMARK_ESTROGEN_RESPONSE_LATE: NES -2.16, q=0.0088, 200 genes, leading edge SLC2A8 CALCR GPER1 CCN5 DCXR CXCL12 BATF BLVRB …
+    HALLMARK_INTERFERON_GAMMA_RESPONSE: NES 2.00, q=0.0088, 200 genes, leading edge NUP93 PSMB2 RIPK2 PELI1 PTPN2 PFKP PIM1 IFNAR2 …
+    HALLMARK_UNFOLDED_PROTEIN_RESPONSE: NES 1.91, q=0.0088, 113 genes, leading edge PSAT1 SLC7A5 CKS1B CEBPB DKC1 PDIA6 ASNS CHAC1 …
+    HALLMARK_GLYCOLYSIS: NES 1.75, q=0.0088, 200 genes, leading edge CENPA DEPDC1 AURKA KIF20A STMN1 ENO1 DSC2 CDK1 …
+  CAVEAT (tell the user): Welch's t-test per gene on rna_seq_v2_mrna_median_all_sample_Zscores as stored, Basal-like minus Luminal A, each patient contributing the mean of their samples; computed from three running totals per gene and cohort, so it is exact across sites. 20,006 genes tested, 14 left out (fewer than 3 patients with a value in a cohort, or no variance); q is Benjamini–Hochberg across every gene tested. The 1500 most significant genes are drawn as points, the rest as density. Z-scores are standardised within each study, so a difference is in study-specific units. A t-test on thousands of patients finds tiny differences significant — read the effect, not only the q. Enrichment is preranked GSEA on the unrounded t (Subramanian et al. 2005, weight 1), significance from 1000 random sets of the same size drawn from the same ranking, seeded by the set's name so a shared link reproduces it; p = (b+1)/(m+1) over the same-sign nulls, q is Benjamini–Hochberg across the sets tested (not GSEA's own NES-based FDR). Gene-set permutation ignores gene–gene correlation, so it is anti-conservative next to phenotype permutation, which needs patient-level values. 
+  METHOD:
+    - Sum every gene's values [Basal-like · brca_tcga_pan_can_atlas_2018]: The diffexpr:moments query above, on “rna_seq_v2_mrna_median_all_sample_Zscores” as stored. Per gene, over the cohort's patients with a value (a patient with several samples contributes their average): n, Σv and Σv². Every gene in the profile, no individual value.
+    - Sum every gene's values [Luminal A · brca_tcga_pan_can_atlas_2018]: The diffexpr:moments query above, on “rna_seq_v2_mrna_median_all_sample_Zscores” as stored. Per gene, over the cohort's patients with a value (a patient with several samples contributes their average): n, Σv and Σv². Every gene in the profile, no individual value.
+    - Leave out genes that cannot be tested (20,020 → 20,006 rows): A gene with fewer than 3 patients with a value in either cohort, or with no variance (Var A + Var B ≤ 1e-9 · (1 + mean A² + mean B²) — judged to a tolerance, since a constant gene's variance from the sums is rounding noise).
+    - Welch's t-test per gene: Mean A − mean B (Basal-like minus Luminal A) with each cohort's variance (Σv² − (Σv)²/n)/(n − 1); unequal-variance t, Welch–Satterthwaite df, two-sided. With thousands of patients a tiny difference is significant: read the effect too.
+        genes tested: 20,006
+    - Benjamini–Hochberg across genes: q-values over all 20,006 genes tested.
+    - Preranked gene-set enrichment (GSEA): Genes ranked by the unrounded t, Basal-like over Luminal A first. Enrichment score as in Subramanian et al. 2005 with weight 1 (each member's step is |t| / Σ|t| of the members). Significance from 1000 random sets of the same size drawn from the same ranking, from a generator seeded by the set's name — so a shared link gives the same p — with p = (b + 1)/(m + 1) over the m null scores of the same sign and NES = ES / mean of those. Gene-set permutation ignores correlation between genes, so it is anti-conservative next to phenotype permutation. A set with fewer than 10 ranked members is not tested. q is Benjamini–Hochberg across the sets tested.
+        sets tested: 50
+    Welch's t from moments matches scipy 1.18.1 ttest_ind(equal_var=False) and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/moments-golden.test.ts, test/tables-golden.test.ts); enrichment scores match gseapy 1.3.1 prerank, and NES and p match the script's port of our seeded null exactly (test/gsea-golden.test.ts).
diff --git a/test/snapshots/enrichment.json b/test/snapshots/enrichment.json
new file mode 100644
index 00000000..c769d37c
--- /dev/null
+++ b/test/snapshots/enrichment.json
@@ -0,0 +1,737 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "Colorectal, MSS": {
+      "count": 4957,
+      "text": "CANCER_TYPE in [Colorectal Cancer] (any sample) AND NOT MSI_TYPE in [Instable] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "Pancreatic, MSS": {
+      "count": 3095,
+      "text": "CANCER_TYPE in [Pancreatic Cancer] (any sample) AND NOT MSI_TYPE in [Instable] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "enrichment",
+      "cohorts": [
+        "Colorectal, MSS",
+        "Pancreatic, MSS"
+      ],
+      "enrichment": {
+        "alteration": "mutation",
+        "measure": "any",
+        "groups": [
+          "Colorectal, MSS",
+          "Pancreatic, MSS"
+        ],
+        "tested": 494,
+        "untested": 8,
+        "significant": 316,
+        "minAltered": 5,
+        "test": "two-sided Fisher's exact test",
+        "rows": [
+          {
+            "gene": "APC",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 3815,
+                "profiled": 4986,
+                "pct": 76.5
+              },
+              {
+                "cohort": "Pancreatic, MSS",
+                "altered": 45,
+                "profiled": 3117,
+                "pct": 1.4
+              }
+            ],
+            "log2Ratio": 5.727889517526085,
+            "p": 0,
+            "q": 0,
+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "5q22.2"
+          },
+          {
+            "gene": "KRAS",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 2178,
+                "profiled": 4986,
+                "pct": 43.7
+              },
+              {
+                "cohort": "Pancreatic, MSS",
+                "altered": 2518,
+                "profiled": 3117,
+                "pct": 80.8
+              }
+            ],
+            "log2Ratio": -0.8869990568682424,
+            "p": 1.5404597664259493e-250,
+            "q": 3.804935623072094e-248,
+            "enrichedIn": "Pancreatic, MSS",
+            "cytoband": "12p12.1"
+          },
+          {
+            "gene": "CDKN2A",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 52,
+                "profiled": 4986,
+                "pct": 1
+              },
+              {
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+                "pct": 20.2
+              }
+            ],
+            "log2Ratio": -4.27420121660433,
+            "p": 5.002627218146018e-211,
+            "q": 8.237659485880442e-209,
+            "enrichedIn": "Pancreatic, MSS",
+            "cytoband": "9p21.3"
+          },
+          {
+            "gene": "TCF7L2",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 547,
+                "profiled": 4806,
+                "pct": 11.4
+              },
+              {
+                "cohort": "Pancreatic, MSS",
+                "altered": 3,
+                "profiled": 3083,
+                "pct": 0.1
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+            ],
+            "log2Ratio": 6.869932763048709,
+            "p": 1.3769916760974181e-117,
+            "q": 1.7005847199803115e-115,
+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "10q25.2-q25.3"
+          },
+          {
+            "gene": "PIK3CA",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 876,
+                "profiled": 4986,
+                "pct": 17.6
+              },
+              {
+                "cohort": "Pancreatic, MSS",
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+            "log2Ratio": 2.9473152122303587,
+            "p": 1.9289791968532027e-117,
+            "q": 1.9058314464909642e-115,
+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "3q26.32"
+          },
+          {
+            "gene": "FBXW7",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 635,
+                "profiled": 4986,
+                "pct": 12.7
+              },
+              {
+                "cohort": "Pancreatic, MSS",
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+            "log2Ratio": 3.545425212543056,
+            "p": 1.7529225317192021e-97,
+            "q": 1.4432395511154764e-95,
+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "4q31.3"
+          },
+          {
+            "gene": "SOX9",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 470,
+                "profiled": 4986,
+                "pct": 9.4
+              },
+              {
+                "cohort": "Pancreatic, MSS",
+                "altered": 25,
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+                "pct": 0.8
+              }
+            ],
+            "log2Ratio": 3.554936028924142,
+            "p": 2.450019178878529e-71,
+            "q": 1.729013534808562e-69,
+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "17q24.3"
+          },
+          {
+            "gene": "BRAF",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 373,
+                "profiled": 4986,
+                "pct": 7.5
+              },
+              {
+                "cohort": "Pancreatic, MSS",
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+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "7q34"
+          },
+          {
+            "gene": "AMER1",
+            "cells": [
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+                "cohort": "Colorectal, MSS",
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+            "enrichedIn": "Colorectal, MSS",
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+          },
+          {
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+                "pct": 0.5
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+                "cohort": "Pancreatic, MSS",
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+                "pct": 4.1
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+            "enrichedIn": "Pancreatic, MSS",
+            "cytoband": "11q13"
+          },
+          {
+            "gene": "TP53",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 3890,
+                "profiled": 4986,
+                "pct": 78
+              },
+              {
+                "cohort": "Pancreatic, MSS",
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+            "cytoband": "17p13.1"
+          },
+          {
+            "gene": "CTNNB1",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
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+                "profiled": 4986,
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+          },
+          {
+            "gene": "NRAS",
+            "cells": [
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+                "cohort": "Colorectal, MSS",
+                "altered": 196,
+                "profiled": 4986,
+                "pct": 3.9
+              },
+              {
+                "cohort": "Pancreatic, MSS",
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+                "profiled": 3117,
+                "pct": 0.5
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+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "1p13.2"
+          },
+          {
+            "gene": "PIK3R1",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 178,
+                "profiled": 4986,
+                "pct": 3.6
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+            "cytoband": "5q13.1"
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+          {
+            "gene": "PTPRT",
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+                "cohort": "Colorectal, MSS",
+                "altered": 315,
+                "profiled": 4986,
+                "pct": 6.3
+              },
+              {
+                "cohort": "Pancreatic, MSS",
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+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "20q12-q13.11"
+          },
+          {
+            "gene": "PTPRS",
+            "cells": [
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+                "pct": 5.2
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+            "cytoband": "19p13.3"
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+          {
+            "gene": "EPHA3",
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+                "pct": 3.5
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+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "3p11.1"
+          },
+          {
+            "gene": "CARD11",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 166,
+                "profiled": 4986,
+                "pct": 3.3
+              },
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+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "7p22.2"
+          },
+          {
+            "gene": "GRIN2A",
+            "cells": [
+              {
+                "cohort": "Colorectal, MSS",
+                "altered": 188,
+                "profiled": 4986,
+                "pct": 3.8
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+            "q": 1.3115350363993611e-14,
+            "enrichedIn": "Colorectal, MSS",
+            "cytoband": "16p13.2"
+          },
+          {
+            "…": "280 more",
+            "sha256": "5e8d6b42f8f2c356"
+          }
+        ],
+        "grid": {
+          "xMax": 10,
+          "yMax": 500,
+          "nx": 80,
+          "ny": 50,
+          "cells": [
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+              0,
+              4
+            ],
+            [
+              49,
+              0,
+              4
+            ],
+            [
+              39,
+              0,
+              9
+            ],
+            [
+              37,
+              0,
+              3
+            ],
+            [
+              40,
+              0,
+              10
+            ]
+          ]
+        },
+        "labels": [
+          "APC",
+          "KRAS",
+          "CDKN2A",
+          "TCF7L2",
+          "PIK3CA",
+          "FBXW7",
+          "SOX9",
+          "BRAF",
+          "AMER1",
+          "SMAD2",
+          "MEN1",
+          "TP53"
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Every gene's mutation frequency in each cohort, counted in SAMPLES over the samples profiled for that gene (a panel that lists it, or whole-exome; a sample altered off-panel counts as measured), uncalled mutations excluded as the portal excludes them. 494 genes tested with a two-sided Fisher's exact test; q is Benjamini–Hochberg across all of them. 8 altered genes were not tested: fewer than 5 altered samples across the cohorts, or not profiled in one of them. The log ratio is log2 of the first cohort's altered share over the second's, undefined (drawn at the edge) when either is zero. A gene enriched here is more often altered, not necessarily more important: a cohort holding hypermutated tumours (MSI-high, POLE) is enriched for mutations in almost every gene at once — exclude them in the cohort definition to see past that. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count altered and profiled samples per gene",
+            "detail": "The enrichment queries above: per gene, samples with a mutation; per gene, samples on a panel listing it (counted per panel, then joined to its genes); and the whole-exome samples, profiled for every gene.",
+            "scope": "Colorectal, MSS · msk_chord_2024",
+            "rowsOut": 502,
+            "params": [
+              [
+                "whole-exome samples",
+                "0"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count altered and profiled samples per gene",
+            "detail": "The enrichment queries above: per gene, samples with a mutation; per gene, samples on a panel listing it (counted per panel, then joined to its genes); and the whole-exome samples, profiled for every gene.",
+            "scope": "Pancreatic, MSS · msk_chord_2024",
+            "rowsOut": 487,
+            "params": [
+              [
+                "whole-exome samples",
+                "0"
+              ]
+            ]
+          },
+          {
+            "kind": "transform",
+            "title": "Profiled = panel samples + whole-exome samples, summed across studies",
+            "detail": "A sample altered in a gene its panel does not list still counts as measured. Genes with fewer than 5 altered samples across the cohorts, or not profiled in one of them, are not tested.",
+            "params": [
+              [
+                "tested",
+                "494"
+              ],
+              [
+                "not tested",
+                "8"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Fisher's exact test per gene, then Benjamini–Hochberg",
+            "detail": "A two-sided Fisher's exact test on altered vs not among the profiled, per gene; q across every gene tested."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "fisherExact",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "enrichment:tested": {
+            "v": 494
+          },
+          "enrichment:APC:p": {
+            "v": 0
+          },
+          "enrichment:APC:q": {
+            "v": 0
+          },
+          "enrichment:KRAS:p": {
+            "v": 1.5404597664259493e-250
+          },
+          "enrichment:KRAS:q": {
+            "v": 3.804935623072094e-248
+          },
+          "enrichment:CDKN2A:p": {
+            "v": 5.002627218146018e-211
+          },
+          "enrichment:CDKN2A:q": {
+            "v": 8.237659485880442e-209
+          },
+          "enrichment:TCF7L2:p": {
+            "v": 1.3769916760974181e-117
+          },
+          "enrichment:TCF7L2:q": {
+            "v": 1.7005847199803115e-115
+          },
+          "enrichment:PIK3CA:p": {
+            "v": 1.9289791968532027e-117
+          },
+          "enrichment:PIK3CA:q": {
+            "v": 1.9058314464909642e-115
+          },
+          "enrichment:FBXW7:p": {
+            "v": 1.7529225317192021e-97
+          },
+          "enrichment:FBXW7:q": {
+            "v": 1.4432395511154764e-95
+          },
+          "enrichment:SOX9:p": {
+            "v": 2.450019178878529e-71
+          },
+          "enrichment:SOX9:q": {
+            "v": 1.729013534808562e-69
+          },
+          "enrichment:BRAF:p": {
+            "v": 6.782245235505447e-41
+          },
+          "enrichment:BRAF:q": {
+            "v": 4.188036432924613e-39
+          },
+          "enrichment:AMER1:p": {
+            "v": 5.62219558260204e-34
+          },
+          "enrichment:AMER1:q": {
+            "v": 3.085960686450453e-32
+          },
+          "enrichment:SMAD2:p": {
+            "v": 2.540614676524035e-31
+          },
+          "…": {
+            "more": 61,
+            "sha256": "1b96d5efa60a3d06"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/enrichment.sql b/test/snapshots/enrichment.sql
new file mode 100644
index 00000000..4287e649
--- /dev/null
+++ b/test/snapshots/enrichment.sql
@@ -0,0 +1,162 @@
+-- cohort Colorectal, MSS
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable')))))
+
+-- cohort Pancreatic, MSS
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable')))))
+
+-- view 0 enrichment: cytobands · msk_chord_2024
+SELECT g.hugo_gene_symbol AS gene, any(rgg.cytoband) AS cytoband FROM snapshot_db.gene g
+JOIN snapshot_db.reference_genome_gene rgg ON rgg.entrez_gene_id = g.entrez_gene_id
+WHERE rgg.reference_genome_id = (SELECT reference_genome_id FROM snapshot_db.cancer_study WHERE cancer_study_identifier = 'msk_chord_2024')
+  AND g.hugo_gene_symbol IN ('APC','KRAS','CDKN2A','TCF7L2','PIK3CA','FBXW7','SOX9','BRAF','AMER1','SMAD2','MEN1','TP53','CTNNB1','NRAS','PIK3R1','PTPRT','PTPRS','EPHA3','CARD11','GRIN2A','ASXL1','MTOR','PLCG2','KDR','ERBB2','NF1','EPHA5','PIK3CG','ERBB3','PDGFRA','PTEN','AR','ALK','ZFHX3','PIK3C2G','AXIN2','EPHA7','B2M','SOX17','KDM6A','SLX4','CREBBP','FAT1','NCOR1','NOTCH3','SMO','TP53BP1','INHBA','ATM','NOTCH4','TET1','EPHB1','IKZF1','ATR','MITF','SPEN','ROS1','IRS1','DAXX','MAP3K1','FOXP1','U2AF1','PREX2','TBX3','POLE','PTCH1','HGF','ERBB4','RASA1','TGFBR2','NSD1','KMT2A','FLT4','IGF1R','DICER1','ARID1B','MUTYH','SMAD3','SMAD4','TET2','KMT2B','TEK','PPP2R1A','AXL','DNMT3B','STAT3','PIK3CD','ESR1','AKT1','GATA3','MRE11','ANKRD11','FLT3','PMS1','NSD3','CDK8','BRIP1','CTCF','TP63','MGA','LATS2','STK11','DROSHA','BRD4','KDM5A','DNMT1','PRKN','ASXL2','NTRK3','RAF1','CSF3R','PTPRD','EP300','SMARCA2','MAP2K1','LZTR1','PRDM1','POLD1','FLCN','CYLD','BMPR1A','PAK5','GLI1','ERCC4','KIT','SESN1','FGFR1','ALB','MDC1','PGR','DDR2','ALOX12B','ETV1','PTPN11','MYOD1','CASP8','NOTCH2','BRCA1','CYSLTR2','INPP4B','TERT','BARD1','ARID5B','RET','RNF43','CIC','MSH2','PAX5','JAK3','DOT1L','ELF3','CHEK2','IRF4','ERCC5','TSHR','TCF3','NEGR1','HLA-B','INSR','WT1','PHOX2B','JAK1','RECQL4','MSH3','PRKD1','MED12','FGF3','PPP6C','MALT1','TNFAIP3','INPP4A','RRAGC','RB1','STAG2','PIK3R2','UPF1','IRS2','MPL','EZH2','HNF1A','PIK3CB','LATS1','DIS3','MST1','MSI1','WWTR1','NTRK2','RAD50','JUN','SH2B3','RAD21','KDM5C','TAP2','SHQ1','CDKN1B','IDH1','MET','FLT1','CDK6','EGFR','PPM1D','NUF2','RPS6KA4','CDKN2B','LMO1','NKX3-1','RIT1','MST1R','NTRK1','FGFR3','MYC','NOTCH1','ZNRF3','GAB1','ARAF','AGO2','SESN3','MAP3K13','ERCC2','NKX2-1','H3-5','MDM4','PMS2','RECQL','NCOA3','BLM','SOS1','NBN','GNAQ','GAB2','MYD88','TAP1','ABRAXAS1','RAD54L','CDH1','PALB2','TNFRSF14','KLF5','FANCA','RAB35','XPO1','SMARCB1','SHOC2','AURKA','FYN','IL7R','E2F3','FOXF1','TOP1','MLLT1','JAK2','ERG','PRDM14','ARID2','EIF4E','HOXB13','EGFL7','SUFU','USP8','CBL','GREM1','EIF4A2','ARID1A','CXCR4','MSH6','CHEK1','DCUN1D1','FGF4','GATA2','SDHA','CDC73','FGFR2','BCL10','H3C12','COP1','ACVR1','CBFB','EPCAM','ABL1','IGF1','RPS6KB2','FGFR4','KMT5A','PPP4R2','RRAS','RAD51C','SMARCD1','NSD2','STK40','ERCC3','SDHB','CSDE1','IFNGR1','RPTOR','TGFBR1','TSC1','MAP2K4','CDK12','HLA-A','BRCA2')
+GROUP BY gene
+
+-- view 0 enrichment: enrichment:altered:Colorectal, MSS · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable'))))))
+SELECT g.hugo_gene_symbol AS gene, uniqExact(g.sample_unique_id) AS altered
+FROM snapshot_db.genomic_event_derived g
+WHERE g.cancer_study_identifier = 'msk_chord_2024' AND g.variant_type = 'mutation' AND g.mutation_status != 'UNCALLED'
+  AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+GROUP BY gene
+
+-- view 0 enrichment: enrichment:altered:Pancreatic, MSS · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable'))))))
+SELECT g.hugo_gene_symbol AS gene, uniqExact(g.sample_unique_id) AS altered
+FROM snapshot_db.genomic_event_derived g
+WHERE g.cancer_study_identifier = 'msk_chord_2024' AND g.variant_type = 'mutation' AND g.mutation_status != 'UNCALLED'
+  AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+GROUP BY gene
+
+-- view 0 enrichment: enrichment:panels:Colorectal, MSS · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable')))))),
+pc AS (
+  SELECT sp.gene_panel_id AS panel, uniqExact(sp.sample_unique_id) AS n
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND sp.gene_panel_id != 'WES' AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY panel
+)
+SELECT gptg.gene AS gene, sum(pc.n) AS profiled
+FROM snapshot_db.gene_panel_to_gene_derived gptg INNER JOIN pc ON pc.panel = gptg.gene_panel_id
+GROUP BY gene
+
+-- view 0 enrichment: enrichment:panels:Pancreatic, MSS · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable')))))),
+pc AS (
+  SELECT sp.gene_panel_id AS panel, uniqExact(sp.sample_unique_id) AS n
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND sp.gene_panel_id != 'WES' AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY panel
+)
+SELECT gptg.gene AS gene, sum(pc.n) AS profiled
+FROM snapshot_db.gene_panel_to_gene_derived gptg INNER JOIN pc ON pc.panel = gptg.gene_panel_id
+GROUP BY gene
+
+-- view 0 enrichment: enrichment:wes:Colorectal, MSS · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable'))))))
+SELECT uniqExact(sp.sample_unique_id) AS n
+FROM snapshot_db.sample_to_gene_panel_derived sp
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+  AND sp.gene_panel_id = 'WES' AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+
+-- view 0 enrichment: enrichment:wes:Pancreatic, MSS · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'MSI_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Instable'))))))
+SELECT uniqExact(sp.sample_unique_id) AS n
+FROM snapshot_db.sample_to_gene_panel_derived sp
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+  AND sp.gene_panel_id = 'WES' AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
diff --git a/test/snapshots/enrichment.svg b/test/snapshots/enrichment.svg
new file mode 100644
index 00000000..7819c308
--- /dev/null
+++ b/test/snapshots/enrichment.svg
@@ -0,0 +1,2 @@
+<!-- view 0 enrichment -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 828" width="100%" role="img" aria-label="Alteration enrichment — mutation — Colorectal, MSS vs Pancreatic, MSS" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Alteration enrichment — mutation — Colorectal, MSS vs Pancreatic, MSS</title><rect width="960" height="828" fill="#ffffff"/><text x="8" y="14" font-size="11" fill="#5b6b76">494 genes tested for mutation · 316 at q &lt; 0.05 · two-sided Fisher's exact test</text><rect x="51" y="45" width="287" height="248" fill="none" stroke="#dde4e8" stroke-width="0.75"/><line x1="51" y1="288" x2="338" y2="288" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="291.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="51" y1="240.4" x2="338" y2="240.4" stroke="#dde4e8" stroke-width="0.75"/><text x="45" y="243.9" font-size="10" fill="#5b6b76" text-anchor="end">100</text><line x1="51" y1="192.8" x2="338" y2="192.8" 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stroke-width="0.75"/><line x1="125.3" y1="293" x2="125.3" y2="296" stroke="#5b6b76" stroke-width="1"/><text x="119.3" y="309" font-size="10" fill="#5b6b76">−5</text><line x1="194.5" y1="45" x2="194.5" y2="293" stroke="#dde4e8" stroke-width="0.75"/><line x1="194.5" y1="293" x2="194.5" y2="296" stroke="#5b6b76" stroke-width="1"/><text x="191.5" y="309" font-size="10" fill="#5b6b76">0</text><line x1="263.8" y1="45" x2="263.8" y2="293" stroke="#dde4e8" stroke-width="0.75"/><line x1="263.8" y1="293" x2="263.8" y2="296" stroke="#5b6b76" stroke-width="1"/><text x="260.8" y="309" font-size="10" fill="#5b6b76">5</text><line x1="333" y1="45" x2="333" y2="293" stroke="#dde4e8" stroke-width="0.75"/><line x1="333" y1="293" x2="333" y2="296" stroke="#5b6b76" stroke-width="1"/><text x="327" y="309" font-size="10" fill="#5b6b76">10</text><line x1="56" y1="293" x2="333" y2="293" stroke="#5b6b76" stroke-width="1"/><text x="194.5" y="328" font-size="11" fill="#16232b" text-anchor="middle">Log2 Ratio</text><line x1="194.5" y1="45" x2="194.5" y2="293" stroke="#5b6b76" stroke-width="0.75"/><rect x="208.4" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.74"/><rect x="215.3" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.59"/><rect x="218.7" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.53"/><rect x="211.8" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.65"/><rect x="201.4" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.75"/><rect x="198" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.73"/><rect x="232.6" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.43"/><rect x="204.9" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.72"/><rect x="222.2" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.39"/><rect x="180.6" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.27"/><rect x="170.3" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.27"/><rect x="329.5" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.39"/><rect x="229.1" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.27"/><rect x="187.6" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.43"/><rect x="225.7" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.43"/><rect x="191" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.55"/><rect x="184.1" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.39"/><rect x="194.5" y="283.2" width="3.8" height="5.1" fill="#9aa5ad" opacity="0.57"/><circle cx="273.8" cy="145.2" r="3" fill="#0072B2" opacity="0.8"><title>APC: Colorectal, MSS 76.5% (3,815 / 4,986), Pancreatic, MSS 1.4% (45 / 3,117) · q &lt;1e-300</title></circle><circle cx="182.2" cy="169.1" r="3" fill="#E69F00" opacity="0.8"><title>KRAS: Colorectal, MSS 43.7% (2,178 / 4,986), Pancreatic, MSS 80.8% (2,518 / 3,117) · q 3.8e-248</title></circle><circle cx="135.3" cy="187.9" r="3" fill="#E69F00" opacity="0.8"><title>CDKN2A: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 20.2% (629 / 3,117) · q 8.2e-209</title></circle><circle cx="289.6" cy="232.4" r="3" fill="#0072B2" opacity="0.8"><title>TCF7L2: Colorectal, MSS 11.4% (547 / 4,806), Pancreatic, MSS 0.1% (3 / 3,083) · q 1.7e-115</title></circle><circle cx="235.3" cy="232.4" r="3" fill="#0072B2" opacity="0.8"><title>PIK3CA: Colorectal, MSS 17.6% (876 / 4,986), Pancreatic, MSS 2.3% (71 / 3,117) · q 1.9e-115</title></circle><circle cx="243.6" cy="241.9" r="3" fill="#0072B2" opacity="0.8"><title>FBXW7: Colorectal, MSS 12.7% (635 / 4,986), Pancreatic, MSS 1.1% (34 / 3,117) · q 1.4e-95</title></circle><circle cx="243.7" cy="254.4" r="3" fill="#0072B2" opacity="0.8"><title>SOX9: Colorectal, MSS 9.4% (470 / 4,986), Pancreatic, MSS 0.8% (25 / 3,117) · q 1.7e-69</title></circle><circle cx="229.2" cy="268.9" r="3" fill="#0072B2" opacity="0.8"><title>BRAF: Colorectal, MSS 7.5% (373 / 4,986), Pancreatic, MSS 1.3% (41 / 3,117) · q 4.2e-39</title></circle><circle cx="232.7" cy="272.2" r="3" fill="#0072B2" opacity="0.8"><title>AMER1: Colorectal, MSS 5.6% (281 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 3.1e-32</title></circle><circle cx="242.4" cy="273.4" r="3" fill="#0072B2" opacity="0.8"><title>SMAD2: Colorectal, MSS 4.2% (211 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 1.3e-29</title></circle><circle cx="152.5" cy="273.6" r="3" fill="#E69F00" opacity="0.8"><title>MEN1: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 4.1% (128 / 3,117) · q 3e-29</title></circle><circle cx="197.7" cy="274.4" r="3" fill="#0072B2" opacity="0.8"><title>TP53: Colorectal, MSS 78.0% (3,890 / 4,986), Pancreatic, MSS 66.6% (2,077 / 3,117) · q 1.2e-27</title></circle><circle cx="226.4" cy="275.5" r="3" fill="#0072B2" opacity="0.8"><title>CTNNB1: Colorectal, MSS 5.4% (269 / 4,986), Pancreatic, MSS 1.1% (34 / 3,117) · q 2.1e-25</title></circle><circle cx="235.2" cy="276.3" r="3" fill="#0072B2" opacity="0.8"><title>NRAS: Colorectal, MSS 3.9% (196 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 8.8e-24</title></circle><circle cx="239" cy="276.5" r="3" fill="#0072B2" opacity="0.8"><title>PIK3R1: Colorectal, MSS 3.6% (178 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 2e-23</title></circle><circle cx="218.9" cy="277.4" r="3" fill="#0072B2" opacity="0.8"><title>PTPRT: Colorectal, MSS 6.3% (315 / 4,986), Pancreatic, MSS 1.9% (58 / 3,117) · q 2.1e-21</title></circle><circle cx="218.5" cy="279.4" r="3" fill="#0072B2" opacity="0.8"><title>PTPRS: Colorectal, MSS 5.2% (261 / 4,986), Pancreatic, MSS 1.6% (49 / 3,117) · q 2.3e-17</title></circle><circle cx="224.9" cy="280.2" r="3" fill="#0072B2" opacity="0.8"><title>EPHA3: Colorectal, MSS 3.5% (176 / 4,986), Pancreatic, MSS 0.8% (24 / 3,117) · q 1.2e-15</title></circle><circle cx="225.5" cy="280.5" r="3" fill="#0072B2" opacity="0.8"><title>CARD11: Colorectal, MSS 3.3% (166 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 5.6e-15</title></circle><circle cx="221.8" cy="280.7" r="3" fill="#0072B2" opacity="0.8"><title>GRIN2A: Colorectal, MSS 3.8% (188 / 4,986), Pancreatic, MSS 1.0% (30 / 3,117) · q 1.3e-14</title></circle><circle cx="230.8" cy="281.1" r="3" fill="#0072B2" opacity="0.8"><title>ASXL1: Colorectal, MSS 2.6% (128 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.7e-14</title></circle><circle cx="225.4" cy="281.2" r="3" fill="#0072B2" opacity="0.8"><title>MTOR: Colorectal, MSS 3.0% (150 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 1.5e-13</title></circle><circle cx="230.5" cy="281.3" r="3" fill="#0072B2" opacity="0.8"><title>PLCG2: Colorectal, MSS 2.6% (123 / 4,806), Pancreatic, MSS 0.4% (13 / 3,083) · q 2.2e-13</title></circle><circle cx="225.1" cy="281.4" r="3" fill="#0072B2" opacity="0.8"><title>KDR: Colorectal, MSS 3.0% (148 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 3.3e-13</title></circle><circle cx="223.2" cy="281.6" r="3" fill="#0072B2" opacity="0.8"><title>ERBB2: Colorectal, MSS 3.1% (155 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 6.2e-13</title></circle><circle cx="221.4" cy="281.7" r="3" fill="#0072B2" opacity="0.8"><title>NF1: Colorectal, MSS 3.3% (166 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 1e-12</title></circle><circle cx="219.7" cy="281.7" r="3" fill="#0072B2" opacity="0.8"><title>EPHA5: Colorectal, MSS 3.6% (181 / 4,986), Pancreatic, MSS 1.0% (32 / 3,117) · q 1e-12</title></circle><circle cx="221.7" cy="281.9" r="3" fill="#0072B2" opacity="0.8"><title>PIK3CG: Colorectal, MSS 3.1% (156 / 4,986), Pancreatic, MSS 0.8% (25 / 3,117) · q 3.3e-12</title></circle><circle cx="220.9" cy="281.9" r="3" fill="#0072B2" opacity="0.8"><title>ERBB3: Colorectal, MSS 3.2% (162 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 3.3e-12</title></circle><circle cx="231.1" cy="282" r="3" fill="#0072B2" opacity="0.8"><title>PDGFRA: Colorectal, MSS 2.2% (110 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 4.1e-12</title></circle><circle cx="217.5" cy="282" r="3" fill="#0072B2" opacity="0.8"><title>PTEN: Colorectal, MSS 3.9% (192 / 4,986), Pancreatic, MSS 1.2% (38 / 3,117) · q 5e-12</title></circle><circle cx="222.1" cy="282.5" r="3" fill="#0072B2" opacity="0.8"><title>AR: Colorectal, MSS 2.8% (140 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 3.5e-11</title></circle><circle cx="219.8" cy="282.6" r="3" fill="#0072B2" opacity="0.8"><title>ALK: Colorectal, MSS 3.1% (153 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 8.1e-11</title></circle><circle cx="215.4" cy="282.9" r="3" fill="#0072B2" opacity="0.8"><title>ZFHX3: Colorectal, MSS 3.8% (182 / 4,806), Pancreatic, MSS 1.3% (41 / 3,083) · q 2.7e-10</title></circle><circle cx="222.3" cy="283.1" r="3" fill="#0072B2" opacity="0.8"><title>PIK3C2G: Colorectal, MSS 2.4% (122 / 4,986), Pancreatic, MSS 0.6% (19 / 3,117) · q 8.8e-10</title></circle><circle cx="218.9" cy="283.2" r="3" fill="#0072B2" opacity="0.8"><title>AXIN2: Colorectal, MSS 2.8% (141 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 1.3e-9</title></circle><circle cx="222.1" cy="283.3" r="3" fill="#0072B2" opacity="0.8"><title>EPHA7: Colorectal, MSS 2.5% (118 / 4,806), Pancreatic, MSS 0.6% (19 / 3,083) · q 1.6e-9</title></circle><circle cx="235.8" cy="283.3" r="3" fill="#0072B2" opacity="0.8"><title>B2M: Colorectal, MSS 1.5% (76 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 1.8e-9</title></circle><circle cx="226.1" cy="283.3" r="3" fill="#0072B2" opacity="0.8"><title>SOX17: Colorectal, MSS 2.0% (101 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 1.9e-9</title></circle><circle cx="176.1" cy="283.4" r="3" fill="#E69F00" opacity="0.8"><title>KDM6A: Colorectal, MSS 1.5% (74 / 4,986), Pancreatic, MSS 3.7% (116 / 3,117) · q 2.5e-9</title></circle><circle cx="231.5" cy="283.5" r="3" fill="#0072B2" opacity="0.8"><title>SLX4: Colorectal, MSS 2.0% (80 / 4,059), Pancreatic, MSS 0.3% (8 / 2,588) · q 4e-9</title></circle><circle cx="216.7" cy="283.6" r="3" fill="#0072B2" opacity="0.8"><title>CREBBP: Colorectal, MSS 2.9% (146 / 4,986), Pancreatic, MSS 1.0% (30 / 3,117) · q 6.3e-9</title></circle><circle cx="214.3" cy="283.6" r="3" fill="#0072B2" opacity="0.8"><title>FAT1: Colorectal, MSS 3.4% (172 / 4,986), Pancreatic, MSS 1.3% (40 / 3,117) · q 7.8e-9</title></circle><circle cx="220.4" cy="283.7" r="3" fill="#0072B2" opacity="0.8"><title>NCOR1: Colorectal, MSS 2.3% (117 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 8.9e-9</title></circle><circle cx="214.7" cy="283.7" r="3" fill="#0072B2" opacity="0.8"><title>NOTCH3: Colorectal, MSS 3.3% (163 / 4,986), Pancreatic, MSS 1.2% (37 / 3,117) · q 1e-8</title></circle><circle cx="230.4" cy="283.8" r="3" fill="#0072B2" opacity="0.8"><title>SMO: Colorectal, MSS 1.5% (77 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 1.9e-8</title></circle><circle cx="225.8" cy="283.8" r="3" fill="#0072B2" opacity="0.8"><title>TP53BP1: Colorectal, MSS 2.2% (90 / 4,059), Pancreatic, MSS 0.5% (12 / 2,588) · q 1.9e-8</title></circle><circle cx="227.7" cy="283.9" r="3" fill="#0072B2" opacity="0.8"><title>INHBA: Colorectal, MSS 1.7% (82 / 4,806), Pancreatic, MSS 0.3% (10 / 3,083) · q 2.1e-8</title></circle><circle cx="207.5" cy="283.9" r="3" fill="#0072B2" opacity="0.8"><title>ATM: Colorectal, MSS 6.0% (298 / 4,986), Pancreatic, MSS 3.1% (97 / 3,117) · q 2.3e-8</title></circle><circle cx="219.4" cy="283.9" r="3" fill="#0072B2" opacity="0.8"><title>NOTCH4: Colorectal, MSS 2.3% (117 / 4,986), Pancreatic, MSS 0.7% (21 / 3,117) · q 2.6e-8</title></circle><circle cx="221.3" cy="284" r="3" fill="#0072B2" opacity="0.8"><title>TET1: Colorectal, MSS 2.1% (104 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 4e-8</title></circle><circle cx="215.2" cy="284.1" r="3" fill="#0072B2" opacity="0.8"><title>EPHB1: Colorectal, MSS 2.9% (144 / 4,986), Pancreatic, MSS 1.0% (32 / 3,117) · q 6e-8</title></circle><circle cx="220.9" cy="284.2" r="3" fill="#0072B2" opacity="0.8"><title>IKZF1: Colorectal, MSS 2.0% (102 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 9.1e-8</title></circle><circle cx="217.8" cy="284.2" r="3" fill="#0072B2" opacity="0.8"><title>ATR: Colorectal, MSS 2.4% (118 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 1.1e-7</title></circle><circle cx="238.2" cy="284.3" r="3" fill="#0072B2" opacity="0.8"><title>MITF: Colorectal, MSS 1.1% (57 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 1.4e-7</title></circle><circle cx="217.4" cy="284.3" r="3" fill="#0072B2" opacity="0.8"><title>SPEN: Colorectal, MSS 2.3% (116 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 1.6e-7</title></circle><circle cx="217.6" cy="284.3" r="3" fill="#0072B2" opacity="0.8"><title>ROS1: Colorectal, MSS 2.3% (117 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 1.6e-7</title></circle><circle cx="223.1" cy="284.3" r="3" fill="#0072B2" opacity="0.8"><title>IRS1: Colorectal, MSS 1.7% (87 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 1.7e-7</title></circle><circle cx="171.6" cy="284.3" r="3" fill="#E69F00" opacity="0.8"><title>DAXX: Colorectal, MSS 0.7% (34 / 4,986), Pancreatic, MSS 2.1% (67 / 3,117) · q 1.8e-7</title></circle><circle cx="221.6" cy="284.3" r="3" fill="#0072B2" opacity="0.8"><title>MAP3K1: Colorectal, MSS 1.9% (93 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 1.8e-7</title></circle><circle cx="227.5" cy="284.4" r="3" fill="#0072B2" opacity="0.8"><title>FOXP1: Colorectal, MSS 1.5% (75 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 1.8e-7</title></circle><circle cx="167.5" cy="284.4" r="3" fill="#E69F00" opacity="0.8"><title>U2AF1: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 1.7% (53 / 3,117) · q 2e-7</title></circle><circle cx="210.9" cy="284.5" r="3" fill="#0072B2" opacity="0.8"><title>PREX2: Colorectal, MSS 4.3% (175 / 4,059), Pancreatic, MSS 1.9% (49 / 2,588) · q 3.2e-7</title></circle><circle cx="220.5" cy="284.5" r="3" fill="#0072B2" opacity="0.8"><title>TBX3: Colorectal, MSS 1.9% (94 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 3.9e-7</title></circle><circle cx="215.4" cy="284.6" r="3" fill="#0072B2" opacity="0.8"><title>POLE: Colorectal, MSS 2.5% (123 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 4.6e-7</title></circle><circle cx="222.4" cy="284.6" r="3" fill="#0072B2" opacity="0.8"><title>PTCH1: Colorectal, MSS 1.7% (84 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 5.5e-7</title></circle><circle cx="220.9" cy="284.6" r="3" fill="#0072B2" opacity="0.8"><title>HGF: Colorectal, MSS 1.8% (90 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 5.7e-7</title></circle><circle cx="210.3" cy="284.6" r="3" fill="#0072B2" opacity="0.8"><title>ERBB4: Colorectal, MSS 3.7% (183 / 4,986), Pancreatic, MSS 1.7% (52 / 3,117) · q 6.1e-7</title></circle><circle cx="221.9" cy="284.7" r="3" fill="#0072B2" opacity="0.8"><title>RASA1: Colorectal, MSS 1.6% (82 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 8.1e-7</title></circle><circle cx="179.8" cy="284.9" r="3" fill="#E69F00" opacity="0.8"><title>TGFBR2: Colorectal, MSS 1.7% (86 / 4,986), Pancreatic, MSS 3.6% (112 / 3,117) · q 1.8e-6</title></circle><circle cx="220" cy="284.9" r="3" fill="#0072B2" opacity="0.8"><title>NSD1: Colorectal, MSS 1.7% (86 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 1.8e-6</title></circle><circle cx="213.1" cy="284.9" r="3" fill="#0072B2" opacity="0.8"><title>KMT2A: Colorectal, MSS 2.6% (130 / 4,986), Pancreatic, MSS 1.0% (32 / 3,117) · q 2.1e-6</title></circle><circle cx="211.6" cy="284.9" r="3" fill="#0072B2" opacity="0.8"><title>FLT4: Colorectal, MSS 2.9% (147 / 4,986), Pancreatic, MSS 1.3% (39 / 3,117) · q 2.4e-6</title></circle><circle cx="221.2" cy="285" r="3" fill="#0072B2" opacity="0.8"><title>IGF1R: Colorectal, MSS 1.6% (79 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 2.6e-6</title></circle><circle cx="220.4" cy="285" r="3" fill="#0072B2" opacity="0.8"><title>DICER1: Colorectal, MSS 1.6% (82 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.6e-6</title></circle><circle cx="212.4" cy="285" r="3" fill="#0072B2" opacity="0.8"><title>ARID1B: Colorectal, MSS 2.7% (133 / 4,986), Pancreatic, MSS 1.1% (34 / 3,117) · q 3.1e-6</title></circle><circle cx="333" cy="285" r="3" fill="#0072B2" opacity="0.8"><title>MUTYH: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0% (0 / 3,117) · q 3.5e-6</title></circle><circle cx="208.8" cy="285.1" r="3" fill="#0072B2" opacity="0.8"><title>SMAD3: Colorectal, MSS 3.7% (183 / 4,986), Pancreatic, MSS 1.8% (56 / 3,117) · q 4.4e-6</title></circle><circle cx="189.5" cy="285.1" r="3" fill="#E69F00" opacity="0.8"><title>SMAD4: Colorectal, MSS 15.1% (752 / 4,986), Pancreatic, MSS 19.3% (603 / 3,117) · q 4.4e-6</title></circle><circle cx="217.5" cy="285.1" r="3" fill="#0072B2" opacity="0.8"><title>TET2: Colorectal, MSS 1.8% (91 / 4,986), Pancreatic, MSS 0.6% (18 / 3,117) · q 4.7e-6</title></circle><circle cx="213" cy="285.1" r="3" fill="#0072B2" opacity="0.8"><title>KMT2B: Colorectal, MSS 2.9% (119 / 4,059), Pancreatic, MSS 1.2% (30 / 2,588) · q 5.7e-6</title></circle><circle cx="222.2" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>TEK: Colorectal, MSS 1.7% (69 / 4,059), Pancreatic, MSS 0.4% (11 / 2,588) · q 7.5e-6</title></circle><circle cx="224.3" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>PPP2R1A: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 7.5e-6</title></circle><circle cx="218.8" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>AXL: Colorectal, MSS 1.6% (81 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 7.7e-6</title></circle><circle cx="222.8" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>DNMT3B: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 7.7e-6</title></circle><circle cx="255.5" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>STAT3: Colorectal, MSS 0.7% (33 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 7.7e-6</title></circle><circle cx="220.1" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>PIK3CD: Colorectal, MSS 1.5% (75 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.7e-6</title></circle><circle cx="216.4" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>ESR1: Colorectal, MSS 1.8% (91 / 4,986), Pancreatic, MSS 0.6% (19 / 3,117) · q 8.7e-6</title></circle><circle cx="226.7" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>AKT1: Colorectal, MSS 1.1% (56 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 8.9e-6</title></circle><circle cx="226.7" cy="285.2" r="3" fill="#0072B2" opacity="0.8"><title>GATA3: Colorectal, MSS 1.1% (56 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 8.9e-6</title></circle><circle cx="230.7" cy="285.3" r="3" fill="#0072B2" opacity="0.8"><title>MRE11: Colorectal, MSS 1.0% (49 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 1e-5</title></circle><circle cx="212" cy="285.3" r="3" fill="#0072B2" opacity="0.8"><title>ANKRD11: Colorectal, MSS 2.5% (120 / 4,806), Pancreatic, MSS 1.0% (32 / 3,083) · q 1.1e-5</title></circle><circle cx="220.6" cy="285.3" r="3" fill="#0072B2" opacity="0.8"><title>FLT3: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 1.1e-5</title></circle><circle cx="233.5" cy="285.3" r="3" fill="#0072B2" opacity="0.8"><title>PMS1: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 1.2e-5</title></circle><circle cx="226.7" cy="285.3" r="3" fill="#0072B2" opacity="0.8"><title>NSD3: Colorectal, MSS 1.4% (55 / 4,059), Pancreatic, MSS 0.3% (7 / 2,588) · q 1.2e-5</title></circle><circle cx="226.3" cy="285.3" r="3" fill="#0072B2" opacity="0.8"><title>CDK8: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 1.3e-5</title></circle><circle cx="222.2" cy="285.4" r="3" fill="#0072B2" opacity="0.8"><title>BRIP1: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 1.5e-5</title></circle><circle cx="222.2" cy="285.4" r="3" fill="#0072B2" opacity="0.8"><title>CTCF: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 1.5e-5</title></circle><circle cx="221.2" cy="285.4" r="3" fill="#0072B2" opacity="0.8"><title>TP63: Colorectal, MSS 1.3% (67 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 1.6e-5</title></circle><circle cx="209.6" cy="285.4" r="3" fill="#0072B2" opacity="0.8"><title>MGA: Colorectal, MSS 3.1% (149 / 4,806), Pancreatic, MSS 1.5% (45 / 3,083) · q 1.6e-5</title></circle><circle cx="216" cy="285.4" r="3" fill="#0072B2" opacity="0.8"><title>LATS2: Colorectal, MSS 1.8% (89 / 4,986), Pancreatic, MSS 0.6% (19 / 3,117) · q 1.6e-5</title></circle><circle cx="173.7" cy="285.4" r="3" fill="#E69F00" opacity="0.8"><title>STK11: Colorectal, MSS 0.6% (30 / 4,986), Pancreatic, MSS 1.7% (53 / 3,117) · q 1.7e-5</title></circle><circle cx="218.5" cy="285.4" r="3" fill="#0072B2" opacity="0.8"><title>DROSHA: Colorectal, MSS 1.8% (73 / 4,059), Pancreatic, MSS 0.5% (14 / 2,588) · q 1.9e-5</title></circle><circle cx="217.8" cy="285.4" r="3" fill="#0072B2" opacity="0.8"><title>BRD4: Colorectal, MSS 1.5% (77 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 2e-5</title></circle><circle cx="218.4" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>KDM5A: Colorectal, MSS 1.5% (74 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.1e-5</title></circle><circle cx="218.7" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>DNMT1: Colorectal, MSS 1.5% (75 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.1e-5</title></circle><circle cx="220.9" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>PRKN: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 2.2e-5</title></circle><circle cx="216.6" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>ASXL2: Colorectal, MSS 1.6% (82 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 2.5e-5</title></circle><circle cx="212.6" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>NTRK3: Colorectal, MSS 2.1% (107 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 2.7e-5</title></circle><circle cx="225.6" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>RAF1: Colorectal, MSS 1.1% (53 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 2.8e-5</title></circle><circle cx="222.9" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>CSF3R: Colorectal, MSS 1.2% (58 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 2.8e-5</title></circle><circle cx="212.1" cy="285.5" r="3" fill="#0072B2" opacity="0.8"><title>PTPRD: Colorectal, MSS 2.2% (112 / 4,986), Pancreatic, MSS 0.9% (29 / 3,117) · q 2.9e-5</title></circle><circle cx="210.5" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>EP300: Colorectal, MSS 2.5% (125 / 4,986), Pancreatic, MSS 1.1% (35 / 3,117) · q 3.4e-5</title></circle><circle cx="227.4" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>SMARCA2: Colorectal, MSS 2.8% (45 / 1,591), Pancreatic, MSS 0.5% (6 / 1,100) · q 3.6e-5</title></circle><circle cx="227.1" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>MAP2K1: Colorectal, MSS 1.0% (49 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 3.8e-5</title></circle><circle cx="241.9" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>LZTR1: Colorectal, MSS 1.9% (31 / 1,591), Pancreatic, MSS 0.2% (2 / 1,100) · q 3.8e-5</title></circle><circle cx="225.2" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>PRDM1: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 4.2e-5</title></circle><circle cx="222.5" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>POLD1: Colorectal, MSS 1.2% (57 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 4.2e-5</title></circle><circle cx="223.6" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>FLCN: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 4.4e-5</title></circle><circle cx="229" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>CYLD: Colorectal, MSS 1.1% (44 / 4,059), Pancreatic, MSS 0.2% (5 / 2,588) · q 4.5e-5</title></circle><circle cx="222.3" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>BMPR1A: Colorectal, MSS 1.2% (58 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 4.5e-5</title></circle><circle cx="221.2" cy="285.6" r="3" fill="#0072B2" opacity="0.8"><title>PAK5: Colorectal, MSS 1.2% (61 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 4.6e-5</title></circle><circle cx="215.8" cy="285.7" r="3" fill="#0072B2" opacity="0.8"><title>GLI1: Colorectal, MSS 1.6% (77 / 4,806), Pancreatic, MSS 0.6% (17 / 3,083) · q 6.2e-5</title></circle><circle cx="228.6" cy="285.8" r="3" fill="#0072B2" opacity="0.8"><title>ERCC4: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 8.1e-5</title></circle><circle cx="216.7" cy="285.8" r="3" fill="#0072B2" opacity="0.8"><title>KIT: Colorectal, MSS 1.5% (73 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 8.1e-5</title></circle><circle cx="252.1" cy="285.8" r="3" fill="#0072B2" opacity="0.8"><title>SESN1: Colorectal, MSS 0.7% (28 / 4,059), Pancreatic, MSS 0% (1 / 2,588) · q 8.7e-5</title></circle><circle cx="218.6" cy="285.8" r="3" fill="#0072B2" opacity="0.8"><title>FGFR1: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 9.3e-5</title></circle><circle cx="333" cy="285.9" r="3" fill="#0072B2" opacity="0.8"><title>ALB: Colorectal, MSS 1.3% (20 / 1,591), Pancreatic, MSS 0% (0 / 1,100) · q 1.3e-4</title></circle><circle cx="215.9" cy="285.9" r="3" fill="#0072B2" opacity="0.8"><title>MDC1: Colorectal, MSS 1.4% (70 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 1.6e-4</title></circle><circle cx="213.6" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>PGR: Colorectal, MSS 1.7% (81 / 4,806), Pancreatic, MSS 0.6% (20 / 3,083) · q 1.9e-4</title></circle><circle cx="217.6" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>DDR2: Colorectal, MSS 1.2% (61 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 2e-4</title></circle><circle cx="224.9" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>ALOX12B: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 2e-4</title></circle><circle cx="224.9" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>ETV1: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 2e-4</title></circle><circle cx="223.2" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>PTPN11: Colorectal, MSS 0.9% (47 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 2.1e-4</title></circle><circle cx="219.2" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>MYOD1: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 2.1e-4</title></circle><circle cx="216.1" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>CASP8: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.4e-4</title></circle><circle cx="216.1" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>NOTCH2: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.4e-4</title></circle><circle cx="214.2" cy="286" r="3" fill="#0072B2" opacity="0.8"><title>BRCA1: Colorectal, MSS 1.5% (73 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 2.6e-4</title></circle><circle cx="232.8" cy="286.1" r="3" fill="#0072B2" opacity="0.8"><title>CYSLTR2: Colorectal, MSS 0.8% (32 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 2.9e-4</title></circle><circle cx="218.8" cy="286.1" r="3" fill="#0072B2" opacity="0.8"><title>INPP4B: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 3e-4</title></circle><circle cx="208.2" cy="286.1" r="3" fill="#0072B2" opacity="0.8"><title>TERT: Colorectal, MSS 2.5% (124 / 4,986), Pancreatic, MSS 1.3% (39 / 3,117) · q 3e-4</title></circle><circle cx="219.8" cy="286.1" r="3" fill="#0072B2" opacity="0.8"><title>BARD1: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 3.1e-4</title></circle><circle cx="220.2" cy="286.1" r="3" fill="#0072B2" opacity="0.8"><title>ARID5B: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 3.2e-4</title></circle><circle cx="210.8" cy="286.1" r="3" fill="#0072B2" opacity="0.8"><title>RET: Colorectal, MSS 1.9% (94 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 3.2e-4</title></circle><circle cx="186.4" cy="286.1" r="3" fill="#E69F00" opacity="0.8"><title>RNF43: Colorectal, MSS 3.7% (182 / 4,986), Pancreatic, MSS 5.5% (171 / 3,117) · q 3.7e-4</title></circle><circle cx="211.9" cy="286.1" r="3" fill="#0072B2" opacity="0.8"><title>CIC: Colorectal, MSS 1.7% (84 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 3.9e-4</title></circle><circle cx="219.4" cy="286.2" r="3" fill="#0072B2" opacity="0.8"><title>MSH2: Colorectal, MSS 1.0% (50 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 4.4e-4</title></circle><circle cx="222.3" cy="286.2" r="3" fill="#0072B2" opacity="0.8"><title>PAX5: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 4.7e-4</title></circle><circle cx="213.7" cy="286.2" r="3" fill="#0072B2" opacity="0.8"><title>JAK3: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 5e-4</title></circle><circle cx="211.6" cy="286.2" r="3" fill="#0072B2" opacity="0.8"><title>DOT1L: Colorectal, MSS 1.7% (83 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 5.3e-4</title></circle><circle cx="209.1" cy="286.2" r="3" fill="#0072B2" opacity="0.8"><title>ELF3: Colorectal, MSS 2.6% (104 / 4,059), Pancreatic, MSS 1.2% (32 / 2,588) · q 5.5e-4</title></circle><circle cx="225.1" cy="286.2" r="3" fill="#0072B2" opacity="0.8"><title>CHEK2: Colorectal, MSS 0.7% (37 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 6.2e-4</title></circle><circle cx="220.1" cy="286.2" r="3" fill="#0072B2" opacity="0.8"><title>IRF4: Colorectal, MSS 0.9% (46 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 6.6e-4</title></circle><circle cx="215.3" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>ERCC5: Colorectal, MSS 1.2% (59 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.1e-4</title></circle><circle cx="215.3" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>TSHR: Colorectal, MSS 1.2% (59 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.1e-4</title></circle><circle cx="218.7" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>TCF3: Colorectal, MSS 1.0% (47 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 8.8e-4</title></circle><circle cx="219.7" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>NEGR1: Colorectal, MSS 0.9% (44 / 4,806), Pancreatic, MSS 0.3% (8 / 3,083) · q 9e-4</title></circle><circle cx="220.8" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>HLA-B: Colorectal, MSS 1.0% (41 / 4,059), Pancreatic, MSS 0.3% (7 / 2,588) · q 9.2e-4</title></circle><circle cx="218.6" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>INSR: Colorectal, MSS 1.0% (48 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 9.2e-4</title></circle><circle cx="218.6" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>WT1: Colorectal, MSS 1.0% (48 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 9.2e-4</title></circle><circle cx="220.9" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>PHOX2B: Colorectal, MSS 0.8% (42 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 9.6e-4</title></circle><circle cx="215" cy="286.3" r="3" fill="#0072B2" opacity="0.8"><title>JAK1: Colorectal, MSS 1.2% (58 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 9.8e-4</title></circle><circle cx="213.4" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>RECQL4: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.001</title></circle><circle cx="217.3" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>MSH3: Colorectal, MSS 1.2% (49 / 4,059), Pancreatic, MSS 0.4% (10 / 2,588) · q 0.0012</title></circle><circle cx="209.1" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>PRKD1: Colorectal, MSS 2.2% (91 / 4,059), Pancreatic, MSS 1.1% (28 / 2,588) · q 0.0012</title></circle><circle cx="207.6" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>MED12: Colorectal, MSS 2.2% (111 / 4,986), Pancreatic, MSS 1.2% (36 / 3,117) · q 0.0012</title></circle><circle cx="217.7" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>FGF3: Colorectal, MSS 0.9% (46 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0013</title></circle><circle cx="235.3" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>PPP6C: Colorectal, MSS 0.5% (24 / 4,806), Pancreatic, MSS 0.1% (2 / 3,083) · q 0.0014</title></circle><circle cx="236.1" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>MALT1: Colorectal, MSS 0.5% (25 / 4,806), Pancreatic, MSS 0.1% (2 / 3,083) · q 0.0014</title></circle><circle cx="222" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>TNFAIP3: Colorectal, MSS 0.8% (38 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.0014</title></circle><circle cx="215.2" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>INPP4A: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.0015</title></circle><circle cx="245.4" cy="286.4" r="3" fill="#0072B2" opacity="0.8"><title>RRAGC: Colorectal, MSS 0.5% (20 / 4,059), Pancreatic, MSS 0% (1 / 2,588) · q 0.0016</title></circle><circle cx="183.2" cy="286.5" r="3" fill="#E69F00" opacity="0.8"><title>RB1: Colorectal, MSS 1.4% (70 / 4,986), Pancreatic, MSS 2.5% (77 / 3,117) · q 0.0017</title></circle><circle cx="213.9" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>STAG2: Colorectal, MSS 1.2% (59 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0017</title></circle><circle cx="217.3" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>PIK3R2: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0018</title></circle><circle cx="218.6" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>UPF1: Colorectal, MSS 1.0% (42 / 4,059), Pancreatic, MSS 0.3% (8 / 2,588) · q 0.0018</title></circle><circle cx="209.8" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>IRS2: Colorectal, MSS 1.6% (79 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 0.0019</title></circle><circle cx="226" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>MPL: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0019</title></circle><circle cx="218.7" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>EZH2: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 0.002</title></circle><circle cx="219.9" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>HNF1A: Colorectal, MSS 0.8% (40 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.002</title></circle><circle cx="214.8" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>PIK3CB: Colorectal, MSS 1.1% (53 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.002</title></circle><circle cx="210.7" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>LATS1: Colorectal, MSS 1.4% (72 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0021</title></circle><circle cx="215.4" cy="286.5" r="3" fill="#0072B2" opacity="0.8"><title>DIS3: Colorectal, MSS 1.0% (50 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.0022</title></circle><circle cx="228.8" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>MST1: Colorectal, MSS 0.5% (26 / 4,806), Pancreatic, MSS 0.1% (3 / 3,083) · q 0.0025</title></circle><circle cx="228.7" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>MSI1: Colorectal, MSS 0.6% (26 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0025</title></circle><circle cx="228.7" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>WWTR1: Colorectal, MSS 0.6% (26 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0025</title></circle><circle cx="212.2" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>NTRK2: Colorectal, MSS 1.2% (62 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.0025</title></circle><circle cx="212.2" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>RAD50: Colorectal, MSS 1.2% (62 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.0025</title></circle><circle cx="213.6" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>JUN: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 0.0025</title></circle><circle cx="214.5" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>SH2B3: Colorectal, MSS 1.1% (51 / 4,806), Pancreatic, MSS 0.4% (12 / 3,083) · q 0.0027</title></circle><circle cx="219.4" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>RAD21: Colorectal, MSS 0.8% (38 / 4,806), Pancreatic, MSS 0.2% (7 / 3,083) · q 0.0027</title></circle><circle cx="210.4" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>KDM5C: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0027</title></circle><circle cx="222.6" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>TAP2: Colorectal, MSS 0.8% (32 / 4,059), Pancreatic, MSS 0.2% (5 / 2,588) · q 0.0027</title></circle><circle cx="225.4" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>SHQ1: Colorectal, MSS 0.6% (30 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0028</title></circle><circle cx="224.7" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>CDKN1B: Colorectal, MSS 0.6% (29 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0028</title></circle><circle cx="222.8" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>IDH1: Colorectal, MSS 0.7% (33 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.0029</title></circle><circle cx="211.5" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>MET: Colorectal, MSS 1.2% (60 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.0033</title></circle><circle cx="207.5" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>FLT1: Colorectal, MSS 1.8% (92 / 4,986), Pancreatic, MSS 1.0% (30 / 3,117) · q 0.0034</title></circle><circle cx="233" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>CDK6: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 0.1% (2 / 3,117) · q 0.0034</title></circle><circle cx="209.9" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>EGFR: Colorectal, MSS 1.4% (69 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0035</title></circle><circle cx="217.3" cy="286.6" r="3" fill="#0072B2" opacity="0.8"><title>PPM1D: Colorectal, MSS 0.8% (39 / 4,806), Pancreatic, MSS 0.3% (8 / 3,083) · q 0.0036</title></circle><circle cx="227.9" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>NUF2: Colorectal, MSS 0.6% (25 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0037</title></circle><circle cx="217.3" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>RPS6KA4: Colorectal, MSS 0.8% (40 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 0.0037</title></circle><circle cx="227.5" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>CDKN2B: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0039</title></circle><circle cx="242.9" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>LMO1: Colorectal, MSS 0.4% (18 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0039</title></circle><circle cx="242.9" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>NKX3-1: Colorectal, MSS 0.4% (18 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0039</title></circle><circle cx="242.9" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>RIT1: Colorectal, MSS 0.4% (18 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0039</title></circle><circle cx="214.6" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>MST1R: Colorectal, MSS 1.0% (47 / 4,806), Pancreatic, MSS 0.4% (11 / 3,083) · q 0.0039</title></circle><circle cx="209.5" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>NTRK1: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.7% (21 / 3,117) · q 0.004</title></circle><circle cx="221.6" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>FGFR3: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.0041</title></circle><circle cx="221.6" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>MYC: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.0041</title></circle><circle cx="206.5" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>NOTCH1: Colorectal, MSS 2.0% (102 / 4,986), Pancreatic, MSS 1.1% (35 / 3,117) · q 0.0043</title></circle><circle cx="227.8" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>ZNRF3: Colorectal, MSS 1.4% (23 / 1,591), Pancreatic, MSS 0.3% (3 / 1,100) · q 0.0046</title></circle><circle cx="242.5" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>GAB1: Colorectal, MSS 1.0% (16 / 1,591), Pancreatic, MSS 0.1% (1 / 1,100) · q 0.0051</title></circle><circle cx="216.8" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>ARAF: Colorectal, MSS 0.8% (39 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 0.0052</title></circle><circle cx="213.7" cy="286.7" r="3" fill="#0072B2" opacity="0.8"><title>AGO2: Colorectal, MSS 1.1% (45 / 4,059), Pancreatic, MSS 0.4% (11 / 2,588) · q 0.0054</title></circle><circle cx="227.1" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>SESN3: Colorectal, MSS 0.6% (24 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0054</title></circle><circle cx="212.1" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>MAP3K13: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0054</title></circle><circle cx="213.7" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>ERCC2: Colorectal, MSS 0.9% (46 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.0055</title></circle><circle cx="217.8" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>NKX2-1: Colorectal, MSS 0.7% (36 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.0055</title></circle><circle cx="226.7" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>H3-5: Colorectal, MSS 0.5% (24 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0056</title></circle><circle cx="226.7" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>MDM4: Colorectal, MSS 0.5% (24 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0056</title></circle><circle cx="219.2" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>PMS2: Colorectal, MSS 0.7% (33 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.0057</title></circle><circle cx="221.3" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>RECQL: Colorectal, MSS 0.7% (30 / 4,059), Pancreatic, MSS 0.2% (5 / 2,588) · q 0.0057</title></circle><circle cx="212.5" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>NCOA3: Colorectal, MSS 1.0% (50 / 4,806), Pancreatic, MSS 0.4% (13 / 3,083) · q 0.0057</title></circle><circle cx="211.1" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>BLM: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.0063</title></circle><circle cx="212.8" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>SOS1: Colorectal, MSS 1.2% (47 / 4,059), Pancreatic, MSS 0.5% (12 / 2,588) · q 0.0064</title></circle><circle cx="214.9" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>NBN: Colorectal, MSS 0.8% (40 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0066</title></circle><circle cx="231.1" cy="286.8" r="3" fill="#0072B2" opacity="0.8"><title>GNAQ: Colorectal, MSS 0.4% (20 / 4,986), Pancreatic, MSS 0.1% (2 / 3,117) · q 0.0076</title></circle><circle cx="333" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>GAB2: Colorectal, MSS 0.7% (11 / 1,591), Pancreatic, MSS 0% (0 / 1,100) · q 0.0084</title></circle><circle cx="225.8" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>MYD88: Colorectal, MSS 0.5% (23 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0085</title></circle><circle cx="222.9" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>TAP1: Colorectal, MSS 0.6% (26 / 4,059), Pancreatic, MSS 0.2% (4 / 2,588) · q 0.0086</title></circle><circle cx="222.5" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>ABRAXAS1: Colorectal, MSS 0.5% (26 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0087</title></circle><circle cx="214.4" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>RAD54L: Colorectal, MSS 0.8% (39 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0092</title></circle><circle cx="210.9" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>CDH1: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0096</title></circle><circle cx="210.9" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>PALB2: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0096</title></circle><circle cx="240.5" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>TNFRSF14: Colorectal, MSS 0.3% (16 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0097</title></circle><circle cx="217.9" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>KLF5: Colorectal, MSS 1.8% (28 / 1,591), Pancreatic, MSS 0.5% (6 / 1,100) · q 0.0099</title></circle><circle cx="208.4" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>FANCA: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0099</title></circle><circle cx="333" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>RAB35: Colorectal, MSS 0.2% (11 / 4,806), Pancreatic, MSS 0% (0 / 3,083) · q 0.01</title></circle><circle cx="212.8" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>XPO1: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.01</title></circle><circle cx="213.3" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>SMARCB1: Colorectal, MSS 0.8% (41 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 0.011</title></circle><circle cx="230.5" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>SHOC2: Colorectal, MSS 0.5% (19 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.012</title></circle><circle cx="217.9" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>AURKA: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.012</title></circle><circle cx="213.9" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>FYN: Colorectal, MSS 0.8% (37 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 0.012</title></circle><circle cx="211.5" cy="286.9" r="3" fill="#0072B2" opacity="0.8"><title>IL7R: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.012</title></circle><circle cx="221.7" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>E2F3: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.013</title></circle><circle cx="239.9" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>FOXF1: Colorectal, MSS 0.9% (14 / 1,591), Pancreatic, MSS 0.1% (1 / 1,100) · q 0.013</title></circle><circle cx="212.4" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>TOP1: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.014</title></circle><circle cx="333" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>MLLT1: Colorectal, MSS 0.6% (10 / 1,591), Pancreatic, MSS 0% (0 / 1,100) · q 0.014</title></circle><circle cx="209.6" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>JAK2: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.015</title></circle><circle cx="210" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>ERG: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.015</title></circle><circle cx="212.7" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>PRDM14: Colorectal, MSS 1.0% (39 / 4,059), Pancreatic, MSS 0.4% (10 / 2,588) · q 0.015</title></circle><circle cx="203.8" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>ARID2: Colorectal, MSS 2.2% (112 / 4,986), Pancreatic, MSS 1.4% (44 / 3,117) · q 0.015</title></circle><circle cx="238.4" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>EIF4E: Colorectal, MSS 0.3% (14 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.015</title></circle><circle cx="239.7" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>HOXB13: Colorectal, MSS 0.3% (15 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.015</title></circle><circle cx="239.2" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>EGFL7: Colorectal, MSS 0.3% (15 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.015</title></circle><circle cx="239.2" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>SUFU: Colorectal, MSS 0.3% (15 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.015</title></circle><circle cx="229.9" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>USP8: Colorectal, MSS 1.1% (17 / 1,591), Pancreatic, MSS 0.2% (2 / 1,100) · q 0.015</title></circle><circle cx="211.1" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>CBL: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.016</title></circle><circle cx="333" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>GREM1: Colorectal, MSS 0.2% (11 / 4,986), Pancreatic, MSS 0% (0 / 3,117) · q 0.017</title></circle><circle cx="220.6" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>EIF4A2: Colorectal, MSS 0.5% (23 / 4,806), Pancreatic, MSS 0.1% (4 / 3,083) · q 0.018</title></circle><circle cx="190.1" cy="287" r="3" fill="#E69F00" opacity="0.8"><title>ARID1A: Colorectal, MSS 6.2% (307 / 4,986), Pancreatic, MSS 7.7% (239 / 3,117) · q 0.018</title></circle><circle cx="223.5" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>CXCR4: Colorectal, MSS 0.4% (20 / 4,806), Pancreatic, MSS 0.1% (3 / 3,083) · q 0.018</title></circle><circle cx="206" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>MSH6: Colorectal, MSS 1.5% (74 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 0.018</title></circle><circle cx="220.1" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>CHEK1: Colorectal, MSS 0.5% (23 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.018</title></circle><circle cx="224" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>DCUN1D1: Colorectal, MSS 0.4% (21 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.019</title></circle><circle cx="224" cy="287" r="3" fill="#0072B2" opacity="0.8"><title>FGF4: Colorectal, MSS 0.4% (21 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.019</title></circle><circle cx="214.8" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>GATA2: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.021</title></circle><circle cx="214.8" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>SDHA: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.021</title></circle><circle cx="210.6" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>CDC73: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.021</title></circle><circle cx="210.6" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>FGFR2: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.021</title></circle><circle cx="236.9" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>BCL10: Colorectal, MSS 0.3% (13 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.023</title></circle><circle cx="236.9" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>H3C12: Colorectal, MSS 0.3% (13 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.023</title></circle><circle cx="212.8" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>COP1: Colorectal, MSS 0.7% (36 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.023</title></circle><circle cx="217.8" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>ACVR1: Colorectal, MSS 0.5% (25 / 4,806), Pancreatic, MSS 0.2% (5 / 3,083) · q 0.024</title></circle><circle cx="237.8" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>CBFB: Colorectal, MSS 0.3% (14 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.024</title></circle><circle cx="237.8" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>EPCAM: Colorectal, MSS 0.3% (14 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.024</title></circle><circle cx="208.8" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>ABL1: Colorectal, MSS 1.0% (49 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.024</title></circle><circle cx="217.3" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>IGF1: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.024</title></circle><circle cx="217.3" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>RPS6KB2: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.024</title></circle><circle cx="209.5" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>FGFR4: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 0.024</title></circle><circle cx="227.1" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>KMT5A: Colorectal, MSS 0.4% (16 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.025</title></circle><circle cx="227.1" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>PPP4R2: Colorectal, MSS 0.4% (16 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.025</title></circle><circle cx="227.1" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>RRAS: Colorectal, MSS 0.4% (16 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.025</title></circle><circle cx="219.2" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>RAD51C: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.026</title></circle><circle cx="219.2" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>SMARCD1: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.026</title></circle><circle cx="208.3" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>NSD2: Colorectal, MSS 1.2% (50 / 4,059), Pancreatic, MSS 0.6% (16 / 2,588) · q 0.026</title></circle><circle cx="222" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>STK40: Colorectal, MSS 0.4% (19 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.026</title></circle><circle cx="211.3" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>ERCC3: Colorectal, MSS 0.7% (37 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 0.027</title></circle><circle cx="227.9" cy="287.1" r="3" fill="#0072B2" opacity="0.8"><title>SDHB: Colorectal, MSS 0.3% (17 / 4,986), Pancreatic, MSS 0.1% (2 / 3,117) · q 0.027</title></circle><circle cx="210.3" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>CSDE1: Colorectal, MSS 0.9% (38 / 4,059), Pancreatic, MSS 0.4% (11 / 2,588) · q 0.031</title></circle><circle cx="215.2" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>IFNGR1: Colorectal, MSS 0.5% (27 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.031</title></circle><circle cx="208.4" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>RPTOR: Colorectal, MSS 1.0% (48 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.031</title></circle><circle cx="186.3" cy="287.2" r="3" fill="#E69F00" opacity="0.8"><title>TGFBR1: Colorectal, MSS 1.4% (69 / 4,986), Pancreatic, MSS 2.1% (65 / 3,117) · q 0.033</title></circle><circle cx="205.6" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>TSC1: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 0.033</title></circle><circle cx="203.1" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>MAP2K4: Colorectal, MSS 1.9% (96 / 4,986), Pancreatic, MSS 1.3% (39 / 3,117) · q 0.034</title></circle><circle cx="206.1" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>CDK12: Colorectal, MSS 1.2% (60 / 4,986), Pancreatic, MSS 0.7% (21 / 3,117) · q 0.035</title></circle><circle cx="209.2" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>HLA-A: Colorectal, MSS 0.8% (39 / 4,806), Pancreatic, MSS 0.4% (12 / 3,083) · q 0.036</title></circle><circle cx="201" cy="287.2" r="3" fill="#0072B2" opacity="0.8"><title>BRCA2: Colorectal, MSS 3.1% (155 / 4,986), Pancreatic, MSS 2.2% (70 / 3,117) · q 0.036</title></circle><circle cx="273.8" cy="145.2" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="182.2" cy="169.1" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="135.3" cy="187.9" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="289.6" cy="232.4" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="235.3" cy="232.4" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="243.6" cy="241.9" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="243.7" cy="254.4" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="229.2" cy="268.9" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="232.7" cy="272.2" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="242.4" cy="273.4" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="152.5" cy="273.6" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><circle cx="197.7" cy="274.4" r="4.2" fill="none" stroke="#16232b" stroke-width="1"/><text x="279.8" y="147.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">APC</text><text x="188.2" y="171.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">KRAS</text><text x="141.3" y="190.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">CDKN2A</text><text x="295.6" y="234.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">TCF7L2</text><text x="194.3" y="234.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">PIK3CA</text><text x="249.6" y="244.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">FBXW7</text><text x="212.7" y="256.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">SOX9</text><line x1="233.7" y1="268.9" x2="252.2" y2="268.9" stroke="#5b6b76" stroke-width="0.75"/><text x="253.2" y="271.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">BRAF</text><line x1="228.2" y1="272.2" x2="117.7" y2="272.2" stroke="#5b6b76" stroke-width="0.75"/><text x="83.9" y="274.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">AMER1</text><line x1="242.4" y1="268.9" x2="242.4" y2="218.4" stroke="#5b6b76" stroke-width="0.75"/><text x="225.9" y="214.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">SMAD2</text><text x="139" y="265.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">MEN1</text><text x="185.8" y="265.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">TP53</text><text x="51" y="344" font-size="10" fill="#5b6b76">← Pancreatic, MSS</text><text x="338" y="344" font-size="10" fill="#5b6b76" text-anchor="end">Colorectal, MSS →</text><text x="423" y="45" font-size="10" fill="#5b6b76">the 300 most significant genes</text><line x1="423" y1="249" x2="617" y2="249" stroke="#dde4e8" stroke-width="0.75"/><text x="417" y="252.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="423" y1="210.2" x2="617" y2="210.2" stroke="#dde4e8" stroke-width="0.75"/><text x="417" y="213.7" font-size="10" fill="#5b6b76" text-anchor="end">20</text><line x1="423" y1="171.4" x2="617" y2="171.4" stroke="#dde4e8" stroke-width="0.75"/><text x="417" y="174.9" font-size="10" fill="#5b6b76" text-anchor="end">40</text><line x1="423" y1="132.6" x2="617" y2="132.6" stroke="#dde4e8" stroke-width="0.75"/><text x="417" y="136.1" font-size="10" fill="#5b6b76" text-anchor="end">60</text><line x1="423" y1="93.8" x2="617" y2="93.8" stroke="#dde4e8" stroke-width="0.75"/><text x="417" y="97.3" font-size="10" fill="#5b6b76" text-anchor="end">80</text><line x1="423" y1="55" x2="617" y2="55" stroke="#dde4e8" stroke-width="0.75"/><text x="417" y="58.5" font-size="10" fill="#5b6b76" text-anchor="end">100</text><text x="386.8" y="152" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 386.8 152)"><title>Altered frequency in Pancreatic, MSS (%)</title>Altered frequency in Pancreatic, MSS…</text><line x1="423" y1="55" x2="423" y2="249" stroke="#dde4e8" stroke-width="0.75"/><line x1="423" y1="249" x2="423" y2="252" stroke="#5b6b76" stroke-width="1"/><text x="420" y="265" font-size="10" fill="#5b6b76">0</text><line x1="461.8" y1="55" x2="461.8" y2="249" stroke="#dde4e8" stroke-width="0.75"/><line x1="461.8" y1="249" x2="461.8" y2="252" stroke="#5b6b76" stroke-width="1"/><text x="455.8" y="265" font-size="10" fill="#5b6b76">20</text><line x1="500.6" y1="55" x2="500.6" y2="249" stroke="#dde4e8" stroke-width="0.75"/><line x1="500.6" y1="249" x2="500.6" y2="252" stroke="#5b6b76" stroke-width="1"/><text x="494.6" y="265" font-size="10" fill="#5b6b76">40</text><line x1="539.4" y1="55" x2="539.4" y2="249" stroke="#dde4e8" stroke-width="0.75"/><line x1="539.4" y1="249" x2="539.4" y2="252" stroke="#5b6b76" stroke-width="1"/><text x="533.4" y="265" font-size="10" fill="#5b6b76">60</text><line x1="578.2" y1="55" x2="578.2" y2="249" stroke="#dde4e8" stroke-width="0.75"/><line x1="578.2" y1="249" x2="578.2" y2="252" stroke="#5b6b76" stroke-width="1"/><text x="572.2" y="265" font-size="10" fill="#5b6b76">80</text><line x1="617" y1="55" x2="617" y2="249" stroke="#dde4e8" stroke-width="0.75"/><line x1="617" y1="249" x2="617" y2="252" stroke="#5b6b76" stroke-width="1"/><text x="607" y="265" font-size="10" fill="#5b6b76">100</text><line x1="423" y1="249" x2="617" y2="249" stroke="#5b6b76" stroke-width="1"/><text x="520" y="284" font-size="11" fill="#16232b" text-anchor="middle"><title>Altered frequency in Colorectal, MSS (%)</title>Altered frequency in Colorectal, MSS…</text><line x1="423" y1="249" x2="617" y2="55" stroke="#5b6b76" stroke-width="1" stroke-dasharray="4 3"/><circle cx="571.4" cy="246.3" r="3" fill="#0072B2" opacity="0.75"><title>APC: Colorectal, MSS 76.5% (3,815 / 4,986), Pancreatic, MSS 1.4% (45 / 3,117) · q &lt;1e-300</title></circle><circle cx="507.8" cy="92.2" r="3" fill="#E69F00" opacity="0.75"><title>KRAS: Colorectal, MSS 43.7% (2,178 / 4,986), Pancreatic, MSS 80.8% (2,518 / 3,117) · q 3.8e-248</title></circle><circle cx="424.9" cy="209.8" r="3" fill="#E69F00" opacity="0.75"><title>CDKN2A: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 20.2% (629 / 3,117) · q 8.2e-209</title></circle><circle cx="445.1" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>TCF7L2: Colorectal, MSS 11.4% (547 / 4,806), Pancreatic, MSS 0.1% (3 / 3,083) · q 1.7e-115</title></circle><circle cx="457.1" cy="244.5" r="3" fill="#0072B2" opacity="0.75"><title>PIK3CA: Colorectal, MSS 17.6% (876 / 4,986), Pancreatic, MSS 2.3% (71 / 3,117) · q 1.9e-115</title></circle><circle cx="447.6" cy="246.9" r="3" fill="#0072B2" opacity="0.75"><title>FBXW7: Colorectal, MSS 12.7% (635 / 4,986), Pancreatic, MSS 1.1% (34 / 3,117) · q 1.4e-95</title></circle><circle cx="441.2" cy="247.4" r="3" fill="#0072B2" opacity="0.75"><title>SOX9: Colorectal, MSS 9.4% (470 / 4,986), Pancreatic, MSS 0.8% (25 / 3,117) · q 1.7e-69</title></circle><circle cx="437.6" cy="246.5" r="3" fill="#0072B2" opacity="0.75"><title>BRAF: Colorectal, MSS 7.5% (373 / 4,986), Pancreatic, MSS 1.3% (41 / 3,117) · q 4.2e-39</title></circle><circle cx="433.9" cy="247.4" r="3" fill="#0072B2" opacity="0.75"><title>AMER1: Colorectal, MSS 5.6% (281 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 3.1e-32</title></circle><circle cx="431.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>SMAD2: Colorectal, MSS 4.2% (211 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 1.3e-29</title></circle><circle cx="424" cy="241" r="3" fill="#E69F00" opacity="0.75"><title>MEN1: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 4.1% (128 / 3,117) · q 3e-29</title></circle><circle cx="574.3" cy="119.8" r="3" fill="#0072B2" opacity="0.75"><title>TP53: Colorectal, MSS 78.0% (3,890 / 4,986), Pancreatic, MSS 66.6% (2,077 / 3,117) · q 1.2e-27</title></circle><circle cx="433.5" cy="246.9" r="3" fill="#0072B2" opacity="0.75"><title>CTNNB1: Colorectal, MSS 5.4% (269 / 4,986), Pancreatic, MSS 1.1% (34 / 3,117) · q 2.1e-25</title></circle><circle cx="430.6" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>NRAS: Colorectal, MSS 3.9% (196 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 8.8e-24</title></circle><circle cx="430" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PIK3R1: Colorectal, MSS 3.6% (178 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 2e-23</title></circle><circle cx="435.2" cy="245.3" r="3" fill="#0072B2" opacity="0.75"><title>PTPRT: Colorectal, MSS 6.3% (315 / 4,986), Pancreatic, MSS 1.9% (58 / 3,117) · q 2.1e-21</title></circle><circle cx="433.1" cy="245.9" r="3" fill="#0072B2" opacity="0.75"><title>PTPRS: Colorectal, MSS 5.2% (261 / 4,986), Pancreatic, MSS 1.6% (49 / 3,117) · q 2.3e-17</title></circle><circle cx="429.8" cy="247.4" r="3" fill="#0072B2" opacity="0.75"><title>EPHA3: Colorectal, MSS 3.5% (176 / 4,986), Pancreatic, MSS 0.8% (24 / 3,117) · q 1.2e-15</title></circle><circle cx="429.4" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>CARD11: Colorectal, MSS 3.3% (166 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 5.6e-15</title></circle><circle cx="430.4" cy="247.1" r="3" fill="#0072B2" opacity="0.75"><title>GRIN2A: Colorectal, MSS 3.8% (188 / 4,986), Pancreatic, MSS 1.0% (30 / 3,117) · q 1.3e-14</title></circle><circle cx="428" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>ASXL1: Colorectal, MSS 2.6% (128 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.7e-14</title></circle><circle cx="428.8" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>MTOR: Colorectal, MSS 3.0% (150 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 1.5e-13</title></circle><circle cx="428" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PLCG2: Colorectal, MSS 2.6% (123 / 4,806), Pancreatic, MSS 0.4% (13 / 3,083) · q 2.2e-13</title></circle><circle cx="428.8" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>KDR: Colorectal, MSS 3.0% (148 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 3.3e-13</title></circle><circle cx="429" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>ERBB2: Colorectal, MSS 3.1% (155 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 6.2e-13</title></circle><circle cx="429.4" cy="247.3" r="3" fill="#0072B2" opacity="0.75"><title>NF1: Colorectal, MSS 3.3% (166 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 1e-12</title></circle><circle cx="430" cy="247.1" r="3" fill="#0072B2" opacity="0.75"><title>EPHA5: Colorectal, MSS 3.6% (181 / 4,986), Pancreatic, MSS 1.0% (32 / 3,117) · q 1e-12</title></circle><circle cx="429" cy="247.4" r="3" fill="#0072B2" opacity="0.75"><title>PIK3CG: Colorectal, MSS 3.1% (156 / 4,986), Pancreatic, MSS 0.8% (25 / 3,117) · q 3.3e-12</title></circle><circle cx="429.2" cy="247.3" r="3" fill="#0072B2" opacity="0.75"><title>ERBB3: Colorectal, MSS 3.2% (162 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 3.3e-12</title></circle><circle cx="427.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PDGFRA: Colorectal, MSS 2.2% (110 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 4.1e-12</title></circle><circle cx="430.6" cy="246.7" r="3" fill="#0072B2" opacity="0.75"><title>PTEN: Colorectal, MSS 3.9% (192 / 4,986), Pancreatic, MSS 1.2% (38 / 3,117) · q 5e-12</title></circle><circle cx="428.4" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>AR: Colorectal, MSS 2.8% (140 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 3.5e-11</title></circle><circle cx="429" cy="247.3" r="3" fill="#0072B2" opacity="0.75"><title>ALK: Colorectal, MSS 3.1% (153 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 8.1e-11</title></circle><circle cx="430.4" cy="246.5" r="3" fill="#0072B2" opacity="0.75"><title>ZFHX3: Colorectal, MSS 3.8% (182 / 4,806), Pancreatic, MSS 1.3% (41 / 3,083) · q 2.7e-10</title></circle><circle cx="427.7" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>PIK3C2G: Colorectal, MSS 2.4% (122 / 4,986), Pancreatic, MSS 0.6% (19 / 3,117) · q 8.8e-10</title></circle><circle cx="428.4" cy="247.4" r="3" fill="#0072B2" opacity="0.75"><title>AXIN2: Colorectal, MSS 2.8% (141 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 1.3e-9</title></circle><circle cx="427.9" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>EPHA7: Colorectal, MSS 2.5% (118 / 4,806), Pancreatic, MSS 0.6% (19 / 3,083) · q 1.6e-9</title></circle><circle cx="425.9" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>B2M: Colorectal, MSS 1.5% (76 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 1.8e-9</title></circle><circle cx="426.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>SOX17: Colorectal, MSS 2.0% (101 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 1.9e-9</title></circle><circle cx="425.9" cy="241.8" r="3" fill="#E69F00" opacity="0.75"><title>KDM6A: Colorectal, MSS 1.5% (74 / 4,986), Pancreatic, MSS 3.7% (116 / 3,117) · q 2.5e-9</title></circle><circle cx="426.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>SLX4: Colorectal, MSS 2.0% (80 / 4,059), Pancreatic, MSS 0.3% (8 / 2,588) · q 4e-9</title></circle><circle cx="428.6" cy="247.1" r="3" fill="#0072B2" opacity="0.75"><title>CREBBP: Colorectal, MSS 2.9% (146 / 4,986), Pancreatic, MSS 1.0% (30 / 3,117) · q 6.3e-9</title></circle><circle cx="429.6" cy="246.5" r="3" fill="#0072B2" opacity="0.75"><title>FAT1: Colorectal, MSS 3.4% (172 / 4,986), Pancreatic, MSS 1.3% (40 / 3,117) · q 7.8e-9</title></circle><circle cx="427.5" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>NCOR1: Colorectal, MSS 2.3% (117 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 8.9e-9</title></circle><circle cx="429.4" cy="246.7" r="3" fill="#0072B2" opacity="0.75"><title>NOTCH3: Colorectal, MSS 3.3% (163 / 4,986), Pancreatic, MSS 1.2% (37 / 3,117) · q 1e-8</title></circle><circle cx="425.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>SMO: Colorectal, MSS 1.5% (77 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 1.9e-8</title></circle><circle cx="427.3" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>TP53BP1: Colorectal, MSS 2.2% (90 / 4,059), Pancreatic, MSS 0.5% (12 / 2,588) · q 1.9e-8</title></circle><circle cx="426.3" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>INHBA: Colorectal, MSS 1.7% (82 / 4,806), Pancreatic, MSS 0.3% (10 / 3,083) · q 2.1e-8</title></circle><circle cx="434.6" cy="243" r="3" fill="#0072B2" opacity="0.75"><title>ATM: Colorectal, MSS 6.0% (298 / 4,986), Pancreatic, MSS 3.1% (97 / 3,117) · q 2.3e-8</title></circle><circle cx="427.5" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>NOTCH4: Colorectal, MSS 2.3% (117 / 4,986), Pancreatic, MSS 0.7% (21 / 3,117) · q 2.6e-8</title></circle><circle cx="427.1" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>TET1: Colorectal, MSS 2.1% (104 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 4e-8</title></circle><circle cx="428.6" cy="247.1" r="3" fill="#0072B2" opacity="0.75"><title>EPHB1: Colorectal, MSS 2.9% (144 / 4,986), Pancreatic, MSS 1.0% (32 / 3,117) · q 6e-8</title></circle><circle cx="426.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>IKZF1: Colorectal, MSS 2.0% (102 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 9.1e-8</title></circle><circle cx="427.7" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>ATR: Colorectal, MSS 2.4% (118 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 1.1e-7</title></circle><circle cx="425.1" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>MITF: Colorectal, MSS 1.1% (57 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 1.4e-7</title></circle><circle cx="427.5" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>SPEN: Colorectal, MSS 2.3% (116 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 1.6e-7</title></circle><circle cx="427.5" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>ROS1: Colorectal, MSS 2.3% (117 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 1.6e-7</title></circle><circle cx="426.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>IRS1: Colorectal, MSS 1.7% (87 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 1.7e-7</title></circle><circle cx="424.4" cy="244.9" r="3" fill="#E69F00" opacity="0.75"><title>DAXX: Colorectal, MSS 0.7% (34 / 4,986), Pancreatic, MSS 2.1% (67 / 3,117) · q 1.8e-7</title></circle><circle cx="426.7" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>MAP3K1: Colorectal, MSS 1.9% (93 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 1.8e-7</title></circle><circle cx="425.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>FOXP1: Colorectal, MSS 1.5% (75 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 1.8e-7</title></circle><circle cx="423.8" cy="245.7" r="3" fill="#E69F00" opacity="0.75"><title>U2AF1: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 1.7% (53 / 3,117) · q 2e-7</title></circle><circle cx="431.3" cy="245.3" r="3" fill="#0072B2" opacity="0.75"><title>PREX2: Colorectal, MSS 4.3% (175 / 4,059), Pancreatic, MSS 1.9% (49 / 2,588) · q 3.2e-7</title></circle><circle cx="426.7" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>TBX3: Colorectal, MSS 1.9% (94 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 3.9e-7</title></circle><circle cx="427.9" cy="247.3" r="3" fill="#0072B2" opacity="0.75"><title>POLE: Colorectal, MSS 2.5% (123 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 4.6e-7</title></circle><circle cx="426.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PTCH1: Colorectal, MSS 1.7% (84 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 5.5e-7</title></circle><circle cx="426.5" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>HGF: Colorectal, MSS 1.8% (90 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 5.7e-7</title></circle><circle cx="430.2" cy="245.7" r="3" fill="#0072B2" opacity="0.75"><title>ERBB4: Colorectal, MSS 3.7% (183 / 4,986), Pancreatic, MSS 1.7% (52 / 3,117) · q 6.1e-7</title></circle><circle cx="426.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>RASA1: Colorectal, MSS 1.6% (82 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 8.1e-7</title></circle><circle cx="426.3" cy="242" r="3" fill="#E69F00" opacity="0.75"><title>TGFBR2: Colorectal, MSS 1.7% (86 / 4,986), Pancreatic, MSS 3.6% (112 / 3,117) · q 1.8e-6</title></circle><circle cx="426.3" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>NSD1: Colorectal, MSS 1.7% (86 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 1.8e-6</title></circle><circle cx="428" cy="247.1" r="3" fill="#0072B2" opacity="0.75"><title>KMT2A: Colorectal, MSS 2.6% (130 / 4,986), Pancreatic, MSS 1.0% (32 / 3,117) · q 2.1e-6</title></circle><circle cx="428.6" cy="246.5" r="3" fill="#0072B2" opacity="0.75"><title>FLT4: Colorectal, MSS 2.9% (147 / 4,986), Pancreatic, MSS 1.3% (39 / 3,117) · q 2.4e-6</title></circle><circle cx="426.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>IGF1R: Colorectal, MSS 1.6% (79 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 2.6e-6</title></circle><circle cx="426.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>DICER1: Colorectal, MSS 1.6% (82 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.6e-6</title></circle><circle cx="428.2" cy="246.9" r="3" fill="#0072B2" opacity="0.75"><title>ARID1B: Colorectal, MSS 2.7% (133 / 4,986), Pancreatic, MSS 1.1% (34 / 3,117) · q 3.1e-6</title></circle><circle cx="424.2" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>MUTYH: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0% (0 / 3,117) · q 3.5e-6</title></circle><circle cx="430.2" cy="245.5" r="3" fill="#0072B2" opacity="0.75"><title>SMAD3: Colorectal, MSS 3.7% (183 / 4,986), Pancreatic, MSS 1.8% (56 / 3,117) · q 4.4e-6</title></circle><circle cx="452.3" cy="211.6" r="3" fill="#E69F00" opacity="0.75"><title>SMAD4: Colorectal, MSS 15.1% (752 / 4,986), Pancreatic, MSS 19.3% (603 / 3,117) · q 4.4e-6</title></circle><circle cx="426.5" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>TET2: Colorectal, MSS 1.8% (91 / 4,986), Pancreatic, MSS 0.6% (18 / 3,117) · q 4.7e-6</title></circle><circle cx="428.6" cy="246.7" r="3" fill="#0072B2" opacity="0.75"><title>KMT2B: Colorectal, MSS 2.9% (119 / 4,059), Pancreatic, MSS 1.2% (30 / 2,588) · q 5.7e-6</title></circle><circle cx="426.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>TEK: Colorectal, MSS 1.7% (69 / 4,059), Pancreatic, MSS 0.4% (11 / 2,588) · q 7.5e-6</title></circle><circle cx="425.5" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>PPP2R1A: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 7.5e-6</title></circle><circle cx="426.1" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>AXL: Colorectal, MSS 1.6% (81 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 7.7e-6</title></circle><circle cx="425.5" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>DNMT3B: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 7.7e-6</title></circle><circle cx="424.4" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>STAT3: Colorectal, MSS 0.7% (33 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 7.7e-6</title></circle><circle cx="425.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PIK3CD: Colorectal, MSS 1.5% (75 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.7e-6</title></circle><circle cx="426.5" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>ESR1: Colorectal, MSS 1.8% (91 / 4,986), Pancreatic, MSS 0.6% (19 / 3,117) · q 8.7e-6</title></circle><circle cx="425.1" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>AKT1: Colorectal, MSS 1.1% (56 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 8.9e-6</title></circle><circle cx="425.1" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>GATA3: Colorectal, MSS 1.1% (56 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 8.9e-6</title></circle><circle cx="424.9" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>MRE11: Colorectal, MSS 1.0% (49 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 1e-5</title></circle><circle cx="427.9" cy="247.1" r="3" fill="#0072B2" opacity="0.75"><title>ANKRD11: Colorectal, MSS 2.5% (120 / 4,806), Pancreatic, MSS 1.0% (32 / 3,083) · q 1.1e-5</title></circle><circle cx="425.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>FLT3: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 1.1e-5</title></circle><circle cx="424.7" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>PMS1: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 1.2e-5</title></circle><circle cx="425.7" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>NSD3: Colorectal, MSS 1.4% (55 / 4,059), Pancreatic, MSS 0.3% (7 / 2,588) · q 1.2e-5</title></circle><circle cx="425.1" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>CDK8: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 1.3e-5</title></circle><circle cx="425.5" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>BRIP1: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 1.5e-5</title></circle><circle cx="425.5" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>CTCF: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 1.5e-5</title></circle><circle cx="425.5" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>TP63: Colorectal, MSS 1.3% (67 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 1.6e-5</title></circle><circle cx="429" cy="246.1" r="3" fill="#0072B2" opacity="0.75"><title>MGA: Colorectal, MSS 3.1% (149 / 4,806), Pancreatic, MSS 1.5% (45 / 3,083) · q 1.6e-5</title></circle><circle cx="426.5" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>LATS2: Colorectal, MSS 1.8% (89 / 4,986), Pancreatic, MSS 0.6% (19 / 3,117) · q 1.6e-5</title></circle><circle cx="424.2" cy="245.7" r="3" fill="#E69F00" opacity="0.75"><title>STK11: Colorectal, MSS 0.6% (30 / 4,986), Pancreatic, MSS 1.7% (53 / 3,117) · q 1.7e-5</title></circle><circle cx="426.5" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>DROSHA: Colorectal, MSS 1.8% (73 / 4,059), Pancreatic, MSS 0.5% (14 / 2,588) · q 1.9e-5</title></circle><circle cx="425.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>BRD4: Colorectal, MSS 1.5% (77 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 2e-5</title></circle><circle cx="425.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>KDM5A: Colorectal, MSS 1.5% (74 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.1e-5</title></circle><circle cx="425.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>DNMT1: Colorectal, MSS 1.5% (75 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.1e-5</title></circle><circle cx="425.5" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PRKN: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 2.2e-5</title></circle><circle cx="426.1" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>ASXL2: Colorectal, MSS 1.6% (82 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 2.5e-5</title></circle><circle cx="427.1" cy="247.3" r="3" fill="#0072B2" opacity="0.75"><title>NTRK3: Colorectal, MSS 2.1% (107 / 4,986), Pancreatic, MSS 0.9% (27 / 3,117) · q 2.7e-5</title></circle><circle cx="425.1" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>RAF1: Colorectal, MSS 1.1% (53 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 2.8e-5</title></circle><circle cx="425.3" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>CSF3R: Colorectal, MSS 1.2% (58 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 2.8e-5</title></circle><circle cx="427.3" cy="247.3" r="3" fill="#0072B2" opacity="0.75"><title>PTPRD: Colorectal, MSS 2.2% (112 / 4,986), Pancreatic, MSS 0.9% (29 / 3,117) · q 2.9e-5</title></circle><circle cx="427.9" cy="246.9" r="3" fill="#0072B2" opacity="0.75"><title>EP300: Colorectal, MSS 2.5% (125 / 4,986), Pancreatic, MSS 1.1% (35 / 3,117) · q 3.4e-5</title></circle><circle cx="428.4" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>SMARCA2: Colorectal, MSS 2.8% (45 / 1,591), Pancreatic, MSS 0.5% (6 / 1,100) · q 3.6e-5</title></circle><circle cx="424.9" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>MAP2K1: Colorectal, MSS 1.0% (49 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 3.8e-5</title></circle><circle cx="426.7" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>LZTR1: Colorectal, MSS 1.9% (31 / 1,591), Pancreatic, MSS 0.2% (2 / 1,100) · q 3.8e-5</title></circle><circle cx="424.9" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>PRDM1: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 4.2e-5</title></circle><circle cx="425.3" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>POLD1: Colorectal, MSS 1.2% (57 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 4.2e-5</title></circle><circle cx="425.1" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>FLCN: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 4.4e-5</title></circle><circle cx="425.1" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>CYLD: Colorectal, MSS 1.1% (44 / 4,059), Pancreatic, MSS 0.2% (5 / 2,588) · q 4.5e-5</title></circle><circle cx="425.3" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>BMPR1A: Colorectal, MSS 1.2% (58 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 4.5e-5</title></circle><circle cx="425.3" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>PAK5: Colorectal, MSS 1.2% (61 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 4.6e-5</title></circle><circle cx="426.1" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>GLI1: Colorectal, MSS 1.6% (77 / 4,806), Pancreatic, MSS 0.6% (17 / 3,083) · q 6.2e-5</title></circle><circle cx="424.7" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>ERCC4: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 8.1e-5</title></circle><circle cx="425.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>KIT: Colorectal, MSS 1.5% (73 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 8.1e-5</title></circle><circle cx="424.4" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>SESN1: Colorectal, MSS 0.7% (28 / 4,059), Pancreatic, MSS 0% (1 / 2,588) · q 8.7e-5</title></circle><circle cx="425.5" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>FGFR1: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 9.3e-5</title></circle><circle cx="425.5" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>ALB: Colorectal, MSS 1.3% (20 / 1,591), Pancreatic, MSS 0% (0 / 1,100) · q 1.3e-4</title></circle><circle cx="425.7" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>MDC1: Colorectal, MSS 1.4% (70 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 1.6e-4</title></circle><circle cx="426.3" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>PGR: Colorectal, MSS 1.7% (81 / 4,806), Pancreatic, MSS 0.6% (20 / 3,083) · q 1.9e-4</title></circle><circle cx="425.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>DDR2: Colorectal, MSS 1.2% (61 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 2e-4</title></circle><circle cx="424.7" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>ALOX12B: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 2e-4</title></circle><circle cx="424.7" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>ETV1: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 2e-4</title></circle><circle cx="424.7" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>PTPN11: Colorectal, MSS 0.9% (47 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 2.1e-4</title></circle><circle cx="425.1" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>MYOD1: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 2.1e-4</title></circle><circle cx="425.5" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>CASP8: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.4e-4</title></circle><circle cx="425.5" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>NOTCH2: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 2.4e-4</title></circle><circle cx="425.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>BRCA1: Colorectal, MSS 1.5% (73 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 2.6e-4</title></circle><circle cx="424.6" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>CYSLTR2: Colorectal, MSS 0.8% (32 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 2.9e-4</title></circle><circle cx="425.1" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>INPP4B: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 3e-4</title></circle><circle cx="427.9" cy="246.5" r="3" fill="#0072B2" opacity="0.75"><title>TERT: Colorectal, MSS 2.5% (124 / 4,986), Pancreatic, MSS 1.3% (39 / 3,117) · q 3e-4</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>BARD1: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 3.1e-4</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>ARID5B: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 3.2e-4</title></circle><circle cx="426.7" cy="247.4" r="3" fill="#0072B2" opacity="0.75"><title>RET: Colorectal, MSS 1.9% (94 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 3.2e-4</title></circle><circle cx="430.2" cy="238.3" r="3" fill="#E69F00" opacity="0.75"><title>RNF43: Colorectal, MSS 3.7% (182 / 4,986), Pancreatic, MSS 5.5% (171 / 3,117) · q 3.7e-4</title></circle><circle cx="426.3" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>CIC: Colorectal, MSS 1.7% (84 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 3.9e-4</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>MSH2: Colorectal, MSS 1.0% (50 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 4.4e-4</title></circle><circle cx="424.7" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>PAX5: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 4.7e-4</title></circle><circle cx="425.7" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>JAK3: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.5% (17 / 3,117) · q 5e-4</title></circle><circle cx="426.3" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>DOT1L: Colorectal, MSS 1.7% (83 / 4,986), Pancreatic, MSS 0.7% (22 / 3,117) · q 5.3e-4</title></circle><circle cx="428" cy="246.7" r="3" fill="#0072B2" opacity="0.75"><title>ELF3: Colorectal, MSS 2.6% (104 / 4,059), Pancreatic, MSS 1.2% (32 / 2,588) · q 5.5e-4</title></circle><circle cx="424.4" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>CHEK2: Colorectal, MSS 0.7% (37 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 6.2e-4</title></circle><circle cx="424.7" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>IRF4: Colorectal, MSS 0.9% (46 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 6.6e-4</title></circle><circle cx="425.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>ERCC5: Colorectal, MSS 1.2% (59 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.1e-4</title></circle><circle cx="425.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>TSHR: Colorectal, MSS 1.2% (59 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 7.1e-4</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>TCF3: Colorectal, MSS 1.0% (47 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 8.8e-4</title></circle><circle cx="424.7" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>NEGR1: Colorectal, MSS 0.9% (44 / 4,806), Pancreatic, MSS 0.3% (8 / 3,083) · q 9e-4</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>HLA-B: Colorectal, MSS 1.0% (41 / 4,059), Pancreatic, MSS 0.3% (7 / 2,588) · q 9.2e-4</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>INSR: Colorectal, MSS 1.0% (48 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 9.2e-4</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>WT1: Colorectal, MSS 1.0% (48 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 9.2e-4</title></circle><circle cx="424.6" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>PHOX2B: Colorectal, MSS 0.8% (42 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 9.6e-4</title></circle><circle cx="425.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>JAK1: Colorectal, MSS 1.2% (58 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 9.8e-4</title></circle><circle cx="425.5" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>RECQL4: Colorectal, MSS 1.3% (66 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.001</title></circle><circle cx="425.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>MSH3: Colorectal, MSS 1.2% (49 / 4,059), Pancreatic, MSS 0.4% (10 / 2,588) · q 0.0012</title></circle><circle cx="427.3" cy="246.9" r="3" fill="#0072B2" opacity="0.75"><title>PRKD1: Colorectal, MSS 2.2% (91 / 4,059), Pancreatic, MSS 1.1% (28 / 2,588) · q 0.0012</title></circle><circle cx="427.3" cy="246.7" r="3" fill="#0072B2" opacity="0.75"><title>MED12: Colorectal, MSS 2.2% (111 / 4,986), Pancreatic, MSS 1.2% (36 / 3,117) · q 0.0012</title></circle><circle cx="424.7" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>FGF3: Colorectal, MSS 0.9% (46 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0013</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>PPP6C: Colorectal, MSS 0.5% (24 / 4,806), Pancreatic, MSS 0.1% (2 / 3,083) · q 0.0014</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>MALT1: Colorectal, MSS 0.5% (25 / 4,806), Pancreatic, MSS 0.1% (2 / 3,083) · q 0.0014</title></circle><circle cx="424.6" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>TNFAIP3: Colorectal, MSS 0.8% (38 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.0014</title></circle><circle cx="425.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>INPP4A: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.0015</title></circle><circle cx="424" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>RRAGC: Colorectal, MSS 0.5% (20 / 4,059), Pancreatic, MSS 0% (1 / 2,588) · q 0.0016</title></circle><circle cx="425.7" cy="244.2" r="3" fill="#E69F00" opacity="0.75"><title>RB1: Colorectal, MSS 1.4% (70 / 4,986), Pancreatic, MSS 2.5% (77 / 3,117) · q 0.0017</title></circle><circle cx="425.3" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>STAG2: Colorectal, MSS 1.2% (59 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0017</title></circle><circle cx="424.7" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>PIK3R2: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0018</title></circle><circle cx="424.9" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>UPF1: Colorectal, MSS 1.0% (42 / 4,059), Pancreatic, MSS 0.3% (8 / 2,588) · q 0.0018</title></circle><circle cx="426.1" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>IRS2: Colorectal, MSS 1.6% (79 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 0.0019</title></circle><circle cx="424.2" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>MPL: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0019</title></circle><circle cx="424.7" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>EZH2: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 0.002</title></circle><circle cx="424.6" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>HNF1A: Colorectal, MSS 0.8% (40 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.002</title></circle><circle cx="425.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PIK3CB: Colorectal, MSS 1.1% (53 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.002</title></circle><circle cx="425.7" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>LATS1: Colorectal, MSS 1.4% (72 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0021</title></circle><circle cx="424.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>DIS3: Colorectal, MSS 1.0% (50 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.0022</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>MST1: Colorectal, MSS 0.5% (26 / 4,806), Pancreatic, MSS 0.1% (3 / 3,083) · q 0.0025</title></circle><circle cx="424.2" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>MSI1: Colorectal, MSS 0.6% (26 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0025</title></circle><circle cx="424.2" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>WWTR1: Colorectal, MSS 0.6% (26 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0025</title></circle><circle cx="425.3" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>NTRK2: Colorectal, MSS 1.2% (62 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.0025</title></circle><circle cx="425.3" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>RAD50: Colorectal, MSS 1.2% (62 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.0025</title></circle><circle cx="425.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>JUN: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 0.0025</title></circle><circle cx="425.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>SH2B3: Colorectal, MSS 1.1% (51 / 4,806), Pancreatic, MSS 0.4% (12 / 3,083) · q 0.0027</title></circle><circle cx="424.6" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>RAD21: Colorectal, MSS 0.8% (38 / 4,806), Pancreatic, MSS 0.2% (7 / 3,083) · q 0.0027</title></circle><circle cx="425.7" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>KDM5C: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0027</title></circle><circle cx="424.6" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>TAP2: Colorectal, MSS 0.8% (32 / 4,059), Pancreatic, MSS 0.2% (5 / 2,588) · q 0.0027</title></circle><circle cx="424.2" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>SHQ1: Colorectal, MSS 0.6% (30 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0028</title></circle><circle cx="424.2" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>CDKN1B: Colorectal, MSS 0.6% (29 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0028</title></circle><circle cx="424.4" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>IDH1: Colorectal, MSS 0.7% (33 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.0029</title></circle><circle cx="425.3" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>MET: Colorectal, MSS 1.2% (60 / 4,986), Pancreatic, MSS 0.5% (16 / 3,117) · q 0.0033</title></circle><circle cx="426.5" cy="247.1" r="3" fill="#0072B2" opacity="0.75"><title>FLT1: Colorectal, MSS 1.8% (92 / 4,986), Pancreatic, MSS 1.0% (30 / 3,117) · q 0.0034</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>CDK6: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 0.1% (2 / 3,117) · q 0.0034</title></circle><circle cx="425.7" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>EGFR: Colorectal, MSS 1.4% (69 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0035</title></circle><circle cx="424.6" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>PPM1D: Colorectal, MSS 0.8% (39 / 4,806), Pancreatic, MSS 0.3% (8 / 3,083) · q 0.0036</title></circle><circle cx="424.2" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>NUF2: Colorectal, MSS 0.6% (25 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0037</title></circle><circle cx="424.6" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>RPS6KA4: Colorectal, MSS 0.8% (40 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 0.0037</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>CDKN2B: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0039</title></circle><circle cx="423.8" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>LMO1: Colorectal, MSS 0.4% (18 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0039</title></circle><circle cx="423.8" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>NKX3-1: Colorectal, MSS 0.4% (18 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0039</title></circle><circle cx="423.8" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>RIT1: Colorectal, MSS 0.4% (18 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0039</title></circle><circle cx="424.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>MST1R: Colorectal, MSS 1.0% (47 / 4,806), Pancreatic, MSS 0.4% (11 / 3,083) · q 0.0039</title></circle><circle cx="425.7" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>NTRK1: Colorectal, MSS 1.4% (71 / 4,986), Pancreatic, MSS 0.7% (21 / 3,117) · q 0.004</title></circle><circle cx="424.2" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>FGFR3: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.0041</title></circle><circle cx="424.2" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>MYC: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.0041</title></circle><circle cx="426.9" cy="246.9" r="3" fill="#0072B2" opacity="0.75"><title>NOTCH1: Colorectal, MSS 2.0% (102 / 4,986), Pancreatic, MSS 1.1% (35 / 3,117) · q 0.0043</title></circle><circle cx="425.7" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>ZNRF3: Colorectal, MSS 1.4% (23 / 1,591), Pancreatic, MSS 0.3% (3 / 1,100) · q 0.0046</title></circle><circle cx="424.9" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>GAB1: Colorectal, MSS 1.0% (16 / 1,591), Pancreatic, MSS 0.1% (1 / 1,100) · q 0.0051</title></circle><circle cx="424.6" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>ARAF: Colorectal, MSS 0.8% (39 / 4,986), Pancreatic, MSS 0.3% (8 / 3,117) · q 0.0052</title></circle><circle cx="425.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>AGO2: Colorectal, MSS 1.1% (45 / 4,059), Pancreatic, MSS 0.4% (11 / 2,588) · q 0.0054</title></circle><circle cx="424.2" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>SESN3: Colorectal, MSS 0.6% (24 / 4,059), Pancreatic, MSS 0.1% (3 / 2,588) · q 0.0054</title></circle><circle cx="425.1" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>MAP3K13: Colorectal, MSS 1.1% (54 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0054</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>ERCC2: Colorectal, MSS 0.9% (46 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.0055</title></circle><circle cx="424.4" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>NKX2-1: Colorectal, MSS 0.7% (36 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.0055</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>H3-5: Colorectal, MSS 0.5% (24 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0056</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>MDM4: Colorectal, MSS 0.5% (24 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0056</title></circle><circle cx="424.4" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>PMS2: Colorectal, MSS 0.7% (33 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.0057</title></circle><circle cx="424.4" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>RECQL: Colorectal, MSS 0.7% (30 / 4,059), Pancreatic, MSS 0.2% (5 / 2,588) · q 0.0057</title></circle><circle cx="424.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>NCOA3: Colorectal, MSS 1.0% (50 / 4,806), Pancreatic, MSS 0.4% (13 / 3,083) · q 0.0057</title></circle><circle cx="425.1" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>BLM: Colorectal, MSS 1.1% (55 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.0063</title></circle><circle cx="425.3" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>SOS1: Colorectal, MSS 1.2% (47 / 4,059), Pancreatic, MSS 0.5% (12 / 2,588) · q 0.0064</title></circle><circle cx="424.6" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>NBN: Colorectal, MSS 0.8% (40 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0066</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>GNAQ: Colorectal, MSS 0.4% (20 / 4,986), Pancreatic, MSS 0.1% (2 / 3,117) · q 0.0076</title></circle><circle cx="424.4" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>GAB2: Colorectal, MSS 0.7% (11 / 1,591), Pancreatic, MSS 0% (0 / 1,100) · q 0.0084</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>MYD88: Colorectal, MSS 0.5% (23 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.0085</title></circle><circle cx="424.2" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>TAP1: Colorectal, MSS 0.6% (26 / 4,059), Pancreatic, MSS 0.2% (4 / 2,588) · q 0.0086</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>ABRAXAS1: Colorectal, MSS 0.5% (26 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.0087</title></circle><circle cx="424.6" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>RAD54L: Colorectal, MSS 0.8% (39 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.0092</title></circle><circle cx="424.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>CDH1: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0096</title></circle><circle cx="424.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PALB2: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.4% (14 / 3,117) · q 0.0096</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>TNFRSF14: Colorectal, MSS 0.3% (16 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.0097</title></circle><circle cx="426.5" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>KLF5: Colorectal, MSS 1.8% (28 / 1,591), Pancreatic, MSS 0.5% (6 / 1,100) · q 0.0099</title></circle><circle cx="425.5" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>FANCA: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.6% (20 / 3,117) · q 0.0099</title></circle><circle cx="423.4" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>RAB35: Colorectal, MSS 0.2% (11 / 4,806), Pancreatic, MSS 0% (0 / 3,083) · q 0.01</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>XPO1: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.01</title></circle><circle cx="424.6" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>SMARCB1: Colorectal, MSS 0.8% (41 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 0.011</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>SHOC2: Colorectal, MSS 0.5% (19 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.012</title></circle><circle cx="424.2" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>AURKA: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.012</title></circle><circle cx="424.6" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>FYN: Colorectal, MSS 0.8% (37 / 4,806), Pancreatic, MSS 0.3% (9 / 3,083) · q 0.012</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>IL7R: Colorectal, MSS 0.9% (45 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.012</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>E2F3: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.013</title></circle><circle cx="424.7" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>FOXF1: Colorectal, MSS 0.9% (14 / 1,591), Pancreatic, MSS 0.1% (1 / 1,100) · q 0.013</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>TOP1: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.4% (11 / 3,117) · q 0.014</title></circle><circle cx="424.2" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>MLLT1: Colorectal, MSS 0.6% (10 / 1,591), Pancreatic, MSS 0% (0 / 1,100) · q 0.014</title></circle><circle cx="424.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>JAK2: Colorectal, MSS 1.0% (51 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.015</title></circle><circle cx="424.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>ERG: Colorectal, MSS 1.0% (52 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.015</title></circle><circle cx="424.9" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>PRDM14: Colorectal, MSS 1.0% (39 / 4,059), Pancreatic, MSS 0.4% (10 / 2,588) · q 0.015</title></circle><circle cx="427.3" cy="246.3" r="3" fill="#0072B2" opacity="0.75"><title>ARID2: Colorectal, MSS 2.2% (112 / 4,986), Pancreatic, MSS 1.4% (44 / 3,117) · q 0.015</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>EIF4E: Colorectal, MSS 0.3% (14 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.015</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>HOXB13: Colorectal, MSS 0.3% (15 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.015</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>EGFL7: Colorectal, MSS 0.3% (15 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.015</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>SUFU: Colorectal, MSS 0.3% (15 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.015</title></circle><circle cx="425.1" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>USP8: Colorectal, MSS 1.1% (17 / 1,591), Pancreatic, MSS 0.2% (2 / 1,100) · q 0.015</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>CBL: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.016</title></circle><circle cx="423.4" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>GREM1: Colorectal, MSS 0.2% (11 / 4,986), Pancreatic, MSS 0% (0 / 3,117) · q 0.017</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>EIF4A2: Colorectal, MSS 0.5% (23 / 4,806), Pancreatic, MSS 0.1% (4 / 3,083) · q 0.018</title></circle><circle cx="435" cy="234.1" r="3" fill="#E69F00" opacity="0.75"><title>ARID1A: Colorectal, MSS 6.2% (307 / 4,986), Pancreatic, MSS 7.7% (239 / 3,117) · q 0.018</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>CXCR4: Colorectal, MSS 0.4% (20 / 4,806), Pancreatic, MSS 0.1% (3 / 3,083) · q 0.018</title></circle><circle cx="425.9" cy="247.4" r="3" fill="#0072B2" opacity="0.75"><title>MSH6: Colorectal, MSS 1.5% (74 / 4,986), Pancreatic, MSS 0.8% (26 / 3,117) · q 0.018</title></circle><circle cx="424" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>CHEK1: Colorectal, MSS 0.5% (23 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.018</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>DCUN1D1: Colorectal, MSS 0.4% (21 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.019</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>FGF4: Colorectal, MSS 0.4% (21 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.019</title></circle><circle cx="424.2" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>GATA2: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.021</title></circle><circle cx="424.2" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>SDHA: Colorectal, MSS 0.6% (31 / 4,986), Pancreatic, MSS 0.2% (7 / 3,117) · q 0.021</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>CDC73: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.021</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>FGFR2: Colorectal, MSS 0.9% (43 / 4,986), Pancreatic, MSS 0.4% (12 / 3,117) · q 0.021</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>BCL10: Colorectal, MSS 0.3% (13 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.023</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>H3C12: Colorectal, MSS 0.3% (13 / 4,806), Pancreatic, MSS 0% (1 / 3,083) · q 0.023</title></circle><circle cx="424.4" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>COP1: Colorectal, MSS 0.7% (36 / 4,986), Pancreatic, MSS 0.3% (9 / 3,117) · q 0.023</title></circle><circle cx="424" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>ACVR1: Colorectal, MSS 0.5% (25 / 4,806), Pancreatic, MSS 0.2% (5 / 3,083) · q 0.024</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>CBFB: Colorectal, MSS 0.3% (14 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.024</title></circle><circle cx="423.6" cy="249" r="3" fill="#0072B2" opacity="0.75"><title>EPCAM: Colorectal, MSS 0.3% (14 / 4,986), Pancreatic, MSS 0% (1 / 3,117) · q 0.024</title></circle><circle cx="424.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>ABL1: Colorectal, MSS 1.0% (49 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.024</title></circle><circle cx="424" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>IGF1: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.024</title></circle><circle cx="424" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>RPS6KB2: Colorectal, MSS 0.5% (25 / 4,986), Pancreatic, MSS 0.2% (5 / 3,117) · q 0.024</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>FGFR4: Colorectal, MSS 0.9% (44 / 4,986), Pancreatic, MSS 0.4% (13 / 3,117) · q 0.024</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>KMT5A: Colorectal, MSS 0.4% (16 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.025</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>PPP4R2: Colorectal, MSS 0.4% (16 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.025</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>RRAS: Colorectal, MSS 0.4% (16 / 4,059), Pancreatic, MSS 0.1% (2 / 2,588) · q 0.025</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>RAD51C: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.026</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>SMARCD1: Colorectal, MSS 0.4% (22 / 4,986), Pancreatic, MSS 0.1% (4 / 3,117) · q 0.026</title></circle><circle cx="425.3" cy="247.8" r="3" fill="#0072B2" opacity="0.75"><title>NSD2: Colorectal, MSS 1.2% (50 / 4,059), Pancreatic, MSS 0.6% (16 / 2,588) · q 0.026</title></circle><circle cx="423.8" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>STK40: Colorectal, MSS 0.4% (19 / 4,986), Pancreatic, MSS 0.1% (3 / 3,117) · q 0.026</title></circle><circle cx="424.4" cy="248.4" r="3" fill="#0072B2" opacity="0.75"><title>ERCC3: Colorectal, MSS 0.7% (37 / 4,986), Pancreatic, MSS 0.3% (10 / 3,117) · q 0.027</title></circle><circle cx="423.6" cy="248.8" r="3" fill="#0072B2" opacity="0.75"><title>SDHB: Colorectal, MSS 0.3% (17 / 4,986), Pancreatic, MSS 0.1% (2 / 3,117) · q 0.027</title></circle><circle cx="424.7" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>CSDE1: Colorectal, MSS 0.9% (38 / 4,059), Pancreatic, MSS 0.4% (11 / 2,588) · q 0.031</title></circle><circle cx="424" cy="248.6" r="3" fill="#0072B2" opacity="0.75"><title>IFNGR1: Colorectal, MSS 0.5% (27 / 4,986), Pancreatic, MSS 0.2% (6 / 3,117) · q 0.031</title></circle><circle cx="424.9" cy="248" r="3" fill="#0072B2" opacity="0.75"><title>RPTOR: Colorectal, MSS 1.0% (48 / 4,986), Pancreatic, MSS 0.5% (15 / 3,117) · q 0.031</title></circle><circle cx="425.7" cy="244.9" r="3" fill="#E69F00" opacity="0.75"><title>TGFBR1: Colorectal, MSS 1.4% (69 / 4,986), Pancreatic, MSS 2.1% (65 / 3,117) · q 0.033</title></circle><circle cx="425.5" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>TSC1: Colorectal, MSS 1.3% (64 / 4,986), Pancreatic, MSS 0.7% (23 / 3,117) · q 0.033</title></circle><circle cx="426.7" cy="246.5" r="3" fill="#0072B2" opacity="0.75"><title>MAP2K4: Colorectal, MSS 1.9% (96 / 4,986), Pancreatic, MSS 1.3% (39 / 3,117) · q 0.034</title></circle><circle cx="425.3" cy="247.6" r="3" fill="#0072B2" opacity="0.75"><title>CDK12: Colorectal, MSS 1.2% (60 / 4,986), Pancreatic, MSS 0.7% (21 / 3,117) · q 0.035</title></circle><circle cx="424.6" cy="248.2" r="3" fill="#0072B2" opacity="0.75"><title>HLA-A: Colorectal, MSS 0.8% (39 / 4,806), Pancreatic, MSS 0.4% (12 / 3,083) · q 0.036</title></circle><circle cx="429" cy="244.7" r="3" fill="#0072B2" opacity="0.75"><title>BRCA2: Colorectal, MSS 3.1% (155 / 4,986), Pancreatic, MSS 2.2% (70 / 3,117) · q 0.036</title></circle><text x="659" y="47" font-size="12" fill="#16232b" font-weight="600">Genes with highest frequency in any group</text><line x1="704" y1="247" x2="956" y2="247" stroke="#dde4e8" stroke-width="0.75"/><text x="698" y="250.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="704" y1="209.4" x2="956" y2="209.4" stroke="#dde4e8" stroke-width="0.75"/><text x="698" y="212.9" font-size="10" fill="#5b6b76" text-anchor="end">20</text><line x1="704" y1="171.8" x2="956" y2="171.8" stroke="#dde4e8" stroke-width="0.75"/><text x="698" y="175.3" font-size="10" fill="#5b6b76" text-anchor="end">40</text><line x1="704" y1="134.2" x2="956" y2="134.2" stroke="#dde4e8" stroke-width="0.75"/><text x="698" y="137.7" font-size="10" fill="#5b6b76" text-anchor="end">60</text><line x1="704" y1="96.6" x2="956" y2="96.6" stroke="#dde4e8" stroke-width="0.75"/><text x="698" y="100.1" font-size="10" fill="#5b6b76" text-anchor="end">80</text><line x1="704" y1="59" x2="956" y2="59" stroke="#dde4e8" stroke-width="0.75"/><text x="698" y="62.5" font-size="10" fill="#5b6b76" text-anchor="end">100</text><text x="667.8" y="153" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 667.8 153)">Alteration frequency (%)</text><rect x="707" y="164.8" width="8.6" height="82.2" fill="#0072B2"><title>KRAS in Colorectal, MSS: 43.7% (2,178 / 4,986)</title></rect><rect x="716.6" y="95.1" width="8.6" height="151.9" fill="#E69F00"><title>KRAS in Pancreatic, MSS: 80.8% (2,518 / 3,117)</title></rect><rect x="732.2" y="100.4" width="8.6" height="146.6" fill="#0072B2"><title>TP53 in Colorectal, MSS: 78.0% (3,890 / 4,986)</title></rect><rect x="741.8" y="121.8" width="8.6" height="125.2" fill="#E69F00"><title>TP53 in Pancreatic, MSS: 66.6% (2,077 / 3,117)</title></rect><rect x="757.4" y="103.2" width="8.6" height="143.8" fill="#0072B2"><title>APC in Colorectal, MSS: 76.5% (3,815 / 4,986)</title></rect><rect x="767" y="244.4" width="8.6" height="2.6" fill="#E69F00"><title>APC in Pancreatic, MSS: 1.4% (45 / 3,117)</title></rect><rect x="782.6" y="245.1" width="8.6" height="1.9" fill="#0072B2"><title>CDKN2A in Colorectal, MSS: 1.0% (52 / 4,986)</title></rect><rect x="792.2" y="209" width="8.6" height="38" fill="#E69F00"><title>CDKN2A in Pancreatic, MSS: 20.2% (629 / 3,117)</title></rect><rect x="807.8" y="218.6" width="8.6" height="28.4" fill="#0072B2"><title>SMAD4 in Colorectal, MSS: 15.1% (752 / 4,986)</title></rect><rect x="817.4" y="210.7" width="8.6" height="36.3" fill="#E69F00"><title>SMAD4 in Pancreatic, MSS: 19.3% (603 / 3,117)</title></rect><rect x="833" y="213.9" width="8.6" height="33.1" fill="#0072B2"><title>PIK3CA in Colorectal, MSS: 17.6% (876 / 4,986)</title></rect><rect x="842.6" y="242.7" width="8.6" height="4.3" fill="#E69F00"><title>PIK3CA in Pancreatic, MSS: 2.3% (71 / 3,117)</title></rect><rect x="858.2" y="223.1" width="8.6" height="23.9" fill="#0072B2"><title>FBXW7 in Colorectal, MSS: 12.7% (635 / 4,986)</title></rect><rect x="867.8" y="244.9" width="8.6" height="2.1" fill="#E69F00"><title>FBXW7 in Pancreatic, MSS: 1.1% (34 / 3,117)</title></rect><rect x="883.4" y="225.6" width="8.6" height="21.4" fill="#0072B2"><title>TCF7L2 in Colorectal, MSS: 11.4% (547 / 4,806)</title></rect><rect x="893" y="246.8" width="8.6" height="0.2" fill="#E69F00"><title>TCF7L2 in Pancreatic, MSS: 0.1% (3 / 3,083)</title></rect><rect x="908.6" y="229.3" width="8.6" height="17.7" fill="#0072B2"><title>SOX9 in Colorectal, MSS: 9.4% (470 / 4,986)</title></rect><rect x="918.2" y="245.5" width="8.6" height="1.5" fill="#E69F00"><title>SOX9 in Pancreatic, MSS: 0.8% (25 / 3,117)</title></rect><rect x="933.8" y="235.3" width="8.6" height="11.7" fill="#0072B2"><title>ARID1A in Colorectal, MSS: 6.2% (307 / 4,986)</title></rect><rect x="943.4" y="232.5" width="8.6" height="14.5" fill="#E69F00"><title>ARID1A in Pancreatic, MSS: 7.7% (239 / 3,117)</title></rect><line x1="704" y1="247" x2="956" y2="247" stroke="#5b6b76" stroke-width="1"/><text x="719.6" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 719.6 253)">KRAS *</text><text x="744.8" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 744.8 253)">TP53 *</text><text x="770" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 770 253)">APC *</text><text x="795.2" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 795.2 253)">CDKN2A *</text><text x="820.4" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 820.4 253)">SMAD4 *</text><text x="845.6" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 845.6 253)">PIK3CA *</text><text x="870.8" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 870.8 253)">FBXW7 *</text><text x="896" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 896 253)">TCF7L2 *</text><text x="921.2" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 921.2 253)">SOX9 *</text><text x="946.4" y="253" font-size="10" fill="#16232b" text-anchor="end" transform="rotate(-45 946.4 253)">ARID1A *</text><rect x="704" y="306.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="719" y="315" font-size="10" fill="#16232b">Colorectal, MSS</text><rect x="805.71" y="306.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="820.7" y="315" font-size="10" fill="#16232b">Pancreatic, MSS</text><text x="704" y="333" font-size="10" fill="#5b6b76"><title>* q &lt; 0.05: the gene's frequency differs between the groups</title>* q &lt; 0.05: the gene's frequency differs between the g…</text><text x="8" y="372" font-size="11" fill="#16232b" font-weight="600">Gene</text><text x="67" y="372" font-size="11" fill="#16232b" font-weight="600">Cytoband</text><text x="239" y="372" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">Colorectal, MSS</text><text x="338" y="372" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">Pancreatic, MSS</text><text x="407" y="372" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">Log2 Ratio</text><text x="468" y="372" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">p-Value</text><text x="530" y="372" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">q-Value</text><text x="544" y="372" font-size="11" fill="#16232b" font-weight="600">Enriched in</text><line x1="8" y1="377" x2="632.7" y2="377" stroke="#dde4e8" stroke-width="2"/><text x="8" y="396" font-size="11" fill="#16232b" font-weight="600">APC</text><text x="67" y="396" font-size="11" fill="#16232b">5q22.2</text><text x="239" y="396" font-size="11" fill="#16232b" text-anchor="end">3,815 (76.5%)</text><text x="338" y="396" font-size="11" fill="#16232b" text-anchor="end">45 (1.4%)</text><text x="407" y="396" font-size="11" fill="#16232b" text-anchor="end">5.73</text><text x="468" y="396" font-size="11" fill="#16232b" text-anchor="end">&lt;1e-300</text><text x="530" y="396" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">&lt;1e-300</text><rect x="544" y="385" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="396" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="414" font-size="11" fill="#16232b" font-weight="600">KRAS</text><text x="67" y="414" font-size="11" fill="#16232b">12p12.1</text><text x="239" y="414" font-size="11" fill="#16232b" text-anchor="end">2,178 (43.7%)</text><text x="338" y="414" font-size="11" fill="#16232b" text-anchor="end">2,518 (80.8%)</text><text x="407" y="414" font-size="11" fill="#16232b" text-anchor="end">−0.89</text><text x="468" y="414" font-size="11" fill="#16232b" text-anchor="end">1.5e-250</text><text x="530" y="414" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">3.8e-248</text><rect x="544" y="403" width="88.7" height="15" rx="3" fill="#E69F00"/><text x="550" y="414" font-size="10" fill="#16232b" font-weight="600">Pancreatic, MSS</text><text x="8" y="432" font-size="11" fill="#16232b" font-weight="600">CDKN2A</text><text x="67" y="432" font-size="11" fill="#16232b">9p21.3</text><text x="239" y="432" font-size="11" fill="#16232b" text-anchor="end">52 (1.0%)</text><text x="338" y="432" font-size="11" fill="#16232b" text-anchor="end">629 (20.2%)</text><text x="407" y="432" font-size="11" fill="#16232b" text-anchor="end">−4.27</text><text x="468" y="432" font-size="11" fill="#16232b" text-anchor="end">5e-211</text><text x="530" y="432" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">8.2e-209</text><rect x="544" y="421" width="88.7" height="15" rx="3" fill="#E69F00"/><text x="550" y="432" font-size="10" fill="#16232b" font-weight="600">Pancreatic, MSS</text><text x="8" y="450" font-size="11" fill="#16232b" font-weight="600">TCF7L2</text><text x="67" y="450" font-size="11" fill="#16232b">10q25.2-q25.3</text><text x="239" y="450" font-size="11" fill="#16232b" text-anchor="end">547 (11.4%)</text><text x="338" y="450" font-size="11" fill="#16232b" text-anchor="end">3 (0.1%)</text><text x="407" y="450" font-size="11" fill="#16232b" text-anchor="end">6.87</text><text x="468" y="450" font-size="11" fill="#16232b" text-anchor="end">1.4e-117</text><text x="530" y="450" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">1.7e-115</text><rect x="544" y="439" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="450" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="468" font-size="11" fill="#16232b" font-weight="600">PIK3CA</text><text x="67" y="468" font-size="11" fill="#16232b">3q26.32</text><text x="239" y="468" font-size="11" fill="#16232b" text-anchor="end">876 (17.6%)</text><text x="338" y="468" font-size="11" fill="#16232b" text-anchor="end">71 (2.3%)</text><text x="407" y="468" font-size="11" fill="#16232b" text-anchor="end">2.95</text><text x="468" y="468" font-size="11" fill="#16232b" text-anchor="end">1.9e-117</text><text x="530" y="468" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">1.9e-115</text><rect x="544" y="457" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="468" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="486" font-size="11" fill="#16232b" font-weight="600">FBXW7</text><text x="67" y="486" font-size="11" fill="#16232b">4q31.3</text><text x="239" y="486" font-size="11" fill="#16232b" text-anchor="end">635 (12.7%)</text><text x="338" y="486" font-size="11" fill="#16232b" text-anchor="end">34 (1.1%)</text><text x="407" y="486" font-size="11" fill="#16232b" text-anchor="end">3.55</text><text x="468" y="486" font-size="11" fill="#16232b" text-anchor="end">1.8e-97</text><text x="530" y="486" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">1.4e-95</text><rect x="544" y="475" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="486" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="504" font-size="11" fill="#16232b" font-weight="600">SOX9</text><text x="67" y="504" font-size="11" fill="#16232b">17q24.3</text><text x="239" y="504" font-size="11" fill="#16232b" text-anchor="end">470 (9.4%)</text><text x="338" y="504" font-size="11" fill="#16232b" text-anchor="end">25 (0.8%)</text><text x="407" y="504" font-size="11" fill="#16232b" text-anchor="end">3.55</text><text x="468" y="504" font-size="11" fill="#16232b" text-anchor="end">2.5e-71</text><text x="530" y="504" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">1.7e-69</text><rect x="544" y="493" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="504" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="522" font-size="11" fill="#16232b" font-weight="600">BRAF</text><text x="67" y="522" font-size="11" fill="#16232b">7q34</text><text x="239" y="522" font-size="11" fill="#16232b" text-anchor="end">373 (7.5%)</text><text x="338" y="522" font-size="11" fill="#16232b" text-anchor="end">41 (1.3%)</text><text x="407" y="522" font-size="11" fill="#16232b" text-anchor="end">2.51</text><text x="468" y="522" font-size="11" fill="#16232b" text-anchor="end">6.8e-41</text><text x="530" y="522" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">4.2e-39</text><rect x="544" y="511" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="522" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="540" font-size="11" fill="#16232b" font-weight="600">AMER1</text><text x="67" y="540" font-size="11" fill="#16232b">Xq11.2</text><text x="239" y="540" font-size="11" fill="#16232b" text-anchor="end">281 (5.6%)</text><text x="338" y="540" font-size="11" fill="#16232b" text-anchor="end">26 (0.8%)</text><text x="407" y="540" font-size="11" fill="#16232b" text-anchor="end">2.76</text><text x="468" y="540" font-size="11" fill="#16232b" text-anchor="end">5.6e-34</text><text x="530" y="540" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">3.1e-32</text><rect x="544" y="529" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="540" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="558" font-size="11" fill="#16232b" font-weight="600">SMAD2</text><text x="67" y="558" font-size="11" fill="#16232b">18q21.1</text><text x="239" y="558" font-size="11" fill="#16232b" text-anchor="end">211 (4.2%)</text><text x="338" y="558" font-size="11" fill="#16232b" text-anchor="end">12 (0.4%)</text><text x="407" y="558" font-size="11" fill="#16232b" text-anchor="end">3.46</text><text x="468" y="558" font-size="11" fill="#16232b" text-anchor="end">2.5e-31</text><text x="530" y="558" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">1.3e-29</text><rect x="544" y="547" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="558" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="576" font-size="11" fill="#16232b" font-weight="600">MEN1</text><text x="67" y="576" font-size="11" fill="#16232b">11q13</text><text x="239" y="576" font-size="11" fill="#16232b" text-anchor="end">25 (0.5%)</text><text x="338" y="576" font-size="11" fill="#16232b" text-anchor="end">128 (4.1%)</text><text x="407" y="576" font-size="11" fill="#16232b" text-anchor="end">−3.03</text><text x="468" y="576" font-size="11" fill="#16232b" text-anchor="end">6.6e-31</text><text x="530" y="576" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">3e-29</text><rect x="544" y="565" width="88.7" height="15" rx="3" fill="#E69F00"/><text x="550" y="576" font-size="10" fill="#16232b" font-weight="600">Pancreatic, MSS</text><text x="8" y="594" font-size="11" fill="#16232b" font-weight="600">TP53</text><text x="67" y="594" font-size="11" fill="#16232b">17p13.1</text><text x="239" y="594" font-size="11" fill="#16232b" text-anchor="end">3,890 (78.0%)</text><text x="338" y="594" font-size="11" fill="#16232b" text-anchor="end">2,077 (66.6%)</text><text x="407" y="594" font-size="11" fill="#16232b" text-anchor="end">0.23</text><text x="468" y="594" font-size="11" fill="#16232b" text-anchor="end">3e-29</text><text x="530" y="594" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">1.2e-27</text><rect x="544" y="583" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="594" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="612" font-size="11" fill="#16232b" font-weight="600">CTNNB1</text><text x="67" y="612" font-size="11" fill="#16232b">3p22.1</text><text x="239" y="612" font-size="11" fill="#16232b" text-anchor="end">269 (5.4%)</text><text x="338" y="612" font-size="11" fill="#16232b" text-anchor="end">34 (1.1%)</text><text x="407" y="612" font-size="11" fill="#16232b" text-anchor="end">2.31</text><text x="468" y="612" font-size="11" fill="#16232b" text-anchor="end">5.6e-27</text><text x="530" y="612" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">2.1e-25</text><rect x="544" y="601" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="612" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="630" font-size="11" fill="#16232b" font-weight="600">NRAS</text><text x="67" y="630" font-size="11" fill="#16232b">1p13.2</text><text x="239" y="630" font-size="11" fill="#16232b" text-anchor="end">196 (3.9%)</text><text x="338" y="630" font-size="11" fill="#16232b" text-anchor="end">16 (0.5%)</text><text x="407" y="630" font-size="11" fill="#16232b" text-anchor="end">2.94</text><text x="468" y="630" font-size="11" fill="#16232b" text-anchor="end">2.5e-25</text><text x="530" y="630" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">8.8e-24</text><rect x="544" y="619" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="630" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="648" font-size="11" fill="#16232b" font-weight="600">PIK3R1</text><text x="67" y="648" font-size="11" fill="#16232b">5q13.1</text><text x="239" y="648" font-size="11" fill="#16232b" text-anchor="end">178 (3.6%)</text><text x="338" y="648" font-size="11" fill="#16232b" text-anchor="end">12 (0.4%)</text><text x="407" y="648" font-size="11" fill="#16232b" text-anchor="end">3.21</text><text x="468" y="648" font-size="11" fill="#16232b" text-anchor="end">6e-25</text><text x="530" y="648" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">2e-23</text><rect x="544" y="637" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="648" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="666" font-size="11" fill="#16232b" font-weight="600">PTPRT</text><text x="67" y="666" font-size="11" fill="#16232b">20q12-q13.11</text><text x="239" y="666" font-size="11" fill="#16232b" text-anchor="end">315 (6.3%)</text><text x="338" y="666" font-size="11" fill="#16232b" text-anchor="end">58 (1.9%)</text><text x="407" y="666" font-size="11" fill="#16232b" text-anchor="end">1.76</text><text x="468" y="666" font-size="11" fill="#16232b" text-anchor="end">6.7e-23</text><text x="530" y="666" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">2.1e-21</text><rect x="544" y="655" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="666" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="684" font-size="11" fill="#16232b" font-weight="600">PTPRS</text><text x="67" y="684" font-size="11" fill="#16232b">19p13.3</text><text x="239" y="684" font-size="11" fill="#16232b" text-anchor="end">261 (5.2%)</text><text x="338" y="684" font-size="11" fill="#16232b" text-anchor="end">49 (1.6%)</text><text x="407" y="684" font-size="11" fill="#16232b" text-anchor="end">1.74</text><text x="468" y="684" font-size="11" fill="#16232b" text-anchor="end">7.9e-19</text><text x="530" y="684" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">2.3e-17</text><rect x="544" y="673" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="684" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="702" font-size="11" fill="#16232b" font-weight="600">EPHA3</text><text x="67" y="702" font-size="11" fill="#16232b">3p11.1</text><text x="239" y="702" font-size="11" fill="#16232b" text-anchor="end">176 (3.5%)</text><text x="338" y="702" font-size="11" fill="#16232b" text-anchor="end">24 (0.8%)</text><text x="407" y="702" font-size="11" fill="#16232b" text-anchor="end">2.20</text><text x="468" y="702" font-size="11" fill="#16232b" text-anchor="end">4.4e-17</text><text x="530" y="702" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">1.2e-15</text><rect x="544" y="691" width="86.3" height="15" rx="3" fill="#0072B2"/><text x="550" y="702" font-size="10" fill="#ffffff" font-weight="600">Colorectal, MSS</text><text x="8" y="722" font-size="10" fill="#5b6b76">+476 more genes tested (the full table is in the result and the MCP text)</text><text x="8" y="740" font-size="10" fill="#5b6b76">Every gene's mutation frequency in each cohort, counted in SAMPLES over the samples profiled for that gene (a panel that lists it, or whole-exome; a sample altered</text><text x="8" y="754" font-size="10" fill="#5b6b76">off-panel counts as measured), uncalled mutations excluded as the portal excludes them. 494 genes tested with a two-sided Fisher's exact test; q is</text><text x="8" y="768" font-size="10" fill="#5b6b76">Benjamini–Hochberg across all of them. 8 altered genes were not tested: fewer than 5 altered samples across the cohorts, or not profiled in one of them. The log ratio is</text><text x="8" y="782" font-size="10" fill="#5b6b76">log2 of the first cohort's altered share over the second's, undefined (drawn at the edge) when either is zero. A gene enriched here is more often altered, not necessarily</text><text x="8" y="796" font-size="10" fill="#5b6b76">more important: a cohort holding hypermutated tumours (MSI-high, POLE) is enriched for mutations in almost every gene at once — exclude them in the cohort definition</text><text x="8" y="810" font-size="10" fill="#5b6b76">to see past that.</text></svg>
diff --git a/test/snapshots/enrichment.txt b/test/snapshots/enrichment.txt
new file mode 100644
index 00000000..7effe8c1
--- /dev/null
+++ b/test/snapshots/enrichment.txt
@@ -0,0 +1,59 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Colorectal, MSS: 4,957 patients — CANCER_TYPE in [Colorectal Cancer] (any sample) AND NOT MSI_TYPE in [Instable] (any sample)
+- Pancreatic, MSS: 3,095 patients — CANCER_TYPE in [Pancreatic Cancer] (any sample) AND NOT MSI_TYPE in [Instable] (any sample)
+
+Alteration enrichment (mutation; two-sided Fisher's exact test, BH q over 494 genes; 316 at q<0.05):
+  APC: Colorectal, MSS 76.5% (3815/4986) | Pancreatic, MSS 1.4% (45/3117), log2 ratio 5.73, q=<1e-300, enriched in Colorectal, MSS
+  KRAS: Colorectal, MSS 43.7% (2178/4986) | Pancreatic, MSS 80.8% (2518/3117), log2 ratio -0.89, q=3.8e-248, enriched in Pancreatic, MSS
+  CDKN2A: Colorectal, MSS 1% (52/4986) | Pancreatic, MSS 20.2% (629/3117), log2 ratio -4.27, q=8.2e-209, enriched in Pancreatic, MSS
+  TCF7L2: Colorectal, MSS 11.4% (547/4806) | Pancreatic, MSS 0.1% (3/3083), log2 ratio 6.87, q=1.7e-115, enriched in Colorectal, MSS
+  PIK3CA: Colorectal, MSS 17.6% (876/4986) | Pancreatic, MSS 2.3% (71/3117), log2 ratio 2.95, q=1.9e-115, enriched in Colorectal, MSS
+  FBXW7: Colorectal, MSS 12.7% (635/4986) | Pancreatic, MSS 1.1% (34/3117), log2 ratio 3.55, q=1.4e-95, enriched in Colorectal, MSS
+  SOX9: Colorectal, MSS 9.4% (470/4986) | Pancreatic, MSS 0.8% (25/3117), log2 ratio 3.55, q=1.7e-69, enriched in Colorectal, MSS
+  BRAF: Colorectal, MSS 7.5% (373/4986) | Pancreatic, MSS 1.3% (41/3117), log2 ratio 2.51, q=4.2e-39, enriched in Colorectal, MSS
+  AMER1: Colorectal, MSS 5.6% (281/4986) | Pancreatic, MSS 0.8% (26/3117), log2 ratio 2.76, q=3.1e-32, enriched in Colorectal, MSS
+  SMAD2: Colorectal, MSS 4.2% (211/4986) | Pancreatic, MSS 0.4% (12/3117), log2 ratio 3.46, q=1.3e-29, enriched in Colorectal, MSS
+  MEN1: Colorectal, MSS 0.5% (25/4986) | Pancreatic, MSS 4.1% (128/3117), log2 ratio -3.03, q=3.0e-29, enriched in Pancreatic, MSS
+  TP53: Colorectal, MSS 78% (3890/4986) | Pancreatic, MSS 66.6% (2077/3117), log2 ratio 0.23, q=1.2e-27, enriched in Colorectal, MSS
+  CTNNB1: Colorectal, MSS 5.4% (269/4986) | Pancreatic, MSS 1.1% (34/3117), log2 ratio 2.31, q=2.1e-25, enriched in Colorectal, MSS
+  NRAS: Colorectal, MSS 3.9% (196/4986) | Pancreatic, MSS 0.5% (16/3117), log2 ratio 2.94, q=8.8e-24, enriched in Colorectal, MSS
+  PIK3R1: Colorectal, MSS 3.6% (178/4986) | Pancreatic, MSS 0.4% (12/3117), log2 ratio 3.21, q=2.0e-23, enriched in Colorectal, MSS
+  PTPRT: Colorectal, MSS 6.3% (315/4986) | Pancreatic, MSS 1.9% (58/3117), log2 ratio 1.76, q=2.1e-21, enriched in Colorectal, MSS
+  PTPRS: Colorectal, MSS 5.2% (261/4986) | Pancreatic, MSS 1.6% (49/3117), log2 ratio 1.74, q=2.3e-17, enriched in Colorectal, MSS
+  EPHA3: Colorectal, MSS 3.5% (176/4986) | Pancreatic, MSS 0.8% (24/3117), log2 ratio 2.20, q=1.2e-15, enriched in Colorectal, MSS
+  CARD11: Colorectal, MSS 3.3% (166/4986) | Pancreatic, MSS 0.7% (22/3117), log2 ratio 2.24, q=5.6e-15, enriched in Colorectal, MSS
+  GRIN2A: Colorectal, MSS 3.8% (188/4986) | Pancreatic, MSS 1% (30/3117), log2 ratio 1.97, q=1.3e-14, enriched in Colorectal, MSS
+  ASXL1: Colorectal, MSS 2.6% (128/4986) | Pancreatic, MSS 0.4% (13/3117), log2 ratio 2.62, q=7.7e-14, enriched in Colorectal, MSS
+  MTOR: Colorectal, MSS 3% (150/4986) | Pancreatic, MSS 0.6% (20/3117), log2 ratio 2.23, q=1.5e-13, enriched in Colorectal, MSS
+  PLCG2: Colorectal, MSS 2.6% (123/4806) | Pancreatic, MSS 0.4% (13/3083), log2 ratio 2.60, q=2.2e-13, enriched in Colorectal, MSS
+  KDR: Colorectal, MSS 3% (148/4986) | Pancreatic, MSS 0.6% (20/3117), log2 ratio 2.21, q=3.3e-13, enriched in Colorectal, MSS
+  ERBB2: Colorectal, MSS 3.1% (155/4986) | Pancreatic, MSS 0.7% (23/3117), log2 ratio 2.07, q=6.2e-13, enriched in Colorectal, MSS
+  NF1: Colorectal, MSS 3.3% (166/4986) | Pancreatic, MSS 0.9% (27/3117), log2 ratio 1.94, q=1.0e-12, enriched in Colorectal, MSS
+  EPHA5: Colorectal, MSS 3.6% (181/4986) | Pancreatic, MSS 1% (32/3117), log2 ratio 1.82, q=1.0e-12, enriched in Colorectal, MSS
+  PIK3CG: Colorectal, MSS 3.1% (156/4986) | Pancreatic, MSS 0.8% (25/3117), log2 ratio 1.96, q=3.3e-12, enriched in Colorectal, MSS
+  ERBB3: Colorectal, MSS 3.2% (162/4986) | Pancreatic, MSS 0.9% (27/3117), log2 ratio 1.91, q=3.3e-12, enriched in Colorectal, MSS
+  PDGFRA: Colorectal, MSS 2.2% (110/4986) | Pancreatic, MSS 0.4% (11/3117), log2 ratio 2.64, q=4.1e-12, enriched in Colorectal, MSS
+  PTEN: Colorectal, MSS 3.9% (192/4986) | Pancreatic, MSS 1.2% (38/3117), log2 ratio 1.66, q=5.0e-12, enriched in Colorectal, MSS
+  AR: Colorectal, MSS 2.8% (140/4986) | Pancreatic, MSS 0.7% (22/3117), log2 ratio 1.99, q=3.5e-11, enriched in Colorectal, MSS
+  ALK: Colorectal, MSS 3.1% (153/4986) | Pancreatic, MSS 0.9% (27/3117), log2 ratio 1.82, q=8.1e-11, enriched in Colorectal, MSS
+  ZFHX3: Colorectal, MSS 3.8% (182/4806) | Pancreatic, MSS 1.3% (41/3083), log2 ratio 1.51, q=2.7e-10, enriched in Colorectal, MSS
+  PIK3C2G: Colorectal, MSS 2.4% (122/4986) | Pancreatic, MSS 0.6% (19/3117), log2 ratio 2.01, q=8.8e-10, enriched in Colorectal, MSS
+  AXIN2: Colorectal, MSS 2.8% (141/4986) | Pancreatic, MSS 0.8% (26/3117), log2 ratio 1.76, q=1.3e-9, enriched in Colorectal, MSS
+  EPHA7: Colorectal, MSS 2.5% (118/4806) | Pancreatic, MSS 0.6% (19/3083), log2 ratio 1.99, q=1.6e-9, enriched in Colorectal, MSS
+  B2M: Colorectal, MSS 1.5% (76/4986) | Pancreatic, MSS 0.2% (6/3117), log2 ratio 2.99, q=1.8e-9, enriched in Colorectal, MSS
+  SOX17: Colorectal, MSS 2% (101/4986) | Pancreatic, MSS 0.4% (13/3117), log2 ratio 2.28, q=1.9e-9, enriched in Colorectal, MSS
+  KDM6A: Colorectal, MSS 1.5% (74/4986) | Pancreatic, MSS 3.7% (116/3117), log2 ratio -1.33, q=2.5e-9, enriched in Pancreatic, MSS
+  … 454 more genes tested
+  CAVEAT (tell the user): Every gene's mutation frequency in each cohort, counted in SAMPLES over the samples profiled for that gene (a panel that lists it, or whole-exome; a sample altered off-panel counts as measured), uncalled mutations excluded as the portal excludes them. 494 genes tested with a two-sided Fisher's exact test; q is Benjamini–Hochberg across all of them. 8 altered genes were not tested: fewer than 5 altered samples across the cohorts, or not profiled in one of them. The log ratio is log2 of the first cohort's altered share over the second's, undefined (drawn at the edge) when either is zero. A gene enriched here is more often altered, not necessarily more important: a cohort holding hypermutated tumours (MSI-high, POLE) is enriched for mutations in almost every gene at once — exclude them in the cohort definition to see past that. 
+  METHOD:
+    - Count altered and profiled samples per gene [Colorectal, MSS · msk_chord_2024]: The enrichment queries above: per gene, samples with a mutation; per gene, samples on a panel listing it (counted per panel, then joined to its genes); and the whole-exome samples, profiled for every gene.
+        whole-exome samples: 0
+    - Count altered and profiled samples per gene [Pancreatic, MSS · msk_chord_2024]: The enrichment queries above: per gene, samples with a mutation; per gene, samples on a panel listing it (counted per panel, then joined to its genes); and the whole-exome samples, profiled for every gene.
+        whole-exome samples: 0
+    - Profiled = panel samples + whole-exome samples, summed across studies: A sample altered in a gene its panel does not list still counts as measured. Genes with fewer than 5 altered samples across the cohorts, or not profiled in one of them, are not tested.
+        tested: 494
+        not tested: 8
+    - Fisher's exact test per gene, then Benjamini–Hochberg: A two-sided Fisher's exact test on altered vs not among the profiled, per gene; q across every gene tested.
+    Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/genome.json b/test/snapshots/genome.json
new file mode 100644
index 00000000..d12a062c
--- /dev/null
+++ b/test/snapshots/genome.json
@@ -0,0 +1,475 @@
+{
+  "study": "msk_impact_50k_2026",
+  "cohorts": {
+    "Colorectal": {
+      "count": 4993,
+      "text": "CANCER_TYPE in [Colorectal Cancer] (any sample)",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    },
+    "Breast": {
+      "count": 5803,
+      "text": "CANCER_TYPE in [Breast Cancer] (any sample)",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "genome",
+      "cohorts": [
+        "Colorectal",
+        "Breast"
+      ],
+      "genome": {
+        "build": "hg19",
+        "binMb": 2,
+        "threshold": 0.2,
+        "includeX": false,
+        "bins": [
+          {
+            "chrom": "1",
+            "start": 0
+          },
+          {
+            "chrom": "1",
+            "start": 2000000
+          },
+          {
+            "chrom": "1",
+            "start": 4000000
+          },
+          {
+            "chrom": "1",
+            "start": 6000000
+          },
+          {
+            "chrom": "1",
+            "start": 8000000
+          },
+          {
+            "chrom": "1",
+            "start": 10000000
+          },
+          {
+            "chrom": "1",
+            "start": 12000000
+          },
+          {
+            "chrom": "1",
+            "start": 14000000
+          },
+          {
+            "chrom": "1",
+            "start": 16000000
+          },
+          {
+            "chrom": "1",
+            "start": 18000000
+          },
+          {
+            "chrom": "1",
+            "start": 20000000
+          },
+          {
+            "chrom": "1",
+            "start": 22000000
+          },
+          {
+            "chrom": "1",
+            "start": 24000000
+          },
+          {
+            "chrom": "1",
+            "start": 26000000
+          },
+          {
+            "chrom": "1",
+            "start": 28000000
+          },
+          {
+            "chrom": "1",
+            "start": 30000000
+          },
+          {
+            "chrom": "1",
+            "start": 32000000
+          },
+          {
+            "chrom": "1",
+            "start": 34000000
+          },
+          {
+            "chrom": "1",
+            "start": 36000000
+          },
+          {
+            "chrom": "1",
+            "start": 38000000
+          },
+          {
+            "…": "1432 more",
+            "sha256": "11021b31f868c895"
+          }
+        ],
+        "series": [
+          {
+            "cohort": "Colorectal",
+            "samples": 5395,
+            "segmented": 5390,
+            "profiled": [
+              829,
+              5254,
+              5310,
+              5310,
+              5120,
+              5327,
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+              5173,
+              5173,
+              5259,
+              5275,
+              {
+                "…": "1432 more",
+                "sha256": "c2218b32c5ea9c1e"
+              }
+            ],
+            "gain": [
+              18,
+              80,
+              72,
+              72,
+              58,
+              50,
+              52,
+              56,
+              54,
+              57,
+              58,
+              69,
+              59,
+              58,
+              22,
+              30,
+              31,
+              31,
+              78,
+              86,
+              {
+                "…": "1432 more",
+                "sha256": "9071064cfbef82a4"
+              }
+            ],
+            "loss": [
+              155,
+              1129,
+              1199,
+              1199,
+              1187,
+              1286,
+              1269,
+              1260,
+              1280,
+              1298,
+              1325,
+              1303,
+              1512,
+              1514,
+              1249,
+              1040,
+              1018,
+              1018,
+              903,
+              902,
+              {
+                "…": "1432 more",
+                "sha256": "05622f1b4e38b879"
+              }
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "samples": 6907,
+            "segmented": 6906,
+            "profiled": [
+              703,
+              6686,
+              6753,
+              6753,
+              6271,
+              6713,
+              6748,
+              6763,
+              6744,
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+              6463,
+              6624,
+              6639,
+              6587,
+              6659,
+              6668,
+              6668,
+              6565,
+              6587,
+              {
+                "…": "1432 more",
+                "sha256": "29fc7d02b1babbec"
+              }
+            ],
+            "gain": [
+              44,
+              182,
+              170,
+              170,
+              141,
+              132,
+              141,
+              161,
+              188,
+              186,
+              188,
+              177,
+              174,
+              175,
+              172,
+              217,
+              225,
+              225,
+              353,
+              365,
+              {
+                "…": "1432 more",
+                "sha256": "b24536dbd6cbad97"
+              }
+            ],
+            "loss": [
+              201,
+              1846,
+              1906,
+              1906,
+              1680,
+              1902,
+              1872,
+              1850,
+              1841,
+              1809,
+              1824,
+              1760,
+              2008,
+              2014,
+              1608,
+              1429,
+              1416,
+              1416,
+              1161,
+              1157,
+              {
+                "…": "1432 more",
+                "sha256": "d31a3cab65c7e604"
+              }
+            ]
+          }
+        ],
+        "test": {
+          "gainQ": [
+            0.00008545878862445828,
+            0.000009030934919032,
+            0.000007052613800163918,
+            0.000007052613800163918,
+            0.000006424362049596166,
+            0.000004690200061493557,
+            0.0000010266350704572462,
+            3.284080897211836e-8,
+            1.8154049214540513e-12,
+            2.3540693323143305e-11,
+            2.9957787840506274e-11,
+            2.1876619846395273e-7,
+            4.1811661928362945e-9,
+            1.7981085382803662e-9,
+            3.822800792218554e-23,
+            7.054747659995672e-27,
+            7.025354497705347e-28,
+            7.025354497705347e-28,
+            1.7450212124036932e-31,
+            2.2841786286715866e-30,
+            {
+              "…": "1432 more",
+              "sha256": "28986f17dabb26fb"
+            }
+          ],
+          "lossQ": [
+            0.000007013609127477512,
+            2.243524316793396e-14,
+            2.790086852777875e-12,
+            2.790086852777875e-12,
+            0.000014166292192959972,
+            3.08684191056184e-7,
+            0.0000011271795899166522,
+            0.0000036741504423566186,
+            0.00007628596613207397,
+            0.002904360074218777,
+            0.012386926935199553,
+            0.03344282841782284,
+            0.11136786598644766,
+            0.09922195566989905,
+            0.5770901208813335,
+            0.08021141405862506,
+            0.04465757638582967,
+            0.04465757638582967,
+            0.500573996127623,
+            0.5297453577870423,
+            {
+              "…": "1432 more",
+              "sha256": "693b40669105adf0"
+            }
+          ],
+          "significantGain": 1255,
+          "significantLoss": 1217
+        }
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_impact_50k_2026",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Share of each cohort's segmented SAMPLES (not patients: a patient with two segmented samples counts twice) gained (log2 segment mean ≥ 0.2) or lost (≤ −0.2) in each 2 Mb bin, hg19. Each bin's denominator is the samples whose segmentation covers it; a bin takes the value of the segment over its midpoint, so an event narrower than 2 Mb can be missed — use a smaller binMb to find it. 12,296 of 12,302 samples have segments. Thresholds on a log ratio are not purity- or ploidy-corrected: a low-purity tumour's real loss can sit inside ±0.2. Chromosome X is not drawn (includeX: true adds it). Per-bin two-sided Fisher tests, Benjamini–Hochberg across bins separately for gains and losses; adjacent bins are not independent, so read a run of significant bins as one region, not many findings.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count gained and lost samples per bin",
+            "detail": "The genome:coverage and genome:bins queries above. Per 2 Mb bin on hg19: the cohort's SAMPLES (a patient with two segmented samples counts twice) whose segmentation covers the bin's midpoint, and of those the ones gained (log2 segment mean ≥ 0.2) or lost (≤ −0.2) there. Counts only.",
+            "scope": "Colorectal · msk_impact_50k_2026",
+            "rowsIn": 4993
+          },
+          {
+            "kind": "fetch",
+            "title": "Count gained and lost samples per bin",
+            "detail": "The genome:coverage and genome:bins queries above. Per 2 Mb bin on hg19: the cohort's SAMPLES (a patient with two segmented samples counts twice) whose segmentation covers the bin's midpoint, and of those the ones gained (log2 segment mean ≥ 0.2) or lost (≤ −0.2) there. Counts only.",
+            "scope": "Breast · msk_impact_50k_2026",
+            "rowsIn": 5803
+          },
+          {
+            "kind": "transform",
+            "title": "Place the bins on one axis",
+            "detail": "Chromosomes 1–22 at hg19 lengths, 1,452 bins; counts add across studies (all on one build).",
+            "params": [
+              [
+                "Colorectal",
+                "5,390 of 5,395 samples segmented"
+              ],
+              [
+                "Breast",
+                "6,906 of 6,907 samples segmented"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Fisher's exact test per bin",
+            "detail": "Two-sided, on gained vs not (and separately lost vs not) among each cohort's covered samples, in every bin both cohorts cover."
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across bins",
+            "detail": "Separately for gains and losses. Adjacent bins are not independent: a run of significant bins is one region, not many findings."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "fisherExact",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "genome:1:0:gain_q": {
+            "v": 0.00008545878862445828
+          },
+          "genome:1:0:loss_q": {
+            "v": 0.000007013609127477512
+          },
+          "genome:1:1:gain_q": {
+            "v": 0.000009030934919032
+          },
+          "genome:1:1:loss_q": {
+            "v": 2.243524316793396e-14
+          },
+          "genome:1:2:gain_q": {
+            "v": 0.000007052613800163918
+          },
+          "genome:1:2:loss_q": {
+            "v": 2.790086852777875e-12
+          },
+          "genome:1:3:gain_q": {
+            "v": 0.000007052613800163918
+          },
+          "genome:1:3:loss_q": {
+            "v": 2.790086852777875e-12
+          },
+          "genome:1:4:gain_q": {
+            "v": 0.000006424362049596166
+          },
+          "genome:1:4:loss_q": {
+            "v": 0.000014166292192959972
+          },
+          "genome:1:5:gain_q": {
+            "v": 0.000004690200061493557
+          },
+          "genome:1:5:loss_q": {
+            "v": 3.08684191056184e-7
+          },
+          "genome:1:6:gain_q": {
+            "v": 0.0000010266350704572462
+          },
+          "genome:1:6:loss_q": {
+            "v": 0.0000011271795899166522
+          },
+          "genome:1:7:gain_q": {
+            "v": 3.284080897211836e-8
+          },
+          "genome:1:7:loss_q": {
+            "v": 0.0000036741504423566186
+          },
+          "genome:1:8:gain_q": {
+            "v": 1.8154049214540513e-12
+          },
+          "genome:1:8:loss_q": {
+            "v": 0.00007628596613207397
+          },
+          "genome:1:9:gain_q": {
+            "v": 2.3540693323143305e-11
+          },
+          "genome:1:9:loss_q": {
+            "v": 0.002904360074218777
+          },
+          "…": {
+            "more": 2752,
+            "sha256": "14aedf4afb06c134"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/genome.sql b/test/snapshots/genome.sql
new file mode 100644
index 00000000..c2b93963
--- /dev/null
+++ b/test/snapshots/genome.sql
@@ -0,0 +1,102 @@
+-- cohort Colorectal
+-- msk_impact_50k_2026
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+
+-- cohort Breast
+-- msk_impact_50k_2026
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))
+
+-- view 0 genome: genome:bins:Breast · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+seg AS (
+  SELECT s.sample_id AS sid, multiIf(s.chr IN ('23', 'X', 'chrX'), 'X', replaceOne(s.chr, 'chr', '')) AS chrom,
+         s.start AS st, s.end AS en, s.segment_mean AS m
+  FROM snapshot_db.copy_number_seg s
+  WHERE s.cancer_study_id = (SELECT cancer_study_id FROM snapshot_db.cancer_study WHERE cancer_study_identifier = 'msk_impact_50k_2026')
+    AND s.sample_id IN (SELECT sid FROM samp) AND isFinite(s.segment_mean)
+),
+binned AS (
+  SELECT sid, chrom, bin, avg(m) AS v FROM (
+    SELECT sid, chrom, m,
+           arrayJoin(range(toUInt64(greatest(0, ceil((st - 1000000) / 2000000))),
+                           toUInt64(greatest(0, floor((en - 1000000) / 2000000) + 1)))) AS bin
+    FROM seg WHERE chrom IN ('1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22') AND en >= st
+  ) GROUP BY sid, chrom, bin
+)
+SELECT chrom, bin, count() AS profiled, countIf(v >= 0.2) AS gain, countIf(v <= -0.2) AS loss
+FROM binned GROUP BY chrom, bin ORDER BY chrom, bin
+
+-- view 0 genome: genome:bins:Colorectal · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+seg AS (
+  SELECT s.sample_id AS sid, multiIf(s.chr IN ('23', 'X', 'chrX'), 'X', replaceOne(s.chr, 'chr', '')) AS chrom,
+         s.start AS st, s.end AS en, s.segment_mean AS m
+  FROM snapshot_db.copy_number_seg s
+  WHERE s.cancer_study_id = (SELECT cancer_study_id FROM snapshot_db.cancer_study WHERE cancer_study_identifier = 'msk_impact_50k_2026')
+    AND s.sample_id IN (SELECT sid FROM samp) AND isFinite(s.segment_mean)
+),
+binned AS (
+  SELECT sid, chrom, bin, avg(m) AS v FROM (
+    SELECT sid, chrom, m,
+           arrayJoin(range(toUInt64(greatest(0, ceil((st - 1000000) / 2000000))),
+                           toUInt64(greatest(0, floor((en - 1000000) / 2000000) + 1)))) AS bin
+    FROM seg WHERE chrom IN ('1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22') AND en >= st
+  ) GROUP BY sid, chrom, bin
+)
+SELECT chrom, bin, count() AS profiled, countIf(v >= 0.2) AS gain, countIf(v <= -0.2) AS loss
+FROM binned GROUP BY chrom, bin ORDER BY chrom, bin
+
+-- view 0 genome: genome:build · msk_impact_50k_2026
+SELECT rg.name AS build FROM snapshot_db.cancer_study cs
+JOIN snapshot_db.reference_genome rg ON rg.reference_genome_id = cs.reference_genome_id
+WHERE cs.cancer_study_identifier = 'msk_impact_50k_2026'
+
+-- view 0 genome: genome:coverage:Breast · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+)
+SELECT (SELECT count() FROM samp) AS samples,
+       (SELECT uniqExact(sample_id) FROM snapshot_db.copy_number_seg
+         WHERE cancer_study_id = (SELECT cancer_study_id FROM snapshot_db.cancer_study WHERE cancer_study_identifier = 'msk_impact_50k_2026')
+           AND sample_id IN (SELECT sid FROM samp)) AS segmented
+FROM system.one
+
+-- view 0 genome: genome:coverage:Colorectal · msk_impact_50k_2026
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+samp AS (
+  SELECT internal_id AS sid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+)
+SELECT (SELECT count() FROM samp) AS samples,
+       (SELECT uniqExact(sample_id) FROM snapshot_db.copy_number_seg
+         WHERE cancer_study_id = (SELECT cancer_study_id FROM snapshot_db.cancer_study WHERE cancer_study_identifier = 'msk_impact_50k_2026')
+           AND sample_id IN (SELECT sid FROM samp)) AS segmented
+FROM system.one
diff --git a/test/snapshots/genome.svg b/test/snapshots/genome.svg
new file mode 100644
index 00000000..6d14f91c
--- /dev/null
+++ b/test/snapshots/genome.svg
@@ -0,0 +1,2 @@
+<!-- view 0 genome -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 464" width="100%" role="img" aria-label="Genome-wide copy number — Colorectal, Breast" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Genome-wide copy number — Colorectal, Breast</title><rect width="960" height="464" fill="#ffffff"/><text x="64" y="17" font-size="12" fill="#16232b" font-weight="600">Colorectal</text><text x="129.8" y="17" font-size="10" fill="#5b6b76">5,390 segmented samples</text><rect x="140.2" y="22" width="74.4" height="115" fill="#dde4e8" opacity="0.45"/><rect x="275.7" y="22" width="58.6" height="115" fill="#dde4e8" opacity="0.45"/><rect x="389.8" y="22" width="52.5" height="115" fill="#dde4e8" opacity="0.45"/><rect x="491.1" y="22" width="45.2" height="115" fill="#dde4e8" opacity="0.45"/><rect x="579.6" y="22" width="41.5" height="115" fill="#dde4e8" opacity="0.45"/><rect x="662.6" y="22" width="40.9" height="115" fill="#dde4e8" 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323h0.6v10h-0.6zM889 323h0.6v10h-0.6zM889.6 323h0.6v10h-0.6zM890.2 323h0.6v10h-0.6zM890.8 323h0.6v10h-0.6zM891.4 323h0.6v10h-0.6zM892 323h0.6v10h-0.6zM892.6 323h0.6v10h-0.6zM893.3 323h0.6v10h-0.6zM893.9 323h0.6v10h-0.6zM894.5 323h0.6v10h-0.6zM895.1 323h0.6v10h-0.6zM895.7 323h0.6v10h-0.6zM896.3 323h0.6v10h-0.6zM896.9 323h0.6v10h-0.6zM897.5 323h0.6v10h-0.6zM898.1 323h0.6v10h-0.6zM906.1 323h0.6v10h-0.6zM906.7 323h0.6v10h-0.6zM907.3 323h0.6v10h-0.6zM907.9 323h0.6v10h-0.6zM908.5 323h0.6v10h-0.6zM909.1 323h0.6v10h-0.6zM909.7 323h0.6v10h-0.6zM910.3 323h0.6v10h-0.6zM910.9 323h0.6v10h-0.6zM911.6 323h0.6v10h-0.6zM912.2 323h0.6v10h-0.6zM912.8 323h0.6v10h-0.6zM913.4 323h0.6v10h-0.6zM914 323h0.6v10h-0.6zM914.6 323h0.6v10h-0.6zM915.2 323h0.6v10h-0.6zM915.8 323h0.6v10h-0.6zM916.4 323h0.6v10h-0.6zM917 323h0.6v10h-0.6zM917.7 323h0.6v10h-0.6zM939 323h0.6v10h-0.6zM939.6 323h0.6v10h-0.6zM940.2 323h0.6v10h-0.6zM940.8 323h0.6v10h-0.6zM941.5 323h0.6v10h-0.6zM942.1 323h0.6v10h-0.6zM942.7 323h0.6v10h-0.6zM943.3 323h0.6v10h-0.6zM943.9 323h0.6v10h-0.6zM944.5 323h0.6v10h-0.6zM945.1 323h0.6v10h-0.6zM945.7 323h0.6v10h-0.6zM946.3 323h0.6v10h-0.6zM946.9 323h0.6v10h-0.6zM947.6 323h0.6v10h-0.6zM948.2 323h0.6v10h-0.6zM948.8 323h0.6v10h-0.6z" fill="#E69F00"/><text x="64" y="353" font-size="10" fill="#5b6b76">q &lt; 0.05, higher in:</text><rect x="158.05" y="344.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="173.1" y="353" font-size="10" fill="#16232b">Colorectal</text><rect x="234.94" y="344.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="249.9" y="353" font-size="10" fill="#16232b">Breast</text><rect x="63.97" y="361.5" width="10" height="10" rx="2" fill="#ff0000" opacity="0.85"/><text x="79" y="370" font-size="10" fill="#16232b">gained</text><rect x="125.06" y="361.5" width="10" height="10" rx="2" fill="#0000ff" opacity="0.85"/><text x="140.1" y="370" font-size="10" fill="#16232b">lost</text><rect x="172.76" y="361.5" width="10" height="10" rx="2" fill="#5b6b76" opacity="0.3"/><text x="187.8" y="370" font-size="10" fill="#16232b">no segment covers this bin</text><text x="8" y="390" font-size="10" fill="#5b6b76">Share of each cohort's segmented SAMPLES (not patients: a patient with two segmented samples counts twice) gained (log2 segment mean ≥ 0.2) or lost (≤ −0.2) in each</text><text x="8" y="404" font-size="10" fill="#5b6b76">2 Mb bin, hg19. Each bin's denominator is the samples whose segmentation covers it; a bin takes the value of the segment over its midpoint, so an event narrower than 2</text><text x="8" y="418" font-size="10" fill="#5b6b76">Mb can be missed — use a smaller binMb to find it. 12,296 of 12,302 samples have segments. Thresholds on a log ratio are not purity- or ploidy-corrected: a low-purity</text><text x="8" y="432" font-size="10" fill="#5b6b76">tumour's real loss can sit inside ±0.2. Chromosome X is not drawn (includeX: true adds it). Per-bin two-sided Fisher tests, Benjamini–Hochberg across bins separately for</text><text x="8" y="446" font-size="10" fill="#5b6b76">gains and losses; adjacent bins are not independent, so read a run of significant bins as one region, not many findings.</text></svg>
diff --git a/test/snapshots/genome.txt b/test/snapshots/genome.txt
new file mode 100644
index 00000000..70a4cba8
--- /dev/null
+++ b/test/snapshots/genome.txt
@@ -0,0 +1,20 @@
+Analysis over msk_impact_50k_2026 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Colorectal: 4,993 patients — CANCER_TYPE in [Colorectal Cancer] (any sample)
+- Breast: 5,803 patients — CANCER_TYPE in [Breast Cancer] (any sample)
+
+Genome-wide copy number (hg19, 2 Mb bins, |log2| ≥ 0.2; peak % gained/lost per chromosome):
+  Colorectal (5,390 segmented samples): chr1 +13/−29, chr2 +4/−5, chr3 +6/−10, chr4 +2/−27, chr5 +11/−17, chr6 +11/−15, chr7 +31/−2, chr8 +35/−40, chr9 +7/−10, chr10 +4/−14, chr11 +5/−10, chr12 +11/−7, chr13 +46/−2, chr14 +4/−21, chr15 +5/−24, chr16 +15/−7, chr17 +9/−39, chr18 +3/−55, chr19 +9/−13, chr20 +61/−10, chr21 +3/−16, chr22 +4/−23
+  Breast (6,906 segmented samples): chr1 +51/−30, chr2 +7/−16, chr3 +16/−25, chr4 +5/−24, chr5 +17/−18, chr6 +17/−32, chr7 +16/−11, chr8 +49/−43, chr9 +7/−28, chr10 +17/−21, chr11 +27/−41, chr12 +14/−15, chr13 +10/−37, chr14 +11/−22, chr15 +13/−27, chr16 +31/−44, chr17 +25/−45, chr18 +7/−25, chr19 +11/−33, chr20 +32/−10, chr21 +14/−12, chr22 +7/−31
+  bins differing at q<0.05: 1255 for gains, 1217 for losses, of 1452
+  CAVEAT (tell the user): Share of each cohort's segmented SAMPLES (not patients: a patient with two segmented samples counts twice) gained (log2 segment mean ≥ 0.2) or lost (≤ −0.2) in each 2 Mb bin, hg19. Each bin's denominator is the samples whose segmentation covers it; a bin takes the value of the segment over its midpoint, so an event narrower than 2 Mb can be missed — use a smaller binMb to find it. 12,296 of 12,302 samples have segments. Thresholds on a log ratio are not purity- or ploidy-corrected: a low-purity tumour's real loss can sit inside ±0.2. Chromosome X is not drawn (includeX: true adds it). Per-bin two-sided Fisher tests, Benjamini–Hochberg across bins separately for gains and losses; adjacent bins are not independent, so read a run of significant bins as one region, not many findings.
+  METHOD:
+    - Count gained and lost samples per bin [Colorectal · msk_impact_50k_2026]: The genome:coverage and genome:bins queries above. Per 2 Mb bin on hg19: the cohort's SAMPLES (a patient with two segmented samples counts twice) whose segmentation covers the bin's midpoint, and of those the ones gained (log2 segment mean ≥ 0.2) or lost (≤ −0.2) there. Counts only.
+    - Count gained and lost samples per bin [Breast · msk_impact_50k_2026]: The genome:coverage and genome:bins queries above. Per 2 Mb bin on hg19: the cohort's SAMPLES (a patient with two segmented samples counts twice) whose segmentation covers the bin's midpoint, and of those the ones gained (log2 segment mean ≥ 0.2) or lost (≤ −0.2) there. Counts only.
+    - Place the bins on one axis: Chromosomes 1–22 at hg19 lengths, 1,452 bins; counts add across studies (all on one build).
+        Colorectal: 5,390 of 5,395 samples segmented
+        Breast: 6,906 of 6,907 samples segmented
+    - Fisher's exact test per bin: Two-sided, on gained vs not (and separately lost vs not) among each cohort's covered samples, in every bin both cohorts cover.
+    - Benjamini–Hochberg across bins: Separately for gains and losses. Adjacent bins are not independent: a run of significant bins is one region, not many findings.
+    Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/hotspots.json b/test/snapshots/hotspots.json
new file mode 100644
index 00000000..209c220f
--- /dev/null
+++ b/test/snapshots/hotspots.json
@@ -0,0 +1,2279 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "CRC": {
+      "count": 5543,
+      "text": "CANCER_TYPE in [Colorectal Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "hotspots",
+      "cohorts": [
+        "CRC"
+      ],
+      "hotspots": [
+        {
+          "gene": "KRAS",
+          "mutated": 2362,
+          "mutations": 2424,
+          "positions": [
+            {
+              "pos": 10,
+              "n": 5,
+              "color": "#993404",
+              "impact": "inframe",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "G10dup",
+                  "n": 5,
+                  "rows": 5,
+                  "driver": true,
+                  "impact": "inframe"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "In_Frame_Ins",
+                  "driver": true,
+                  "rows": 5
+                }
+              ]
+            },
+            {
+              "pos": 12,
+              "n": 1583,
+              "color": "#008000",
+              "impact": "missense",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "G12D",
+                  "n": 688,
+                  "rows": 689,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "G12V",
+                  "n": 487,
+                  "rows": 488,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "G12C",
+                  "n": 162,
+                  "rows": 162,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "G12S",
+                  "n": 109,
+                  "rows": 109,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "G12A",
+                  "n": 108,
+                  "rows": 108,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "G12R",
+                  "n": 26,
+                  "rows": 26,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "G12F",
+                  "n": 3,
+                  "rows": 3,
+                  "driver": true,
+                  "impact": "missense"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "Missense_Mutation",
+                  "driver": true,
+                  "rows": 1585
+                }
+              ]
+            },
+            {
+              "pos": 13,
+              "n": 446,
+              "color": "#008000",
+              "impact": "missense",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "G13D",
+                  "n": 426,
+                  "rows": 426,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "G13C",
+                  "n": 20,
+                  "rows": 20,
+                  "driver": true,
+                  "impact": "missense"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "Missense_Mutation",
+                  "driver": true,
+                  "rows": 446
+                }
+              ]
+            },
+            {
+              "pos": 14,
+              "n": 10,
+              "color": "#008000",
+              "impact": "missense",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "V14I",
+                  "n": 10,
+                  "rows": 10,
+                  "driver": true,
+                  "impact": "missense"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "Missense_Mutation",
+                  "driver": true,
+                  "rows": 10
+                }
+              ]
+            },
+            {
+              "pos": 33,
+              "n": 6,
+              "color": "#008000",
+              "impact": "missense",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "D33E",
+                  "n": 6,
+                  "rows": 6,
+                  "driver": true,
+                  "impact": "missense"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "Missense_Mutation",
+                  "driver": true,
+                  "rows": 6
+                }
+              ]
+            },
+            {
+              "pos": 57,
+              "n": 3,
+              "color": "#53D400",
+              "impact": "missense",
+              "driver": false,
+              "changes": [
+                {
+                  "change": "D57N",
+                  "n": 3,
+                  "rows": 3,
+                  "driver": false,
+                  "impact": "missense"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "Missense_Mutation",
+                  "driver": false,
+                  "rows": 3
+                }
+              ]
+            },
+            {
+              "pos": 59,
+              "n": 13,
+              "color": "#008000",
+              "impact": "missense",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "A59T",
+                  "n": 13,
+                  "rows": 13,
+                  "driver": true,
+                  "impact": "missense"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "Missense_Mutation",
+                  "driver": true,
+                  "rows": 13
+                }
+              ]
+            },
+            {
+              "pos": 60,
+              "n": 18,
+              "color": "#008000",
+              "impact": "missense",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "Q61K",
+                  "n": 18,
+                  "rows": 18,
+                  "driver": true,
+                  "impact": "missense"
+                }
+              ],
+              "types": [
+                {
+                  "mtype": "Missense_Mutation",
+                  "driver": true,
+                  "rows": 18
+                }
+              ]
+            },
+            {
+              "pos": 61,
+              "n": 82,
+              "color": "#008000",
+              "impact": "missense",
+              "driver": true,
+              "changes": [
+                {
+                  "change": "Q61H",
+                  "n": 55,
+                  "rows": 55,
+                  "driver": true,
+                  "impact": "missense"
+                },
+                {
+                  "change": "Q61R",
+                  "n": 15,
+                  "rows": 15,
+                  "driver": true,
+                  "impact": "missense"
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+          "label": {
+            "pos": 175,
+            "text": "R175H/C/L"
+          },
+          "somaticSamples": 4088,
+          "cohortSamples": 5584,
+          "tally": {
+            "missense": {
+              "driver": 2628,
+              "vus": 26
+            },
+            "truncating": {
+              "driver": 796,
+              "vus": 0
+            },
+            "inframe": {
+              "driver": 30,
+              "vus": 0
+            },
+            "splice": {
+              "driver": 244,
+              "vus": 0
+            },
+            "fusion": {
+              "driver": 0,
+              "vus": 0
+            },
+            "other": {
+              "driver": 0,
+              "vus": 0
+            }
+          },
+          "ids": {
+            "refseq": "NM_000546",
+            "ccds": "CCDS11118",
+            "uniprotAccession": "P04637",
+            "uniprotName": "P53_HUMAN",
+            "ensembl": "ENST00000269305.4"
+          }
+        }
+      ],
+      "studies": [
+        "msk_chord_2024"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "Mutations in “CRC” by protein position, as cBioPortal's mutation tab draws them: one stem per codon (the stored protein start position), its height the number of distinct patient + protein change pairs there. Colour is the codon's dominant mutation type, putative drivers before VUS (driver = OncoKB oncogenic, pinned; the portal also counts hotspots and custom annotations). VUS and germline mutations are included, as they are by default on the portal. A suppression floor of 3 patients per change was applied BY EACH SITE before returning anything, withholding 1,112 mutations."
+    }
+  ]
+}
diff --git a/test/snapshots/hotspots.sql b/test/snapshots/hotspots.sql
new file mode 100644
index 00000000..299c20ad
--- /dev/null
+++ b/test/snapshots/hotspots.sql
@@ -0,0 +1,62 @@
+-- cohort CRC
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+
+-- view 0 hotspots: lollipop:changes · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+     m AS (SELECT `GENE.hugoGeneSymbol` AS gene, proteinPosStart AS pos, proteinChange AS change, mutationType AS mtype,
+                  patientId AS pid, sampleId AS sid, mutationStatus AS status, if((`GENE.hugoGeneSymbol`, proteinChange) IN (SELECT hugo_gene_symbol, alteration FROM oncokb_annotation.variant_annotation
+        WHERE oncokb_version = 'v7.5' AND is_driver AND alteration_type = 'MUTATION'), 1, 0) AS driver
+           FROM snapshot_db.mutation_derived
+           WHERE studyId = 'msk_chord_2024' AND `GENE.hugoGeneSymbol` IN ('KRAS','BRAF','PIK3CA','TP53')
+             AND molecularProfileId NOT LIKE '%uncalled%' AND proteinChange != ''
+             AND patientId IN (SELECT pid FROM cohort)),
+     kept AS (SELECT gene, pos, change FROM m GROUP BY gene, pos, change HAVING uniqExact(pid) >= 3)
+SELECT gene, pos, change, uniqExact(pid) AS n, count() AS rows, max(driver) AS driver, topK(1)(multiIf(lowerUTF8(mtype) IN ('missense_mutation', 'missense', 'missense_variant'), 'missense', lowerUTF8(mtype) IN ('frame_shift_ins', 'frame_shift_del', 'frameshift', 'frameshift_deletion', 'frameshift_insertion', 'de_novo_start_outofframe', 'frameshift_variant', 'nonsense_mutation', 'nonsense', 'stopgain_snv', 'stop_gained', 'translation_start_site', 'initiator_codon_variant', 'start_codon_snp', 'start_codon_del', 'nonstop_mutation', 'stop_lost', 'truncating', 'feature_truncation'), 'truncating', lowerUTF8(mtype) IN ('inframe_del', 'inframe_deletion', 'in_frame_del', 'in_frame_deletion', 'inframe_ins', 'inframe_insertion', 'in_frame_ins', 'in_frame_insertion', 'indel', 'nonframeshift_deletion', 'nonframeshift', 'nonframeshift insertion', 'nonframeshift_insertion', 'inframe'), 'inframe', lowerUTF8(mtype) IN ('splice_site', 'splice', 'splice site', 'splicing', 'splice_site_snp', 'splice_site_del', 'splice_site_indel', 'splice_region_variant', 'splice_region'), 'splice', lowerUTF8(mtype) IN ('fusion'), 'fusion', 'other'))[1] AS impact
+FROM m GROUP BY gene, pos, change HAVING n >= 3
+
+-- view 0 hotspots: lollipop:samples · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer')))))
+SELECT uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+
+-- view 0 hotspots: lollipop:totals · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+     m AS (SELECT `GENE.hugoGeneSymbol` AS gene, proteinPosStart AS pos, proteinChange AS change, mutationType AS mtype,
+                  patientId AS pid, sampleId AS sid, mutationStatus AS status, 1 AS driver
+           FROM snapshot_db.mutation_derived
+           WHERE studyId = 'msk_chord_2024' AND `GENE.hugoGeneSymbol` IN ('KRAS','BRAF','PIK3CA','TP53')
+             AND molecularProfileId NOT LIKE '%uncalled%' AND proteinChange != ''
+             AND patientId IN (SELECT pid FROM cohort)),
+     kept AS (SELECT gene, pos, change FROM m GROUP BY gene, pos, change HAVING uniqExact(pid) >= 1)
+SELECT gene, uniqExact(pid) AS patients, uniqExact((pid, change)) AS pairs,
+       uniqExactIf(sid, status != 'GERMLINE') AS somatic_samples
+FROM m GROUP BY gene
+
+-- view 0 hotspots: lollipop:types · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+     m AS (SELECT `GENE.hugoGeneSymbol` AS gene, proteinPosStart AS pos, proteinChange AS change, mutationType AS mtype,
+                  patientId AS pid, sampleId AS sid, mutationStatus AS status, if((`GENE.hugoGeneSymbol`, proteinChange) IN (SELECT hugo_gene_symbol, alteration FROM oncokb_annotation.variant_annotation
+        WHERE oncokb_version = 'v7.5' AND is_driver AND alteration_type = 'MUTATION'), 1, 0) AS driver
+           FROM snapshot_db.mutation_derived
+           WHERE studyId = 'msk_chord_2024' AND `GENE.hugoGeneSymbol` IN ('KRAS','BRAF','PIK3CA','TP53')
+             AND molecularProfileId NOT LIKE '%uncalled%' AND proteinChange != ''
+             AND patientId IN (SELECT pid FROM cohort)),
+     kept AS (SELECT gene, pos, change FROM m GROUP BY gene, pos, change HAVING uniqExact(pid) >= 3)
+SELECT gene, pos, mtype, driver, count() AS rows
+FROM m WHERE (gene, ifNull(pos, -1), change) IN (SELECT gene, ifNull(pos, -1), change FROM kept) GROUP BY gene, pos, mtype, driver
diff --git a/test/snapshots/hotspots.svg b/test/snapshots/hotspots.svg
new file mode 100644
index 00000000..8cd340e8
--- /dev/null
+++ b/test/snapshots/hotspots.svg
@@ -0,0 +1,355 @@
+<!-- view 0 hotspots -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 1204" width="100%" role="img" aria-label="Mutations by protein position — KRAS, BRAF, PIK3CA, TP53" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Mutations by protein position — KRAS, BRAF, PIK3CA, TP53</title><rect width="720" height="1204" fill="#ffffff"/><text x="8" y="19" font-size="12" fill="#16232b" font-weight="600">KRAS</text><text x="50.1" y="19" font-size="10" fill="#5b6b76">2,424 mutations · 2,362 mutated patients</text><line x1="56" y1="192" x2="704" y2="192" stroke="#dde4e8" stroke-width="0.75"/><text x="50" y="195.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="56" y1="154.5" x2="704" y2="154.5" stroke="#dde4e8" stroke-width="0.75"/><text x="50" y="158" font-size="10" fill="#5b6b76" text-anchor="end">500</text><line x1="56" y1="117" x2="704" y2="117" stroke="#dde4e8" stroke-width="0.75"/><text x="50" y="120.5" font-size="10" fill="#5b6b76" text-anchor="end">1,000</text><line x1="56" y1="79.5" x2="704" y2="79.5" stroke="#dde4e8" stroke-width="0.75"/><text x="50" y="83" font-size="10" fill="#5b6b76" text-anchor="end">1,500</text><line x1="56" y1="42" x2="704" y2="42" stroke="#dde4e8" stroke-width="0.75"/><text x="50" y="45.5" font-size="10" fill="#5b6b76" text-anchor="end">2,000</text><line x1="56" y1="192" x2="56" y2="42" stroke="#5b6b76" stroke-width="1"/><text x="10.8" y="117" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 10.8 117)"># KRAS Mutations</text><rect x="62" y="197" width="642" height="14" fill="#BABDB6"/><g><title>Ras (PF00071): 5–164</title><rect x="79.1" y="192" width="543" height="24" fill="#2dcf00"/><text x="350.6" y="207.5" font-size="10" fill="#16232b" text-anchor="middle">Ras</text></g><line x1="62" y1="220" x2="62" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="59" y="236" font-size="10" fill="#5b6b76">0</text><line x1="130.3" y1="220" x2="130.3" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="124.3" y="236" font-size="10" fill="#5b6b76">20</text><line x1="198.6" y1="220" x2="198.6" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="192.6" y="236" font-size="10" fill="#5b6b76">40</text><line x1="266.9" y1="220" x2="266.9" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="260.9" y="236" font-size="10" fill="#5b6b76">60</text><line x1="335.2" y1="220" x2="335.2" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="329.2" y="236" font-size="10" fill="#5b6b76">80</text><line x1="403.5" y1="220" x2="403.5" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="394.5" y="236" font-size="10" fill="#5b6b76">100</text><line x1="471.8" y1="220" x2="471.8" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="462.8" y="236" font-size="10" fill="#5b6b76">120</text><line x1="540.1" y1="220" x2="540.1" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="531.1" y="236" font-size="10" fill="#5b6b76">140</text><line x1="608.4" y1="220" x2="608.4" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="599.4" y="236" font-size="10" fill="#5b6b76">160</text><line x1="704" y1="220" x2="704" y2="223" stroke="#5b6b76" stroke-width="1"/><text x="689.3" y="236" font-size="10" fill="#5b6b76">188aa</text><line x1="62" y1="220" x2="704" y2="220" stroke="#5b6b76" stroke-width="1"/><g><title>codon 10: 5 mutations
+G10dup 5</title><line x1="96.1" y1="192" x2="96.1" y2="191.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="96.1" cy="191.6" r="2.8" fill="#993404"/></g><g><title>codon 12: 1,583 mutations
+G12D 688
+G12V 487
+G12C 162
+G12S 109
+G12A 108
+G12R 26
+G12F 3</title><line x1="103" y1="192" x2="103" y2="73.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="103" cy="73.3" r="2.8" fill="#008000"/></g><g><title>codon 13: 446 mutations
+G13D 426
+G13C 20</title><line x1="106.4" y1="192" x2="106.4" y2="158.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="106.4" cy="158.6" r="2.8" fill="#008000"/></g><g><title>codon 14: 10 mutations
+V14I 10</title><line x1="109.8" y1="192" x2="109.8" y2="191.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="109.8" cy="191.3" r="2.8" fill="#008000"/></g><g><title>codon 33: 6 mutations
+D33E 6</title><line x1="174.7" y1="192" x2="174.7" y2="191.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="174.7" cy="191.6" r="2.8" fill="#008000"/></g><g><title>codon 57: 3 mutations
+D57N 3</title><line x1="256.6" y1="192" x2="256.6" y2="191.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="256.6" cy="191.8" r="2.8" fill="#53D400"/></g><g><title>codon 59: 13 mutations
+A59T 13</title><line x1="263.5" y1="192" x2="263.5" y2="191" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="263.5" cy="191" r="2.8" fill="#008000"/></g><g><title>codon 60: 18 mutations
+Q61K 18</title><line x1="266.9" y1="192" x2="266.9" y2="190.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="266.9" cy="190.6" r="2.8" fill="#008000"/></g><g><title>codon 61: 82 mutations
+Q61H 55
+Q61R 15
+Q61L 8
+Q61K 4</title><line x1="270.3" y1="192" x2="270.3" y2="185.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="270.3" cy="185.9" r="2.8" fill="#008000"/></g><g><title>codon 68: 3 mutations
+R68S 3</title><line x1="294.2" y1="192" x2="294.2" y2="191.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="294.2" cy="191.8" r="2.8" fill="#008000"/></g><g><title>codon 117: 26 mutations
+K117N 26</title><line x1="461.5" y1="192" x2="461.5" y2="190" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="461.5" cy="190" r="2.8" fill="#008000"/></g><g><title>codon 146: 176 mutations
+A146T 140
+A146V 27
+A146P 9</title><line x1="560.6" y1="192" x2="560.6" y2="178.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="560.6" cy="178.8" r="2.8" fill="#008000"/></g><text x="111.5" y="66.3" font-size="10" fill="#16232b" text-anchor="middle">G12D/V/C and 4 more</text><circle cx="67" cy="251.5" r="4" fill="#008000"/><text x="77" y="255" font-size="10" fill="#16232b">Missense</text><circle cx="139.89" cy="251.5" r="4" fill="#53D400"/><text x="149.9" y="255" font-size="10" fill="#16232b">Missense (VUS)</text><circle cx="240.51999999999998" cy="251.5" r="4" fill="#993404"/><text x="250.5" y="255" font-size="10" fill="#16232b">Inframe</text><text x="8" y="288" font-size="12" fill="#16232b" font-weight="600">BRAF</text><text x="49.3" y="288" font-size="10" fill="#5b6b76">658 mutations · 622 mutated patients</text><line x1="47" y1="461" x2="704" y2="461" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="464.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="47" y1="431" x2="704" y2="431" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="434.5" font-size="10" fill="#5b6b76" text-anchor="end">100</text><line x1="47" y1="401" x2="704" y2="401" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="404.5" font-size="10" fill="#5b6b76" text-anchor="end">200</text><line x1="47" y1="371" x2="704" y2="371" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="374.5" font-size="10" fill="#5b6b76" text-anchor="end">300</text><line x1="47" y1="341" x2="704" y2="341" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="344.5" font-size="10" fill="#5b6b76" text-anchor="end">400</text><line x1="47" y1="311" x2="704" y2="311" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="314.5" font-size="10" fill="#5b6b76" text-anchor="end">500</text><line x1="47" y1="461" x2="47" y2="311" stroke="#5b6b76" stroke-width="1"/><text x="10.8" y="386" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 10.8 386)"># BRAF Mutations</text><rect x="53" y="466" width="651" height="14" fill="#BABDB6"/><g><title>RBD (PF02196): 156–227</title><rect x="185.6" y="461" width="60.3" height="24" fill="#2dcf00"/><text x="215.8" y="476.5" font-size="10" fill="#16232b" text-anchor="middle">RBD</text></g><g><title>C1_1 (PF00130): 235–280</title><rect x="252.7" y="461" width="38.2" height="24" fill="#ff5353"/><text x="271.8" y="476.5" font-size="10" fill="#16232b" text-anchor="middle">C1_1</text></g><g><title>Pkinase_Tyr (PF07714): 458–712</title><rect x="442.2" y="461" width="215.9" height="24" fill="#5b5bff"/><text x="550.2" y="476.5" font-size="10" fill="#ffffff" text-anchor="middle">Pkinase_Tyr</text></g><line x1="53" y1="489" x2="53" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="50" y="505" font-size="10" fill="#5b6b76">0</text><line x1="138" y1="489" x2="138" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="129" y="505" font-size="10" fill="#5b6b76">100</text><line x1="223" y1="489" x2="223" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="214" y="505" font-size="10" fill="#5b6b76">200</text><line x1="308" y1="489" x2="308" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="299" y="505" font-size="10" fill="#5b6b76">300</text><line x1="392.9" y1="489" x2="392.9" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="383.9" y="505" font-size="10" fill="#5b6b76">400</text><line x1="477.9" y1="489" x2="477.9" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="468.9" y="505" font-size="10" fill="#5b6b76">500</text><line x1="562.9" y1="489" x2="562.9" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="553.9" y="505" font-size="10" fill="#5b6b76">600</text><line x1="647.9" y1="489" x2="647.9" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="638.9" y="505" font-size="10" fill="#5b6b76">700</text><line x1="704" y1="489" x2="704" y2="492" stroke="#5b6b76" stroke-width="1"/><text x="689.3" y="505" font-size="10" fill="#5b6b76">766aa</text><line x1="53" y1="489" x2="704" y2="489" stroke="#5b6b76" stroke-width="1"/><g><title>codon 241: 4 mutations
+T241M 4</title><line x1="257.8" y1="461" x2="257.8" y2="459.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="257.8" cy="459.8" r="2.8" fill="#008000"/></g><g><title>codon 362: 5 mutations
+R362Q 5</title><line x1="360.7" y1="461" x2="360.7" y2="459.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="360.7" cy="459.5" r="2.8" fill="#53D400"/></g><g><title>codon 403: 19 mutations
+A404Cfs*9 10
+P403Lfs*8 9</title><line x1="395.5" y1="461" x2="395.5" y2="455.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="395.5" cy="455.3" r="2.8" fill="#708090"/></g><g><title>codon 466: 6 mutations
+G466E 3
+G466V 3</title><line x1="449" y1="461" x2="449" y2="459.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="449" cy="459.2" r="2.8" fill="#008000"/></g><g><title>codon 469: 14 mutations
+G469A 10
+G469R 4</title><line x1="451.6" y1="461" x2="451.6" y2="456.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="451.6" cy="456.8" r="2.8" fill="#008000"/></g><g><title>codon 581: 4 mutations
+N581S 4</title><line x1="546.8" y1="461" x2="546.8" y2="459.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="546.8" cy="459.8" r="2.8" fill="#008000"/></g><g><title>codon 594: 46 mutations
+D594G 38
+D594N 8</title><line x1="557.8" y1="461" x2="557.8" y2="447.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="557.8" cy="447.2" r="2.8" fill="#008000"/></g><g><title>codon 595: 4 mutations
+F595L 4</title><line x1="558.7" y1="461" x2="558.7" y2="459.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="558.7" cy="459.8" r="2.8" fill="#008000"/></g><g><title>codon 600: 438 mutations
+V600E 438</title><line x1="562.9" y1="461" x2="562.9" y2="329.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="562.9" cy="329.6" r="2.8" fill="#008000"/></g><g><title>codon 601: 4 mutations
+K601E 4</title><line x1="563.8" y1="461" x2="563.8" y2="459.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="563.8" cy="459.8" r="2.8" fill="#008000"/></g><g><title>codon 762: 3 mutations
+A762V 3</title><line x1="700.6" y1="461" x2="700.6" y2="460.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="700.6" cy="460.1" r="2.8" fill="#53D400"/></g><text x="562.9" y="322.6" font-size="10" fill="#16232b" text-anchor="middle">V600E</text><circle cx="58" cy="520.5" r="4" fill="#008000"/><text x="68" y="524" font-size="10" fill="#16232b">Missense</text><circle cx="130.89" cy="520.5" r="4" fill="#53D400"/><text x="140.9" y="524" font-size="10" fill="#16232b">Missense (VUS)</text><circle cx="231.51999999999998" cy="520.5" r="4" fill="#708090"/><text x="241.5" y="524" font-size="10" fill="#16232b">Truncating (VUS)</text><text x="8" y="557" font-size="12" fill="#16232b" font-weight="600">PIK3CA</text><text x="62.6" y="557" font-size="10" fill="#5b6b76">1,330 mutations · 1,147 mutated patients</text><line x1="47" y1="730" x2="704" y2="730" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="733.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="47" y1="680" x2="704" y2="680" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="683.5" font-size="10" fill="#5b6b76" text-anchor="end">100</text><line x1="47" y1="630" x2="704" y2="630" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="633.5" font-size="10" fill="#5b6b76" text-anchor="end">200</text><line x1="47" y1="580" x2="704" y2="580" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="583.5" font-size="10" fill="#5b6b76" text-anchor="end">300</text><line x1="47" y1="730" x2="47" y2="580" stroke="#5b6b76" stroke-width="1"/><text x="10.8" y="655" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 10.8 655)"># PIK3CA Mutations</text><rect x="53" y="735" width="651" height="14" fill="#BABDB6"/><g><title>PI3K_p85B (PF02192): 32–108</title><rect x="72.5" y="730" width="46.3" height="24" fill="#2dcf00"/><text x="95.7" y="745.5" font-size="10" fill="#16232b" text-anchor="middle"><title>PI3K_p85B</title>PI3K_…</text></g><g><title>PI3K_rbd (PF00794): 174–291</title><rect x="159.1" y="730" width="71.3" height="24" fill="#ff5353"/><text x="194.7" y="745.5" font-size="10" fill="#16232b" text-anchor="middle">PI3K_rbd</text></g><g><title>PI3K_C2 (PF00792): 351–483</title><rect x="267" y="730" width="80.5" height="24" fill="#5b5bff"/><text x="307.2" y="745.5" font-size="10" fill="#ffffff" text-anchor="middle">PI3K_C2</text></g><g><title>PI3Ka (PF00613): 520–703</title><rect x="370" y="730" width="111.5" height="24" fill="#ebd61d"/><text x="425.7" y="745.5" font-size="10" fill="#16232b" text-anchor="middle">PI3Ka</text></g><g><title>PI3_PI4_kinase (PF00454): 798–1014</title><rect x="539.4" y="730" width="131.7" height="24" fill="#ba21e0"/><text x="605.3" y="745.5" font-size="10" fill="#ffffff" text-anchor="middle">PI3_PI4_kinase</text></g><line x1="53" y1="758" x2="53" y2="761" stroke="#5b6b76" stroke-width="1"/><text x="50" y="774" font-size="10" fill="#5b6b76">0</text><line x1="174.9" y1="758" x2="174.9" y2="761" stroke="#5b6b76" stroke-width="1"/><text x="165.9" y="774" font-size="10" fill="#5b6b76">200</text><line x1="296.8" y1="758" x2="296.8" y2="761" stroke="#5b6b76" stroke-width="1"/><text x="287.8" y="774" font-size="10" fill="#5b6b76">400</text><line x1="418.7" y1="758" x2="418.7" y2="761" stroke="#5b6b76" stroke-width="1"/><text x="409.7" y="774" font-size="10" fill="#5b6b76">600</text><line x1="540.6" y1="758" x2="540.6" y2="761" stroke="#5b6b76" stroke-width="1"/><text x="531.6" y="774" font-size="10" fill="#5b6b76">800</text><line x1="704" y1="758" x2="704" y2="761" stroke="#5b6b76" stroke-width="1"/><text x="680.6" y="774" font-size="10" fill="#5b6b76">1,068aa</text><line x1="53" y1="758" x2="704" y2="758" stroke="#5b6b76" stroke-width="1"/><g><title>codon 38: 23 mutations
+R38H 10
+R38C 8
+R38S 5</title><line x1="76.2" y1="730" x2="76.2" y2="718.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="76.2" cy="718.5" r="2.8" fill="#008000"/></g><g><title>codon 81: 9 mutations
+E81K 9</title><line x1="102.4" y1="730" x2="102.4" y2="725.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="102.4" cy="725.5" r="2.8" fill="#53D400"/></g><g><title>codon 83: 3 mutations
+F83K 3</title><line x1="103.6" y1="730" x2="103.6" y2="728.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="103.6" cy="728.5" r="2.8" fill="#53D400"/></g><g><title>codon 88: 47 mutations
+R88Q 47</title><line x1="106.6" y1="730" x2="106.6" y2="706.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="106.6" cy="706.5" r="2.8" fill="#008000"/></g><g><title>codon 93: 23 mutations
+R93W 16
+R93Q 7</title><line x1="109.7" y1="730" x2="109.7" y2="718.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="109.7" cy="718.5" r="2.8" fill="#008000"/></g><g><title>codon 106: 11 mutations
+G106V 11</title><line x1="117.6" y1="730" x2="117.6" y2="724.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="117.6" cy="724.5" r="2.8" fill="#008000"/></g><g><title>codon 108: 27 mutations
+R108H 24
+R108C 3</title><line x1="118.8" y1="730" x2="118.8" y2="716.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="118.8" cy="716.5" r="2.8" fill="#008000"/></g><g><title>codon 109: 14 mutations
+E110del 14</title><line x1="119.4" y1="730" x2="119.4" y2="723" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="119.4" cy="723" r="2.8" fill="#993404"/></g><g><title>codon 111: 17 mutations
+K111E 10
+K111N 7</title><line x1="120.7" y1="730" x2="120.7" y2="721.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="120.7" cy="721.5" r="2.8" fill="#008000"/></g><g><title>codon 118: 10 mutations
+G118D 10</title><line x1="124.9" y1="730" x2="124.9" y2="725" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="124.9" cy="725" r="2.8" fill="#008000"/></g><g><title>codon 227: 5 mutations
+T229Lfs*11 5</title><line x1="191.4" y1="730" x2="191.4" y2="727.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="191.4" cy="727.5" r="2.8" fill="#708090"/></g><g><title>codon 344: 15 mutations
+V344G 11
+V344M 4</title><line x1="262.7" y1="730" x2="262.7" y2="722.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="262.7" cy="722.5" r="2.8" fill="#008000"/></g><g><title>codon 345: 23 mutations
+N345K 20
+N345I 3</title><line x1="263.3" y1="730" x2="263.3" y2="718.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="263.3" cy="718.5" r="2.8" fill="#008000"/></g><g><title>codon 350: 5 mutations
+D350G 5</title><line x1="266.3" y1="730" x2="266.3" y2="727.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="266.3" cy="727.5" r="2.8" fill="#008000"/></g><g><title>codon 357: 5 mutations
+R357Q 5</title><line x1="270.6" y1="730" x2="270.6" y2="727.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="270.6" cy="727.5" r="2.8" fill="#008000"/></g><g><title>codon 364: 3 mutations
+G364R 3</title><line x1="274.9" y1="730" x2="274.9" y2="728.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="274.9" cy="728.5" r="2.8" fill="#008000"/></g><g><title>codon 365: 4 mutations
+E365K 4</title><line x1="275.5" y1="730" x2="275.5" y2="728" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="275.5" cy="728" r="2.8" fill="#008000"/></g><g><title>codon 378: 5 mutations
+C378R 5</title><line x1="283.4" y1="730" x2="283.4" y2="727.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="283.4" cy="727.5" r="2.8" fill="#008000"/></g><g><title>codon 418: 3 mutations
+E418K 3</title><line x1="307.8" y1="730" x2="307.8" y2="728.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="307.8" cy="728.5" r="2.8" fill="#008000"/></g><g><title>codon 420: 39 mutations
+C420R 39</title><line x1="309" y1="730" x2="309" y2="710.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="309" cy="710.5" r="2.8" fill="#008000"/></g><g><title>codon 453: 10 mutations
+E453K 10</title><line x1="329.1" y1="730" x2="329.1" y2="725" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="329.1" cy="725" r="2.8" fill="#008000"/></g><g><title>codon 542: 163 mutations
+E542K 159
+E542Q 4</title><line x1="383.4" y1="730" x2="383.4" y2="648.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="383.4" cy="648.5" r="2.8" fill="#008000"/></g><g><title>codon 545: 276 mutations
+E545K 253
+E545G 19
+E545D 4</title><line x1="385.2" y1="730" x2="385.2" y2="592" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="385.2" cy="592" r="2.8" fill="#008000"/></g><g><title>codon 546: 60 mutations
+Q546K 41
+Q546R 10
+Q546P 5
+Q546H 4</title><line x1="385.8" y1="730" x2="385.8" y2="700" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="385.8" cy="700" r="2.8" fill="#008000"/></g><g><title>codon 726: 12 mutations
+E726K 12</title><line x1="495.5" y1="730" x2="495.5" y2="724" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="495.5" cy="724" r="2.8" fill="#53D400"/></g><g><title>codon 808: 4 mutations
+R808W 4</title><line x1="545.5" y1="730" x2="545.5" y2="728" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="545.5" cy="728" r="2.8" fill="#53D400"/></g><g><title>codon 901: 4 mutations
+C901F 4</title><line x1="602.2" y1="730" x2="602.2" y2="728" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="602.2" cy="728" r="2.8" fill="#008000"/></g><g><title>codon 1004: 3 mutations
+M1004I 3</title><line x1="665" y1="730" x2="665" y2="728.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="665" cy="728.5" r="2.8" fill="#008000"/></g><g><title>codon 1007: 4 mutations
+G1007R 4</title><line x1="666.8" y1="730" x2="666.8" y2="728" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="666.8" cy="728" r="2.8" fill="#008000"/></g><g><title>codon 1021: 10 mutations
+Y1021C 10</title><line x1="675.4" y1="730" x2="675.4" y2="725" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="675.4" cy="725" r="2.8" fill="#53D400"/></g><g><title>codon 1023: 3 mutations
+R1023* 3</title><line x1="676.6" y1="730" x2="676.6" y2="728.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="676.6" cy="728.5" r="2.8" fill="#708090"/></g><g><title>codon 1025: 9 mutations
+T1025A 9</title><line x1="677.8" y1="730" x2="677.8" y2="725.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="677.8" cy="725.5" r="2.8" fill="#008000"/></g><g><title>codon 1043: 12 mutations
+M1043I 9
+M1043V 3</title><line x1="688.8" y1="730" x2="688.8" y2="724" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="688.8" cy="724" r="2.8" fill="#008000"/></g><g><title>codon 1044: 3 mutations
+N1044K 3</title><line x1="689.4" y1="730" x2="689.4" y2="728.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="689.4" cy="728.5" r="2.8" fill="#008000"/></g><g><title>codon 1047: 226 mutations
+H1047R 189
+H1047L 24
+H1047Y 13</title><line x1="691.2" y1="730" x2="691.2" y2="617" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="691.2" cy="617" r="2.8" fill="#008000"/></g><g><title>codon 1049: 6 mutations
+G1049R 6</title><line x1="692.4" y1="730" x2="692.4" y2="727" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="692.4" cy="727" r="2.8" fill="#008000"/></g><g><title>codon 1065: 3 mutations
+H1065Y 3</title><line x1="702.2" y1="730" x2="702.2" y2="728.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="702.2" cy="728.5" r="2.8" fill="#008000"/></g><text x="385.2" y="585" font-size="10" fill="#16232b" text-anchor="middle">E545K/G/D</text><circle cx="58" cy="789.5" r="4" fill="#008000"/><text x="68" y="793" font-size="10" fill="#16232b">Missense</text><circle cx="130.89" cy="789.5" r="4" fill="#53D400"/><text x="140.9" y="793" font-size="10" fill="#16232b">Missense (VUS)</text><circle cx="231.51999999999998" cy="789.5" r="4" fill="#708090"/><text x="241.5" y="793" font-size="10" fill="#16232b">Truncating (VUS)</text><circle cx="338.34999999999997" cy="789.5" r="4" fill="#993404"/><text x="348.3" y="793" font-size="10" fill="#16232b">Inframe</text><text x="8" y="826" font-size="12" fill="#16232b" font-weight="600">TP53</text><text x="47.3" y="826" font-size="10" fill="#5b6b76">4,441 mutations · 4,080 mutated patients</text><line x1="47" y1="999" x2="704" y2="999" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="1002.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="47" y1="969" x2="704" y2="969" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="972.5" font-size="10" fill="#5b6b76" text-anchor="end">100</text><line x1="47" y1="939" x2="704" y2="939" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="942.5" font-size="10" fill="#5b6b76" text-anchor="end">200</text><line x1="47" y1="909" x2="704" y2="909" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="912.5" font-size="10" fill="#5b6b76" text-anchor="end">300</text><line x1="47" y1="879" x2="704" y2="879" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="882.5" font-size="10" fill="#5b6b76" text-anchor="end">400</text><line x1="47" y1="849" x2="704" y2="849" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="852.5" font-size="10" fill="#5b6b76" text-anchor="end">500</text><line x1="47" y1="999" x2="47" y2="849" stroke="#5b6b76" stroke-width="1"/><text x="10.8" y="924" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 10.8 924)"># TP53 Mutations</text><rect x="53" y="1004" width="651" height="14" fill="#BABDB6"/><g><title>P53_TAD (PF08563): 6–29</title><rect x="62.9" y="999" width="38.1" height="24" fill="#2dcf00"/><text x="82" y="1014.5" font-size="10" fill="#16232b" text-anchor="middle"><title>P53_TAD</title>P53_…</text></g><g><title>P53 (PF00870): 95–288</title><rect x="210.4" y="999" width="319.7" height="24" fill="#ff5353"/><text x="370.2" y="1014.5" font-size="10" fill="#16232b" text-anchor="middle">P53</text></g><g><title>P53_tetramer (PF07710): 318–358</title><rect x="579.8" y="999" width="66.3" height="24" fill="#5b5bff"/><text x="612.9" y="1014.5" font-size="10" fill="#ffffff" text-anchor="middle"><title>P53_tetramer</title>P53_tetra…</text></g><line x1="53" y1="1027" x2="53" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="50" y="1043" font-size="10" fill="#5b6b76">0</text><line x1="135.8" y1="1027" x2="135.8" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="129.8" y="1043" font-size="10" fill="#5b6b76">50</text><line x1="218.6" y1="1027" x2="218.6" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="209.6" y="1043" font-size="10" fill="#5b6b76">100</text><line x1="301.5" y1="1027" x2="301.5" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="292.5" y="1043" font-size="10" fill="#5b6b76">150</text><line x1="384.3" y1="1027" x2="384.3" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="375.3" y="1043" font-size="10" fill="#5b6b76">200</text><line x1="467.1" y1="1027" x2="467.1" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="458.1" y="1043" font-size="10" fill="#5b6b76">250</text><line x1="549.9" y1="1027" x2="549.9" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="540.9" y="1043" font-size="10" fill="#5b6b76">300</text><line x1="632.8" y1="1027" x2="632.8" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="623.8" y="1043" font-size="10" fill="#5b6b76">350</text><line x1="704" y1="1027" x2="704" y2="1030" stroke="#5b6b76" stroke-width="1"/><text x="689.3" y="1043" font-size="10" fill="#5b6b76">393aa</text><line x1="53" y1="1027" x2="704" y2="1027" stroke="#5b6b76" stroke-width="1"/><g><title>codon 10: 10 mutations
+X10_splice 10</title><line x1="69.6" y1="999" x2="69.6" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="69.6" cy="996" r="2.8" fill="#e5802b"/></g><g><title>codon 32: 4 mutations
+X32_splice 4</title><line x1="106" y1="999" x2="106" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="106" cy="997.8" r="2.8" fill="#e5802b"/></g><g><title>codon 33: 9 mutations
+X33_splice 9</title><line x1="107.7" y1="999" x2="107.7" y2="996.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="107.7" cy="996.3" r="2.8" fill="#e5802b"/></g><g><title>codon 34: 6 mutations
+P36Afs*7 6</title><line x1="109.3" y1="999" x2="109.3" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="109.3" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 38: 8 mutations
+Q38* 5
+Q38Kfs*6 3</title><line x1="115.9" y1="999" x2="115.9" y2="996.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="115.9" cy="996.6" r="2.8" fill="#000000"/></g><g><title>codon 51: 4 mutations
+E51* 4</title><line x1="137.5" y1="999" x2="137.5" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="137.5" cy="997.8" r="2.8" fill="#000000"/></g><g><title>codon 52: 4 mutations
+Q52* 4</title><line x1="139.1" y1="999" x2="139.1" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="139.1" cy="997.8" r="2.8" fill="#000000"/></g><g><title>codon 53: 12 mutations
+W53* 12</title><line x1="140.8" y1="999" x2="140.8" y2="995.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="140.8" cy="995.4" r="2.8" fill="#000000"/></g><g><title>codon 72: 27 mutations
+V73Rfs*76 14
+V73Wfs*50 13</title><line x1="172.3" y1="999" x2="172.3" y2="990.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="172.3" cy="990.9" r="2.8" fill="#000000"/></g><g><title>codon 75: 6 mutations
+A76Cfs*73 3
+P75Lfs*48 3</title><line x1="177.2" y1="999" x2="177.2" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="177.2" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 89: 16 mutations
+S90Pfs*33 10
+S90Lfs*59 6</title><line x1="200.4" y1="999" x2="200.4" y2="994.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="200.4" cy="994.2" r="2.8" fill="#000000"/></g><g><title>codon 91: 12 mutations
+W91* 12</title><line x1="203.7" y1="999" x2="203.7" y2="995.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="203.7" cy="995.4" r="2.8" fill="#000000"/></g><g><title>codon 104: 6 mutations
+Q104* 6</title><line x1="225.3" y1="999" x2="225.3" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="225.3" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 105: 9 mutations
+G105V 6
+G105C 3</title><line x1="226.9" y1="999" x2="226.9" y2="996.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="226.9" cy="996.3" r="2.8" fill="#008000"/></g><g><title>codon 109: 3 mutations
+F109S 3</title><line x1="233.6" y1="999" x2="233.6" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="233.6" cy="998.1" r="2.8" fill="#008000"/></g><g><title>codon 110: 11 mutations
+R110L 7
+R110P 4</title><line x1="235.2" y1="999" x2="235.2" y2="995.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="235.2" cy="995.7" r="2.8" fill="#008000"/></g><g><title>codon 111: 6 mutations
+L111P 3
+L111Q 3</title><line x1="236.9" y1="999" x2="236.9" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="236.9" cy="997.2" r="2.8" fill="#008000"/></g><g><title>codon 113: 3 mutations
+F113C 3</title><line x1="240.2" y1="999" x2="240.2" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="240.2" cy="998.1" r="2.8" fill="#008000"/></g><g><title>codon 114: 3 mutations
+H115Afs*34 3</title><line x1="241.8" y1="999" x2="241.8" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="241.8" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 122: 8 mutations
+V122Dfs*26 8</title><line x1="255.1" y1="999" x2="255.1" y2="996.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="255.1" cy="996.6" r="2.8" fill="#000000"/></g><g><title>codon 124: 3 mutations
+C124Wfs*25 3</title><line x1="258.4" y1="999" x2="258.4" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="258.4" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 125: 27 mutations
+X125_splice 16
+T125M 11</title><line x1="260.1" y1="999" x2="260.1" y2="990.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="260.1" cy="990.9" r="2.8" fill="#e5802b"/></g><g><title>codon 126: 49 mutations
+X126_splice 34
+Y126C 6
+Y126H 5
+Y126* 4</title><line x1="261.7" y1="999" x2="261.7" y2="984.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="261.7" cy="984.3" r="2.8" fill="#e5802b"/></g><g><title>codon 127: 10 mutations
+S127F 7
+S127P 3</title><line x1="263.4" y1="999" x2="263.4" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="263.4" cy="996" r="2.8" fill="#008000"/></g><g><title>codon 130: 11 mutations
+L130F 4
+L130V 4
+L130Sfs*40 3</title><line x1="268.3" y1="999" x2="268.3" y2="995.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="268.3" cy="995.7" r="2.8" fill="#008000"/></g><g><title>codon 131: 5 mutations
+N131del 5</title><line x1="270" y1="999" x2="270" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="270" cy="997.5" r="2.8" fill="#993404"/></g><g><title>codon 132: 27 mutations
+K132R 13
+K132N 11
+K132E 3</title><line x1="271.7" y1="999" x2="271.7" y2="990.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="271.7" cy="990.9" r="2.8" fill="#008000"/></g><g><title>codon 133: 4 mutations
+M133T 4</title><line x1="273.3" y1="999" x2="273.3" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="273.3" cy="997.8" r="2.8" fill="#008000"/></g><g><title>codon 134: 5 mutations
+F134L 5</title><line x1="275" y1="999" x2="275" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="275" cy="997.5" r="2.8" fill="#008000"/></g><g><title>codon 135: 30 mutations
+C135F 14
+C135Y 13
+C135R 3</title><line x1="276.6" y1="999" x2="276.6" y2="990" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="276.6" cy="990" r="2.8" fill="#008000"/></g><g><title>codon 136: 8 mutations
+Q136* 8</title><line x1="278.3" y1="999" x2="278.3" y2="996.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="278.3" cy="996.6" r="2.8" fill="#000000"/></g><g><title>codon 138: 7 mutations
+A138V 7</title><line x1="281.6" y1="999" x2="281.6" y2="996.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="281.6" cy="996.9" r="2.8" fill="#008000"/></g><g><title>codon 141: 19 mutations
+C141Y 11
+C141R 5
+C141W 3</title><line x1="286.6" y1="999" x2="286.6" y2="993.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="286.6" cy="993.3" r="2.8" fill="#008000"/></g><g><title>codon 143: 9 mutations
+V143M 9</title><line x1="289.9" y1="999" x2="289.9" y2="996.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="289.9" cy="996.3" r="2.8" fill="#008000"/></g><g><title>codon 144: 5 mutations
+Q144* 5</title><line x1="291.5" y1="999" x2="291.5" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="291.5" cy="997.5" r="2.8" fill="#000000"/></g><g><title>codon 146: 20 mutations
+W146* 20</title><line x1="294.8" y1="999" x2="294.8" y2="993" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="294.8" cy="993" r="2.8" fill="#000000"/></g><g><title>codon 151: 30 mutations
+P151S 17
+P151T 6
+P151H 4
+P151R 3</title><line x1="303.1" y1="999" x2="303.1" y2="990" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="303.1" cy="990" r="2.8" fill="#008000"/></g><g><title>codon 152: 48 mutations
+P152L 25
+P153Afs*28 9
+P152Afs*14 8
+P152Rfs*18 6</title><line x1="304.8" y1="999" x2="304.8" y2="984.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="304.8" cy="984.6" r="2.8" fill="#008000"/></g><g><title>codon 154: 5 mutations
+G154V 5</title><line x1="308.1" y1="999" x2="308.1" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="308.1" cy="997.5" r="2.8" fill="#008000"/></g><g><title>codon 156: 3 mutations
+R156P 3</title><line x1="311.4" y1="999" x2="311.4" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="311.4" cy="998.1" r="2.8" fill="#008000"/></g><g><title>codon 157: 8 mutations
+V157F 5
+V157I 3</title><line x1="313.1" y1="999" x2="313.1" y2="996.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="313.1" cy="996.6" r="2.8" fill="#008000"/></g><g><title>codon 158: 38 mutations
+R158H 30
+R158C 5
+R158L 3</title><line x1="314.7" y1="999" x2="314.7" y2="987.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="314.7" cy="987.6" r="2.8" fill="#008000"/></g><g><title>codon 159: 15 mutations
+A159V 8
+A159P 7</title><line x1="316.4" y1="999" x2="316.4" y2="994.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="316.4" cy="994.5" r="2.8" fill="#008000"/></g><g><title>codon 161: 19 mutations
+A161T 16
+A161D 3</title><line x1="319.7" y1="999" x2="319.7" y2="993.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="319.7" cy="993.3" r="2.8" fill="#008000"/></g><g><title>codon 163: 11 mutations
+Y163C 11</title><line x1="323" y1="999" x2="323" y2="995.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="323" cy="995.7" r="2.8" fill="#008000"/></g><g><title>codon 165: 6 mutations
+Q165* 6</title><line x1="326.3" y1="999" x2="326.3" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="326.3" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 166: 4 mutations
+S166* 4</title><line x1="328" y1="999" x2="328" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="328" cy="997.8" r="2.8" fill="#000000"/></g><g><title>codon 167: 6 mutations
+Q167* 6</title><line x1="329.6" y1="999" x2="329.6" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="329.6" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 168: 5 mutations
+H168R 5</title><line x1="331.3" y1="999" x2="331.3" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="331.3" cy="997.5" r="2.8" fill="#008000"/></g><g><title>codon 170: 3 mutations
+T170M 3</title><line x1="334.6" y1="999" x2="334.6" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="334.6" cy="998.1" r="2.8" fill="#008000"/></g><g><title>codon 171: 3 mutations
+E171* 3</title><line x1="336.3" y1="999" x2="336.3" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="336.3" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 172: 5 mutations
+V172F 5</title><line x1="337.9" y1="999" x2="337.9" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="337.9" cy="997.5" r="2.8" fill="#008000"/></g><g><title>codon 173: 40 mutations
+V173M 20
+V173L 17
+V173A 3</title><line x1="339.6" y1="999" x2="339.6" y2="987" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="339.6" cy="987" r="2.8" fill="#008000"/></g><g><title>codon 175: 403 mutations
+R175H 391
+R175C 9
+R175L 3</title><line x1="342.9" y1="999" x2="342.9" y2="878.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="342.9" cy="878.1" r="2.8" fill="#008000"/></g><g><title>codon 176: 58 mutations
+C176F 24
+C176Y 20
+C176S 7
+C176R 4
+C176* 3</title><line x1="344.5" y1="999" x2="344.5" y2="981.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="344.5" cy="981.6" r="2.8" fill="#008000"/></g><g><title>codon 177: 9 mutations
+P177_C182del 9</title><line x1="346.2" y1="999" x2="346.2" y2="996.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="346.2" cy="996.3" r="2.8" fill="#993404"/></g><g><title>codon 178: 12 mutations
+H178Tfs*69 5
+H178P 4
+H178Pfs*3 3</title><line x1="347.9" y1="999" x2="347.9" y2="995.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="347.9" cy="995.4" r="2.8" fill="#000000"/></g><g><title>codon 179: 45 mutations
+H179Y 19
+H179R 12
+H179Q 7
+H179L 4
+H179D 3</title><line x1="349.5" y1="999" x2="349.5" y2="985.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="349.5" cy="985.5" r="2.8" fill="#008000"/></g><g><title>codon 181: 20 mutations
+R181H 11
+R181C 9</title><line x1="352.8" y1="999" x2="352.8" y2="993" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="352.8" cy="993" r="2.8" fill="#008000"/></g><g><title>codon 183: 3 mutations
+S183* 3</title><line x1="356.1" y1="999" x2="356.1" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="356.1" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 187: 36 mutations
+X187_splice 36</title><line x1="362.8" y1="999" x2="362.8" y2="988.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="362.8" cy="988.2" r="2.8" fill="#e5802b"/></g><g><title>codon 190: 23 mutations
+P190L 13
+P190T 7
+P190Lfs*57 3</title><line x1="367.7" y1="999" x2="367.7" y2="992.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="367.7" cy="992.1" r="2.8" fill="#008000"/></g><g><title>codon 191: 5 mutations
+P191del 5</title><line x1="369.4" y1="999" x2="369.4" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="369.4" cy="997.5" r="2.8" fill="#993404"/></g><g><title>codon 192: 8 mutations
+Q192* 8</title><line x1="371" y1="999" x2="371" y2="996.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="371" cy="996.6" r="2.8" fill="#000000"/></g><g><title>codon 193: 26 mutations
+H193R 14
+H193Y 6
+H193L 3
+H193N 3</title><line x1="372.7" y1="999" x2="372.7" y2="991.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="372.7" cy="991.2" r="2.8" fill="#008000"/></g><g><title>codon 194: 16 mutations
+L194R 6
+L194F 4
+L194H 3
+L194P 3</title><line x1="374.4" y1="999" x2="374.4" y2="994.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="374.4" cy="994.2" r="2.8" fill="#008000"/></g><g><title>codon 195: 23 mutations
+I195T 13
+I195F 4
+I195N 3
+I195S 3</title><line x1="376" y1="999" x2="376" y2="992.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="376" cy="992.1" r="2.8" fill="#008000"/></g><g><title>codon 196: 111 mutations
+R196* 107
+R196Q 4</title><line x1="377.7" y1="999" x2="377.7" y2="965.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="377.7" cy="965.7" r="2.8" fill="#000000"/></g><g><title>codon 197: 12 mutations
+V197M 12</title><line x1="379.3" y1="999" x2="379.3" y2="995.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="379.3" cy="995.4" r="2.8" fill="#008000"/></g><g><title>codon 198: 5 mutations
+E198* 5</title><line x1="381" y1="999" x2="381" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="381" cy="997.5" r="2.8" fill="#000000"/></g><g><title>codon 199: 5 mutations
+G199V 5</title><line x1="382.6" y1="999" x2="382.6" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="382.6" cy="997.5" r="2.8" fill="#008000"/></g><g><title>codon 204: 6 mutations
+E204* 6</title><line x1="390.9" y1="999" x2="390.9" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="390.9" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 205: 15 mutations
+Y205C 7
+Y205H 5
+Y205D 3</title><line x1="392.6" y1="999" x2="392.6" y2="994.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="392.6" cy="994.5" r="2.8" fill="#008000"/></g><g><title>codon 209: 13 mutations
+R209Kfs*6 13</title><line x1="399.2" y1="999" x2="399.2" y2="995.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="399.2" cy="995.1" r="2.8" fill="#000000"/></g><g><title>codon 211: 6 mutations
+T211I 6</title><line x1="402.5" y1="999" x2="402.5" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="402.5" cy="997.2" r="2.8" fill="#008000"/></g><g><title>codon 212: 5 mutations
+F212Sfs*3 5</title><line x1="404.2" y1="999" x2="404.2" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="404.2" cy="997.5" r="2.8" fill="#000000"/></g><g><title>codon 213: 160 mutations
+R213* 141
+R213Q 10
+R213L 9</title><line x1="405.8" y1="999" x2="405.8" y2="951" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="405.8" cy="951" r="2.8" fill="#000000"/></g><g><title>codon 214: 11 mutations
+H214R 6
+H214Qfs*7 5</title><line x1="407.5" y1="999" x2="407.5" y2="995.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="407.5" cy="995.7" r="2.8" fill="#008000"/></g><g><title>codon 215: 12 mutations
+S215G 5
+S215N 4
+S215R 3</title><line x1="409.1" y1="999" x2="409.1" y2="995.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="409.1" cy="995.4" r="2.8" fill="#008000"/></g><g><title>codon 216: 15 mutations
+V216M 10
+V216L 5</title><line x1="410.8" y1="999" x2="410.8" y2="994.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="410.8" cy="994.5" r="2.8" fill="#008000"/></g><g><title>codon 220: 35 mutations
+Y220C 35</title><line x1="417.4" y1="999" x2="417.4" y2="988.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="417.4" cy="988.5" r="2.8" fill="#008000"/></g><g><title>codon 222: 3 mutations
+P222L 3</title><line x1="420.7" y1="999" x2="420.7" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="420.7" cy="998.1" r="2.8" fill="#008000"/></g><g><title>codon 224: 33 mutations
+X224_splice 24
+E224D 5
+E224* 4</title><line x1="424.1" y1="999" x2="424.1" y2="989.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="424.1" cy="989.1" r="2.8" fill="#e5802b"/></g><g><title>codon 225: 21 mutations
+X225_splice 21</title><line x1="425.7" y1="999" x2="425.7" y2="992.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="425.7" cy="992.7" r="2.8" fill="#e5802b"/></g><g><title>codon 227: 3 mutations
+D228* 3</title><line x1="429" y1="999" x2="429" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="429" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 229: 5 mutations
+C229Yfs*10 5</title><line x1="432.3" y1="999" x2="432.3" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="432.3" cy="997.5" r="2.8" fill="#000000"/></g><g><title>codon 234: 26 mutations
+Y234C 17
+Y234H 5
+Y234* 4</title><line x1="440.6" y1="999" x2="440.6" y2="991.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="440.6" cy="991.2" r="2.8" fill="#008000"/></g><g><title>codon 236: 15 mutations
+Y236C 10
+Y236N 5</title><line x1="443.9" y1="999" x2="443.9" y2="994.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="443.9" cy="994.5" r="2.8" fill="#008000"/></g><g><title>codon 237: 22 mutations
+M237I 22</title><line x1="445.6" y1="999" x2="445.6" y2="992.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="445.6" cy="992.4" r="2.8" fill="#008000"/></g><g><title>codon 238: 16 mutations
+C238Y 16</title><line x1="447.2" y1="999" x2="447.2" y2="994.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="447.2" cy="994.2" r="2.8" fill="#008000"/></g><g><title>codon 239: 10 mutations
+N239D 7
+N239S 3</title><line x1="448.9" y1="999" x2="448.9" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="448.9" cy="996" r="2.8" fill="#008000"/></g><g><title>codon 241: 26 mutations
+S241F 8
+C242Afs*5 7
+S241Y 5
+S241C 3
+S241P 3</title><line x1="452.2" y1="999" x2="452.2" y2="991.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="452.2" cy="991.2" r="2.8" fill="#008000"/></g><g><title>codon 242: 28 mutations
+C242Y 12
+C242F 10
+C242S 6</title><line x1="453.9" y1="999" x2="453.9" y2="990.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="453.9" cy="990.6" r="2.8" fill="#008000"/></g><g><title>codon 244: 20 mutations
+G244D 10
+G244S 6
+G244C 4</title><line x1="457.2" y1="999" x2="457.2" y2="993" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="457.2" cy="993" r="2.8" fill="#008000"/></g><g><title>codon 245: 144 mutations
+G245S 119
+G245D 13
+G245V 7
+G245C 5</title><line x1="458.8" y1="999" x2="458.8" y2="955.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="458.8" cy="955.8" r="2.8" fill="#008000"/></g><g><title>codon 246: 4 mutations
+M246V 4</title><line x1="460.5" y1="999" x2="460.5" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="460.5" cy="997.8" r="2.8" fill="#008000"/></g><g><title>codon 248: 334 mutations
+R248Q 183
+R248W 146
+R248L 5</title><line x1="463.8" y1="999" x2="463.8" y2="898.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="463.8" cy="898.8" r="2.8" fill="#008000"/></g><g><title>codon 249: 17 mutations
+R249W 6
+R249S 5
+R249G 3
+R249M 3</title><line x1="465.5" y1="999" x2="465.5" y2="993.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="465.5" cy="993.9" r="2.8" fill="#008000"/></g><g><title>codon 250: 12 mutations
+P250L 12</title><line x1="467.1" y1="999" x2="467.1" y2="995.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="467.1" cy="995.4" r="2.8" fill="#008000"/></g><g><title>codon 253: 3 mutations
+T253Nfs*11 3</title><line x1="472.1" y1="999" x2="472.1" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="472.1" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 254: 6 mutations
+I254N 3
+I254T 3</title><line x1="473.7" y1="999" x2="473.7" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="473.7" cy="997.2" r="2.8" fill="#008000"/></g><g><title>codon 255: 13 mutations
+I255T 7
+I255del 6</title><line x1="475.4" y1="999" x2="475.4" y2="995.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="475.4" cy="995.1" r="2.8" fill="#008000"/></g><g><title>codon 257: 10 mutations
+L257P 7
+L257R 3</title><line x1="478.7" y1="999" x2="478.7" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="478.7" cy="996" r="2.8" fill="#008000"/></g><g><title>codon 258: 28 mutations
+E258K 11
+E258* 7
+E258G 7
+E258D 3</title><line x1="480.4" y1="999" x2="480.4" y2="990.6" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="480.4" cy="990.6" r="2.8" fill="#008000"/></g><g><title>codon 259: 7 mutations
+D259Y 7</title><line x1="482" y1="999" x2="482" y2="996.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="482" cy="996.9" r="2.8" fill="#008000"/></g><g><title>codon 261: 26 mutations
+X261_splice 26</title><line x1="485.3" y1="999" x2="485.3" y2="991.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="485.3" cy="991.2" r="2.8" fill="#e5802b"/></g><g><title>codon 262: 6 mutations
+G262V 6</title><line x1="487" y1="999" x2="487" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="487" cy="997.2" r="2.8" fill="#008000"/></g><g><title>codon 264: 5 mutations
+L265del 5</title><line x1="490.3" y1="999" x2="490.3" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="490.3" cy="997.5" r="2.8" fill="#993404"/></g><g><title>codon 265: 5 mutations
+L265P 5</title><line x1="492" y1="999" x2="492" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="492" cy="997.5" r="2.8" fill="#008000"/></g><g><title>codon 266: 43 mutations
+G266E 22
+G266R 10
+G266* 6
+G266V 5</title><line x1="493.6" y1="999" x2="493.6" y2="986.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="493.6" cy="986.1" r="2.8" fill="#008000"/></g><g><title>codon 267: 13 mutations
+R267W 9
+R267Q 4</title><line x1="495.3" y1="999" x2="495.3" y2="995.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="495.3" cy="995.1" r="2.8" fill="#008000"/></g><g><title>codon 270: 14 mutations
+F270L 9
+F270C 5</title><line x1="500.3" y1="999" x2="500.3" y2="994.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="500.3" cy="994.8" r="2.8" fill="#008000"/></g><g><title>codon 271: 10 mutations
+E271* 6
+E271K 4</title><line x1="501.9" y1="999" x2="501.9" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="501.9" cy="996" r="2.8" fill="#000000"/></g><g><title>codon 272: 17 mutations
+V272M 9
+V272L 5
+V272E 3</title><line x1="503.6" y1="999" x2="503.6" y2="993.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="503.6" cy="993.9" r="2.8" fill="#008000"/></g><g><title>codon 273: 371 mutations
+R273C 182
+R273H 179
+R273L 7
+R273P 3</title><line x1="505.2" y1="999" x2="505.2" y2="887.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="505.2" cy="887.7" r="2.8" fill="#008000"/></g><g><title>codon 274: 10 mutations
+V274A 6
+V274F 4</title><line x1="506.9" y1="999" x2="506.9" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="506.9" cy="996" r="2.8" fill="#008000"/></g><g><title>codon 275: 12 mutations
+C275F 7
+C275Y 5</title><line x1="508.5" y1="999" x2="508.5" y2="995.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="508.5" cy="995.4" r="2.8" fill="#008000"/></g><g><title>codon 277: 4 mutations
+C277F 4</title><line x1="511.8" y1="999" x2="511.8" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="511.8" cy="997.8" r="2.8" fill="#008000"/></g><g><title>codon 278: 31 mutations
+P278S 9
+P278L 6
+P278R 5
+P278T 5
+P278A 3
+P278H 3</title><line x1="513.5" y1="999" x2="513.5" y2="989.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="513.5" cy="989.7" r="2.8" fill="#008000"/></g><g><title>codon 280: 11 mutations
+R280K 5
+R280* 3
+R280G 3</title><line x1="516.8" y1="999" x2="516.8" y2="995.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="516.8" cy="995.7" r="2.8" fill="#008000"/></g><g><title>codon 281: 10 mutations
+D281G 5
+D281N 5</title><line x1="518.5" y1="999" x2="518.5" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="518.5" cy="996" r="2.8" fill="#008000"/></g><g><title>codon 282: 219 mutations
+R282W 219</title><line x1="520.1" y1="999" x2="520.1" y2="933.3" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="520.1" cy="933.3" r="2.8" fill="#008000"/></g><g><title>codon 285: 11 mutations
+E285K 11</title><line x1="525.1" y1="999" x2="525.1" y2="995.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="525.1" cy="995.7" r="2.8" fill="#008000"/></g><g><title>codon 286: 22 mutations
+E286K 19
+E286G 3</title><line x1="526.8" y1="999" x2="526.8" y2="992.4" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="526.8" cy="992.4" r="2.8" fill="#008000"/></g><g><title>codon 290: 3 mutations
+R290H 3</title><line x1="533.4" y1="999" x2="533.4" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="533.4" cy="998.1" r="2.8" fill="#53D400"/></g><g><title>codon 294: 10 mutations
+E294* 7
+E294Sfs*51 3</title><line x1="540" y1="999" x2="540" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="540" cy="996" r="2.8" fill="#000000"/></g><g><title>codon 298: 6 mutations
+E298* 6</title><line x1="546.6" y1="999" x2="546.6" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="546.6" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 301: 10 mutations
+P301Qfs*44 7
+G302Rfs*4 3</title><line x1="551.6" y1="999" x2="551.6" y2="996" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="551.6" cy="996" r="2.8" fill="#000000"/></g><g><title>codon 306: 76 mutations
+R306* 76</title><line x1="559.9" y1="999" x2="559.9" y2="976.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="559.9" cy="976.2" r="2.8" fill="#000000"/></g><g><title>codon 307: 27 mutations
+X307_splice 27</title><line x1="561.5" y1="999" x2="561.5" y2="990.9" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="561.5" cy="990.9" r="2.8" fill="#e5802b"/></g><g><title>codon 317: 6 mutations
+Q317* 6</title><line x1="578.1" y1="999" x2="578.1" y2="997.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="578.1" cy="997.2" r="2.8" fill="#000000"/></g><g><title>codon 321: 4 mutations
+K321Nfs*24 4</title><line x1="584.7" y1="999" x2="584.7" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="584.7" cy="997.8" r="2.8" fill="#000000"/></g><g><title>codon 327: 3 mutations
+Y327* 3</title><line x1="594.7" y1="999" x2="594.7" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="594.7" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 331: 26 mutations
+X331_splice 23
+Q331* 3</title><line x1="601.3" y1="999" x2="601.3" y2="991.2" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="601.3" cy="991.2" r="2.8" fill="#e5802b"/></g><g><title>codon 332: 14 mutations
+X332_splice 14</title><line x1="603" y1="999" x2="603" y2="994.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="603" cy="994.8" r="2.8" fill="#e5802b"/></g><g><title>codon 337: 14 mutations
+R337C 14</title><line x1="611.2" y1="999" x2="611.2" y2="994.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="611.2" cy="994.8" r="2.8" fill="#008000"/></g><g><title>codon 339: 4 mutations
+E339* 4</title><line x1="614.5" y1="999" x2="614.5" y2="997.8" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="614.5" cy="997.8" r="2.8" fill="#000000"/></g><g><title>codon 342: 93 mutations
+R342* 90
+R342Efs*3 3</title><line x1="619.5" y1="999" x2="619.5" y2="971.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="619.5" cy="971.1" r="2.8" fill="#000000"/></g><g><title>codon 349: 3 mutations
+E349* 3</title><line x1="631.1" y1="999" x2="631.1" y2="998.1" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="631.1" cy="998.1" r="2.8" fill="#000000"/></g><g><title>codon 373: 5 mutations
+K373Rfs*49 5</title><line x1="670.9" y1="999" x2="670.9" y2="997.5" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="670.9" cy="997.5" r="2.8" fill="#000000"/></g><g><title>codon 382: 21 mutations
+K382Nfs*40 21</title><line x1="685.8" y1="999" x2="685.8" y2="992.7" stroke="#BABDB6" stroke-width="1"/><circle class="lollipop-head" cx="685.8" cy="992.7" r="2.8" fill="#000000"/></g><text x="342.9" y="871.1" font-size="10" fill="#16232b" text-anchor="middle">R175H/C/L</text><circle cx="58" cy="1058.5" r="4" fill="#008000"/><text x="68" y="1062" font-size="10" fill="#16232b">Missense</text><circle cx="130.89" cy="1058.5" r="4" fill="#53D400"/><text x="140.9" y="1062" font-size="10" fill="#16232b">Missense (VUS)</text><circle cx="231.51999999999998" cy="1058.5" r="4" fill="#000000"/><text x="241.5" y="1062" font-size="10" fill="#16232b">Truncating</text><circle cx="310.61" cy="1058.5" r="4" fill="#993404"/><text x="320.6" y="1062" font-size="10" fill="#16232b">Inframe</text><circle cx="377.76" cy="1058.5" r="4" fill="#e5802b"/><text x="387.8" y="1062" font-size="10" fill="#16232b">Splice</text><text x="8" y="1086" font-size="10" fill="#5b6b76">Mutations in “CRC” by protein position, as cBioPortal's mutation tab draws them: one stem per codon (the stored protein start position), its height the</text><text x="8" y="1100" font-size="10" fill="#5b6b76">number of distinct patient + protein change pairs there. Colour is the codon's dominant mutation type, putative drivers before VUS (driver = OncoKB</text><text x="8" y="1114" font-size="10" fill="#5b6b76">oncogenic, pinned; the portal also counts hotspots and custom annotations). VUS and germline mutations are included, as they are by default on the portal. A</text><text x="8" y="1128" font-size="10" fill="#5b6b76">suppression floor of 3 patients per change was applied BY EACH SITE before returning anything, withholding 1,112 mutations. KRAS: somatic mutation</text><text x="8" y="1142" font-size="10" fill="#5b6b76">frequency 42.5%; mutation rows missense 2,365 driver / 3 VUS, inframe 5 driver / 0 VUS. BRAF: somatic mutation frequency 11.2%; mutation rows</text><text x="8" y="1156" font-size="10" fill="#5b6b76">missense 520 driver / 8 VUS, truncating 0 driver / 19 VUS. PIK3CA: somatic mutation frequency 20.5%; mutation rows missense 1,036 driver / 41 VUS,</text><text x="8" y="1170" font-size="10" fill="#5b6b76">truncating 0 driver / 8 VUS, inframe 14 driver / 0 VUS. TP53: somatic mutation frequency 73.2%; mutation rows missense 2,628 driver / 26 VUS, truncating</text><text x="8" y="1184" font-size="10" fill="#5b6b76">796 driver / 0 VUS, inframe 30 driver / 0 VUS, splice 244 driver / 0 VUS.</text></svg>
diff --git a/test/snapshots/hotspots.txt b/test/snapshots/hotspots.txt
new file mode 100644
index 00000000..b1b7c3d9
--- /dev/null
+++ b/test/snapshots/hotspots.txt
@@ -0,0 +1,12 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- CRC: 5,543 patients — CANCER_TYPE in [Colorectal Cancer] (any sample)
+
+Mutations by protein position (commonest changes; share of patients mutated in THAT gene):
+  KRAS: 2,362 mutated, 2,424 mutations on 12 codons — G12D 688 (29.1%), G12V 487 (20.6%), G13D 426 (18%), G12C 162 (6.9%), A146T 140 (5.9%), G12S 109 (4.6%)  [53 below the suppression floor]
+  BRAF: 622 mutated, 658 mutations on 11 codons — V600E 438 (70.4%), D594G 38 (6.1%), G469A 10 (1.6%), A404Cfs*9 10 (1.6%), P403Lfs*8 9 (1.4%), D594N 8 (1.3%)  [111 below the suppression floor]
+  PIK3CA: 1,147 mutated, 1,330 mutations on 37 codons — E545K 253 (22.1%), H1047R 189 (16.5%), E542K 159 (13.9%), R88Q 47 (4.1%), Q546K 41 (3.6%), C420R 39 (3.4%)  [231 below the suppression floor]
+  TP53: 4,080 mutated, 4,441 mutations on 141 codons — R175H 391 (9.6%), R282W 219 (5.4%), R248Q 183 (4.5%), R273C 182 (4.5%), R273H 179 (4.4%), R248W 146 (3.6%)  [717 below the suppression floor]
+  KRAS: somatic mutation frequency 42.5%; mutation rows missense 2,365 driver / 3 VUS, inframe 5 driver / 0 VUS. BRAF: somatic mutation frequency 11.2%; mutation rows missense 520 driver / 8 VUS, truncating 0 driver / 19 VUS. PIK3CA: somatic mutation frequency 20.5%; mutation rows missense 1,036 driver / 41 VUS, truncating 0 driver / 8 VUS, inframe 14 driver / 0 VUS. TP53: somatic mutation frequency 73.2%; mutation rows missense 2,628 driver / 26 VUS, truncating 796 driver / 0 VUS, inframe 30 driver / 0 VUS, splice 244 driver / 0 VUS.
+  Mutations in “CRC” by protein position, as cBioPortal's mutation tab draws them: one stem per codon (the stored protein start position), its height the number of distinct patient + protein change pairs there. Colour is the codon's dominant mutation type, putative drivers before VUS (driver = OncoKB oncogenic, pinned; the portal also counts hotspots and custom annotations). VUS and germline mutations are included, as they are by default on the portal. A suppression floor of 3 patients per change was applied BY EACH SITE before returning anything, withholding 1,112 mutations.
diff --git a/test/snapshots/incidence.json b/test/snapshots/incidence.json
new file mode 100644
index 00000000..27d85a7d
--- /dev/null
+++ b/test/snapshots/incidence.json
@@ -0,0 +1,836 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "NSCLC": {
+      "count": 7809,
+      "text": "CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "Breast": {
+      "count": 5368,
+      "text": "CANCER_TYPE in [Breast Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "Colorectal": {
+      "count": 5543,
+      "text": "CANCER_TYPE in [Colorectal Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "incidence",
+      "cohorts": [
+        "NSCLC",
+        "Breast",
+        "Colorectal"
+      ],
+      "incidence": {
+        "eventLabel": "brain metastasis",
+        "curves": [
+          {
+            "cohort": "NSCLC",
+            "n": 6457,
+            "events": 1406,
+            "competing": 1967,
+            "censored": 3084,
+            "points": [
+              {
+                "t": 0.0329,
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+          "groups": 3
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+      },
+      "participation": {
+        "studies": [
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+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Cumulative incidence of the first brain metastasis after the study's day 0, with death (OS_STATUS) as a competing risk: Aalen–Johansen, 95% intervals from its delta-method standard error. It is not 1 − Kaplan–Meier: that treats the dead as still at risk of the event and overstates incidence whenever death is common — the table sets the two side by side. Excluded rather than counted as event-free: 2101 who already had it by day 0, 0 without follow-up. Gray's test (weight 1) by permutation of cohort labels, 2000 shuffles, seeded; exact under the null that the cohorts share one distribution, entry times included. An event recorded from notes or registries is only as complete as the recording.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Classify and count each patient's outcome",
+            "detail": "The incidence query above. Per patient, months from the study's day 0 (days ÷ 30.4375, rounded to 4 decimals) to the first brain metastasis after it (cause 1), death without it (cause 2, from OS_STATUS), or last follow-up (censored, OS_MONTHS); then counted by (entry, months, cause). Counts only.",
+            "scope": "NSCLC · msk_chord_2024",
+            "rowsIn": 7809
+          },
+          {
+            "kind": "fetch",
+            "title": "Classify and count each patient's outcome",
+            "detail": "The incidence query above. Per patient, months from the study's day 0 (days ÷ 30.4375, rounded to 4 decimals) to the first brain metastasis after it (cause 1), death without it (cause 2, from OS_STATUS), or last follow-up (censored, OS_MONTHS); then counted by (entry, months, cause). Counts only.",
+            "scope": "Breast · msk_chord_2024",
+            "rowsIn": 5368
+          },
+          {
+            "kind": "fetch",
+            "title": "Classify and count each patient's outcome",
+            "detail": "The incidence query above. Per patient, months from the study's day 0 (days ÷ 30.4375, rounded to 4 decimals) to the first brain metastasis after it (cause 1), death without it (cause 2, from OS_STATUS), or last follow-up (censored, OS_MONTHS); then counted by (entry, months, cause). Counts only.",
+            "scope": "Colorectal · msk_chord_2024",
+            "rowsIn": 5543
+          },
+          {
+            "kind": "transform",
+            "title": "Set aside patients who cannot enter the risk set",
+            "detail": "Named and counted rather than treated as event-free. A patient whose first event was recorded after their last follow-up is kept, as censored or dead at follow-up.",
+            "params": [
+              [
+                "NSCLC",
+                "1352 already had it by day 0 · 0 without follow-up"
+              ],
+              [
+                "Breast",
+                "645 already had it by day 0 · 0 without follow-up"
+              ],
+              [
+                "Colorectal",
+                "104 already had it by day 0 · 0 without follow-up"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Estimate cumulative incidence by Aalen–Johansen",
+            "detail": "Death is a competing risk: at each time F1 rises by S(t−)·d1/R, with S the all-cause Kaplan–Meier survival and R those at risk. Standard error by the delta method (Aalen 1978); 95% interval F1 ± 1.959964·SE, clamped to [0, 1]. At each horizon the curve's value there, beside 1 − Kaplan–Meier of the event alone, which counts the dead as still at risk and overstates incidence.",
+            "params": [
+              [
+                "horizons (months)",
+                "12, 24, 60"
+              ],
+              [
+                "NSCLC",
+                "6,457 at risk · 1406 events · 1967 deaths first · 3084 censored"
+              ],
+              [
+                "Breast",
+                "4,723 at risk · 905 events · 956 deaths first · 2862 censored"
+              ],
+              [
+                "Colorectal",
+                "5,439 at risk · 447 events · 1783 deaths first · 3209 censored"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Gray-type test by permutation",
+            "detail": "Per cohort, observed cause-1 events minus those expected from the pooled subdistribution hazard, the subdistribution risk set being Y·(1 − F1)/S (Gray 1988, weight 1), summed as Σ (O − E)²/E over cohorts. This is not Gray's variance-standardised χ², so its p does not come from a χ² table: the cohort labels are shuffled across the pooled records 2,000 times by a generator seeded with 1, and p = (b + 1)/(m + 1). Exact under the null that every cohort's records, entry times included, come from one distribution.",
+            "params": [
+              [
+                "statistic / p",
+                "375.8722 / 0.000500"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "aalenJohansen",
+            "cifAt",
+            "kaplanMeier",
+            "grayPermutationTest"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Cumulative incidence and its standard error match statsmodels 0.15.0 CumIncidenceRight to 1e-12, and with delayed entry lifelines' AalenJohansenFitter; the Gray-type statistic and permutation p match the script's port exactly (test/incidence-golden.test.ts).",
+        "check": {
+          "incidence:NSCLC:12:f1": {
+            "v": 0.13109119321243984,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:12:lo": {
+            "v": 0.12275472329176881,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:12:hi": {
+            "v": 0.13942766313311086,
+            "abs": 1e-9
+          },
+          "incidence:Breast:12:f1": {
+            "v": 0.09076442001016774,
+            "abs": 1e-9
+          },
+          "incidence:Breast:12:lo": {
+            "v": 0.08245310725479074,
+            "abs": 1e-9
+          },
+          "incidence:Breast:12:hi": {
+            "v": 0.09907573276554474,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:12:f1": {
+            "v": 0.0314726097593687,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:12:lo": {
+            "v": 0.026729157383594535,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:12:hi": {
+            "v": 0.03621606213514286,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:24:f1": {
+            "v": 0.18355912082668832,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:24:lo": {
+            "v": 0.17377663726429607,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:24:hi": {
+            "v": 0.19334160438908057,
+            "abs": 1e-9
+          },
+          "incidence:Breast:24:f1": {
+            "v": 0.13557168300700778,
+            "abs": 1e-9
+          },
+          "incidence:Breast:24:lo": {
+            "v": 0.12552839318649045,
+            "abs": 1e-9
+          },
+          "incidence:Breast:24:hi": {
+            "v": 0.1456149728275251,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:24:f1": {
+            "v": 0.057539779761021585,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:24:lo": {
+            "v": 0.05093075326178087,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:24:hi": {
+            "v": 0.0641488062602623,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:60:f1": {
+            "v": 0.24663153687867914,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:60:lo": {
+            "v": 0.23480543347943614,
+            "abs": 1e-9
+          },
+          "incidence:NSCLC:60:hi": {
+            "v": 0.2584576402779222,
+            "abs": 1e-9
+          },
+          "incidence:Breast:60:f1": {
+            "v": 0.20569290620540176,
+            "abs": 1e-9
+          },
+          "incidence:Breast:60:lo": {
+            "v": 0.1930934002582669,
+            "abs": 1e-9
+          },
+          "incidence:Breast:60:hi": {
+            "v": 0.2182924121525366,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:60:f1": {
+            "v": 0.10770994150824832,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:60:lo": {
+            "v": 0.0974147053882567,
+            "abs": 1e-9
+          },
+          "incidence:Colorectal:60:hi": {
+            "v": 0.11800517762823995,
+            "abs": 1e-9
+          },
+          "gray:statistic": {
+            "v": 375.8721732308949,
+            "rel": 1e-12
+          },
+          "gray:p": {
+            "v": 0.0004997501249375312,
+            "abs": 0
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/incidence.sql b/test/snapshots/incidence.sql
new file mode 100644
index 00000000..c5e9feb1
--- /dev/null
+++ b/test/snapshots/incidence.sql
@@ -0,0 +1,170 @@
+-- cohort NSCLC
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))
+
+-- cohort Breast
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))
+
+-- cohort Colorectal
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+
+-- view 0 incidence: incidence:Breast · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), NULL) AS months,
+         anyIf(attribute_value, attribute_name = 'OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient' AND attribute_name IN ('OS_MONTHS', 'OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+anch AS (SELECT pid, toInt64(0) AS day FROM cohort),
+ev AS (SELECT p.stable_id AS pid, ce.start_date AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Diagnosis'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('TUMOR_SITE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('CNS/Brain')))
+    AND p.stable_id IN (SELECT pid FROM cohort)),
+evs AS (
+  SELECT ev.pid AS pid, countIf(ev.day <= anch.day) AS n_prior, countIf(ev.day > anch.day) AS n_after,
+         minIf(ev.day, ev.day > anch.day) AS first_after
+  FROM ev INNER JOIN anch ON anch.pid = ev.pid GROUP BY ev.pid
+),
+per AS (
+  SELECT anch.pid AS pid, anch.day AS a, os.months AS months, os.status LIKE '1%' AS dead,
+         evs.pid != '' AS has_ev, evs.n_prior AS n_prior, evs.n_after AS n_after, evs.first_after AS first_after
+  FROM anch
+  LEFT JOIN os ON os.pid = anch.pid
+  LEFT JOIN evs ON evs.pid = anch.pid
+),
+cls AS (
+  SELECT greatest(0, -a) / 30.4375 AS entry, months - a / 30.4375 AS tend,
+         (first_after - a) / 30.4375 AS tev,
+         multiIf(months IS NULL OR months - a / 30.4375 <= 0, 'nofollowup',
+                 has_ev AND n_prior > 0, 'prior',
+                 has_ev AND n_after > 0 AND first_after < 0, 'unobserved',
+                 'record') AS kind,
+         has_ev AND n_after > 0 AND (first_after - a) / 30.4375 <= months - a / 30.4375 AS hit,
+         has_ev AND n_after > 0 AND (first_after - a) / 30.4375 > months - a / 30.4375 AS late,
+         dead
+  FROM per
+)
+SELECT kind, if(kind = 'record', round(entry, 4), 0) AS entry, if(kind = 'record', round(if(hit, tev, tend), 4), 0) AS t,
+       if(kind != 'record', -1, if(hit, 1, if(dead, 2, 0))) AS cause, late, count() AS n
+FROM cls GROUP BY kind, entry, t, cause, late ORDER BY t
+
+-- view 0 incidence: incidence:Colorectal · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), NULL) AS months,
+         anyIf(attribute_value, attribute_name = 'OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient' AND attribute_name IN ('OS_MONTHS', 'OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+anch AS (SELECT pid, toInt64(0) AS day FROM cohort),
+ev AS (SELECT p.stable_id AS pid, ce.start_date AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Diagnosis'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('TUMOR_SITE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('CNS/Brain')))
+    AND p.stable_id IN (SELECT pid FROM cohort)),
+evs AS (
+  SELECT ev.pid AS pid, countIf(ev.day <= anch.day) AS n_prior, countIf(ev.day > anch.day) AS n_after,
+         minIf(ev.day, ev.day > anch.day) AS first_after
+  FROM ev INNER JOIN anch ON anch.pid = ev.pid GROUP BY ev.pid
+),
+per AS (
+  SELECT anch.pid AS pid, anch.day AS a, os.months AS months, os.status LIKE '1%' AS dead,
+         evs.pid != '' AS has_ev, evs.n_prior AS n_prior, evs.n_after AS n_after, evs.first_after AS first_after
+  FROM anch
+  LEFT JOIN os ON os.pid = anch.pid
+  LEFT JOIN evs ON evs.pid = anch.pid
+),
+cls AS (
+  SELECT greatest(0, -a) / 30.4375 AS entry, months - a / 30.4375 AS tend,
+         (first_after - a) / 30.4375 AS tev,
+         multiIf(months IS NULL OR months - a / 30.4375 <= 0, 'nofollowup',
+                 has_ev AND n_prior > 0, 'prior',
+                 has_ev AND n_after > 0 AND first_after < 0, 'unobserved',
+                 'record') AS kind,
+         has_ev AND n_after > 0 AND (first_after - a) / 30.4375 <= months - a / 30.4375 AS hit,
+         has_ev AND n_after > 0 AND (first_after - a) / 30.4375 > months - a / 30.4375 AS late,
+         dead
+  FROM per
+)
+SELECT kind, if(kind = 'record', round(entry, 4), 0) AS entry, if(kind = 'record', round(if(hit, tev, tend), 4), 0) AS t,
+       if(kind != 'record', -1, if(hit, 1, if(dead, 2, 0))) AS cause, late, count() AS n
+FROM cls GROUP BY kind, entry, t, cause, late ORDER BY t
+
+-- view 0 incidence: incidence:NSCLC · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), NULL) AS months,
+         anyIf(attribute_value, attribute_name = 'OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient' AND attribute_name IN ('OS_MONTHS', 'OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+anch AS (SELECT pid, toInt64(0) AS day FROM cohort),
+ev AS (SELECT p.stable_id AS pid, ce.start_date AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Diagnosis'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('TUMOR_SITE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('CNS/Brain')))
+    AND p.stable_id IN (SELECT pid FROM cohort)),
+evs AS (
+  SELECT ev.pid AS pid, countIf(ev.day <= anch.day) AS n_prior, countIf(ev.day > anch.day) AS n_after,
+         minIf(ev.day, ev.day > anch.day) AS first_after
+  FROM ev INNER JOIN anch ON anch.pid = ev.pid GROUP BY ev.pid
+),
+per AS (
+  SELECT anch.pid AS pid, anch.day AS a, os.months AS months, os.status LIKE '1%' AS dead,
+         evs.pid != '' AS has_ev, evs.n_prior AS n_prior, evs.n_after AS n_after, evs.first_after AS first_after
+  FROM anch
+  LEFT JOIN os ON os.pid = anch.pid
+  LEFT JOIN evs ON evs.pid = anch.pid
+),
+cls AS (
+  SELECT greatest(0, -a) / 30.4375 AS entry, months - a / 30.4375 AS tend,
+         (first_after - a) / 30.4375 AS tev,
+         multiIf(months IS NULL OR months - a / 30.4375 <= 0, 'nofollowup',
+                 has_ev AND n_prior > 0, 'prior',
+                 has_ev AND n_after > 0 AND first_after < 0, 'unobserved',
+                 'record') AS kind,
+         has_ev AND n_after > 0 AND (first_after - a) / 30.4375 <= months - a / 30.4375 AS hit,
+         has_ev AND n_after > 0 AND (first_after - a) / 30.4375 > months - a / 30.4375 AS late,
+         dead
+  FROM per
+)
+SELECT kind, if(kind = 'record', round(entry, 4), 0) AS entry, if(kind = 'record', round(if(hit, tev, tend), 4), 0) AS t,
+       if(kind != 'record', -1, if(hit, 1, if(dead, 2, 0))) AS cause, late, count() AS n
+FROM cls GROUP BY kind, entry, t, cause, late ORDER BY t
diff --git a/test/snapshots/incidence.svg b/test/snapshots/incidence.svg
new file mode 100644
index 00000000..8f291e41
--- /dev/null
+++ b/test/snapshots/incidence.svg
@@ -0,0 +1,2 @@
+<!-- view 0 incidence -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 760 574" width="100%" role="img" aria-label="Cumulative incidence of brain metastasis — NSCLC, Breast, Colorectal" style="max-width:760px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Cumulative incidence of brain metastasis — NSCLC, Breast, Colorectal</title><rect width="760" height="574" fill="#ffffff"/><line x1="57" y1="255" x2="744" y2="255" stroke="#dde4e8" stroke-width="0.75"/><text x="51" y="258.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="57" y1="206.4" x2="744" y2="206.4" stroke="#dde4e8" stroke-width="0.75"/><text x="51" y="209.9" font-size="10" fill="#5b6b76" text-anchor="end">10%</text><line x1="57" y1="157.8" x2="744" y2="157.8" stroke="#dde4e8" stroke-width="0.75"/><text x="51" y="161.3" font-size="10" fill="#5b6b76" text-anchor="end">20%</text><line x1="57" y1="109.2" x2="744" y2="109.2" stroke="#dde4e8" stroke-width="0.75"/><text x="51" y="112.7" font-size="10" fill="#5b6b76" text-anchor="end">30%</text><line x1="57" y1="60.6" x2="744" y2="60.6" stroke="#dde4e8" stroke-width="0.75"/><text x="51" y="64.1" font-size="10" fill="#5b6b76" text-anchor="end">40%</text><line x1="57" y1="12" x2="744" y2="12" stroke="#dde4e8" stroke-width="0.75"/><text x="51" y="15.5" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><text x="16.8" y="133.5" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 16.8 133.5)">Cumulative incidence</text><line x1="57" y1="255" x2="57" y2="258" stroke="#5b6b76" stroke-width="1"/><text x="54" y="271" font-size="10" fill="#5b6b76">0</text><line x1="276.8" y1="255" x2="276.8" y2="258" stroke="#5b6b76" stroke-width="1"/><text x="270.8" y="271" font-size="10" fill="#5b6b76">24</text><line x1="496.7" y1="255" x2="496.7" y2="258" stroke="#5b6b76" stroke-width="1"/><text x="490.7" y="271" font-size="10" fill="#5b6b76">48</text><line x1="716.5" y1="255" x2="716.5" y2="258" stroke="#5b6b76" stroke-width="1"/><text x="710.5" y="271" font-size="10" fill="#5b6b76">72</text><line x1="57" y1="255" x2="744" y2="255" stroke="#5b6b76" stroke-width="1"/><text x="400.5" y="290" font-size="11" fill="#16232b" text-anchor="middle">Months from the study's day 0</text><path d="M57 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fill="none" stroke="#009E73" stroke-width="2" stroke-linejoin="round"/><line x1="8" y1="307.5" x2="26" y2="307.5" stroke="#16232b" stroke-width="2"/><text x="31" y="311" font-size="10" fill="#16232b">brain metastasis</text><line x1="121.38" y1="307.5" x2="139.38" y2="307.5" stroke="#16232b" stroke-width="2" stroke-dasharray="2 2.5"/><text x="144.4" y="311" font-size="10" fill="#16232b">death without it (competing)</text><rect x="287.77" y="302.5" width="10" height="10" rx="2" fill="#16232b" opacity="0.18"/><text x="302.8" y="311" font-size="10" fill="#16232b">95% interval</text><text x="8" y="333" font-size="10" fill="#5b6b76">incidence by</text><text x="104.4" y="333" font-size="10" fill="#5b6b76">12 months</text><text x="300.2" y="333" font-size="10" fill="#5b6b76">24 months</text><text x="496" y="333" font-size="10" fill="#5b6b76">60 months</text><rect x="4" y="339" width="675.8" height="20" fill="#dde4e8" opacity="0.35"/><line x1="8" y1="349" x2="26" y2="349" stroke="#0072B2" stroke-width="2"/><text x="32" y="352.9" font-size="11" fill="#16232b">NSCLC</text><text x="104.4" y="352.9" font-size="11" fill="#16232b">13.1% [12.3%–13.9%]</text><text x="225.4" y="352.9" font-size="10" fill="#5b6b76">1−KM 14.1%</text><text x="300.2" y="352.9" font-size="11" fill="#16232b">18.4% [17.4%–19.3%]</text><text x="421.2" y="352.9" font-size="10" fill="#5b6b76">1−KM 20.8%</text><text x="496" y="352.9" font-size="11" fill="#16232b">24.7% [23.5%–25.8%]</text><text x="617" y="352.9" font-size="10" fill="#5b6b76">1−KM 30.4%</text><line x1="8" y1="369" x2="26" y2="369" stroke="#E69F00" stroke-width="2"/><text x="32" y="372.9" font-size="11" fill="#16232b">Breast</text><text x="104.4" y="372.9" font-size="11" fill="#16232b">9.1% [8.2%–9.9%]</text><text x="225.4" y="372.9" font-size="10" fill="#5b6b76">1−KM 9.5%</text><text x="300.2" y="372.9" font-size="11" fill="#16232b">13.6% [12.6%–14.6%]</text><text x="421.2" y="372.9" font-size="10" fill="#5b6b76">1−KM 14.5%</text><text x="496" y="372.9" font-size="11" fill="#16232b">20.6% [19.3%–21.8%]</text><text x="617" y="372.9" font-size="10" fill="#5b6b76">1−KM 23.3%</text><rect x="4" y="379" width="675.8" height="20" fill="#dde4e8" opacity="0.35"/><line x1="8" y1="389" x2="26" y2="389" stroke="#009E73" stroke-width="2"/><text x="32" y="392.9" font-size="11" fill="#16232b">Colorectal</text><text x="104.4" y="392.9" font-size="11" fill="#16232b">3.1% [2.7%–3.6%]</text><text x="225.4" y="392.9" font-size="10" fill="#5b6b76">1−KM 3.3%</text><text x="300.2" y="392.9" font-size="11" fill="#16232b">5.8% [5.1%–6.4%]</text><text x="421.2" y="392.9" font-size="10" fill="#5b6b76">1−KM 6.6%</text><text x="496" y="392.9" font-size="11" fill="#16232b">10.8% [9.7%–11.8%]</text><text x="617" y="392.9" font-size="10" fill="#5b6b76">1−KM 14.4%</text><line x1="8" y1="413.5" x2="26" y2="413.5" stroke="#0072B2" stroke-width="2"/><text x="32" y="415" font-size="10" fill="#5b6b76">NSCLC — n = 6,457: 1,406 events, 1,967 died first, 3,084 censored · not at risk: 1,352 already had it</text><line x1="8" y1="432.5" x2="26" y2="432.5" stroke="#E69F00" stroke-width="2"/><text x="32" y="434" font-size="10" fill="#5b6b76">Breast — n = 4,723: 905 events, 956 died first, 2,862 censored · not at risk: 645 already had it</text><line x1="8" y1="451.5" x2="26" y2="451.5" stroke="#009E73" stroke-width="2"/><text x="32" y="453" font-size="10" fill="#5b6b76">Colorectal — n = 5,439: 447 events, 1,783 died first, 3,209 censored · not at risk: 104 already had it</text><text x="8" y="477" font-size="11" fill="#16232b">Gray's test across 3 cohorts: statistic 376, permutation p ≤ 5e-4 (no shuffle of 2,000 as extreme)</text><text x="8" y="500" font-size="10" fill="#5b6b76">Cumulative incidence of the first brain metastasis after the study's day 0, with death (OS_STATUS) as a competing risk: Aalen–Johansen, 95% intervals from its</text><text x="8" y="514" font-size="10" fill="#5b6b76">delta-method standard error. It is not 1 − Kaplan–Meier: that treats the dead as still at risk of the event and overstates incidence whenever death is common — the</text><text x="8" y="528" font-size="10" fill="#5b6b76">table sets the two side by side. Excluded rather than counted as event-free: 2101 who already had it by day 0, 0 without follow-up. Gray's test (weight 1) by</text><text x="8" y="542" font-size="10" fill="#5b6b76">permutation of cohort labels, 2000 shuffles, seeded; exact under the null that the cohorts share one distribution, entry times included. An event recorded from notes</text><text x="8" y="556" font-size="10" fill="#5b6b76">or registries is only as complete as the recording.</text></svg>
diff --git a/test/snapshots/incidence.txt b/test/snapshots/incidence.txt
new file mode 100644
index 00000000..11d2fea1
--- /dev/null
+++ b/test/snapshots/incidence.txt
@@ -0,0 +1,32 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- NSCLC: 7,809 patients — CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)
+- Breast: 5,368 patients — CANCER_TYPE in [Breast Cancer] (any sample)
+- Colorectal: 5,543 patients — CANCER_TYPE in [Colorectal Cancer] (any sample)
+
+Cumulative incidence of brain metastasis, death competing (Aalen-Johansen):
+  NSCLC: n=6,457, 1406 events, 1967 died first, 3084 censored; not at risk: 1352 already had it, 0 before observation, 0 no anchor, 0 no follow-up
+  Breast: n=4,723, 905 events, 956 died first, 2862 censored; not at risk: 645 already had it, 0 before observation, 0 no anchor, 0 no follow-up
+  Colorectal: n=5,439, 447 events, 1783 died first, 3209 censored; not at risk: 104 already had it, 0 before observation, 0 no anchor, 0 no follow-up
+  at 12 months: NSCLC 13.1% (95% CI 12.3-13.9; 1-KM would say 14.1%) | Breast 9.1% (95% CI 8.2-9.9; 1-KM would say 9.5%) | Colorectal 3.1% (95% CI 2.7-3.6; 1-KM would say 3.3%)
+  at 24 months: NSCLC 18.4% (95% CI 17.4-19.3; 1-KM would say 20.8%) | Breast 13.6% (95% CI 12.6-14.6; 1-KM would say 14.5%) | Colorectal 5.8% (95% CI 5.1-6.4; 1-KM would say 6.6%)
+  at 60 months: NSCLC 24.7% (95% CI 23.5-25.8; 1-KM would say 30.4%) | Breast 20.6% (95% CI 19.3-21.8; 1-KM would say 23.3%) | Colorectal 10.8% (95% CI 9.7-11.8; 1-KM would say 14.4%)
+  Gray's test: statistic 375.87, permutation p=0.0005 (2000 shuffles)
+  CAVEAT (tell the user): Cumulative incidence of the first brain metastasis after the study's day 0, with death (OS_STATUS) as a competing risk: Aalen–Johansen, 95% intervals from its delta-method standard error. It is not 1 − Kaplan–Meier: that treats the dead as still at risk of the event and overstates incidence whenever death is common — the table sets the two side by side. Excluded rather than counted as event-free: 2101 who already had it by day 0, 0 without follow-up. Gray's test (weight 1) by permutation of cohort labels, 2000 shuffles, seeded; exact under the null that the cohorts share one distribution, entry times included. An event recorded from notes or registries is only as complete as the recording.
+  METHOD:
+    - Classify and count each patient's outcome [NSCLC · msk_chord_2024]: The incidence query above. Per patient, months from the study's day 0 (days ÷ 30.4375, rounded to 4 decimals) to the first brain metastasis after it (cause 1), death without it (cause 2, from OS_STATUS), or last follow-up (censored, OS_MONTHS); then counted by (entry, months, cause). Counts only.
+    - Classify and count each patient's outcome [Breast · msk_chord_2024]: The incidence query above. Per patient, months from the study's day 0 (days ÷ 30.4375, rounded to 4 decimals) to the first brain metastasis after it (cause 1), death without it (cause 2, from OS_STATUS), or last follow-up (censored, OS_MONTHS); then counted by (entry, months, cause). Counts only.
+    - Classify and count each patient's outcome [Colorectal · msk_chord_2024]: The incidence query above. Per patient, months from the study's day 0 (days ÷ 30.4375, rounded to 4 decimals) to the first brain metastasis after it (cause 1), death without it (cause 2, from OS_STATUS), or last follow-up (censored, OS_MONTHS); then counted by (entry, months, cause). Counts only.
+    - Set aside patients who cannot enter the risk set: Named and counted rather than treated as event-free. A patient whose first event was recorded after their last follow-up is kept, as censored or dead at follow-up.
+        NSCLC: 1352 already had it by day 0 · 0 without follow-up
+        Breast: 645 already had it by day 0 · 0 without follow-up
+        Colorectal: 104 already had it by day 0 · 0 without follow-up
+    - Estimate cumulative incidence by Aalen–Johansen: Death is a competing risk: at each time F1 rises by S(t−)·d1/R, with S the all-cause Kaplan–Meier survival and R those at risk. Standard error by the delta method (Aalen 1978); 95% interval F1 ± 1.959964·SE, clamped to [0, 1]. At each horizon the curve's value there, beside 1 − Kaplan–Meier of the event alone, which counts the dead as still at risk and overstates incidence.
+        horizons (months): 12, 24, 60
+        NSCLC: 6,457 at risk · 1406 events · 1967 deaths first · 3084 censored
+        Breast: 4,723 at risk · 905 events · 956 deaths first · 2862 censored
+        Colorectal: 5,439 at risk · 447 events · 1783 deaths first · 3209 censored
+    - Gray-type test by permutation: Per cohort, observed cause-1 events minus those expected from the pooled subdistribution hazard, the subdistribution risk set being Y·(1 − F1)/S (Gray 1988, weight 1), summed as Σ (O − E)²/E over cohorts. This is not Gray's variance-standardised χ², so its p does not come from a χ² table: the cohort labels are shuffled across the pooled records 2,000 times by a generator seeded with 1, and p = (b + 1)/(m + 1). Exact under the null that every cohort's records, entry times included, come from one distribution.
+        statistic / p: 375.8722 / 0.000500
+    Cumulative incidence and its standard error match statsmodels 0.15.0 CumIncidenceRight to 1e-12, and with delayed entry lifelines' AalenJohansenFitter; the Gray-type statistic and permutation p match the script's port exactly (test/incidence-golden.test.ts).
diff --git a/test/snapshots/labs.json b/test/snapshots/labs.json
new file mode 100644
index 00000000..c9dac5f4
--- /dev/null
+++ b/test/snapshots/labs.json
@@ -0,0 +1,568 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "Prostate, TP53 mutant": {
+      "count": 849,
+      "text": "CANCER_TYPE in [Prostate Cancer] (any sample) AND OQL[TP53: MUT]",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "Prostate, TP53 wild-type": {
+      "count": 2284,
+      "text": "CANCER_TYPE in [Prostate Cancer] (any sample) AND NOT OQL[TP53]",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "labs",
+      "cohorts": [
+        "Prostate, TP53 mutant",
+        "Prostate, TP53 wild-type"
+      ],
+      "labs": {
+        "test": "PSA",
+        "anchoredTo": "Treatment [SUBTYPE=Hormone]",
+        "responseDrop": 50,
+        "fromWeeks": -12,
+        "toWeeks": 52,
+        "binWeeks": 4,
+        "baselineDays": 90,
+        "panels": [
+          {
+            "cohort": "Prostate, TP53 mutant",
+            "anchored": 792,
+            "baseline": 649,
+            "positiveBaseline": 631,
+            "evaluable": 586,
+            "course": [
+              {
+                "week": -12,
+                "n": 84,
+                "q1": -36.5,
+                "median": -13,
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+              },
+              {
+                "week": -8,
+                "n": 119,
+                "q1": -32,
+                "median": -12,
+                "q3": 4
+              },
+              {
+                "week": -4,
+                "n": 149,
+                "q1": -18,
+                "median": -6,
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+              },
+              {
+                "week": 0,
+                "n": 208,
+                "q1": -84,
+                "median": -56,
+                "q3": -6
+              },
+              {
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+                "n": 407,
+                "q1": -96,
+                "median": -86,
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+              },
+              {
+                "week": 8,
+                "n": 319,
+                "q1": -98,
+                "median": -94,
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+              },
+              {
+                "week": 12,
+                "n": 327,
+                "q1": -100,
+                "median": -96,
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+              },
+              {
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+                "q1": -100,
+                "median": -98,
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+              },
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+                "q1": -100,
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+                "q1": -100,
+                "median": -96,
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+              },
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+                "n": 310,
+                "q1": -100,
+                "median": -98,
+                "q3": -74.5
+              },
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+                "n": 234,
+                "q1": -100,
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+                "q1": -100,
+                "median": -98,
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+              },
+              {
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+                "n": 238,
+                "q1": -100,
+                "median": -95,
+                "q3": -44.5
+              },
+              {
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+                "n": 213,
+                "q1": -100,
+                "median": -94,
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+              },
+              {
+                "week": 52,
+                "n": 23,
+                "q1": -100,
+                "median": -82,
+                "q3": 31
+              }
+            ],
+            "best": [
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+              1,
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+              1,
+              2,
+              {
+                "…": "181 more",
+                "sha256": "33dc4736a2e19428"
+              }
+            ],
+            "responders": 503,
+            "waterfall": [
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+              {
+                "…": "566 more",
+                "sha256": "f1b10b1b2f434557"
+              }
+            ]
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+          {
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+              {
+                "week": 20,
+                "n": 563,
+                "q1": -100,
+                "median": -100,
+                "q3": -92
+              },
+              {
+                "week": 24,
+                "n": 582,
+                "q1": -100,
+                "median": -100,
+                "q3": -92
+              },
+              {
+                "week": 28,
+                "n": 733,
+                "q1": -100,
+                "median": -100,
+                "q3": -94
+              },
+              {
+                "week": 32,
+                "n": 464,
+                "q1": -100,
+                "median": -100,
+                "q3": -88
+              },
+              {
+                "week": 36,
+                "n": 488,
+                "q1": -100,
+                "median": -100,
+                "q3": -77.5
+              },
+              {
+                "week": 40,
+                "n": 615,
+                "q1": -100,
+                "median": -100,
+                "q3": -90
+              },
+              {
+                "week": 44,
+                "n": 508,
+                "q1": -100,
+                "median": -100,
+                "q3": -78
+              },
+              {
+                "week": 48,
+                "n": 480,
+                "q1": -100,
+                "median": -100,
+                "q3": -69.5
+              },
+              {
+                "week": 52,
+                "n": 56,
+                "q1": -100,
+                "median": -98,
+                "q3": -21.5
+              }
+            ],
+            "best": [
+              966,
+              121,
+              49,
+              43,
+              32,
+              15,
+              17,
+              13,
+              8,
+              4,
+              8,
+              11,
+              7,
+              3,
+              9,
+              8,
+              3,
+              3,
+              3,
+              5,
+              {
+                "…": "181 more",
+                "sha256": "c24b835620b51a4f"
+              }
+            ],
+            "responders": 1347,
+            "waterfall": [
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              -100,
+              {
+                "…": "1429 more",
+                "sha256": "96ae3b8f4f34c36f"
+              }
+            ]
+          }
+        ],
+        "responseP": 0.0000014345634433689822
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": true,
+        "aggregateOnly": []
+      },
+      "caveat": "PSA as % change from each patient's baseline — the mean of their readings on the last day with any, in the 90 days up to and including the first Treatment [SUBTYPE=Hormone] — over -12 to 52 weeks around it, in 4-week bins; a patient tested several times in a bin counts once, at their mean, and no reading from the baseline day is drawn. Median and interquartile band are read from 2-point histograms, so they are exact to within a point, and the figure draws only bins with at least 5 patients; values above +300% share the top bin. Best response is each patient's lowest value after the anchor within the window; a responder fell by at least 50% (PCWG3's PSA50), counted exactly. Denominators: Prostate, TP53 mutant — 792 anchored, 649 with a baseline (18 of them left out: a baseline of 0 or below has no % change), 586 with a value after; Prostate, TP53 wild-type — 1,888 anchored, 1,568 with a baseline (25 of them left out: a baseline of 0 or below has no % change), 1,449 with a value after. Patients tested more often are over-represented in any bin they appear in, and testing itself is more frequent when a value is moving; the course is of those measured, not of everyone. Response rates compared with a two-sided Fisher test. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients' % change from baseline",
+            "detail": "The labs:course and labs:best queries above (and labs:waterfall, one bar per patient, from a patient-level participant). Counts per bin and per rule, no individual value except the waterfall's bars.",
+            "scope": "Prostate, TP53 mutant · msk_chord_2024",
+            "rowsIn": 849
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients' % change from baseline",
+            "detail": "The labs:course and labs:best queries above (and labs:waterfall, one bar per patient, from a patient-level participant). Counts per bin and per rule, no individual value except the waterfall's bars.",
+            "scope": "Prostate, TP53 wild-type · msk_chord_2024",
+            "rowsIn": 2284
+          },
+          {
+            "kind": "transform",
+            "title": "Anchor, baseline and change",
+            "detail": "Day 0 is each patient's first Treatment [SUBTYPE=Hormone] (event type, key and value matched ignoring case); a patient without one is not anchored. PSA readings are those whose result parses as a number. The baseline is the mean of a patient's readings on the last day with any in the 90 days up to and including day 0; a patient without one has no baseline, and one whose baseline is 0 or below is left out, since a % change from it means nothing. Every later reading becomes 100·(value − baseline)/baseline; readings on the baseline day itself are not used.",
+            "params": [
+              [
+                "Prostate, TP53 mutant",
+                "792 anchored → 649 with a baseline → 631 with a positive one → 586 with a reading after day 0"
+              ],
+              [
+                "Prostate, TP53 wild-type",
+                "1,888 anchored → 1,568 with a baseline → 1,543 with a positive one → 1,449 with a reading after day 0"
+              ]
+            ]
+          },
+          {
+            "kind": "transform",
+            "title": "Bin the course",
+            "detail": "Weeks -12 to 52 around day 0 in 4-week bins; a patient tested several times in a bin counts once, at their mean. % change in 2-point bins from -100 to 300, values beyond either end in the end bin. The figure draws a bin only from 5 patients."
+          },
+          {
+            "kind": "statistic",
+            "title": "Median and interquartile range per week",
+            "detail": "From each bin's histogram, with the bin midpoints as values and linear interpolation between ranks — exact to within a 2-point bin."
+          },
+          {
+            "kind": "statistic",
+            "title": "Best response",
+            "detail": "Each patient's lowest % change after day 0 within the window; a responder fell by at least 50%, counted from the unbinned values. The two cohorts' response rates are compared with a two-sided Fisher's exact test.",
+            "params": [
+              [
+                "Prostate, TP53 mutant",
+                "503 responders of 586"
+              ],
+              [
+                "Prostate, TP53 wild-type",
+                "1,347 responders of 1,449"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "quantilesFromHistogram",
+            "fisherExact"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Quantiles from histograms match numpy percentile on the expanded values, and Fisher's exact test scipy 1.18.1 fisher_exact, to a relative 1e-6 (test/moments-golden.test.ts, test/tables-golden.test.ts).",
+        "check": {
+          "labs:Prostate, TP53 mutant:-12:q1": {
+            "v": -36.5,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-12:median": {
+            "v": -13,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-12:q3": {
+            "v": 32,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-8:q1": {
+            "v": -32,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-8:median": {
+            "v": -12,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-8:q3": {
+            "v": 4,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-4:q1": {
+            "v": -18,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-4:median": {
+            "v": -6,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:-4:q3": {
+            "v": 8,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:0:q1": {
+            "v": -84,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:0:median": {
+            "v": -56,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:0:q3": {
+            "v": -6,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:4:q1": {
+            "v": -96,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:4:median": {
+            "v": -86,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:4:q3": {
+            "v": -48,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:8:q1": {
+            "v": -98,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:8:median": {
+            "v": -94,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:8:q3": {
+            "v": -66,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:12:q1": {
+            "v": -100,
+            "abs": 0.000500001
+          },
+          "labs:Prostate, TP53 mutant:12:median": {
+            "v": -96,
+            "abs": 0.000500001
+          },
+          "…": {
+            "more": 83,
+            "sha256": "c0351cf9c722cb35"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/labs.sql b/test/snapshots/labs.sql
new file mode 100644
index 00000000..80137716
--- /dev/null
+++ b/test/snapshots/labs.sql
@@ -0,0 +1,325 @@
+-- cohort Prostate, TP53 mutant
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort Prostate, TP53 wild-type
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+
+-- view 0 labs: labs:best:Prostate, TP53 mutant · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('Hormone')))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+lab AS (
+  SELECT p.stable_id AS pid, ce.start_date AS day, if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN snapshot_db.clinical_event_data t ON t.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(t.key)) = lowerUTF8(trimBoth('TEST')) AND lowerUTF8(trimBoth(t.value)) = lowerUTF8(trimBoth('PSA'))
+  JOIN snapshot_db.clinical_event_data r ON r.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(r.key)) = lowerUTF8(trimBoth('RESULT'))
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Lab_Test'))
+    AND p.stable_id IN (SELECT pid FROM anch) AND if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) IS NOT NULL
+),
+base AS (
+  SELECT lab.pid AS pid, max(lab.day) AS bday,
+         arrayAvg(arrayMap((x) -> assumeNotNull(x.1), arrayFilter((x) -> x.2 = bday, groupArray((lab.v, lab.day))))) AS b
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid
+  WHERE lab.day >= anch.day - 90 AND lab.day <= anch.day GROUP BY lab.pid
+),
+chg AS (
+  SELECT lab.pid AS pid, lab.day - anch.day AS rel, 100 * (lab.v - base.b) / base.b AS pc
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid INNER JOIN base ON base.pid = lab.pid
+  WHERE base.b > 0 AND lab.day != base.bday
+    AND lab.day - anch.day >= -84 AND lab.day - anch.day <= 364
+)
+SELECT 'best' AS what, toInt32(floor((least(300, greatest(-100, best)) - (-100) + 1) / 2)) AS pbin, count() AS n FROM (SELECT pid, min(pc) AS best FROM chg WHERE rel > 0 GROUP BY pid) GROUP BY pbin
+UNION ALL SELECT 'responders' AS what, 0 AS pbin, countIf(best <= -50) AS n FROM (SELECT pid, min(pc) AS best FROM chg WHERE rel > 0 GROUP BY pid)
+UNION ALL SELECT 'anchored' AS what, 0 AS pbin, count() AS n FROM anch
+UNION ALL SELECT 'baseline' AS what, 0 AS pbin, count() AS n FROM base
+UNION ALL SELECT 'positive_baseline' AS what, 0 AS pbin, countIf(b > 0) AS n FROM base
+UNION ALL SELECT 'after' AS what, 0 AS pbin, uniqExactIf(pid, rel > 0) AS n FROM chg
+
+-- view 0 labs: labs:best:Prostate, TP53 wild-type · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('Hormone')))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+lab AS (
+  SELECT p.stable_id AS pid, ce.start_date AS day, if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN snapshot_db.clinical_event_data t ON t.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(t.key)) = lowerUTF8(trimBoth('TEST')) AND lowerUTF8(trimBoth(t.value)) = lowerUTF8(trimBoth('PSA'))
+  JOIN snapshot_db.clinical_event_data r ON r.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(r.key)) = lowerUTF8(trimBoth('RESULT'))
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Lab_Test'))
+    AND p.stable_id IN (SELECT pid FROM anch) AND if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) IS NOT NULL
+),
+base AS (
+  SELECT lab.pid AS pid, max(lab.day) AS bday,
+         arrayAvg(arrayMap((x) -> assumeNotNull(x.1), arrayFilter((x) -> x.2 = bday, groupArray((lab.v, lab.day))))) AS b
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid
+  WHERE lab.day >= anch.day - 90 AND lab.day <= anch.day GROUP BY lab.pid
+),
+chg AS (
+  SELECT lab.pid AS pid, lab.day - anch.day AS rel, 100 * (lab.v - base.b) / base.b AS pc
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid INNER JOIN base ON base.pid = lab.pid
+  WHERE base.b > 0 AND lab.day != base.bday
+    AND lab.day - anch.day >= -84 AND lab.day - anch.day <= 364
+)
+SELECT 'best' AS what, toInt32(floor((least(300, greatest(-100, best)) - (-100) + 1) / 2)) AS pbin, count() AS n FROM (SELECT pid, min(pc) AS best FROM chg WHERE rel > 0 GROUP BY pid) GROUP BY pbin
+UNION ALL SELECT 'responders' AS what, 0 AS pbin, countIf(best <= -50) AS n FROM (SELECT pid, min(pc) AS best FROM chg WHERE rel > 0 GROUP BY pid)
+UNION ALL SELECT 'anchored' AS what, 0 AS pbin, count() AS n FROM anch
+UNION ALL SELECT 'baseline' AS what, 0 AS pbin, count() AS n FROM base
+UNION ALL SELECT 'positive_baseline' AS what, 0 AS pbin, countIf(b > 0) AS n FROM base
+UNION ALL SELECT 'after' AS what, 0 AS pbin, uniqExactIf(pid, rel > 0) AS n FROM chg
+
+-- view 0 labs: labs:course:Prostate, TP53 mutant · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('Hormone')))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+lab AS (
+  SELECT p.stable_id AS pid, ce.start_date AS day, if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN snapshot_db.clinical_event_data t ON t.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(t.key)) = lowerUTF8(trimBoth('TEST')) AND lowerUTF8(trimBoth(t.value)) = lowerUTF8(trimBoth('PSA'))
+  JOIN snapshot_db.clinical_event_data r ON r.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(r.key)) = lowerUTF8(trimBoth('RESULT'))
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Lab_Test'))
+    AND p.stable_id IN (SELECT pid FROM anch) AND if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) IS NOT NULL
+),
+base AS (
+  SELECT lab.pid AS pid, max(lab.day) AS bday,
+         arrayAvg(arrayMap((x) -> assumeNotNull(x.1), arrayFilter((x) -> x.2 = bday, groupArray((lab.v, lab.day))))) AS b
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid
+  WHERE lab.day >= anch.day - 90 AND lab.day <= anch.day GROUP BY lab.pid
+),
+chg AS (
+  SELECT lab.pid AS pid, lab.day - anch.day AS rel, 100 * (lab.v - base.b) / base.b AS pc
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid INNER JOIN base ON base.pid = lab.pid
+  WHERE base.b > 0 AND lab.day != base.bday
+    AND lab.day - anch.day >= -84 AND lab.day - anch.day <= 364
+)
+SELECT tbin, pbin, count() AS n FROM (
+  SELECT pid, toInt32(floor(rel / 28)) AS tbin, toInt32(floor((least(300, greatest(-100, avg(pc))) - (-100) + 1) / 2)) AS pbin
+  FROM chg GROUP BY pid, tbin
+) GROUP BY tbin, pbin ORDER BY tbin, pbin
+
+-- view 0 labs: labs:course:Prostate, TP53 wild-type · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('Hormone')))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+lab AS (
+  SELECT p.stable_id AS pid, ce.start_date AS day, if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN snapshot_db.clinical_event_data t ON t.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(t.key)) = lowerUTF8(trimBoth('TEST')) AND lowerUTF8(trimBoth(t.value)) = lowerUTF8(trimBoth('PSA'))
+  JOIN snapshot_db.clinical_event_data r ON r.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(r.key)) = lowerUTF8(trimBoth('RESULT'))
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Lab_Test'))
+    AND p.stable_id IN (SELECT pid FROM anch) AND if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) IS NOT NULL
+),
+base AS (
+  SELECT lab.pid AS pid, max(lab.day) AS bday,
+         arrayAvg(arrayMap((x) -> assumeNotNull(x.1), arrayFilter((x) -> x.2 = bday, groupArray((lab.v, lab.day))))) AS b
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid
+  WHERE lab.day >= anch.day - 90 AND lab.day <= anch.day GROUP BY lab.pid
+),
+chg AS (
+  SELECT lab.pid AS pid, lab.day - anch.day AS rel, 100 * (lab.v - base.b) / base.b AS pc
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid INNER JOIN base ON base.pid = lab.pid
+  WHERE base.b > 0 AND lab.day != base.bday
+    AND lab.day - anch.day >= -84 AND lab.day - anch.day <= 364
+)
+SELECT tbin, pbin, count() AS n FROM (
+  SELECT pid, toInt32(floor(rel / 28)) AS tbin, toInt32(floor((least(300, greatest(-100, avg(pc))) - (-100) + 1) / 2)) AS pbin
+  FROM chg GROUP BY pid, tbin
+) GROUP BY tbin, pbin ORDER BY tbin, pbin
+
+-- view 0 labs: labs:waterfall:Prostate, TP53 mutant · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('Hormone')))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+lab AS (
+  SELECT p.stable_id AS pid, ce.start_date AS day, if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN snapshot_db.clinical_event_data t ON t.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(t.key)) = lowerUTF8(trimBoth('TEST')) AND lowerUTF8(trimBoth(t.value)) = lowerUTF8(trimBoth('PSA'))
+  JOIN snapshot_db.clinical_event_data r ON r.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(r.key)) = lowerUTF8(trimBoth('RESULT'))
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Lab_Test'))
+    AND p.stable_id IN (SELECT pid FROM anch) AND if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) IS NOT NULL
+),
+base AS (
+  SELECT lab.pid AS pid, max(lab.day) AS bday,
+         arrayAvg(arrayMap((x) -> assumeNotNull(x.1), arrayFilter((x) -> x.2 = bday, groupArray((lab.v, lab.day))))) AS b
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid
+  WHERE lab.day >= anch.day - 90 AND lab.day <= anch.day GROUP BY lab.pid
+),
+chg AS (
+  SELECT lab.pid AS pid, lab.day - anch.day AS rel, 100 * (lab.v - base.b) / base.b AS pc
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid INNER JOIN base ON base.pid = lab.pid
+  WHERE base.b > 0 AND lab.day != base.bday
+    AND lab.day - anch.day >= -84 AND lab.day - anch.day <= 364
+)
+SELECT round(best, 1) AS best FROM (SELECT pid, min(pc) AS best FROM chg WHERE rel > 0 GROUP BY pid)
+ORDER BY best LIMIT 5000
+
+-- view 0 labs: labs:waterfall:Prostate, TP53 wild-type · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Prostate Cancer'))))
+INTERSECT DISTINCT
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))),
+anch AS (
+  SELECT p.stable_id AS pid, min(ce.start_date) AS day
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_data WHERE lowerUTF8(trimBoth(key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(value)) = lowerUTF8(trimBoth('Hormone')))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+lab AS (
+  SELECT p.stable_id AS pid, ce.start_date AS day, if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) AS v
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  JOIN snapshot_db.clinical_event_data t ON t.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(t.key)) = lowerUTF8(trimBoth('TEST')) AND lowerUTF8(trimBoth(t.value)) = lowerUTF8(trimBoth('PSA'))
+  JOIN snapshot_db.clinical_event_data r ON r.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(r.key)) = lowerUTF8(trimBoth('RESULT'))
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Lab_Test'))
+    AND p.stable_id IN (SELECT pid FROM anch) AND if(isFinite(ifNull(toFloat64OrNull(r.value), nan)), toFloat64OrNull(r.value), NULL) IS NOT NULL
+),
+base AS (
+  SELECT lab.pid AS pid, max(lab.day) AS bday,
+         arrayAvg(arrayMap((x) -> assumeNotNull(x.1), arrayFilter((x) -> x.2 = bday, groupArray((lab.v, lab.day))))) AS b
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid
+  WHERE lab.day >= anch.day - 90 AND lab.day <= anch.day GROUP BY lab.pid
+),
+chg AS (
+  SELECT lab.pid AS pid, lab.day - anch.day AS rel, 100 * (lab.v - base.b) / base.b AS pc
+  FROM lab INNER JOIN anch ON anch.pid = lab.pid INNER JOIN base ON base.pid = lab.pid
+  WHERE base.b > 0 AND lab.day != base.bday
+    AND lab.day - anch.day >= -84 AND lab.day - anch.day <= 364
+)
+SELECT round(best, 1) AS best FROM (SELECT pid, min(pc) AS best FROM chg WHERE rel > 0 GROUP BY pid)
+ORDER BY best LIMIT 5000
diff --git a/test/snapshots/labs.svg b/test/snapshots/labs.svg
new file mode 100644
index 00000000..cf0fd370
--- /dev/null
+++ b/test/snapshots/labs.svg
@@ -0,0 +1,2 @@
+<!-- view 0 labs -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 510" width="100%" role="img" aria-label="Lab course — PSA from Treatment [SUBTYPE=Hormone] — Prostate, TP53 mutant, Prostate, TP53 wild-type" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Lab course — PSA from Treatment [SUBTYPE=Hormone] — Prostate, TP53 mutant, Prostate, TP53 wild-type</title><rect width="960" height="510" fill="#ffffff"/><text x="69" y="14" font-size="12" fill="#16232b" font-weight="600">PSA, % change from baseline</text><line x1="69" y1="251" x2="487" y2="251" stroke="#dde4e8" stroke-width="0.75"/><text x="63" y="254.5" font-size="10" fill="#5b6b76" text-anchor="end">−100%</text><line x1="69" y1="194.8" x2="487" y2="194.8" stroke="#dde4e8" stroke-width="0.75"/><text x="63" y="198.3" font-size="10" fill="#5b6b76" text-anchor="end">−50%</text><line x1="69" y1="138.5" x2="487" y2="138.5" stroke="#dde4e8" stroke-width="0.75"/><text x="63" y="142" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="69" y1="82.3" x2="487" y2="82.3" stroke="#dde4e8" stroke-width="0.75"/><text x="63" y="85.8" font-size="10" fill="#5b6b76" text-anchor="end">+50%</text><line x1="69" y1="26" x2="487" y2="26" stroke="#dde4e8" stroke-width="0.75"/><text x="63" y="29.5" font-size="10" fill="#5b6b76" text-anchor="end">+100%</text><text x="16.8" y="138.5" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 16.8 138.5)">% change from baseline</text><line x1="69" y1="138.5" x2="487" y2="138.5" stroke="#5b6b76" stroke-width="1"/><line x1="69" y1="251" x2="69" y2="254" stroke="#5b6b76" stroke-width="1"/><text x="60" y="267" font-size="10" fill="#5b6b76">−12</text><line x1="147.4" y1="251" x2="147.4" y2="254" stroke="#5b6b76" stroke-width="1"/><text x="144.4" y="267" font-size="10" fill="#5b6b76">0</text><line x1="225.8" y1="251" x2="225.8" y2="254" stroke="#5b6b76" stroke-width="1"/><text x="219.8" y="267" font-size="10" fill="#5b6b76">12</text><line x1="304.1" y1="251" x2="304.1" y2="254" stroke="#5b6b76" stroke-width="1"/><text x="298.1" y="267" font-size="10" fill="#5b6b76">24</text><line x1="382.5" y1="251" x2="382.5" y2="254" stroke="#5b6b76" stroke-width="1"/><text x="376.5" y="267" font-size="10" fill="#5b6b76">36</text><line x1="460.9" y1="251" x2="460.9" y2="254" stroke="#5b6b76" stroke-width="1"/><text x="454.9" y="267" font-size="10" fill="#5b6b76">48</text><line x1="69" y1="251" x2="487" y2="251" stroke="#5b6b76" stroke-width="1"/><text x="278" y="286" font-size="11" fill="#16232b" text-anchor="middle">Weeks from Treatment [SUBTYPE=Hormone]</text><line x1="69" y1="194.8" x2="487" y2="194.8" stroke="#5b6b76" stroke-width="1" stroke-dasharray="4 3"/><text x="485" y="190.8" font-size="10" fill="#5b6b76" text-anchor="end">responder: fell ≥50%</text><line x1="147.4" y1="26" x2="147.4" y2="251" stroke="#16232b" stroke-width="1" stroke-dasharray="2 2"/><path d="M82.1 102.5L108.2 134L134.3 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r="2.5" fill="#0072B2"/><circle cx="264.9" cy="248.8" r="2.5" fill="#0072B2"/><circle cx="291.1" cy="248.8" r="2.5" fill="#0072B2"/><circle cx="317.2" cy="246.5" r="2.5" fill="#0072B2"/><circle cx="343.3" cy="248.8" r="2.5" fill="#0072B2"/><circle cx="369.4" cy="246.5" r="2.5" fill="#0072B2"/><circle cx="395.6" cy="246.5" r="2.5" fill="#0072B2"/><circle cx="421.7" cy="248.8" r="2.5" fill="#0072B2"/><circle cx="447.8" cy="245.4" r="2.5" fill="#0072B2"/><circle cx="473.9" cy="244.3" r="2.5" fill="#0072B2"/><circle cx="500.1" cy="230.8" r="2.5" fill="#0072B2"/><path d="M82.1 143L108.2 136.3L134.3 131.8L160.4 159.9L186.6 219.5L212.7 233.6L238.8 237.5L264.9 244.3L291.1 242L317.2 242L343.3 244.3L369.4 237.5L395.6 225.7L421.7 239.8L447.8 226.3L473.9 216.7L500.1 162.7L500.1 251L473.9 251L447.8 251L421.7 251L395.6 251L369.4 251L343.3 251L317.2 251L291.1 251L264.9 251L238.8 251L212.7 251L186.6 246.5L160.4 233L134.3 154.3L108.2 172.3L82.1 184.6Z" fill="#E69F00" opacity="0.15"/><path d="M82.1 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r="2.5" fill="#E69F00"/><circle cx="473.9" cy="251" r="2.5" fill="#E69F00"/><circle cx="500.1" cy="248.8" r="2.5" fill="#E69F00"/><text x="531" y="14" font-size="12" fill="#16232b" font-weight="600">Best % change after the start, worst to best</text><text x="592" y="37" font-size="11" fill="#0072B2" font-weight="600">Prostate, TP53 mutant</text><text x="717.3" y="37" font-size="11" fill="#16232b">85.8% fell ≥50% (503/586)</text><line x1="592" y1="141" x2="948" y2="141" stroke="#dde4e8" stroke-width="0.75"/><text x="586" y="144.5" font-size="10" fill="#5b6b76" text-anchor="end">−100%</text><line x1="592" y1="117" x2="948" y2="117" stroke="#dde4e8" stroke-width="0.75"/><text x="586" y="120.5" font-size="10" fill="#5b6b76" text-anchor="end">−50%</text><line x1="592" y1="93" x2="948" y2="93" stroke="#dde4e8" stroke-width="0.75"/><text x="586" y="96.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="592" y1="69" x2="948" y2="69" stroke="#dde4e8" stroke-width="0.75"/><text x="586" y="72.5" font-size="10" fill="#5b6b76" text-anchor="end">+50%</text><line x1="592" y1="45" x2="948" y2="45" stroke="#dde4e8" stroke-width="0.75"/><text x="586" y="48.5" font-size="10" fill="#5b6b76" text-anchor="end">+100%</text><text x="539.8" y="93" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 539.8 93)">best % change</text><path d="M592 45h0.6v48h-0.6zM592.6 45h0.6v48h-0.6zM593.2 45h0.6v48h-0.6zM593.8 45h0.6v48h-0.6zM594.4 45h0.6v48h-0.6zM595 45h0.6v48h-0.6zM595.6 45h0.6v48h-0.6zM596.3 45h0.6v48h-0.6zM596.9 45h0.6v48h-0.6zM597.5 45h0.6v48h-0.6zM598.1 48.6h0.6v44.4h-0.6zM598.7 51.9h0.6v41.1h-0.6zM599.3 58h0.6v35h-0.6zM599.9 60.2h0.6v32.8h-0.6zM600.5 60.4h0.6v32.6h-0.6zM601.1 60.6h0.6v32.4h-0.6zM601.7 62.5h0.6v30.5h-0.6zM602.3 69.5h0.6v23.5h-0.6zM602.9 70.7h0.6v22.3h-0.6zM603.5 74.4h0.6v18.6h-0.6zM604.2 77.1h0.6v15.9h-0.6zM604.8 77.9h0.6v15.1h-0.6zM605.4 79.9h0.6v13.1h-0.6zM606 80.3h0.6v12.7h-0.6zM606.6 81h0.6v12h-0.6zM607.2 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236h0.5v48h-0.5zM938.2 236h0.5v48h-0.5zM938.4 236h0.5v48h-0.5zM938.7 236h0.5v48h-0.5zM938.9 236h0.5v48h-0.5zM939.2 236h0.5v48h-0.5zM939.4 236h0.5v48h-0.5zM939.6 236h0.5v48h-0.5zM939.9 236h0.5v48h-0.5zM940.1 236h0.5v48h-0.5zM940.4 236h0.5v48h-0.5zM940.6 236h0.5v48h-0.5zM940.9 236h0.5v48h-0.5zM941.1 236h0.5v48h-0.5zM941.4 236h0.5v48h-0.5zM941.6 236h0.5v48h-0.5zM941.9 236h0.5v48h-0.5zM942.1 236h0.5v48h-0.5zM942.3 236h0.5v48h-0.5zM942.6 236h0.5v48h-0.5zM942.8 236h0.5v48h-0.5zM943.1 236h0.5v48h-0.5zM943.3 236h0.5v48h-0.5zM943.6 236h0.5v48h-0.5zM943.8 236h0.5v48h-0.5zM944.1 236h0.5v48h-0.5zM944.3 236h0.5v48h-0.5zM944.6 236h0.5v48h-0.5zM944.8 236h0.5v48h-0.5zM945.1 236h0.5v48h-0.5zM945.3 236h0.5v48h-0.5zM945.5 236h0.5v48h-0.5zM945.8 236h0.5v48h-0.5zM946 236h0.5v48h-0.5zM946.3 236h0.5v48h-0.5zM946.5 236h0.5v48h-0.5zM946.8 236h0.5v48h-0.5zM947 236h0.5v48h-0.5zM947.3 236h0.5v48h-0.5zM947.5 236h0.5v48h-0.5zM947.8 236h0.5v48h-0.5z" fill="#E69F00" opacity="0.85"/><line x1="592" y1="236" x2="948" y2="236" stroke="#5b6b76" stroke-width="1"/><line x1="592" y1="260" x2="948" y2="260" stroke="#16232b" stroke-width="1" stroke-dasharray="3 3"/><text x="592" y="298" font-size="10" fill="#5b6b76">patients, worst to best: 1,449</text><rect x="8" y="324" width="10" height="10" rx="2" fill="#0072B2" opacity="1"/><text x="24" y="331" font-size="11" fill="#16232b">Prostate, TP53 mutant · 792 started, 649 with a baseline, 586 measured after</text><rect x="8" y="344" width="10" height="10" rx="2" fill="#E69F00" opacity="1"/><text x="24" y="351" font-size="11" fill="#16232b">Prostate, TP53 wild-type · 1,888 started, 1,568 with a baseline, 1,449 measured after</text><text x="8" y="371" font-size="11" fill="#16232b">response rates differ: Fisher p = 1.4e-6</text><text x="8" y="394" font-size="10" fill="#5b6b76">PSA as % change from each patient's baseline — the mean of their readings on the last day with any, in the 90 days up to and including the first Treatment</text><text x="8" y="408" font-size="10" fill="#5b6b76">[SUBTYPE=Hormone] — over -12 to 52 weeks around it, in 4-week bins; a patient tested several times in a bin counts once, at their mean, and no reading from the</text><text x="8" y="422" font-size="10" fill="#5b6b76">baseline day is drawn. Median and interquartile band are read from 2-point histograms, so they are exact to within a point, and the figure draws only bins with at least 5</text><text x="8" y="436" font-size="10" fill="#5b6b76">patients; values above +300% share the top bin. Best response is each patient's lowest value after the anchor within the window; a responder fell by at least 50%</text><text x="8" y="450" font-size="10" fill="#5b6b76">(PCWG3's PSA50), counted exactly. Denominators: Prostate, TP53 mutant — 792 anchored, 649 with a baseline (18 of them left out: a baseline of 0 or below has no %</text><text x="8" y="464" font-size="10" fill="#5b6b76">change), 586 with a value after; Prostate, TP53 wild-type — 1,888 anchored, 1,568 with a baseline (25 of them left out: a baseline of 0 or below has no % change), 1,449</text><text x="8" y="478" font-size="10" fill="#5b6b76">with a value after. Patients tested more often are over-represented in any bin they appear in, and testing itself is more frequent when a value is moving; the course is of</text><text x="8" y="492" font-size="10" fill="#5b6b76">those measured, not of everyone. Response rates compared with a two-sided Fisher test.</text></svg>
diff --git a/test/snapshots/labs.txt b/test/snapshots/labs.txt
new file mode 100644
index 00000000..c8e69b85
--- /dev/null
+++ b/test/snapshots/labs.txt
@@ -0,0 +1,25 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Prostate, TP53 mutant: 849 patients — CANCER_TYPE in [Prostate Cancer] (any sample) AND OQL[TP53: MUT]
+- Prostate, TP53 wild-type: 2,284 patients — CANCER_TYPE in [Prostate Cancer] (any sample) AND NOT OQL[TP53]
+
+Lab course: PSA % change from baseline, weeks from Treatment [SUBTYPE=Hormone]:
+  Prostate, TP53 mutant: 792 started, 649 with a baseline, 586 measured after; 85.8% fell ≥50% (503/586)
+    median [IQR] by week: -12w -13% [-36.5, 32], -8w -12% [-32, 4], -4w -6% [-18, 8], 0w -56% [-84, -6], 4w -86% [-96, -48], 8w -94% [-98, -66], 12w -96% [-100, -63], 16w -98% [-100, -82], 20w -98% [-100, -64], 24w -96% [-100, -62], 28w -98% [-100, -74.5], 32w -96% [-100, -72], 36w -96% [-100, -42], 40w -98% [-100, -54], 44w -95% [-100, -44.5], 48w -94% [-100, -22], 52w -82% [-100, 31]
+  Prostate, TP53 wild-type: 1,888 started, 1,568 with a baseline, 1,449 measured after; 93.0% fell ≥50% (1347/1449)
+    median [IQR] by week: -12w -22% [-41, -4], -8w -14% [-30, 2], -4w -6% [-14, 6], 0w -64% [-84, -19], 4w -90% [-96, -72], 8w -98% [-100, -84.5], 12w -98% [-100, -88], 16w -100% [-100, -94], 20w -100% [-100, -92], 24w -100% [-100, -92], 28w -100% [-100, -94], 32w -100% [-100, -88], 36w -100% [-100, -77.5], 40w -100% [-100, -90], 44w -100% [-100, -78], 48w -100% [-100, -69.5], 52w -98% [-100, -21.5]
+  response rates differ: Fisher p=1.4e-6
+  CAVEAT (tell the user): PSA as % change from each patient's baseline — the mean of their readings on the last day with any, in the 90 days up to and including the first Treatment [SUBTYPE=Hormone] — over -12 to 52 weeks around it, in 4-week bins; a patient tested several times in a bin counts once, at their mean, and no reading from the baseline day is drawn. Median and interquartile band are read from 2-point histograms, so they are exact to within a point, and the figure draws only bins with at least 5 patients; values above +300% share the top bin. Best response is each patient's lowest value after the anchor within the window; a responder fell by at least 50% (PCWG3's PSA50), counted exactly. Denominators: Prostate, TP53 mutant — 792 anchored, 649 with a baseline (18 of them left out: a baseline of 0 or below has no % change), 586 with a value after; Prostate, TP53 wild-type — 1,888 anchored, 1,568 with a baseline (25 of them left out: a baseline of 0 or below has no % change), 1,449 with a value after. Patients tested more often are over-represented in any bin they appear in, and testing itself is more frequent when a value is moving; the course is of those measured, not of everyone. Response rates compared with a two-sided Fisher test. 
+  METHOD:
+    - Count patients' % change from baseline [Prostate, TP53 mutant · msk_chord_2024]: The labs:course and labs:best queries above (and labs:waterfall, one bar per patient, from a patient-level participant). Counts per bin and per rule, no individual value except the waterfall's bars.
+    - Count patients' % change from baseline [Prostate, TP53 wild-type · msk_chord_2024]: The labs:course and labs:best queries above (and labs:waterfall, one bar per patient, from a patient-level participant). Counts per bin and per rule, no individual value except the waterfall's bars.
+    - Anchor, baseline and change: Day 0 is each patient's first Treatment [SUBTYPE=Hormone] (event type, key and value matched ignoring case); a patient without one is not anchored. PSA readings are those whose result parses as a number. The baseline is the mean of a patient's readings on the last day with any in the 90 days up to and including day 0; a patient without one has no baseline, and one whose baseline is 0 or below is left out, since a % change from it means nothing. Every later reading becomes 100·(value − baseline)/baseline; readings on the baseline day itself are not used.
+        Prostate, TP53 mutant: 792 anchored → 649 with a baseline → 631 with a positive one → 586 with a reading after day 0
+        Prostate, TP53 wild-type: 1,888 anchored → 1,568 with a baseline → 1,543 with a positive one → 1,449 with a reading after day 0
+    - Bin the course: Weeks -12 to 52 around day 0 in 4-week bins; a patient tested several times in a bin counts once, at their mean. % change in 2-point bins from -100 to 300, values beyond either end in the end bin. The figure draws a bin only from 5 patients.
+    - Median and interquartile range per week: From each bin's histogram, with the bin midpoints as values and linear interpolation between ranks — exact to within a 2-point bin.
+    - Best response: Each patient's lowest % change after day 0 within the window; a responder fell by at least 50%, counted from the unbinned values. The two cohorts' response rates are compared with a two-sided Fisher's exact test.
+        Prostate, TP53 mutant: 503 responders of 586
+        Prostate, TP53 wild-type: 1,347 responders of 1,449
+    Quantiles from histograms match numpy percentile on the expanded values, and Fisher's exact test scipy 1.18.1 fisher_exact, to a relative 1e-6 (test/moments-golden.test.ts, test/tables-golden.test.ts).
diff --git a/test/snapshots/logistic.json b/test/snapshots/logistic.json
new file mode 100644
index 00000000..76a7e055
--- /dev/null
+++ b/test/snapshots/logistic.json
@@ -0,0 +1,140 @@
+{
+  "study": "crc_apc_impact_2020",
+  "cohorts": {
+    "all patients": {
+      "count": 471,
+      "text": "all patients in the study",
+      "studies": [
+        "crc_apc_impact_2020"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "logistic",
+      "cohorts": [
+        "all patients"
+      ],
+      "logistic": {
+        "coefficients": [
+          {
+            "label": "APC mutation status: Mutated",
+            "reference": "Not mutated",
+            "level": "Mutated",
+            "axisIndex": 0,
+            "beta": -1.264274030987992,
+            "se": 0.4053957847207888,
+            "or": 0.28244426751592355,
+            "ciLow": 0.12760251879097487,
+            "ciHigh": 0.6251817362891188,
+            "p": 0.001817022203426293
+          }
+        ],
+        "n": 470,
+        "events": 27,
+        "outcomeOther": [
+          "Do not report",
+          "Indeterminate",
+          "Stable"
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "crc_apc_impact_2020",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Logistic regression on 470 patients, 27 with MSI_TYPE = Instable (1 term). Every other observed value of MSI_TYPE (Do not report, Indeterminate, Stable) was treated as “not event.” 1 patient from patient-level participants lacked the outcome or a covariate and were excluded. Computed from real per-patient rows within each local study — not pooled aggregate counts across untrusted remote peers.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Fetch one row per patient",
+            "detail": "The logisticRows query above. Patients with a value for MSI_TYPE and for every covariate are kept. 1 cohort patient had no row.",
+            "scope": "crc_apc_impact_2020",
+            "rowsIn": 471,
+            "rowsOut": 470
+          },
+          {
+            "kind": "transform",
+            "title": "Code the outcome",
+            "detail": "1 if MSI_TYPE = “Instable”, else 0 — every other observed value (Do not report, Indeterminate, Stable) counts as not the event."
+          },
+          {
+            "kind": "transform",
+            "title": "Code the covariates",
+            "detail": "Each numeric covariate is one term. Each categorical covariate becomes one 0/1 term per level, measured against a reference level that gets no term.",
+            "params": [
+              [
+                "APC mutation status: Mutated",
+                "1 if c0 = “Mutated”, else 0 · 325 of 470 patients · reference “Not mutated”: the fixed default for a mutated-vs-not axis"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Fit a logistic regression",
+            "detail": "Maximum likelihood for a binomial GLM with a logit link and an intercept, by Newton–Raphson with step-halving.",
+            "params": [
+              [
+                "patients / events",
+                "470 / 27"
+              ],
+              [
+                "terms",
+                "1 plus an intercept (not drawn)"
+              ],
+              [
+                "standard errors",
+                "from the Fisher information at the estimate"
+              ],
+              [
+                "95% interval",
+                "exp(β ± 1.959964·SE), Wald"
+              ],
+              [
+                "p-value",
+                "Wald χ² on 1 df, (β/SE)²"
+              ],
+              [
+                "fit",
+                "converged in 8 iterations"
+              ],
+              [
+                "log likelihood",
+                "-98.4061"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "logisticRegression"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Matches statsmodels 0.15.0 Logit to a relative 1e-6 on coefficients, SEs, p-values and intervals (test/regression-golden.test.ts).",
+        "check": {
+          "logistic:APC mutation status: Mutated:beta": {
+            "v": -1.264274030987992
+          },
+          "logistic:APC mutation status: Mutated:se": {
+            "v": 0.4053957847207888
+          },
+          "logistic:APC mutation status: Mutated:p": {
+            "v": 0.001817022203426293,
+            "rel": 0.00001,
+            "abs": 1e-14
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/logistic.sql b/test/snapshots/logistic.sql
new file mode 100644
index 00000000..61335237
--- /dev/null
+++ b/test/snapshots/logistic.sql
@@ -0,0 +1,84 @@
+-- cohort all patients
+-- crc_apc_impact_2020
+SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'
+
+-- view 0 logistic: logisticNotProfiled · crc_apc_impact_2020
+SELECT countIf(c0 = 'Not profiled') AS n FROM (
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'),
+y0 AS (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND attribute_name = 'MSI_TYPE' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND attribute_name = 'MSI_TYPE' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+),
+c0 AS (
+SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'APC'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS alt_case, if(countIf(g.variant_type = 'mutation') > 0, 'Mutated', 'Not mutated') AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND hugo_gene_symbol IN ('APC')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'crc_apc_impact_2020' AND replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case)
+)
+SELECT y0.v AS y, c0.v AS c0
+FROM y0
+  INNER JOIN c0 ON c0.id = y0.id
+)
+
+-- view 0 logistic: logisticRows · crc_apc_impact_2020
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020'),
+y0 AS (
+SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND attribute_name = 'MSI_TYPE' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND attribute_name = 'MSI_TYPE' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id
+),
+c0 AS (
+SELECT id, v FROM (
+SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'crc_apc_impact_2020' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'APC'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') AS alt_case, if(countIf(g.variant_type = 'mutation') > 0, 'Mutated', 'Not mutated') AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND hugo_gene_symbol IN ('APC')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'crc_apc_impact_2020' AND replaceOne(sd.patient_unique_id, 'crc_apc_impact_2020_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case)
+) WHERE v != 'Not profiled'
+)
+SELECT y0.v AS y, c0.v AS c0
+FROM y0
+  INNER JOIN c0 ON c0.id = y0.id
diff --git a/test/snapshots/logistic.svg b/test/snapshots/logistic.svg
new file mode 100644
index 00000000..bf605c10
--- /dev/null
+++ b/test/snapshots/logistic.svg
@@ -0,0 +1,2 @@
+<!-- view 0 logistic -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 760 151" width="100%" role="img" aria-label="Logistic regression — odds ratios" style="max-width:760px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Logistic regression — odds ratios</title><rect width="760" height="151" fill="#ffffff"/><text x="617.6" y="14" font-size="10" fill="#5b6b76">OR [95% CI]</text><text x="752" y="14" font-size="10" fill="#5b6b76" text-anchor="end">p</text><rect x="4" y="24" width="752" height="22" fill="#dde4e8" opacity="0.35"/><text x="8" y="38.9" font-size="11" fill="#16232b">APC mutation status: Mutated vs Not mutated</text><line x1="264.7" y1="35" x2="514.7" y2="35" stroke="#0072B2" stroke-width="2"/><rect x="385.7" y="31" width="8" height="8" fill="#0072B2"/><text x="617.6" y="38.9" font-size="11" fill="#16232b">0.28 [0.13–0.63]</text><text x="752" y="38.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">0.0018</text><line x1="588.6" y1="20" x2="588.6" y2="46" stroke="#5b6b76" stroke-width="1" stroke-dasharray="3 3"/><text x="588.6" y="14" font-size="10" fill="#5b6b76" text-anchor="middle">OR = 1</text><line x1="335.4" y1="50" x2="335.4" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="328" y="66" font-size="10" fill="#5b6b76">0.2</text><line x1="444.4" y1="50" x2="444.4" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="437.1" y="66" font-size="10" fill="#5b6b76">0.4</text><line x1="508.2" y1="50" x2="508.2" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="500.9" y="66" font-size="10" fill="#5b6b76">0.6</text><line x1="553.5" y1="50" x2="553.5" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="546.1" y="66" font-size="10" fill="#5b6b76">0.8</text><line x1="588.6" y1="50" x2="588.6" y2="53" stroke="#5b6b76" stroke-width="1"/><text x="585.6" y="66" font-size="10" fill="#5b6b76">1</text><line x1="248.127" y1="50" x2="603.594" y2="50" stroke="#5b6b76" stroke-width="1"/><text x="425.9" y="85" font-size="11" fill="#16232b" text-anchor="middle">OR (log scale) — left of 1 is lower, right is higher</text><text x="8" y="105" font-size="10" fill="#5b6b76">Logistic regression on 470 patients, 27 with MSI_TYPE = Instable (1 term). Every other observed value of MSI_TYPE (Do not report, Indeterminate, Stable) was</text><text x="8" y="119" font-size="10" fill="#5b6b76">treated as “not event.” 1 patient from patient-level participants lacked the outcome or a covariate and were excluded. Computed from real per-patient rows within</text><text x="8" y="133" font-size="10" fill="#5b6b76">each local study — not pooled aggregate counts across untrusted remote peers.</text></svg>
diff --git a/test/snapshots/logistic.txt b/test/snapshots/logistic.txt
new file mode 100644
index 00000000..6792d418
--- /dev/null
+++ b/test/snapshots/logistic.txt
@@ -0,0 +1,22 @@
+Analysis over crc_apc_impact_2020 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- all patients: 471 patients — all patients in the study
+
+Logistic regression (n=470, 27 with the outcome):
+  APC mutation status: Mutated vs Not mutated: OR 0.28 (95% CI 0.13-0.63) p=0.0018
+  CAVEAT (tell the user): Logistic regression on 470 patients, 27 with MSI_TYPE = Instable (1 term). Every other observed value of MSI_TYPE (Do not report, Indeterminate, Stable) was treated as “not event.” 1 patient from patient-level participants lacked the outcome or a covariate and were excluded. Computed from real per-patient rows within each local study — not pooled aggregate counts across untrusted remote peers.
+  METHOD:
+    - Fetch one row per patient [crc_apc_impact_2020] (471 → 470 rows): The logisticRows query above. Patients with a value for MSI_TYPE and for every covariate are kept. 1 cohort patient had no row.
+    - Code the outcome: 1 if MSI_TYPE = “Instable”, else 0 — every other observed value (Do not report, Indeterminate, Stable) counts as not the event.
+    - Code the covariates: Each numeric covariate is one term. Each categorical covariate becomes one 0/1 term per level, measured against a reference level that gets no term.
+        APC mutation status: Mutated: 1 if c0 = “Mutated”, else 0 · 325 of 470 patients · reference “Not mutated”: the fixed default for a mutated-vs-not axis
+    - Fit a logistic regression: Maximum likelihood for a binomial GLM with a logit link and an intercept, by Newton–Raphson with step-halving.
+        patients / events: 470 / 27
+        terms: 1 plus an intercept (not drawn)
+        standard errors: from the Fisher information at the estimate
+        95% interval: exp(β ± 1.959964·SE), Wald
+        p-value: Wald χ² on 1 df, (β/SE)²
+        fit: converged in 8 iterations
+        log likelihood: -98.4061
+    Matches statsmodels 0.15.0 Logit to a relative 1e-6 on coefficients, SEs, p-values and intervals (test/regression-golden.test.ts).
diff --git a/test/snapshots/mutations.json b/test/snapshots/mutations.json
new file mode 100644
index 00000000..88ffb9a3
--- /dev/null
+++ b/test/snapshots/mutations.json
@@ -0,0 +1,709 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "Colorectal": {
+      "count": 5543,
+      "text": "CANCER_TYPE in [Colorectal Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "mutations",
+      "cohorts": [
+        "Colorectal"
+      ],
+      "mutations": {
+        "gene": "KRAS",
+        "columns": [
+          {
+            "id": "sample",
+            "name": "Sample ID",
+            "numeric": false
+          },
+          {
+            "id": "cancerType",
+            "name": "Cancer Type Detailed",
+            "numeric": false
+          },
+          {
+            "id": "proteinChange",
+            "name": "Protein Change",
+            "numeric": false
+          },
+          {
+            "id": "annotation",
+            "name": "Annotation (OncoKB)",
+            "numeric": false
+          },
+          {
+            "id": "mutationType",
+            "name": "Mutation Type",
+            "numeric": false
+          },
+          {
+            "id": "alteration",
+            "name": "Alteration",
+            "numeric": false
+          },
+          {
+            "id": "variantType",
+            "name": "Variant Type",
+            "numeric": false
+          },
+          {
+            "id": "vaf",
+            "name": "Allele Freq (T)",
+            "numeric": true
+          },
+          {
+            "id": "altCount",
+            "name": "Variant Reads",
+            "numeric": true
+          },
+          {
+            "id": "refCount",
+            "name": "Ref Reads",
+            "numeric": true
+          },
+          {
+            "id": "mutCount",
+            "name": "# Mut in Sample",
+            "numeric": true
+          },
+          {
+            "id": "chr",
+            "name": "Chromosome",
+            "numeric": false
+          },
+          {
+            "id": "start",
+            "name": "Start Pos",
+            "numeric": true
+          },
+          {
+            "id": "end",
+            "name": "End Pos",
+            "numeric": true
+          },
+          {
+            "id": "ref",
+            "name": "Ref",
+            "numeric": false
+          },
+          {
+            "id": "var",
+            "name": "Var",
+            "numeric": false
+          },
+          {
+            "id": "status",
+            "name": "MS",
+            "numeric": false
+          }
+        ],
+        "shown": [
+          "sample",
+          "proteinChange",
+          "annotation",
+          "vaf",
+          "mutCount"
+        ],
+        "defaults": [
+          "sample",
+          "cancerType",
+          "proteinChange",
+          "annotation",
+          "mutationType",
+          "mutCount"
+        ],
+        "query": {
+          "columns": [
+            "sample",
+            "proteinChange",
+            "annotation",
+            "vaf",
+            "mutCount"
+          ],
+          "filters": {
+            "proteinChange": {
+              "op": "regex",
+              "value": "^G1[23]"
+            },
+            "vaf": {
+              "min": 0.2
+            }
+          },
+          "sort": {
+            "column": "vaf",
+            "desc": true
+          }
+        },
+        "rows": [
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0044990",
+            "sample": "P-0044990-T01-IM6",
+            "values": [
+              "P-0044990-T01-IM6",
+              "Colon Adenocarcinoma",
+              "G12D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.928,
+              257,
+              20,
+              9,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0074217",
+            "sample": "P-0074217-T02-IM7",
+            "values": [
+              "P-0074217-T02-IM7",
+              "Large Cell Neuroendocrine Carcinoma",
+              "G12C",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.922,
+              665,
+              56,
+              3,
+              "12",
+              25398285,
+              25398285,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0066632",
+            "sample": "P-0066632-T01-IM7",
+            "values": [
+              "P-0066632-T01-IM7",
+              "Colon Adenocarcinoma",
+              "G12D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.896,
+              121,
+              14,
+              7,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0011108",
+            "sample": "P-0011108-T01-IM5",
+            "values": [
+              "P-0011108-T01-IM5",
+              "Colon Adenocarcinoma",
+              "G12D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.888,
+              772,
+              97,
+              9,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0046395",
+            "sample": "P-0046395-T01-IM6",
+            "values": [
+              "P-0046395-T01-IM6",
+              "Colorectal Adenocarcinoma",
+              "G13D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.814,
+              415,
+              95,
+              10,
+              "12",
+              25398281,
+              25398281,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0018448",
+            "sample": "P-0018448-T01-IM6",
+            "values": [
+              "P-0018448-T01-IM6",
+              "Colon Adenocarcinoma",
+              "G12D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.799,
+              860,
+              217,
+              16,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0055331",
+            "sample": "P-0055331-T01-IM6",
+            "values": [
+              "P-0055331-T01-IM6",
+              "Colon Adenocarcinoma",
+              "G12A",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.796,
+              266,
+              68,
+              9,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "G",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0057020",
+            "sample": "P-0057020-T01-IM6",
+            "values": [
+              "P-0057020-T01-IM6",
+              "Colorectal Adenocarcinoma",
+              "G12C",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.792,
+              179,
+              47,
+              7,
+              "12",
+              25398285,
+              25398285,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0012877",
+            "sample": "P-0012877-T01-IM5",
+            "values": [
+              "P-0012877-T01-IM5",
+              "Colon Adenocarcinoma",
+              "G12D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.791,
+              457,
+              121,
+              10,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0005029",
+            "sample": "P-0005029-T01-IM5",
+            "values": [
+              "P-0005029-T01-IM5",
+              "Rectal Adenocarcinoma",
+              "G13D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.786,
+              434,
+              118,
+              3,
+              "12",
+              25398281,
+              25398281,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0015210",
+            "sample": "P-0015210-T01-IM6",
+            "values": [
+              "P-0015210-T01-IM6",
+              "Colon Adenocarcinoma",
+              "G12D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.775,
+              856,
+              248,
+              5,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0000739",
+            "sample": "P-0000739-T01-IM3",
+            "values": [
+              "P-0000739-T01-IM3",
+              "Colon Adenocarcinoma",
+              "G12C",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.768,
+              540,
+              163,
+              7,
+              "12",
+              25398285,
+              25398285,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0078660",
+            "sample": "P-0078660-T01-IM7",
+            "values": [
+              "P-0078660-T01-IM7",
+              "Colorectal Adenocarcinoma",
+              "G12C",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.763,
+              161,
+              50,
+              13,
+              "12",
+              25398285,
+              25398285,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0007990",
+            "sample": "P-0007990-T01-IM5",
+            "values": [
+              "P-0007990-T01-IM5",
+              "Colon Adenocarcinoma",
+              "G12S",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.759,
+              1216,
+              386,
+              7,
+              "12",
+              25398285,
+              25398285,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0050517",
+            "sample": "P-0050517-T01-IM6",
+            "values": [
+              "P-0050517-T01-IM6",
+              "Colon Adenocarcinoma",
+              "G12V",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.751,
+              175,
+              58,
+              10,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0039168",
+            "sample": "P-0039168-T01-IM6",
+            "values": [
+              "P-0039168-T01-IM6",
+              "Mucinous Adenocarcinoma of the Colon and Rectum",
+              "G12S",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.744,
+              886,
+              305,
+              4,
+              "12",
+              25398285,
+              25398285,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0041600",
+            "sample": "P-0041600-T01-IM6",
+            "values": [
+              "P-0041600-T01-IM6",
+              "Colorectal Adenocarcinoma",
+              "G12V",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.741,
+              304,
+              106,
+              6,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0046742",
+            "sample": "P-0046742-T01-IM6",
+            "values": [
+              "P-0046742-T01-IM6",
+              "Colon Adenocarcinoma",
+              "G13D",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.724,
+              184,
+              70,
+              7,
+              "12",
+              25398281,
+              25398281,
+              "C",
+              "T",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0008735",
+            "sample": "P-0008735-T01-IM5",
+            "values": [
+              "P-0008735-T01-IM5",
+              "Colon Adenocarcinoma",
+              "G12C",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.723,
+              748,
+              286,
+              10,
+              "12",
+              25398285,
+              25398285,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "patient": "P-0089247",
+            "sample": "P-0089247-T01-IM7",
+            "values": [
+              "P-0089247-T01-IM7",
+              "Colon Adenocarcinoma",
+              "G12V",
+              "Oncogenic · Level 1",
+              "Missense_Mutation",
+              "Missense · Driver",
+              "SNP",
+              0.722,
+              255,
+              98,
+              8,
+              "12",
+              25398284,
+              25398284,
+              "C",
+              "A",
+              "SOMATIC"
+            ]
+          },
+          {
+            "…": "5 more",
+            "sha256": "a704ba8fc2fb562f"
+          }
+        ],
+        "offset": 0,
+        "pageSize": 25,
+        "lists": [
+          {
+            "cohort": "Colorectal",
+            "study": "msk_chord_2024",
+            "total": 1378,
+            "all": 2426
+          }
+        ],
+        "total": 1378,
+        "all": 2426,
+        "oncokbPin": "v7.5"
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": true,
+        "aggregateOnly": []
+      },
+      "caveat": "Every KRAS mutation call in the cohort's samples, one row per call; uncalled mutations are left out, as the portal leaves them out. Annotation is OncoKB v7.5 (oncogenicity and its highest sensitivity level for the gene). Allele frequency is variant reads over variant + reference reads; # Mut in Sample is the study's own MUTATION_COUNT, blank where it records none. Showing 1,378 of 2,426 mutations: rows Protein Change matches /^G1[23]/; Allele Freq (T) ≥ 0.2. Sorted by Allele Freq (T) (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count the KRAS mutation calls per cohort and study, and those the query keeps",
+            "detail": "The mutations:counts query above: every called KRAS mutation in each cohort's samples (uncalled calls left out), with the sample's CANCER_TYPE_DETAILED and MUTATION_COUNT where recorded and the OncoKB v7.5 call for its protein change; allele frequency = variant / (variant + reference) reads. The query keeps rows Protein Change matches /^G1[23]/; Allele Freq (T) ≥ 0.2.",
+            "params": [
+              [
+                "Colorectal · msk_chord_2024",
+                "1,378 of 2,426"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Sort them together and take one page",
+            "detail": "The mutations:rows query above: the KRAS mutation calls of every cohort and study combined in one query, kept to rows Protein Change matches /^G1[23]/; Allele Freq (T) ≥ 0.2, sorted by Allele Freq (T) descending, missing last, then sample, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "mutationRowSource",
+            "pageSql",
+            "countsSql"
+          ],
+          "file": "lib/mutation-table.ts, lib/table-query.ts",
+          "commit": null
+        },
+        "verification": "A listing: no statistic is computed, so there is no script — the queries are the numbers."
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/mutations.sql b/test/snapshots/mutations.sql
new file mode 100644
index 00000000..9db37e09
--- /dev/null
+++ b/test/snapshots/mutations.sql
@@ -0,0 +1,77 @@
+-- cohort Colorectal
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+
+-- view 0 mutations: mutations:counts · msk_chord_2024
+WITH ann AS (SELECT alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'MUTATION' AND hugo_gene_symbol = 'KRAS'
+  GROUP BY alteration),
+c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+m0 AS (
+  SELECT patientId AS pid, sampleId AS sid, proteinChange, mutationType, variantType, chr, startPosition AS start, endPosition AS end,
+         referenceAllele AS ref, tumorSeqAllele AS var, tumorAltCount AS altCount, tumorRefCount AS refCount, mutationStatus AS status, proteinPosStart AS pos
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND `GENE.hugoGeneSymbol` = 'KRAS'
+    AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+),
+ct0 AS (SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, any(attribute_value) AS v FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample' AND attribute_name = 'CANCER_TYPE_DETAILED'
+    AND sample_unique_id IN (SELECT concat('msk_chord_2024_', sid) FROM m0) GROUP BY sid),
+mc0 AS (SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, any(attribute_value) AS v FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample' AND attribute_name = 'MUTATION_COUNT'
+    AND sample_unique_id IN (SELECT concat('msk_chord_2024_', sid) FROM m0) GROUP BY sid),
+rows AS (
+  SELECT 'Colorectal' AS cohort, 'msk_chord_2024' AS study, 0 AS ord,
+         m.pid AS patient, m.sid AS sid, ct.v AS cancerType, m.proteinChange AS proteinChange,
+         ann.oncogenicity AS oncogenicity, ann.level AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         m.mutationType AS mutationType, m.variantType AS variantType,
+         if(m.altCount + m.refCount > 0, m.altCount / (m.altCount + m.refCount), NULL) AS vaf, m.altCount AS altCount, m.refCount AS refCount,
+         toInt64OrNull(mc.v) AS mutCount, m.chr AS chr, m.start AS start, m.end AS end, m.ref AS ref, m.var AS var, m.status AS status, m.pos AS pos
+  FROM m0 AS m
+  LEFT JOIN ann ON ann.alteration = m.proteinChange
+  LEFT JOIN ct0 AS ct ON ct.sid = m.sid LEFT JOIN mc0 AS mc ON mc.sid = m.sid
+)
+SELECT cohort, study, countIf((match(ifNull(toString(proteinChange), ''), '^G1[23]')) AND (CAST(vaf AS Nullable(Float64)) IS NOT NULL AND CAST(vaf AS Nullable(Float64)) >= 0.2)) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 mutations: mutations:rows · msk_chord_2024
+WITH ann AS (SELECT alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'MUTATION' AND hugo_gene_symbol = 'KRAS'
+  GROUP BY alteration),
+c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+m0 AS (
+  SELECT patientId AS pid, sampleId AS sid, proteinChange, mutationType, variantType, chr, startPosition AS start, endPosition AS end,
+         referenceAllele AS ref, tumorSeqAllele AS var, tumorAltCount AS altCount, tumorRefCount AS refCount, mutationStatus AS status, proteinPosStart AS pos
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND `GENE.hugoGeneSymbol` = 'KRAS'
+    AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+),
+ct0 AS (SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, any(attribute_value) AS v FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample' AND attribute_name = 'CANCER_TYPE_DETAILED'
+    AND sample_unique_id IN (SELECT concat('msk_chord_2024_', sid) FROM m0) GROUP BY sid),
+mc0 AS (SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, any(attribute_value) AS v FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample' AND attribute_name = 'MUTATION_COUNT'
+    AND sample_unique_id IN (SELECT concat('msk_chord_2024_', sid) FROM m0) GROUP BY sid),
+rows AS (
+  SELECT 'Colorectal' AS cohort, 'msk_chord_2024' AS study, 0 AS ord,
+         m.pid AS patient, m.sid AS sid, ct.v AS cancerType, m.proteinChange AS proteinChange,
+         ann.oncogenicity AS oncogenicity, ann.level AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         m.mutationType AS mutationType, m.variantType AS variantType,
+         if(m.altCount + m.refCount > 0, m.altCount / (m.altCount + m.refCount), NULL) AS vaf, m.altCount AS altCount, m.refCount AS refCount,
+         toInt64OrNull(mc.v) AS mutCount, m.chr AS chr, m.start AS start, m.end AS end, m.ref AS ref, m.var AS var, m.status AS status, m.pos AS pos
+  FROM m0 AS m
+  LEFT JOIN ann ON ann.alteration = m.proteinChange
+  LEFT JOIN ct0 AS ct ON ct.sid = m.sid LEFT JOIN mc0 AS mc ON mc.sid = m.sid
+)
+SELECT *, count() OVER () AS total FROM rows
+WHERE (match(ifNull(toString(proteinChange), ''), '^G1[23]')) AND (CAST(vaf AS Nullable(Float64)) IS NOT NULL AND CAST(vaf AS Nullable(Float64)) >= 0.2)
+ORDER BY CAST(vaf AS Nullable(Float64)) DESC NULLS LAST, sid, ord, proteinChange
+LIMIT 25 OFFSET 0
diff --git a/test/snapshots/mutations.svg b/test/snapshots/mutations.svg
new file mode 100644
index 00000000..4ce19f79
--- /dev/null
+++ b/test/snapshots/mutations.svg
@@ -0,0 +1,2 @@
+<!-- view 0 mutations -->
+(no figure)
diff --git a/test/snapshots/mutations.txt b/test/snapshots/mutations.txt
new file mode 100644
index 00000000..5ec15ce6
--- /dev/null
+++ b/test/snapshots/mutations.txt
@@ -0,0 +1,39 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Colorectal: 5,543 patients — CANCER_TYPE in [Colorectal Cancer] (any sample)
+
+KRAS mutations: 1,378 of 2,426 rows kept by the query; the first 25, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	sample	proteinChange	annotation	vaf	mutCount
+  Colorectal	P-0044990-T01-IM6	G12D	Oncogenic · Level 1	0.928	9
+  Colorectal	P-0074217-T02-IM7	G12C	Oncogenic · Level 1	0.922	3
+  Colorectal	P-0066632-T01-IM7	G12D	Oncogenic · Level 1	0.896	7
+  Colorectal	P-0011108-T01-IM5	G12D	Oncogenic · Level 1	0.888	9
+  Colorectal	P-0046395-T01-IM6	G13D	Oncogenic · Level 1	0.814	10
+  Colorectal	P-0018448-T01-IM6	G12D	Oncogenic · Level 1	0.799	16
+  Colorectal	P-0055331-T01-IM6	G12A	Oncogenic · Level 1	0.796	9
+  Colorectal	P-0057020-T01-IM6	G12C	Oncogenic · Level 1	0.792	7
+  Colorectal	P-0012877-T01-IM5	G12D	Oncogenic · Level 1	0.791	10
+  Colorectal	P-0005029-T01-IM5	G13D	Oncogenic · Level 1	0.786	3
+  Colorectal	P-0015210-T01-IM6	G12D	Oncogenic · Level 1	0.775	5
+  Colorectal	P-0000739-T01-IM3	G12C	Oncogenic · Level 1	0.768	7
+  Colorectal	P-0078660-T01-IM7	G12C	Oncogenic · Level 1	0.763	13
+  Colorectal	P-0007990-T01-IM5	G12S	Oncogenic · Level 1	0.759	7
+  Colorectal	P-0050517-T01-IM6	G12V	Oncogenic · Level 1	0.751	10
+  Colorectal	P-0039168-T01-IM6	G12S	Oncogenic · Level 1	0.744	4
+  Colorectal	P-0041600-T01-IM6	G12V	Oncogenic · Level 1	0.741	6
+  Colorectal	P-0046742-T01-IM6	G13D	Oncogenic · Level 1	0.724	7
+  Colorectal	P-0008735-T01-IM5	G12C	Oncogenic · Level 1	0.723	10
+  Colorectal	P-0089247-T01-IM7	G12V	Oncogenic · Level 1	0.722	8
+  Colorectal	P-0057012-T01-IM6	G12V	Oncogenic · Level 1	0.72	9
+  Colorectal	P-0012653-T01-IM5	G13D	Oncogenic · Level 1	0.718	4
+  Colorectal	P-0042211-T01-IM6	G12D	Oncogenic · Level 1	0.718	8
+  Colorectal	P-0087879-T01-IM7	G13D	Oncogenic · Level 1	0.718	8
+  Colorectal	P-0077659-T01-IM7	G12R	Oncogenic · Level 1	0.716	38
+  … 1,353 more rows
+  CAVEAT (tell the user): Every KRAS mutation call in the cohort's samples, one row per call; uncalled mutations are left out, as the portal leaves them out. Annotation is OncoKB v7.5 (oncogenicity and its highest sensitivity level for the gene). Allele frequency is variant reads over variant + reference reads; # Mut in Sample is the study's own MUTATION_COUNT, blank where it records none. Showing 1,378 of 2,426 mutations: rows Protein Change matches /^G1[23]/; Allele Freq (T) ≥ 0.2. Sorted by Allele Freq (T) (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. 
+  METHOD:
+    - Count the KRAS mutation calls per cohort and study, and those the query keeps: The mutations:counts query above: every called KRAS mutation in each cohort's samples (uncalled calls left out), with the sample's CANCER_TYPE_DETAILED and MUTATION_COUNT where recorded and the OncoKB v7.5 call for its protein change; allele frequency = variant / (variant + reference) reads. The query keeps rows Protein Change matches /^G1[23]/; Allele Freq (T) ≥ 0.2.
+        Colorectal · msk_chord_2024: 1,378 of 2,426
+    - Sort them together and take one page: The mutations:rows query above: the KRAS mutation calls of every cohort and study combined in one query, kept to rows Protein Change matches /^G1[23]/; Allele Freq (T) ≥ 0.2, sorted by Allele Freq (T) descending, missing last, then sample, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset.
+    A listing: no statistic is computed, so there is no script — the queries are the numbers.
diff --git a/test/snapshots/overlap.json b/test/snapshots/overlap.json
new file mode 100644
index 00000000..3425ccba
--- /dev/null
+++ b/test/snapshots/overlap.json
@@ -0,0 +1,237 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "TP53 mutant": {
+      "count": 13105,
+      "text": "OQL[TP53: MUT]",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "KRAS mutant": {
+      "count": 7115,
+      "text": "OQL[KRAS: MUT]",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "Immunotherapy": {
+      "count": 3341,
+      "text": "has Immuno treatment",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "NSCLC": {
+      "count": 7809,
+      "text": "CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "overlap",
+      "cohorts": [
+        "TP53 mutant",
+        "KRAS mutant",
+        "Immunotherapy",
+        "NSCLC"
+      ],
+      "overlap": {
+        "sets": [
+          {
+            "name": "TP53 mutant",
+            "size": 13105,
+            "samples": 13172
+          },
+          {
+            "name": "KRAS mutant",
+            "size": 7115,
+            "samples": 7161
+          },
+          {
+            "name": "Immunotherapy",
+            "size": 3341,
+            "samples": 3372
+          },
+          {
+            "name": "NSCLC",
+            "size": 7809,
+            "samples": 7876
+          }
+        ],
+        "intersections": [
+          {
+            "members": [
+              "TP53 mutant"
+            ],
+            "n": 5314,
+            "samples": 5320
+          },
+          {
+            "members": [
+              "TP53 mutant",
+              "KRAS mutant"
+            ],
+            "n": 3391,
+            "samples": 3402
+          },
+          {
+            "members": [
+              "TP53 mutant",
+              "NSCLC"
+            ],
+            "n": 2043,
+            "samples": 2055
+          },
+          {
+            "members": [
+              "NSCLC"
+            ],
+            "n": 1887,
+            "samples": 1897
+          },
+          {
+            "members": [
+              "KRAS mutant"
+            ],
+            "n": 1381,
+            "samples": 1381
+          },
+          {
+            "members": [
+              "TP53 mutant",
+              "Immunotherapy",
+              "NSCLC"
+            ],
+            "n": 1167,
+            "samples": 1176
+          },
+          {
+            "members": [
+              "KRAS mutant",
+              "NSCLC"
+            ],
+            "n": 838,
+            "samples": 842
+          },
+          {
+            "members": [
+              "KRAS mutant",
+              "Immunotherapy",
+              "NSCLC"
+            ],
+            "n": 546,
+            "samples": 548
+          },
+          {
+            "members": [
+              "Immunotherapy",
+              "NSCLC"
+            ],
+            "n": 518,
+            "samples": 523
+          },
+          {
+            "members": [
+              "TP53 mutant",
+              "KRAS mutant",
+              "Immunotherapy",
+              "NSCLC"
+            ],
+            "n": 435,
+            "samples": 444
+          },
+          {
+            "members": [
+              "TP53 mutant",
+              "KRAS mutant",
+              "NSCLC"
+            ],
+            "n": 375,
+            "samples": 391
+          },
+          {
+            "members": [
+              "TP53 mutant",
+              "Immunotherapy"
+            ],
+            "n": 292,
+            "samples": 293
+          },
+          {
+            "members": [
+              "Immunotherapy"
+            ],
+            "n": 234,
+            "samples": 235
+          },
+          {
+            "members": [
+              "TP53 mutant",
+              "KRAS mutant",
+              "Immunotherapy"
+            ],
+            "n": 88,
+            "samples": 91
+          },
+          {
+            "members": [
+              "KRAS mutant",
+              "Immunotherapy"
+            ],
+            "n": 61,
+            "samples": 62
+          }
+        ],
+        "union": 18570
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Patients in exactly the marked cohorts and none of the others, for every combination that has any — computed inside each study's database, which returns one count per combination. 18,570 patients are in at least one of the 4 cohorts. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients per combination of cohorts",
+            "detail": "The overlap query above: every patient in any of TP53 mutant, KRAS mutant, Immunotherapy, NSCLC gets a bitmask of the cohorts it is in, and the study returns one count per bitmask. Counts only.",
+            "scope": "msk_chord_2024",
+            "rowsOut": 18570
+          },
+          {
+            "kind": "transform",
+            "title": "Add the counts across studies and name each combination",
+            "detail": "Each study's bits are mapped back to the view's cohort order; combinations add exactly, and a patient can only overlap within one study.",
+            "params": [
+              [
+                "patients in any cohort",
+                "18,570"
+              ],
+              [
+                "combinations with patients",
+                "15"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [],
+          "file": "lib/views/overlap.ts",
+          "commit": null
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/overlap.sql b/test/snapshots/overlap.sql
new file mode 100644
index 00000000..0d3a9969
--- /dev/null
+++ b/test/snapshots/overlap.sql
@@ -0,0 +1,82 @@
+-- cohort TP53 mutant
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort KRAS mutant
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort Immunotherapy
+-- msk_chord_2024
+(WITH
+study_patients AS (
+  SELECT p.internal_id FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+),
+sel_a AS (
+SELECT ce.patient_id, ce.start_date, ce.stop_date
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.clinical_event_data a0 ON a0.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(a0.key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(a0.value)) = lowerUTF8(trimBoth('Immuno'))
+  WHERE lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.patient_id IN (SELECT internal_id FROM study_patients)
+)
+SELECT DISTINCT p.stable_id AS pid
+FROM sel_a
+  JOIN snapshot_db.patient p ON p.internal_id = sel_a.patient_id
+WHERE 1
+ORDER BY pid LIMIT 1000000)
+
+-- cohort NSCLC
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))
+
+-- view 0 overlap: overlap · msk_chord_2024
+WITH c0 AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+c1 AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+c2 AS ((WITH
+study_patients AS (
+  SELECT p.internal_id FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+),
+sel_a AS (
+SELECT ce.patient_id, ce.start_date, ce.stop_date
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.clinical_event_data a0 ON a0.clinical_event_id = ce.clinical_event_id AND lowerUTF8(trimBoth(a0.key)) = lowerUTF8(trimBoth('SUBTYPE')) AND lowerUTF8(trimBoth(a0.value)) = lowerUTF8(trimBoth('Immuno'))
+  WHERE lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('Treatment'))
+    AND ce.patient_id IN (SELECT internal_id FROM study_patients)
+)
+SELECT DISTINCT p.stable_id AS pid
+FROM sel_a
+  JOIN snapshot_db.patient p ON p.internal_id = sel_a.patient_id
+WHERE 1
+ORDER BY pid LIMIT 1000000)),
+c3 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))),
+u AS (SELECT pid FROM c0 UNION DISTINCT SELECT pid FROM c1 UNION DISTINCT SELECT pid FROM c2 UNION DISTINCT SELECT pid FROM c3),
+m AS (SELECT pid, (pid IN (SELECT pid FROM c0)) * 1 + (pid IN (SELECT pid FROM c1)) * 2 + (pid IN (SELECT pid FROM c2)) * 4 + (pid IN (SELECT pid FROM c3)) * 8 AS mask FROM u),
+s AS (SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, count() AS ns FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM u) GROUP BY pid)
+SELECT mask, count() AS n, sum(s.ns) AS samples FROM m LEFT JOIN s ON s.pid = m.pid GROUP BY mask ORDER BY n DESC
diff --git a/test/snapshots/overlap.svg b/test/snapshots/overlap.svg
new file mode 100644
index 00000000..4793182f
--- /dev/null
+++ b/test/snapshots/overlap.svg
@@ -0,0 +1,2 @@
+<!-- view 0 overlap -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 860 361" width="100%" role="img" aria-label="Cohort overlap — TP53 mutant, KRAS mutant, Immunotherapy, NSCLC" style="max-width:860px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Cohort overlap — TP53 mutant, KRAS mutant, Immunotherapy, NSCLC</title><rect width="860" height="361" fill="#ffffff"/><line x1="221.69" y1="191" x2="852" y2="191" stroke="#dde4e8" stroke-width="0.75"/><text x="215.7" y="194.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="221.69" y1="134.7" x2="852" y2="134.7" stroke="#dde4e8" stroke-width="0.75"/><text x="215.7" y="138.2" font-size="10" fill="#5b6b76" text-anchor="end">2,000</text><line x1="221.69" y1="78.3" x2="852" y2="78.3" stroke="#dde4e8" stroke-width="0.75"/><text x="215.7" y="81.8" font-size="10" fill="#5b6b76" text-anchor="end">4,000</text><line x1="221.69" y1="22" x2="852" y2="22" stroke="#dde4e8" stroke-width="0.75"/><text x="215.7" y="25.5" font-size="10" fill="#5b6b76" text-anchor="end">6,000</text><text x="176.5" y="106.5" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 176.5 106.5)">patients in exactly these cohorts</text><rect x="234.57" y="41.32" width="24" height="149.68" fill="#16232b" opacity="0.85"><title>TP53 mutant: 5,314 patients</title></rect><text x="246.6" y="37.3" font-size="10" fill="#16232b" text-anchor="middle">5,314</text><rect x="276.32" y="95.49" width="24" height="95.51" fill="#16232b" opacity="0.85"><title>TP53 mutant ∩ KRAS mutant: 3,391 patients</title></rect><text x="288.3" y="91.5" font-size="10" fill="#16232b" text-anchor="middle">3,391</text><rect x="318.08" y="133.46" width="24" height="57.54" fill="#16232b" opacity="0.85"><title>TP53 mutant ∩ NSCLC: 2,043 patients</title></rect><text x="330.1" y="129.5" font-size="10" fill="#16232b" text-anchor="middle">2,043</text><rect x="359.83" y="137.85" width="24" height="53.15" fill="#16232b" opacity="0.85"><title>NSCLC: 1,887 patients</title></rect><text x="371.8" y="133.8" font-size="10" fill="#16232b" text-anchor="middle">1,887</text><rect x="401.58" y="152.1" width="24" height="38.9" fill="#16232b" opacity="0.85"><title>KRAS mutant: 1,381 patients</title></rect><text x="413.6" y="148.1" font-size="10" fill="#16232b" text-anchor="middle">1,381</text><rect x="443.34" y="158.13" width="24" height="32.87" fill="#16232b" opacity="0.85"><title>TP53 mutant ∩ Immunotherapy ∩ NSCLC: 1,167 patients</title></rect><text x="455.3" y="154.1" font-size="10" fill="#16232b" text-anchor="middle">1,167</text><rect x="485.09" y="167.4" width="24" height="23.6" fill="#16232b" opacity="0.85"><title>KRAS mutant ∩ NSCLC: 838 patients</title></rect><text x="497.1" y="163.4" font-size="10" fill="#16232b" text-anchor="middle">838</text><rect x="526.84" y="175.62" width="24" height="15.38" fill="#16232b" opacity="0.85"><title>KRAS mutant ∩ Immunotherapy ∩ NSCLC: 546 patients</title></rect><text x="538.8" y="171.6" font-size="10" fill="#16232b" text-anchor="middle">546</text><rect x="568.6" y="176.41" width="24" height="14.59" fill="#16232b" opacity="0.85"><title>Immunotherapy ∩ NSCLC: 518 patients</title></rect><text x="580.6" y="172.4" font-size="10" fill="#16232b" text-anchor="middle">518</text><rect x="610.35" y="178.75" width="24" height="12.25" fill="#16232b" opacity="0.85"><title>TP53 mutant ∩ KRAS mutant ∩ Immunotherapy ∩ NSCLC: 435 patients</title></rect><text x="622.4" y="174.7" font-size="10" fill="#16232b" text-anchor="middle">435</text><rect x="652.11" y="180.44" width="24" height="10.56" fill="#16232b" opacity="0.85"><title>TP53 mutant ∩ KRAS mutant ∩ NSCLC: 375 patients</title></rect><text x="664.1" y="176.4" font-size="10" fill="#16232b" text-anchor="middle">375</text><rect x="693.86" y="182.78" width="24" height="8.22" fill="#16232b" opacity="0.85"><title>TP53 mutant ∩ Immunotherapy: 292 patients</title></rect><text x="705.9" y="178.8" font-size="10" fill="#16232b" text-anchor="middle">292</text><rect x="735.62" y="184.41" width="24" height="6.59" fill="#16232b" opacity="0.85"><title>Immunotherapy: 234 patients</title></rect><text x="747.6" y="180.4" font-size="10" fill="#16232b" text-anchor="middle">234</text><rect x="777.37" y="188.52" width="24" height="2.48" fill="#16232b" opacity="0.85"><title>TP53 mutant ∩ KRAS mutant ∩ Immunotherapy: 88 patients</title></rect><text x="789.4" y="184.5" font-size="10" fill="#16232b" text-anchor="middle">88</text><rect x="819.12" y="189.28" width="24" height="1.72" fill="#16232b" opacity="0.85"><title>KRAS mutant ∩ Immunotherapy: 61 patients</title></rect><text x="831.1" y="185.3" font-size="10" fill="#16232b" text-anchor="middle">61</text><text x="132.9" y="205" font-size="10" fill="#5b6b76" text-anchor="end">patients</text><rect x="225.69" y="209" width="626.31" height="22" fill="#dde4e8" opacity="0.35"/><rect x="140.89" y="214" width="68.8" height="12" fill="#0072B2" opacity="0.85"/><text x="88.2" y="223.9" font-size="11" fill="#16232b" text-anchor="end">TP53 mutant</text><text x="132.9" y="223.5" font-size="10" fill="#5b6b76" text-anchor="end">13,105</text><circle cx="246.57" cy="220" r="5" fill="#16232b"/><circle cx="288.32" cy="220" r="5" fill="#16232b"/><circle cx="330.08" cy="220" r="5" fill="#16232b"/><circle cx="371.83" cy="220" r="5" fill="#dde4e8"/><circle cx="413.58" cy="220" r="5" fill="#dde4e8"/><circle cx="455.34" cy="220" r="5" fill="#16232b"/><circle cx="497.09" cy="220" r="5" fill="#dde4e8"/><circle cx="538.84" cy="220" r="5" fill="#dde4e8"/><circle cx="580.6" cy="220" r="5" fill="#dde4e8"/><circle cx="622.35" cy="220" r="5" fill="#16232b"/><circle cx="664.11" cy="220" r="5" fill="#16232b"/><circle cx="705.86" cy="220" r="5" fill="#16232b"/><circle cx="747.62" cy="220" r="5" fill="#dde4e8"/><circle cx="789.37" cy="220" r="5" fill="#16232b"/><circle cx="831.12" cy="220" r="5" fill="#dde4e8"/><rect x="140.89" y="236" width="37.35" height="12" fill="#E69F00" opacity="0.85"/><text x="88.2" y="245.9" font-size="11" fill="#16232b" text-anchor="end">KRAS mutant</text><text x="132.9" y="245.5" font-size="10" fill="#5b6b76" text-anchor="end">7,115</text><circle cx="246.57" cy="242" r="5" fill="#dde4e8"/><circle cx="288.32" cy="242" r="5" fill="#16232b"/><circle cx="330.08" cy="242" r="5" fill="#dde4e8"/><circle cx="371.83" cy="242" r="5" fill="#dde4e8"/><circle cx="413.58" cy="242" r="5" fill="#16232b"/><circle cx="455.34" cy="242" r="5" fill="#dde4e8"/><circle cx="497.09" cy="242" r="5" fill="#16232b"/><circle cx="538.84" cy="242" r="5" fill="#16232b"/><circle cx="580.6" cy="242" r="5" fill="#dde4e8"/><circle cx="622.35" cy="242" r="5" fill="#16232b"/><circle cx="664.11" cy="242" r="5" fill="#16232b"/><circle cx="705.86" cy="242" r="5" fill="#dde4e8"/><circle cx="747.62" cy="242" r="5" fill="#dde4e8"/><circle cx="789.37" cy="242" r="5" fill="#16232b"/><circle cx="831.12" cy="242" r="5" fill="#16232b"/><rect x="225.69" y="253" width="626.31" height="22" fill="#dde4e8" opacity="0.35"/><rect x="140.89" y="258" width="17.54" height="12" fill="#009E73" opacity="0.85"/><text x="88.2" y="267.9" font-size="11" fill="#16232b" text-anchor="end">Immunotherapy</text><text x="132.9" y="267.5" font-size="10" fill="#5b6b76" text-anchor="end">3,341</text><circle cx="246.57" cy="264" r="5" fill="#dde4e8"/><circle cx="288.32" cy="264" r="5" fill="#dde4e8"/><circle cx="330.08" cy="264" r="5" fill="#dde4e8"/><circle cx="371.83" cy="264" r="5" fill="#dde4e8"/><circle cx="413.58" cy="264" r="5" fill="#dde4e8"/><circle cx="455.34" cy="264" r="5" fill="#16232b"/><circle cx="497.09" cy="264" r="5" fill="#dde4e8"/><circle cx="538.84" cy="264" r="5" fill="#16232b"/><circle cx="580.6" cy="264" r="5" fill="#16232b"/><circle cx="622.35" cy="264" r="5" fill="#16232b"/><circle cx="664.11" cy="264" r="5" fill="#dde4e8"/><circle cx="705.86" cy="264" r="5" fill="#16232b"/><circle cx="747.62" cy="264" r="5" fill="#16232b"/><circle cx="789.37" cy="264" r="5" fill="#16232b"/><circle cx="831.12" cy="264" r="5" fill="#16232b"/><rect x="140.89" y="280" width="41" height="12" fill="#CC79A7" opacity="0.85"/><text x="88.2" y="289.9" font-size="11" fill="#16232b" text-anchor="end">NSCLC</text><text x="132.9" y="289.5" font-size="10" fill="#5b6b76" text-anchor="end">7,809</text><circle cx="246.57" cy="286" r="5" fill="#dde4e8"/><circle cx="288.32" cy="286" r="5" fill="#dde4e8"/><circle cx="330.08" cy="286" r="5" fill="#16232b"/><circle cx="371.83" cy="286" r="5" fill="#16232b"/><circle cx="413.58" cy="286" r="5" fill="#dde4e8"/><circle cx="455.34" cy="286" r="5" fill="#16232b"/><circle cx="497.09" cy="286" r="5" fill="#16232b"/><circle cx="538.84" cy="286" r="5" fill="#16232b"/><circle cx="580.6" cy="286" r="5" fill="#16232b"/><circle cx="622.35" cy="286" r="5" fill="#16232b"/><circle cx="664.11" cy="286" r="5" fill="#16232b"/><circle cx="705.86" cy="286" r="5" fill="#dde4e8"/><circle cx="747.62" cy="286" r="5" fill="#dde4e8"/><circle cx="789.37" cy="286" r="5" fill="#dde4e8"/><circle cx="831.12" cy="286" r="5" fill="#dde4e8"/><line x1="288.32" y1="220" x2="288.32" y2="242" stroke="#16232b" stroke-width="2"/><line x1="330.08" y1="220" x2="330.08" y2="286" stroke="#16232b" stroke-width="2"/><line x1="455.34" y1="220" x2="455.34" y2="286" stroke="#16232b" stroke-width="2"/><line x1="497.09" y1="242" x2="497.09" y2="286" stroke="#16232b" stroke-width="2"/><line x1="538.84" y1="242" x2="538.84" y2="286" stroke="#16232b" stroke-width="2"/><line x1="580.6" y1="264" x2="580.6" y2="286" stroke="#16232b" stroke-width="2"/><line x1="622.35" y1="220" x2="622.35" y2="286" stroke="#16232b" stroke-width="2"/><line x1="664.11" y1="220" x2="664.11" y2="286" stroke="#16232b" stroke-width="2"/><line x1="705.86" y1="220" x2="705.86" y2="264" stroke="#16232b" stroke-width="2"/><line x1="789.37" y1="220" x2="789.37" y2="264" stroke="#16232b" stroke-width="2"/><line x1="831.12" y1="242" x2="831.12" y2="264" stroke="#16232b" stroke-width="2"/><text x="8" y="316" font-size="11" fill="#16232b">18,570 patients in at least one cohort · 15 non-empty combinations of 15</text><text x="8" y="329" font-size="10" fill="#5b6b76">Patients in exactly the marked cohorts and none of the others, for every combination that has any — computed inside each study's database, which returns one count per</text><text x="8" y="343" font-size="10" fill="#5b6b76">combination. 18,570 patients are in at least one of the 4 cohorts.</text></svg>
diff --git a/test/snapshots/overlap.txt b/test/snapshots/overlap.txt
new file mode 100644
index 00000000..ae1afece
--- /dev/null
+++ b/test/snapshots/overlap.txt
@@ -0,0 +1,32 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- TP53 mutant: 13,105 patients — OQL[TP53: MUT]
+- KRAS mutant: 7,115 patients — OQL[KRAS: MUT]
+- Immunotherapy: 3,341 patients — has Immuno treatment
+- NSCLC: 7,809 patients — CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)
+
+Cohort overlap (patients in exactly these cohorts and no other):
+  cohort sizes: TP53 mutant 13,105, KRAS mutant 7,115, Immunotherapy 3,341, NSCLC 7,809
+  TP53 mutant only: 5,314
+  TP53 mutant ∩ KRAS mutant only: 3,391
+  TP53 mutant ∩ NSCLC only: 2,043
+  NSCLC only: 1,887
+  KRAS mutant only: 1,381
+  TP53 mutant ∩ Immunotherapy ∩ NSCLC only: 1,167
+  KRAS mutant ∩ NSCLC only: 838
+  KRAS mutant ∩ Immunotherapy ∩ NSCLC only: 546
+  Immunotherapy ∩ NSCLC only: 518
+  TP53 mutant ∩ KRAS mutant ∩ Immunotherapy ∩ NSCLC: 435
+  TP53 mutant ∩ KRAS mutant ∩ NSCLC only: 375
+  TP53 mutant ∩ Immunotherapy only: 292
+  Immunotherapy only: 234
+  TP53 mutant ∩ KRAS mutant ∩ Immunotherapy only: 88
+  KRAS mutant ∩ Immunotherapy only: 61
+  18,570 patients in at least one cohort
+  CAVEAT (tell the user): Patients in exactly the marked cohorts and none of the others, for every combination that has any — computed inside each study's database, which returns one count per combination. 18,570 patients are in at least one of the 4 cohorts. 
+  METHOD:
+    - Count patients per combination of cohorts [msk_chord_2024]: The overlap query above: every patient in any of TP53 mutant, KRAS mutant, Immunotherapy, NSCLC gets a bitmask of the cohorts it is in, and the study returns one count per bitmask. Counts only.
+    - Add the counts across studies and name each combination: Each study's bits are mapped back to the view's cohort order; combinations add exactly, and a patient can only overlap within one study.
+        patients in any cohort: 18,570
+        combinations with patients: 15
diff --git a/test/snapshots/paired.json b/test/snapshots/paired.json
new file mode 100644
index 00000000..84b66e1d
--- /dev/null
+++ b/test/snapshots/paired.json
@@ -0,0 +1,233 @@
+{
+  "study": "msk_impact_50k_2026",
+  "cohorts": {
+    "all patients": {
+      "count": 48179,
+      "text": "all patients in the study",
+      "studies": [
+        "msk_impact_50k_2026"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "paired",
+      "cohorts": [
+        "all patients"
+      ],
+      "paired": {
+        "pairBy": {
+          "attribute": "SAMPLE_TYPE",
+          "a": [
+            "Primary"
+          ],
+          "b": [
+            "Metastasis"
+          ]
+        },
+        "labels": {
+          "a": "Primary",
+          "b": "Metastasis"
+        },
+        "measure": "driver",
+        "panels": [
+          {
+            "cohort": "all patients",
+            "patients": 2278,
+            "aSamples": 2558,
+            "bSamples": 2674,
+            "genes": [
+              {
+                "gene": "TP53",
+                "pairs": 2278,
+                "both": 930,
+                "aOnly": 108,
+                "bOnly": 161,
+                "neither": 1079,
+                "concordance": 0.776,
+                "p": 0.0014752559593677614,
+                "q": 0.002360409534988418
+              },
+              {
+                "gene": "KRAS",
+                "pairs": 2278,
+                "both": 235,
+                "aOnly": 58,
+                "bOnly": 75,
+                "neither": 1910,
+                "concordance": 0.639,
+                "p": 0.16508338915579682,
+                "q": 0.18866673046376778
+              },
+              {
+                "gene": "PIK3CA",
+                "pairs": 2278,
+                "both": 199,
+                "aOnly": 87,
+                "bOnly": 64,
+                "neither": 1928,
+                "concordance": 0.569,
+                "p": 0.07304915944162735,
+                "q": 0.09739887925550313
+              },
+              {
+                "gene": "ESR1",
+                "pairs": 2278,
+                "both": 4,
+                "aOnly": 7,
+                "bOnly": 49,
+                "neither": 2218,
+                "concordance": 0.067,
+                "p": 7.455210310203303e-9,
+                "q": 2.982084124081321e-8
+              },
+              {
+                "gene": "AR",
+                "pairs": 2278,
+                "both": 9,
+                "aOnly": 1,
+                "bOnly": 71,
+                "neither": 2197,
+                "concordance": 0.111,
+                "p": 3.091670257478201e-20,
+                "q": 2.473336205982561e-19
+              },
+              {
+                "gene": "CDKN2A",
+                "pairs": 2278,
+                "both": 222,
+                "aOnly": 123,
+                "bOnly": 180,
+                "neither": 1753,
+                "concordance": 0.423,
+                "p": 0.0012575668195445922,
+                "q": 0.002360409534988418
+              },
+              {
+                "gene": "MYC",
+                "pairs": 2278,
+                "both": 65,
+                "aOnly": 47,
+                "bOnly": 113,
+                "neither": 2053,
+                "concordance": 0.289,
+                "p": 1.876911518039637e-7,
+                "q": 5.005097381439033e-7
+              },
+              {
+                "gene": "TERT",
+                "pairs": 2278,
+                "both": 298,
+                "aOnly": 72,
+                "bOnly": 75,
+                "neither": 1833,
+                "concordance": 0.67,
+                "p": 0.8690499708342935,
+                "q": 0.8690499708342935
+              }
+            ]
+          }
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_impact_50k_2026",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Within patients with samples on both sides, Primary and Metastasis (SAMPLE_TYPE = Primary vs Metastasis; all patients: 2,278 patients), per gene, among those profiled for it on BOTH sides by each side's own panels: an OncoKB driver (v7.5) on both sides, on the Primary side only, or the Metastasis side only; a patient with several samples on a side counts as altered there if any is. Concordance is both ÷ altered anywhere. The exact McNemar test asks whether discordance runs one way; q is Benjamini–Hochberg across the genes of each cohort. A sample's SAMPLE_TYPE records where the tissue came from, not when it was taken, so Metastasis-only is not the same as \"acquired later\"; and patients sampled twice are a selected group, typically with longer or harder disease courses.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count each gene's state in paired samples",
+            "detail": "The paired:patients and paired:genes queries above. Patients with both a SAMPLE_TYPE = Primary sample (Primary) and a SAMPLE_TYPE = Metastasis sample (Metastasis); per gene, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the Primary side only, or the Metastasis side only. A patient with several samples on one side counts as altered there if any is. Counts only.",
+            "scope": "all patients · msk_impact_50k_2026",
+            "rowsIn": 48179
+          },
+          {
+            "kind": "statistic",
+            "title": "Exact McNemar test per gene",
+            "detail": "Only the discordant patients carry information about direction: of the Primary-only and Metastasis-only patients, each is a fair coin under the null, so p is twice the smaller binomial tail (capped at 1). Concordance is both ÷ altered anywhere.",
+            "params": [
+              [
+                "all patients",
+                "2,278 patients with both kinds of sample"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across genes, per cohort",
+            "detail": "q-values over the genes with at least one discordant patient, separately for each cohort."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "mcnemarExact",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "The exact McNemar test matches statsmodels 0.15.0 mcnemar(exact=True) and Benjamini–Hochberg multipletests, to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "paired:all patients:TP53:p": {
+            "v": 0.0014752559593677614
+          },
+          "paired:all patients:TP53:q": {
+            "v": 0.002360409534988418
+          },
+          "paired:all patients:KRAS:p": {
+            "v": 0.16508338915579682
+          },
+          "paired:all patients:KRAS:q": {
+            "v": 0.18866673046376778
+          },
+          "paired:all patients:PIK3CA:p": {
+            "v": 0.07304915944162735
+          },
+          "paired:all patients:PIK3CA:q": {
+            "v": 0.09739887925550313
+          },
+          "paired:all patients:ESR1:p": {
+            "v": 7.455210310203303e-9
+          },
+          "paired:all patients:ESR1:q": {
+            "v": 2.982084124081321e-8
+          },
+          "paired:all patients:AR:p": {
+            "v": 3.091670257478201e-20
+          },
+          "paired:all patients:AR:q": {
+            "v": 2.473336205982561e-19
+          },
+          "paired:all patients:CDKN2A:p": {
+            "v": 0.0012575668195445922
+          },
+          "paired:all patients:CDKN2A:q": {
+            "v": 0.002360409534988418
+          },
+          "paired:all patients:MYC:p": {
+            "v": 1.876911518039637e-7
+          },
+          "paired:all patients:MYC:q": {
+            "v": 5.005097381439033e-7
+          },
+          "paired:all patients:TERT:p": {
+            "v": 0.8690499708342935
+          },
+          "paired:all patients:TERT:q": {
+            "v": 0.8690499708342935
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/paired.sql b/test/snapshots/paired.sql
new file mode 100644
index 00000000..b9917039
--- /dev/null
+++ b/test/snapshots/paired.sql
@@ -0,0 +1,61 @@
+-- cohort all patients
+-- msk_impact_50k_2026
+SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026'
+
+-- view 0 paired: paired:genes:all patients · msk_impact_50k_2026
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Primary'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Metastasis'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'SAMPLE_TYPE'
+    AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired)),
+alt AS (
+  SELECT g.hugo_gene_symbol AS gene, ps.pid AS pid, ps.side AS side, max(ann.is_driver) AS hit
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  INNER JOIN ps ON ps.suid = g.sample_unique_id
+  WHERE g.cancer_study_identifier = 'msk_impact_50k_2026' AND g.hugo_gene_symbol IN ('TP53','KRAS','PIK3CA','ESR1','AR','CDKN2A','MYC','TERT') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND g.sample_unique_id IN (SELECT suid FROM ps)
+  GROUP BY gene, pid, side
+),
+cov AS (
+  SELECT DISTINCT pg.gene AS gene, ps.pid AS pid, ps.side AS side
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('TP53','KRAS','PIK3CA','ESR1','AR','CDKN2A','MYC','TERT')
+  INNER JOIN ps ON ps.suid = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_impact_50k_2026'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+),
+per AS (
+  SELECT gene, pid, max(side = 'a') AS pa, max(side = 'b') AS pb,
+         max(side = 'a' AND hit > 0) AS aa, max(side = 'b' AND hit > 0) AS ab
+  FROM (SELECT gene, pid, side, hit FROM alt UNION ALL SELECT gene, pid, side, 0 AS hit FROM cov)
+  GROUP BY gene, pid
+)
+SELECT gene, count() AS pairs, countIf(aa AND ab) AS both, countIf(aa AND NOT ab) AS a_only, countIf(ab AND NOT aa) AS b_only
+FROM per WHERE pa AND pb GROUP BY gene
+
+-- view 0 paired: paired:patients:all patients · msk_impact_50k_2026
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Primary'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Metastasis'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_impact_50k_2026' AND type = 'sample' AND attribute_name = 'SAMPLE_TYPE'
+    AND replaceOne(patient_unique_id, 'msk_impact_50k_2026_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired))
+SELECT uniqExact(pid) AS patients, countIf(side = 'a') AS a_samples, countIf(side = 'b') AS b_samples FROM ps
diff --git a/test/snapshots/paired.svg b/test/snapshots/paired.svg
new file mode 100644
index 00000000..b0e6ac2b
--- /dev/null
+++ b/test/snapshots/paired.svg
@@ -0,0 +1,2 @@
+<!-- view 0 paired -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 354" width="100%" role="img" aria-label="Paired samples — Primary vs Metastasis" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Paired samples — Primary vs Metastasis</title><rect width="960" height="354" fill="#ffffff"/><rect x="8" y="6.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="15" font-size="10" fill="#16232b">Primary only</text><rect x="95.31" y="6.5" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="110.3" y="15" font-size="10" fill="#16232b">both</text><rect x="146.9" y="6.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="161.9" y="15" font-size="10" fill="#16232b">Metastasis only</text><text x="8" y="39" font-size="12" fill="#16232b" font-weight="600">all patients</text><text x="79.6" y="39" font-size="10" fill="#5b6b76">2,278 patients with both · 2,558 Primary and 2,674 Metastasis samples</text><text x="701.8" y="57" font-size="10" fill="#5b6b76" text-anchor="end">concordant</text><text x="775.1" y="57" font-size="10" fill="#5b6b76" text-anchor="end">profiled pairs</text><text x="840.8" y="57" font-size="10" fill="#5b6b76" text-anchor="end">McNemar q</text><line x1="66.6" y1="61" x2="66.6" y2="221" stroke="#dde4e8" stroke-width="0.75"/><line x1="161.6" y1="61" x2="161.6" y2="221" stroke="#dde4e8" stroke-width="0.75"/><line x1="256.7" y1="61" x2="256.7" y2="221" stroke="#dde4e8" stroke-width="0.75"/><line x1="351.7" y1="61" x2="351.7" y2="221" stroke="#dde4e8" stroke-width="0.75"/><line x1="446.8" y1="61" x2="446.8" y2="221" stroke="#dde4e8" stroke-width="0.75"/><line x1="541.8" y1="61" x2="541.8" y2="221" stroke="#dde4e8" stroke-width="0.75"/><line x1="636.8" y1="61" x2="636.8" y2="221" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="61" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="52.6" y="74.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">TP53</text><rect x="66.6" y="64" width="51.3" height="14" fill="#0072B2"><title>TP53 — 108 patients altered Primary only</title></rect><text x="92.2" y="74.5" font-size="10" fill="#ffffff" text-anchor="middle">108</text><rect x="117.9" y="64" width="441.9" height="14" fill="#9aa5ad"><title>TP53 — 930 patients altered both</title></rect><text x="338.9" y="74.5" font-size="10" fill="#16232b" text-anchor="middle">930</text><rect x="559.9" y="64" width="76.5" height="14" fill="#E69F00"><title>TP53 — 161 patients altered Metastasis only</title></rect><text x="598.1" y="74.5" font-size="10" fill="#16232b" text-anchor="middle">161</text><text x="701.8" y="74.5" font-size="10" fill="#16232b" text-anchor="end">77.6%</text><text x="775.1" y="74.5" font-size="10" fill="#16232b" text-anchor="end">2,278</text><text x="840.8" y="74.5" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">0.0024</text><text x="854.8" y="74.5" font-size="10" fill="#16232b">more Metastasis-only</text><text x="52.6" y="94.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">KRAS</text><rect x="66.6" y="84" width="27.6" height="14" fill="#0072B2"><title>KRAS — 58 patients altered Primary only</title></rect><text x="80.4" y="94.5" font-size="10" fill="#ffffff" text-anchor="middle">58</text><rect x="94.1" y="84" width="111.7" height="14" fill="#9aa5ad"><title>KRAS — 235 patients altered both</title></rect><text x="150" y="94.5" font-size="10" fill="#16232b" text-anchor="middle">235</text><rect x="205.8" y="84" width="35.6" height="14" fill="#E69F00"><title>KRAS — 75 patients altered Metastasis only</title></rect><text x="223.6" y="94.5" font-size="10" fill="#16232b" text-anchor="middle">75</text><text x="701.8" y="94.5" font-size="10" fill="#5b6b76" text-anchor="end">63.9%</text><text x="775.1" y="94.5" font-size="10" fill="#5b6b76" text-anchor="end">2,278</text><text x="840.8" y="94.5" font-size="10" fill="#5b6b76" text-anchor="end">0.19</text><text x="854.8" y="94.5" font-size="10" fill="#5b6b76"></text><rect x="4" y="101" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="52.6" y="114.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">PIK3CA</text><rect x="66.6" y="104" width="41.3" height="14" fill="#0072B2"><title>PIK3CA — 87 patients altered Primary only</title></rect><text x="87.3" y="114.5" font-size="10" fill="#ffffff" text-anchor="middle">87</text><rect x="107.9" y="104" width="94.6" height="14" fill="#9aa5ad"><title>PIK3CA — 199 patients altered both</title></rect><text x="155.2" y="114.5" font-size="10" fill="#16232b" text-anchor="middle">199</text><rect x="202.5" y="104" width="30.4" height="14" fill="#E69F00"><title>PIK3CA — 64 patients altered Metastasis only</title></rect><text x="217.7" y="114.5" font-size="10" fill="#16232b" text-anchor="middle">64</text><text x="701.8" y="114.5" font-size="10" fill="#5b6b76" text-anchor="end">56.9%</text><text x="775.1" y="114.5" font-size="10" fill="#5b6b76" text-anchor="end">2,278</text><text x="840.8" y="114.5" font-size="10" fill="#5b6b76" text-anchor="end">0.097</text><text x="854.8" y="114.5" font-size="10" fill="#5b6b76"></text><text x="52.6" y="134.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">ESR1</text><rect x="66.6" y="124" width="3.3" height="14" fill="#0072B2"><title>ESR1 — 7 patients altered Primary only</title></rect><rect x="69.9" y="124" width="1.9" height="14" fill="#9aa5ad"><title>ESR1 — 4 patients altered both</title></rect><rect x="71.8" y="124" width="23.3" height="14" fill="#E69F00"><title>ESR1 — 49 patients altered Metastasis only</title></rect><text x="83.5" y="134.5" font-size="10" fill="#16232b" text-anchor="middle">49</text><text x="701.8" y="134.5" font-size="10" fill="#16232b" text-anchor="end">6.7%</text><text x="775.1" y="134.5" font-size="10" fill="#16232b" text-anchor="end">2,278</text><text x="840.8" y="134.5" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">3e-8</text><text x="854.8" y="134.5" font-size="10" fill="#16232b">more Metastasis-only</text><rect x="4" y="141" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="52.6" y="154.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">AR</text><rect x="66.6" y="144" width="1" height="14" fill="#0072B2"><title>AR — 1 patients altered Primary only</title></rect><rect x="67.6" y="144" width="4.3" height="14" fill="#9aa5ad"><title>AR — 9 patients altered both</title></rect><rect x="71.9" y="144" width="33.7" height="14" fill="#E69F00"><title>AR — 71 patients altered Metastasis only</title></rect><text x="88.7" y="154.5" font-size="10" fill="#16232b" text-anchor="middle">71</text><text x="701.8" y="154.5" font-size="10" fill="#16232b" text-anchor="end">11.1%</text><text x="775.1" y="154.5" font-size="10" fill="#16232b" text-anchor="end">2,278</text><text x="840.8" y="154.5" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">2.5e-19</text><text x="854.8" y="154.5" font-size="10" fill="#16232b">more Metastasis-only</text><text x="52.6" y="174.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">CDKN2A</text><rect x="66.6" y="164" width="58.5" height="14" fill="#0072B2"><title>CDKN2A — 123 patients altered Primary only</title></rect><text x="95.8" y="174.5" font-size="10" fill="#ffffff" text-anchor="middle">123</text><rect x="125" y="164" width="105.5" height="14" fill="#9aa5ad"><title>CDKN2A — 222 patients altered both</title></rect><text x="177.8" y="174.5" font-size="10" fill="#16232b" text-anchor="middle">222</text><rect x="230.5" y="164" width="85.5" height="14" fill="#E69F00"><title>CDKN2A — 180 patients altered Metastasis only</title></rect><text x="273.3" y="174.5" font-size="10" fill="#16232b" text-anchor="middle">180</text><text x="701.8" y="174.5" font-size="10" fill="#16232b" text-anchor="end">42.3%</text><text x="775.1" y="174.5" font-size="10" fill="#16232b" text-anchor="end">2,278</text><text x="840.8" y="174.5" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">0.0024</text><text x="854.8" y="174.5" font-size="10" fill="#16232b">more Metastasis-only</text><rect x="4" y="181" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="52.6" y="194.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">MYC</text><rect x="66.6" y="184" width="22.3" height="14" fill="#0072B2"><title>MYC — 47 patients altered Primary only</title></rect><text x="77.8" y="194.5" font-size="10" fill="#ffffff" text-anchor="middle">47</text><rect x="88.9" y="184" width="30.9" height="14" fill="#9aa5ad"><title>MYC — 65 patients altered both</title></rect><text x="104.4" y="194.5" font-size="10" fill="#16232b" text-anchor="middle">65</text><rect x="119.8" y="184" width="53.7" height="14" fill="#E69F00"><title>MYC — 113 patients altered Metastasis only</title></rect><text x="146.7" y="194.5" font-size="10" fill="#16232b" text-anchor="middle">113</text><text x="701.8" y="194.5" font-size="10" fill="#16232b" text-anchor="end">28.9%</text><text x="775.1" y="194.5" font-size="10" fill="#16232b" text-anchor="end">2,278</text><text x="840.8" y="194.5" font-size="10" fill="#16232b" text-anchor="end" font-weight="600">5e-7</text><text x="854.8" y="194.5" font-size="10" fill="#16232b">more Metastasis-only</text><text x="52.6" y="214.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">TERT</text><rect x="66.6" y="204" width="34.2" height="14" fill="#0072B2"><title>TERT — 72 patients altered Primary only</title></rect><text x="83.7" y="214.5" font-size="10" fill="#ffffff" text-anchor="middle">72</text><rect x="100.8" y="204" width="141.6" height="14" fill="#9aa5ad"><title>TERT — 298 patients altered both</title></rect><text x="171.6" y="214.5" font-size="10" fill="#16232b" text-anchor="middle">298</text><rect x="242.4" y="204" width="35.6" height="14" fill="#E69F00"><title>TERT — 75 patients altered Metastasis only</title></rect><text x="260.2" y="214.5" font-size="10" fill="#16232b" text-anchor="middle">75</text><text x="701.8" y="214.5" font-size="10" fill="#5b6b76" text-anchor="end">67.0%</text><text x="775.1" y="214.5" font-size="10" fill="#5b6b76" text-anchor="end">2,278</text><text x="840.8" y="214.5" font-size="10" fill="#5b6b76" text-anchor="end">0.87</text><text x="854.8" y="214.5" font-size="10" fill="#5b6b76"></text><line x1="66.6" y1="221" x2="66.6" y2="224" stroke="#5b6b76" stroke-width="1"/><text x="63.6" y="237" font-size="10" fill="#5b6b76">0</text><line x1="161.6" y1="221" x2="161.6" y2="224" stroke="#5b6b76" stroke-width="1"/><text x="152.6" y="237" font-size="10" fill="#5b6b76">200</text><line x1="256.7" y1="221" x2="256.7" y2="224" stroke="#5b6b76" stroke-width="1"/><text x="247.7" y="237" font-size="10" fill="#5b6b76">400</text><line x1="351.7" y1="221" x2="351.7" y2="224" stroke="#5b6b76" stroke-width="1"/><text x="342.7" y="237" font-size="10" fill="#5b6b76">600</text><line x1="446.8" y1="221" x2="446.8" y2="224" stroke="#5b6b76" stroke-width="1"/><text x="437.8" y="237" font-size="10" fill="#5b6b76">800</text><line x1="541.8" y1="221" x2="541.8" y2="224" stroke="#5b6b76" stroke-width="1"/><text x="528.4" y="237" font-size="10" fill="#5b6b76">1,000</text><line x1="636.8" y1="221" x2="636.8" y2="224" stroke="#5b6b76" stroke-width="1"/><text x="623.5" y="237" font-size="10" fill="#5b6b76">1,200</text><line x1="66.583" y1="221" x2="636.8399999999999" y2="221" stroke="#5b6b76" stroke-width="1"/><text x="351.7" y="256" font-size="11" fill="#16232b" text-anchor="middle">patients altered on either side</text><text x="8" y="278" font-size="10" fill="#5b6b76">Within patients with samples on both sides, Primary and Metastasis (SAMPLE_TYPE = Primary vs Metastasis; all patients: 2,278 patients), per gene, among those profiled</text><text x="8" y="292" font-size="10" fill="#5b6b76">for it on BOTH sides by each side's own panels: an OncoKB driver (v7.5) on both sides, on the Primary side only, or the Metastasis side only; a patient with several</text><text x="8" y="306" font-size="10" fill="#5b6b76">samples on a side counts as altered there if any is. Concordance is both ÷ altered anywhere. The exact McNemar test asks whether discordance runs one way; q is</text><text x="8" y="320" font-size="10" fill="#5b6b76">Benjamini–Hochberg across the genes of each cohort. A sample's SAMPLE_TYPE records where the tissue came from, not when it was taken, so Metastasis-only is not</text><text x="8" y="334" font-size="10" fill="#5b6b76">the same as &quot;acquired later&quot;; and patients sampled twice are a selected group, typically with longer or harder disease courses.</text></svg>
diff --git a/test/snapshots/paired.txt b/test/snapshots/paired.txt
new file mode 100644
index 00000000..9af231c2
--- /dev/null
+++ b/test/snapshots/paired.txt
@@ -0,0 +1,22 @@
+Analysis over msk_impact_50k_2026 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- all patients: 48,179 patients — all patients in the study
+
+Paired samples, Primary vs Metastasis (SAMPLE_TYPE Primary vs Metastasis; drivers; profiled on both sides):
+  all patients (2,278 patients with both):
+    TP53: both 930, Primary-only 108, Metastasis-only 161 of 2278 pairs, 78% concordant, McNemar q=0.0024
+    KRAS: both 235, Primary-only 58, Metastasis-only 75 of 2278 pairs, 64% concordant, McNemar q=0.1887
+    PIK3CA: both 199, Primary-only 87, Metastasis-only 64 of 2278 pairs, 57% concordant, McNemar q=0.0974
+    ESR1: both 4, Primary-only 7, Metastasis-only 49 of 2278 pairs, 7% concordant, McNemar q=3.0e-8
+    AR: both 9, Primary-only 1, Metastasis-only 71 of 2278 pairs, 11% concordant, McNemar q=2.5e-19
+    CDKN2A: both 222, Primary-only 123, Metastasis-only 180 of 2278 pairs, 42% concordant, McNemar q=0.0024
+    MYC: both 65, Primary-only 47, Metastasis-only 113 of 2278 pairs, 29% concordant, McNemar q=5.0e-7
+    TERT: both 298, Primary-only 72, Metastasis-only 75 of 2278 pairs, 67% concordant, McNemar q=0.8690
+  CAVEAT (tell the user): Within patients with samples on both sides, Primary and Metastasis (SAMPLE_TYPE = Primary vs Metastasis; all patients: 2,278 patients), per gene, among those profiled for it on BOTH sides by each side's own panels: an OncoKB driver (v7.5) on both sides, on the Primary side only, or the Metastasis side only; a patient with several samples on a side counts as altered there if any is. Concordance is both ÷ altered anywhere. The exact McNemar test asks whether discordance runs one way; q is Benjamini–Hochberg across the genes of each cohort. A sample's SAMPLE_TYPE records where the tissue came from, not when it was taken, so Metastasis-only is not the same as "acquired later"; and patients sampled twice are a selected group, typically with longer or harder disease courses.
+  METHOD:
+    - Count each gene's state in paired samples [all patients · msk_impact_50k_2026]: The paired:patients and paired:genes queries above. Patients with both a SAMPLE_TYPE = Primary sample (Primary) and a SAMPLE_TYPE = Metastasis sample (Metastasis); per gene, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the Primary side only, or the Metastasis side only. A patient with several samples on one side counts as altered there if any is. Counts only.
+    - Exact McNemar test per gene: Only the discordant patients carry information about direction: of the Primary-only and Metastasis-only patients, each is a fair coin under the null, so p is twice the smaller binomial tail (capped at 1). Concordance is both ÷ altered anywhere.
+        all patients: 2,278 patients with both kinds of sample
+    - Benjamini–Hochberg across genes, per cohort: q-values over the genes with at least one discordant patient, separately for each cohort.
+    The exact McNemar test matches statsmodels 0.15.0 mcnemar(exact=True) and Benjamini–Hochberg multipletests, to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/pathways.json b/test/snapshots/pathways.json
new file mode 100644
index 00000000..fccf54ce
--- /dev/null
+++ b/test/snapshots/pathways.json
@@ -0,0 +1,1557 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "Colorectal": {
+      "count": 5543,
+      "text": "CANCER_TYPE in [Colorectal Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "Pancreatic": {
+      "count": 3109,
+      "text": "CANCER_TYPE in [Pancreatic Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "pathways",
+      "cohorts": [
+        "Colorectal",
+        "Pancreatic"
+      ],
+      "annotation": "OncoKB v7.5",
+      "pathways": {
+        "measure": "driver",
+        "rows": [
+          {
+            "name": "RTK-RAS",
+            "source": "Sanchez-Vega 2018",
+            "genes": 85,
+            "profiledGenes": 47,
+            "cells": [
+              {
+                "cohort": "Colorectal",
+                "profiled": 5543,
+                "altered": 3738,
+                "pct": 67.4
+              },
+              {
+                "cohort": "Pancreatic",
+                "profiled": 3109,
+                "altered": 2600,
+                "pct": 83.6
+              }
+            ],
+            "p": 6.382707592331983e-63,
+            "top": [
+              {
+                "gene": "KRAS",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 2395,
+                    "pct": 43.2
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 2517,
+                    "pct": 81
+                  }
+                ],
+                "q": 7.736787074641247e-267
+              },
+              {
+                "gene": "BRAF",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 547,
+                    "pct": 9.9
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 35,
+                    "pct": 1.1
+                  }
+                ],
+                "q": 7.371186914678062e-68
+              },
+              {
+                "gene": "ERBB2",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 291,
+                    "pct": 5.2
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 36,
+                    "pct": 1.2
+                  }
+                ],
+                "q": 2.4107184931598108e-24
+              },
+              {
+                "gene": "NRAS",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 189,
+                    "pct": 3.4
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 14,
+                    "pct": 0.5
+                  }
+                ],
+                "q": 1.6486842887630062e-21
+              },
+              {
+                "gene": "FGFR1",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 136,
+                    "pct": 2.5
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 53,
+                    "pct": 1.7
+                  }
+                ],
+                "q": 0.03664470704728582
+              },
+              {
+                "gene": "ERBB3",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 130,
+                    "pct": 2.3
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 28,
+                    "pct": 0.9
+                  }
+                ],
+                "q": 0.0000015378844163248904
+              }
+            ],
+            "otherAltered": 32,
+            "notProfiled": [
+              "GRB2",
+              "CBLB",
+              "CBLC",
+              "INSRR",
+              "SOS2",
+              "SHC1",
+              "SHC2",
+              "SHC3",
+              "SHC4",
+              "RASGRP1",
+              "RASGRP2",
+              "RASGRP3",
+              "RASGRP4",
+              "RAPGEF1",
+              "RAPGEF2",
+              "RASGRF1",
+              "RASGRF2",
+              "FNTA",
+              "FNTB",
+              "RCE1",
+              {
+                "…": "18 more",
+                "sha256": "143af5862385bf8b"
+              }
+            ],
+            "q": 1.2765415184663967e-62
+          },
+          {
+            "name": "PI3K",
+            "source": "Sanchez-Vega 2018",
+            "genes": 29,
+            "profiledGenes": 19,
+            "cells": [
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+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 177,
+                    "pct": 3.2
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 16,
+                    "pct": 0.5
+                  }
+                ],
+                "q": 2.37601062548213e-18
+              },
+              {
+                "gene": "LATS1",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 49,
+                    "pct": 0.9
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 10,
+                    "pct": 0.3
+                  }
+                ],
+                "q": 0.0033279240530356964
+              },
+              {
+                "gene": "LATS2",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 34,
+                    "pct": 0.6
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 5,
+                    "pct": 0.2
+                  }
+                ],
+                "q": 0.0041179603472771
+              },
+              {
+                "gene": "NF2",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 9,
+                    "pct": 0.2
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 9,
+                    "pct": 0.3
+                  }
+                ],
+                "q": 0.3109147064391383
+              },
+              {
+                "gene": "YAP1",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 10,
+                    "pct": 0.2
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 1,
+                    "pct": 0
+                  }
+                ],
+                "q": 0.15986587736599803
+              }
+            ],
+            "otherAltered": 0,
+            "notProfiled": [
+              "STK4",
+              "STK3",
+              "SAV1",
+              "MOB1A",
+              "MOB1B",
+              "TEAD1",
+              "TEAD2",
+              "TEAD3",
+              "TEAD4",
+              "PTPN14",
+              "WWC1",
+              "TAOK1",
+              "TAOK2",
+              "TAOK3",
+              "CRB1",
+              "CRB2",
+              "CRB3",
+              "LLGL1",
+              "LLGL2",
+              "HMCN1",
+              {
+                "…": "12 more",
+                "sha256": "fbb5c6a2c9e6c94a"
+              }
+            ],
+            "q": 1.0025375551557823e-18
+          },
+          {
+            "name": "TGF-β",
+            "source": "Sanchez-Vega 2018",
+            "genes": 7,
+            "profiledGenes": 5,
+            "cells": [
+              {
+                "cohort": "Colorectal",
+                "profiled": 5543,
+                "altered": 1194,
+                "pct": 21.5
+              },
+              {
+                "cohort": "Pancreatic",
+                "profiled": 3109,
+                "altered": 732,
+                "pct": 23.5
+              }
+            ],
+            "p": 0.03335684499535692,
+            "top": [
+              {
+                "gene": "SMAD4",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 859,
+                    "pct": 15.5
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 655,
+                    "pct": 21.1
+                  }
+                ],
+                "q": 3.7270880898502765e-10
+              },
+              {
+                "gene": "SMAD2",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 225,
+                    "pct": 4.1
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 11,
+                    "pct": 0.4
+                  }
+                ],
+                "q": 4.060527821165915e-30
+              },
+              {
+                "gene": "SMAD3",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 180,
+                    "pct": 3.2
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 31,
+                    "pct": 1
+                  }
+                ],
+                "q": 2.6199648767682504e-11
+              },
+              {
+                "gene": "TGFBR1",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 48,
+                    "pct": 0.9
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 42,
+                    "pct": 1.4
+                  }
+                ],
+                "q": 0.05793031858365166
+              },
+              {
+                "gene": "TGFBR2",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 22,
+                    "pct": 0.4
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 22,
+                    "pct": 0.7
+                  }
+                ],
+                "q": 0.0908517357789052
+              }
+            ],
+            "otherAltered": 0,
+            "notProfiled": [
+              "ACVR2A",
+              "ACVR1B"
+            ],
+            "q": 0.037063161105952126
+          },
+          {
+            "name": "NRF2",
+            "source": "Sanchez-Vega 2018",
+            "genes": 3,
+            "profiledGenes": 3,
+            "cells": [
+              {
+                "cohort": "Colorectal",
+                "profiled": 5543,
+                "altered": 54,
+                "pct": 1
+              },
+              {
+                "cohort": "Pancreatic",
+                "profiled": 3109,
+                "altered": 24,
+                "pct": 0.8
+              }
+            ],
+            "p": 0.4067674356699012,
+            "top": [
+              {
+                "gene": "KEAP1",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 33,
+                    "pct": 0.6
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 19,
+                    "pct": 0.6
+                  }
+                ],
+                "q": 1
+              },
+              {
+                "gene": "CUL3",
+                "role": "TSG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 20,
+                    "pct": 0.4
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 1,
+                    "pct": 0
+                  }
+                ],
+                "q": 0.004058675700131532
+              },
+              {
+                "gene": "NFE2L2",
+                "role": "OG",
+                "cells": [
+                  {
+                    "cohort": "Colorectal",
+                    "profiled": 5543,
+                    "altered": 1,
+                    "pct": 0
+                  },
+                  {
+                    "cohort": "Pancreatic",
+                    "profiled": 3109,
+                    "altered": 4,
+                    "pct": 0.1
+                  }
+                ],
+                "q": 0.0908517357789052
+              }
+            ],
+            "otherAltered": 0,
+            "notProfiled": [],
+            "q": 0.4067674356699012
+          }
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Share of patients with an OncoKB driver (v7.5) in at least one member gene — drivers only, as Sanchez-Vega et al. counted curated oncogenic events. A pathway's denominator is the patients profiled for at least one of its genes, so a panel that covers few members makes a pathway look rarer than a genome-wide assay would; each member gene's own % is over the patients profiled for THAT gene. 206 member-gene slots were on no participating panel and are listed as not profiled rather than counted as unaltered. Pathway membership is the paper's Table S3; a gene's pathway role (oncogene / tumour suppressor) is not used in the count. Two-sided Fisher tests; q is Benjamini–Hochberg across the pathways drawn, and separately across every member gene tested.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients altered per pathway and per gene",
+            "detail": "The geneCounts and pathways:sets queries above. Per pathway: patients profiled for at least one member gene (the denominator) and of those, patients with an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in at least one member. Per member gene: the same over the patients profiled for that gene. Counts only.",
+            "scope": "Colorectal · msk_chord_2024",
+            "rowsIn": 5543,
+            "params": [
+              [
+                "pathways / sets",
+                "RTK-RAS (85), PI3K (29), TP53 (6), Cell cycle (15), MYC (13), WNT (68), NOTCH (71), Hippo (38), TGF-β (7), NRF2 (3)"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients altered per pathway and per gene",
+            "detail": "The geneCounts and pathways:sets queries above. Per pathway: patients profiled for at least one member gene (the denominator) and of those, patients with an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in at least one member. Per member gene: the same over the patients profiled for that gene. Counts only.",
+            "scope": "Pancreatic · msk_chord_2024",
+            "rowsIn": 3109,
+            "params": [
+              [
+                "pathways / sets",
+                "RTK-RAS (85), PI3K (29), TP53 (6), Cell cycle (15), MYC (13), WNT (68), NOTCH (71), Hippo (38), TGF-β (7), NRF2 (3)"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Fisher's exact test per pathway and per member gene",
+            "detail": "Two-sided, altered vs not among the profiled, between the two cohorts."
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg",
+            "detail": "Across the pathways drawn, and separately across every member gene tested, each gene once even when it sits in two pathways."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "fisherExact",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "pathways:pathway:RTK-RAS:p": {
+            "v": 6.382707592331983e-63
+          },
+          "pathways:pathway:RTK-RAS:q": {
+            "v": 1.2765415184663967e-62
+          },
+          "pathways:pathway:PI3K:p": {
+            "v": 5.785174993259107e-125
+          },
+          "pathways:pathway:PI3K:q": {
+            "v": 1.4462937483147768e-124
+          },
+          "pathways:pathway:TP53:p": {
+            "v": 8.772026952886191e-18
+          },
+          "pathways:pathway:TP53:q": {
+            "v": 1.2531467075551702e-17
+          },
+          "pathways:pathway:Cell cycle:p": {
+            "v": 5.19604894684864e-245
+          },
+          "pathways:pathway:Cell cycle:q": {
+            "v": 2.59802447342432e-244
+          },
+          "pathways:pathway:MYC:p": {
+            "v": 2.905584066517031e-8
+          },
+          "pathways:pathway:MYC:q": {
+            "v": 3.6319800831462886e-8
+          },
+          "pathways:pathway:WNT:p": {
+            "v": 0
+          },
+          "pathways:pathway:WNT:q": {
+            "v": 0
+          },
+          "pathways:pathway:NOTCH:p": {
+            "v": 2.6198371106471583e-139
+          },
+          "pathways:pathway:NOTCH:q": {
+            "v": 8.732790368823861e-139
+          },
+          "pathways:pathway:Hippo:p": {
+            "v": 6.015225330934694e-19
+          },
+          "pathways:pathway:Hippo:q": {
+            "v": 1.0025375551557823e-18
+          },
+          "pathways:pathway:TGF-β:p": {
+            "v": 0.03335684499535692
+          },
+          "pathways:pathway:TGF-β:q": {
+            "v": 0.037063161105952126
+          },
+          "pathways:pathway:NRF2:p": {
+            "v": 0.4067674356699012
+          },
+          "pathways:pathway:NRF2:q": {
+            "v": 0.4067674356699012
+          },
+          "…": {
+            "more": 53,
+            "sha256": "dcbde1c378cc0869"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/pathways.sql b/test/snapshots/pathways.sql
new file mode 100644
index 00000000..1ab78a1a
--- /dev/null
+++ b/test/snapshots/pathways.sql
@@ -0,0 +1,203 @@
+-- cohort Colorectal
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))
+
+-- cohort Pancreatic
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))
+
+-- view 0 pathways: geneCounts · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid, g.variant_type AS vtype,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid, vtype
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         multiIf(sp.alteration_type = 'MUTATION_EXTENDED', 'mutation',
+                 sp.alteration_type = 'COPY_NUMBER_ALTERATION', 'cna', 'structural_variant') AS vtype
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+raw AS (
+  SELECT gene, pid, vtype, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, vtype, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, vtype, 0 AS is_alt, 0 AS is_drv FROM covered
+  )
+  GROUP BY gene, pid, vtype
+),
+perPatient AS (
+  SELECT gene, pid, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM raw GROUP BY gene, pid
+)
+SELECT gene, vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM raw GROUP BY gene, vtype
+UNION ALL
+SELECT gene, 'any' AS vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM perPatient GROUP BY gene
+
+-- view 0 pathways: geneCounts · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid, g.variant_type AS vtype,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid, vtype
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         multiIf(sp.alteration_type = 'MUTATION_EXTENDED', 'mutation',
+                 sp.alteration_type = 'COPY_NUMBER_ALTERATION', 'cna', 'structural_variant') AS vtype
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+raw AS (
+  SELECT gene, pid, vtype, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, vtype, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, vtype, 0 AS is_alt, 0 AS is_drv FROM covered
+  )
+  GROUP BY gene, pid, vtype
+),
+perPatient AS (
+  SELECT gene, pid, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM raw GROUP BY gene, pid
+)
+SELECT gene, vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM raw GROUP BY gene, vtype
+UNION ALL
+SELECT gene, 'any' AS vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM perPatient GROUP BY gene
+
+-- view 0 pathways: pathways:sets:Colorectal · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Colorectal Cancer'))))),
+members AS (SELECT tupleElement(m, 1) AS set_name, tupleElement(m, 2) AS gene FROM (SELECT arrayJoin([('RTK-RAS','ABL1'),('RTK-RAS','EGFR'),('RTK-RAS','ERBB2'),('RTK-RAS','ERBB3'),('RTK-RAS','ERBB4'),('RTK-RAS','PDGFRA'),('RTK-RAS','PDGFRB'),('RTK-RAS','MET'),('RTK-RAS','FGFR1'),('RTK-RAS','FGFR2'),('RTK-RAS','FGFR3'),('RTK-RAS','FGFR4'),('RTK-RAS','FLT3'),('RTK-RAS','ALK'),('RTK-RAS','RET'),('RTK-RAS','ROS1'),('RTK-RAS','KIT'),('RTK-RAS','IGF1R'),('RTK-RAS','NTRK1'),('RTK-RAS','NTRK2'),('RTK-RAS','NTRK3'),('RTK-RAS','SOS1'),('RTK-RAS','GRB2'),('RTK-RAS','PTPN11'),('RTK-RAS','KRAS'),('RTK-RAS','HRAS'),('RTK-RAS','NRAS'),('RTK-RAS','RIT1'),('RTK-RAS','ARAF'),('RTK-RAS','BRAF'),('RTK-RAS','RAF1'),('RTK-RAS','RAC1'),('RTK-RAS','MAP2K1'),('RTK-RAS','MAP2K2'),('RTK-RAS','MAPK1'),('RTK-RAS','NF1'),('RTK-RAS','RASA1'),('RTK-RAS','CBL'),('RTK-RAS','ERRFI1'),('RTK-RAS','CBLB'),('RTK-RAS','CBLC'),('RTK-RAS','INSR'),('RTK-RAS','INSRR'),('RTK-RAS','IRS1'),('RTK-RAS','SOS2'),('RTK-RAS','SHC1'),('RTK-RAS','SHC2'),('RTK-RAS','SHC3'),('RTK-RAS','SHC4'),('RTK-RAS','RASGRP1'),('RTK-RAS','RASGRP2'),('RTK-RAS','RASGRP3'),('RTK-RAS','RASGRP4'),('RTK-RAS','RAPGEF1'),('RTK-RAS','RAPGEF2'),('RTK-RAS','RASGRF1'),('RTK-RAS','RASGRF2'),('RTK-RAS','FNTA'),('RTK-RAS','FNTB'),('RTK-RAS','RCE1'),('RTK-RAS','ICMT'),('RTK-RAS','MRAS'),('RTK-RAS','PLXNB1'),('RTK-RAS','MAPK3'),('RTK-RAS','ARHGAP35'),('RTK-RAS','RASA2'),('RTK-RAS','RASA3'),('RTK-RAS','RASAL1'),('RTK-RAS','RASAL2'),('RTK-RAS','RASAL3'),('RTK-RAS','SPRED1'),('RTK-RAS','SPRED2'),('RTK-RAS','SPRED3'),('RTK-RAS','DAB2IP'),('RTK-RAS','SHOC2'),('RTK-RAS','PPP1CA'),('RTK-RAS','SCRIB'),('RTK-RAS','PIN1'),('RTK-RAS','KSR1'),('RTK-RAS','KSR2'),('RTK-RAS','PEBP1'),('RTK-RAS','ERF'),('RTK-RAS','PEA15'),('RTK-RAS','JAK2'),('RTK-RAS','IRS2'),('PI3K','EIF4EBP1'),('PI3K','AKT1'),('PI3K','AKT2'),('PI3K','AKT3'),('PI3K','AKT1S1'),('PI3K','DEPDC5'),('PI3K','DEPTOR'),('PI3K','INPP4B'),('PI3K','MAPKAP1'),('PI3K','MLST8'),('PI3K','MTOR'),('PI3K','NPRL2'),('PI3K','NPRL3'),('PI3K','PDK1'),('PI3K','PIK3CA'),('PI3K','PIK3CB'),('PI3K','PIK3R1'),('PI3K','PIK3R2'),('PI3K','PIK3R3'),('PI3K','PPP2R1A'),('PI3K','PTEN'),('PI3K','RHEB'),('PI3K','RICTOR'),('PI3K','RPTOR'),('PI3K','RPS6'),('PI3K','RPS6KB1'),('PI3K','STK11'),('PI3K','TSC1'),('PI3K','TSC2'),('TP53','TP53'),('TP53','MDM2'),('TP53','MDM4'),('TP53','ATM'),('TP53','CHEK2'),('TP53','RPS6KA3'),('Cell cycle','CDKN1A'),('Cell cycle','CDKN1B'),('Cell cycle','CDKN2A'),('Cell cycle','CDKN2B'),('Cell cycle','CDKN2C'),('Cell cycle','CCND1'),('Cell cycle','CCND2'),('Cell cycle','CCND3'),('Cell cycle','CCNE1'),('Cell cycle','CDK2'),('Cell cycle','CDK4'),('Cell cycle','CDK6'),('Cell cycle','RB1'),('Cell cycle','E2F1'),('Cell cycle','E2F3'),('MYC','MAX'),('MYC','MGA'),('MYC','MLX'),('MYC','MLXIP'),('MYC','MLXIPL'),('MYC','MNT'),('MYC','MXD1'),('MYC','MXD3'),('MYC','MXD4'),('MYC','MXI1'),('MYC','MYC'),('MYC','MYCL'),('MYC','MYCN'),('WNT','CHD8'),('WNT','LEF1'),('WNT','LGR4'),('WNT','LGR5'),('WNT','LRP5'),('WNT','LRP6'),('WNT','LZTR1'),('WNT','NDP'),('WNT','PORCN'),('WNT','RSPO1'),('WNT','SFRP1'),('WNT','SFRP2'),('WNT','SFRP4'),('WNT','SFRP5'),('WNT','SOST'),('WNT','TCF7L1'),('WNT','TLE1'),('WNT','TLE2'),('WNT','TLE3'),('WNT','TLE4'),('WNT','WIF1'),('WNT','ZNRF3'),('WNT','CTNNB1'),('WNT','DVL1'),('WNT','DVL2'),('WNT','DVL3'),('WNT','FRAT1'),('WNT','FRAT2'),('WNT','FZD1'),('WNT','FZD10'),('WNT','FZD2'),('WNT','FZD3'),('WNT','FZD4'),('WNT','FZD5'),('WNT','FZD6'),('WNT','FZD7'),('WNT','FZD8'),('WNT','FZD9'),('WNT','WNT1'),('WNT','WNT10A'),('WNT','WNT10B'),('WNT','WNT11'),('WNT','WNT16'),('WNT','WNT2'),('WNT','WNT3A'),('WNT','WNT4'),('WNT','WNT5A'),('WNT','WNT5B'),('WNT','WNT6'),('WNT','WNT7A'),('WNT','WNT7B'),('WNT','WNT8A'),('WNT','WNT8B'),('WNT','WNT9A'),('WNT','WNT9B'),('WNT','AMER1'),('WNT','APC'),('WNT','AXIN1'),('WNT','AXIN2'),('WNT','DKK1'),('WNT','DKK2'),('WNT','DKK3'),('WNT','DKK4'),('WNT','GSK3B'),('WNT','RNF43'),('WNT','TCF7'),('WNT','TCF7L2'),('WNT','CHD4'),('NOTCH','ARRDC1'),('NOTCH','CNTN6'),('NOTCH','CREBBP'),('NOTCH','EP300'),('NOTCH','HES1'),('NOTCH','HES2'),('NOTCH','HES3'),('NOTCH','HES4'),('NOTCH','HES5'),('NOTCH','HEY1'),('NOTCH','HEY2'),('NOTCH','HEYL'),('NOTCH','KAT2B'),('NOTCH','KDM5A'),('NOTCH','NOTCH1'),('NOTCH','NOTCH2'),('NOTCH','NOTCH3'),('NOTCH','NOTCH4'),('NOTCH','NOV'),('NOTCH','NRARP'),('NOTCH','PSEN2'),('NOTCH','LFNG'),('NOTCH','ITCH'),('NOTCH','NCSTN'),('NOTCH','SPEN'),('NOTCH','JAG1'),('NOTCH','APH1A'),('NOTCH','FBXW7'),('NOTCH','FHL1'),('NOTCH','THBS2'),('NOTCH','HDAC2'),('NOTCH','MFAP2'),('NOTCH','CUL1'),('NOTCH','RFNG'),('NOTCH','NCOR1'),('NOTCH','NCOR2'),('NOTCH','MFAP5'),('NOTCH','HDAC1'),('NOTCH','NUMB'),('NOTCH','JAG2'),('NOTCH','MAML3'),('NOTCH','MFNG'),('NOTCH','CIR1'),('NOTCH','CNTN1'),('NOTCH','MAML1'),('NOTCH','MAML2'),('NOTCH','NUMBL'),('NOTCH','PSEN1'),('NOTCH','PSENEN'),('NOTCH','RBPJ'),('NOTCH','RBPJL'),('NOTCH','RBX1'),('NOTCH','SAP30'),('NOTCH','SKP1'),('NOTCH','SNW1'),('NOTCH','CTBP1'),('NOTCH','CTBP2'),('NOTCH','ADAM10'),('NOTCH','APH1B'),('NOTCH','ADAM17'),('NOTCH','DLK1'),('NOTCH','DLL1'),('NOTCH','DLL3'),('NOTCH','DLL4'),('NOTCH','DNER'),('NOTCH','DTX1'),('NOTCH','DTX2'),('NOTCH','DTX3'),('NOTCH','DTX3L'),('NOTCH','DTX4'),('NOTCH','EGFL7'),('Hippo','STK4'),('Hippo','STK3'),('Hippo','SAV1'),('Hippo','LATS1'),('Hippo','LATS2'),('Hippo','MOB1A'),('Hippo','MOB1B'),('Hippo','YAP1'),('Hippo','WWTR1'),('Hippo','TEAD1'),('Hippo','TEAD2'),('Hippo','TEAD3'),('Hippo','TEAD4'),('Hippo','PTPN14'),('Hippo','NF2'),('Hippo','WWC1'),('Hippo','TAOK1'),('Hippo','TAOK2'),('Hippo','TAOK3'),('Hippo','CRB1'),('Hippo','CRB2'),('Hippo','CRB3'),('Hippo','LLGL1'),('Hippo','LLGL2'),('Hippo','HMCN1'),('Hippo','SCRIB'),('Hippo','HIPK2'),('Hippo','FAT1'),('Hippo','FAT2'),('Hippo','FAT3'),('Hippo','FAT4'),('Hippo','DCHS1'),('Hippo','DCHS2'),('Hippo','CSNK1E'),('Hippo','CSNK1D'),('Hippo','AJUBA'),('Hippo','LIMD1'),('Hippo','WTIP'),('TGF-β','TGFBR1'),('TGF-β','TGFBR2'),('TGF-β','ACVR2A'),('TGF-β','ACVR1B'),('TGF-β','SMAD2'),('TGF-β','SMAD3'),('TGF-β','SMAD4'),('NRF2','NFE2L2'),('NRF2','KEAP1'),('NRF2','CUL3')]) AS m)),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+per AS (
+  SELECT members.set_name AS set_name, x.pid AS pid, max(x.is_alt) AS is_alt, max(x.is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, 0 AS is_alt, 0 AS is_drv FROM covered
+  ) x
+  INNER JOIN members ON members.gene = x.gene
+  GROUP BY set_name, pid
+)
+SELECT set_name, uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM per GROUP BY set_name
+
+-- view 0 pathways: pathways:sets:Pancreatic · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))),
+members AS (SELECT tupleElement(m, 1) AS set_name, tupleElement(m, 2) AS gene FROM (SELECT arrayJoin([('RTK-RAS','ABL1'),('RTK-RAS','EGFR'),('RTK-RAS','ERBB2'),('RTK-RAS','ERBB3'),('RTK-RAS','ERBB4'),('RTK-RAS','PDGFRA'),('RTK-RAS','PDGFRB'),('RTK-RAS','MET'),('RTK-RAS','FGFR1'),('RTK-RAS','FGFR2'),('RTK-RAS','FGFR3'),('RTK-RAS','FGFR4'),('RTK-RAS','FLT3'),('RTK-RAS','ALK'),('RTK-RAS','RET'),('RTK-RAS','ROS1'),('RTK-RAS','KIT'),('RTK-RAS','IGF1R'),('RTK-RAS','NTRK1'),('RTK-RAS','NTRK2'),('RTK-RAS','NTRK3'),('RTK-RAS','SOS1'),('RTK-RAS','GRB2'),('RTK-RAS','PTPN11'),('RTK-RAS','KRAS'),('RTK-RAS','HRAS'),('RTK-RAS','NRAS'),('RTK-RAS','RIT1'),('RTK-RAS','ARAF'),('RTK-RAS','BRAF'),('RTK-RAS','RAF1'),('RTK-RAS','RAC1'),('RTK-RAS','MAP2K1'),('RTK-RAS','MAP2K2'),('RTK-RAS','MAPK1'),('RTK-RAS','NF1'),('RTK-RAS','RASA1'),('RTK-RAS','CBL'),('RTK-RAS','ERRFI1'),('RTK-RAS','CBLB'),('RTK-RAS','CBLC'),('RTK-RAS','INSR'),('RTK-RAS','INSRR'),('RTK-RAS','IRS1'),('RTK-RAS','SOS2'),('RTK-RAS','SHC1'),('RTK-RAS','SHC2'),('RTK-RAS','SHC3'),('RTK-RAS','SHC4'),('RTK-RAS','RASGRP1'),('RTK-RAS','RASGRP2'),('RTK-RAS','RASGRP3'),('RTK-RAS','RASGRP4'),('RTK-RAS','RAPGEF1'),('RTK-RAS','RAPGEF2'),('RTK-RAS','RASGRF1'),('RTK-RAS','RASGRF2'),('RTK-RAS','FNTA'),('RTK-RAS','FNTB'),('RTK-RAS','RCE1'),('RTK-RAS','ICMT'),('RTK-RAS','MRAS'),('RTK-RAS','PLXNB1'),('RTK-RAS','MAPK3'),('RTK-RAS','ARHGAP35'),('RTK-RAS','RASA2'),('RTK-RAS','RASA3'),('RTK-RAS','RASAL1'),('RTK-RAS','RASAL2'),('RTK-RAS','RASAL3'),('RTK-RAS','SPRED1'),('RTK-RAS','SPRED2'),('RTK-RAS','SPRED3'),('RTK-RAS','DAB2IP'),('RTK-RAS','SHOC2'),('RTK-RAS','PPP1CA'),('RTK-RAS','SCRIB'),('RTK-RAS','PIN1'),('RTK-RAS','KSR1'),('RTK-RAS','KSR2'),('RTK-RAS','PEBP1'),('RTK-RAS','ERF'),('RTK-RAS','PEA15'),('RTK-RAS','JAK2'),('RTK-RAS','IRS2'),('PI3K','EIF4EBP1'),('PI3K','AKT1'),('PI3K','AKT2'),('PI3K','AKT3'),('PI3K','AKT1S1'),('PI3K','DEPDC5'),('PI3K','DEPTOR'),('PI3K','INPP4B'),('PI3K','MAPKAP1'),('PI3K','MLST8'),('PI3K','MTOR'),('PI3K','NPRL2'),('PI3K','NPRL3'),('PI3K','PDK1'),('PI3K','PIK3CA'),('PI3K','PIK3CB'),('PI3K','PIK3R1'),('PI3K','PIK3R2'),('PI3K','PIK3R3'),('PI3K','PPP2R1A'),('PI3K','PTEN'),('PI3K','RHEB'),('PI3K','RICTOR'),('PI3K','RPTOR'),('PI3K','RPS6'),('PI3K','RPS6KB1'),('PI3K','STK11'),('PI3K','TSC1'),('PI3K','TSC2'),('TP53','TP53'),('TP53','MDM2'),('TP53','MDM4'),('TP53','ATM'),('TP53','CHEK2'),('TP53','RPS6KA3'),('Cell cycle','CDKN1A'),('Cell cycle','CDKN1B'),('Cell cycle','CDKN2A'),('Cell cycle','CDKN2B'),('Cell cycle','CDKN2C'),('Cell cycle','CCND1'),('Cell cycle','CCND2'),('Cell cycle','CCND3'),('Cell cycle','CCNE1'),('Cell cycle','CDK2'),('Cell cycle','CDK4'),('Cell cycle','CDK6'),('Cell cycle','RB1'),('Cell cycle','E2F1'),('Cell cycle','E2F3'),('MYC','MAX'),('MYC','MGA'),('MYC','MLX'),('MYC','MLXIP'),('MYC','MLXIPL'),('MYC','MNT'),('MYC','MXD1'),('MYC','MXD3'),('MYC','MXD4'),('MYC','MXI1'),('MYC','MYC'),('MYC','MYCL'),('MYC','MYCN'),('WNT','CHD8'),('WNT','LEF1'),('WNT','LGR4'),('WNT','LGR5'),('WNT','LRP5'),('WNT','LRP6'),('WNT','LZTR1'),('WNT','NDP'),('WNT','PORCN'),('WNT','RSPO1'),('WNT','SFRP1'),('WNT','SFRP2'),('WNT','SFRP4'),('WNT','SFRP5'),('WNT','SOST'),('WNT','TCF7L1'),('WNT','TLE1'),('WNT','TLE2'),('WNT','TLE3'),('WNT','TLE4'),('WNT','WIF1'),('WNT','ZNRF3'),('WNT','CTNNB1'),('WNT','DVL1'),('WNT','DVL2'),('WNT','DVL3'),('WNT','FRAT1'),('WNT','FRAT2'),('WNT','FZD1'),('WNT','FZD10'),('WNT','FZD2'),('WNT','FZD3'),('WNT','FZD4'),('WNT','FZD5'),('WNT','FZD6'),('WNT','FZD7'),('WNT','FZD8'),('WNT','FZD9'),('WNT','WNT1'),('WNT','WNT10A'),('WNT','WNT10B'),('WNT','WNT11'),('WNT','WNT16'),('WNT','WNT2'),('WNT','WNT3A'),('WNT','WNT4'),('WNT','WNT5A'),('WNT','WNT5B'),('WNT','WNT6'),('WNT','WNT7A'),('WNT','WNT7B'),('WNT','WNT8A'),('WNT','WNT8B'),('WNT','WNT9A'),('WNT','WNT9B'),('WNT','AMER1'),('WNT','APC'),('WNT','AXIN1'),('WNT','AXIN2'),('WNT','DKK1'),('WNT','DKK2'),('WNT','DKK3'),('WNT','DKK4'),('WNT','GSK3B'),('WNT','RNF43'),('WNT','TCF7'),('WNT','TCF7L2'),('WNT','CHD4'),('NOTCH','ARRDC1'),('NOTCH','CNTN6'),('NOTCH','CREBBP'),('NOTCH','EP300'),('NOTCH','HES1'),('NOTCH','HES2'),('NOTCH','HES3'),('NOTCH','HES4'),('NOTCH','HES5'),('NOTCH','HEY1'),('NOTCH','HEY2'),('NOTCH','HEYL'),('NOTCH','KAT2B'),('NOTCH','KDM5A'),('NOTCH','NOTCH1'),('NOTCH','NOTCH2'),('NOTCH','NOTCH3'),('NOTCH','NOTCH4'),('NOTCH','NOV'),('NOTCH','NRARP'),('NOTCH','PSEN2'),('NOTCH','LFNG'),('NOTCH','ITCH'),('NOTCH','NCSTN'),('NOTCH','SPEN'),('NOTCH','JAG1'),('NOTCH','APH1A'),('NOTCH','FBXW7'),('NOTCH','FHL1'),('NOTCH','THBS2'),('NOTCH','HDAC2'),('NOTCH','MFAP2'),('NOTCH','CUL1'),('NOTCH','RFNG'),('NOTCH','NCOR1'),('NOTCH','NCOR2'),('NOTCH','MFAP5'),('NOTCH','HDAC1'),('NOTCH','NUMB'),('NOTCH','JAG2'),('NOTCH','MAML3'),('NOTCH','MFNG'),('NOTCH','CIR1'),('NOTCH','CNTN1'),('NOTCH','MAML1'),('NOTCH','MAML2'),('NOTCH','NUMBL'),('NOTCH','PSEN1'),('NOTCH','PSENEN'),('NOTCH','RBPJ'),('NOTCH','RBPJL'),('NOTCH','RBX1'),('NOTCH','SAP30'),('NOTCH','SKP1'),('NOTCH','SNW1'),('NOTCH','CTBP1'),('NOTCH','CTBP2'),('NOTCH','ADAM10'),('NOTCH','APH1B'),('NOTCH','ADAM17'),('NOTCH','DLK1'),('NOTCH','DLL1'),('NOTCH','DLL3'),('NOTCH','DLL4'),('NOTCH','DNER'),('NOTCH','DTX1'),('NOTCH','DTX2'),('NOTCH','DTX3'),('NOTCH','DTX3L'),('NOTCH','DTX4'),('NOTCH','EGFL7'),('Hippo','STK4'),('Hippo','STK3'),('Hippo','SAV1'),('Hippo','LATS1'),('Hippo','LATS2'),('Hippo','MOB1A'),('Hippo','MOB1B'),('Hippo','YAP1'),('Hippo','WWTR1'),('Hippo','TEAD1'),('Hippo','TEAD2'),('Hippo','TEAD3'),('Hippo','TEAD4'),('Hippo','PTPN14'),('Hippo','NF2'),('Hippo','WWC1'),('Hippo','TAOK1'),('Hippo','TAOK2'),('Hippo','TAOK3'),('Hippo','CRB1'),('Hippo','CRB2'),('Hippo','CRB3'),('Hippo','LLGL1'),('Hippo','LLGL2'),('Hippo','HMCN1'),('Hippo','SCRIB'),('Hippo','HIPK2'),('Hippo','FAT1'),('Hippo','FAT2'),('Hippo','FAT3'),('Hippo','FAT4'),('Hippo','DCHS1'),('Hippo','DCHS2'),('Hippo','CSNK1E'),('Hippo','CSNK1D'),('Hippo','AJUBA'),('Hippo','LIMD1'),('Hippo','WTIP'),('TGF-β','TGFBR1'),('TGF-β','TGFBR2'),('TGF-β','ACVR2A'),('TGF-β','ACVR1B'),('TGF-β','SMAD2'),('TGF-β','SMAD3'),('TGF-β','SMAD4'),('NRF2','NFE2L2'),('NRF2','KEAP1'),('NRF2','CUL3')]) AS m)),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('ABL1','EGFR','ERBB2','ERBB3','ERBB4','PDGFRA','PDGFRB','MET','FGFR1','FGFR2','FGFR3','FGFR4','FLT3','ALK','RET','ROS1','KIT','IGF1R','NTRK1','NTRK2','NTRK3','SOS1','GRB2','PTPN11','KRAS','HRAS','NRAS','RIT1','ARAF','BRAF','RAF1','RAC1','MAP2K1','MAP2K2','MAPK1','NF1','RASA1','CBL','ERRFI1','CBLB','CBLC','INSR','INSRR','IRS1','SOS2','SHC1','SHC2','SHC3','SHC4','RASGRP1','RASGRP2','RASGRP3','RASGRP4','RAPGEF1','RAPGEF2','RASGRF1','RASGRF2','FNTA','FNTB','RCE1','ICMT','MRAS','PLXNB1','MAPK3','ARHGAP35','RASA2','RASA3','RASAL1','RASAL2','RASAL3','SPRED1','SPRED2','SPRED3','DAB2IP','SHOC2','PPP1CA','SCRIB','PIN1','KSR1','KSR2','PEBP1','ERF','PEA15','JAK2','IRS2','EIF4EBP1','AKT1','AKT2','AKT3','AKT1S1','DEPDC5','DEPTOR','INPP4B','MAPKAP1','MLST8','MTOR','NPRL2','NPRL3','PDK1','PIK3CA','PIK3CB','PIK3R1','PIK3R2','PIK3R3','PPP2R1A','PTEN','RHEB','RICTOR','RPTOR','RPS6','RPS6KB1','STK11','TSC1','TSC2','TP53','MDM2','MDM4','ATM','CHEK2','RPS6KA3','CDKN1A','CDKN1B','CDKN2A','CDKN2B','CDKN2C','CCND1','CCND2','CCND3','CCNE1','CDK2','CDK4','CDK6','RB1','E2F1','E2F3','MAX','MGA','MLX','MLXIP','MLXIPL','MNT','MXD1','MXD3','MXD4','MXI1','MYC','MYCL','MYCN','CHD8','LEF1','LGR4','LGR5','LRP5','LRP6','LZTR1','NDP','PORCN','RSPO1','SFRP1','SFRP2','SFRP4','SFRP5','SOST','TCF7L1','TLE1','TLE2','TLE3','TLE4','WIF1','ZNRF3','CTNNB1','DVL1','DVL2','DVL3','FRAT1','FRAT2','FZD1','FZD10','FZD2','FZD3','FZD4','FZD5','FZD6','FZD7','FZD8','FZD9','WNT1','WNT10A','WNT10B','WNT11','WNT16','WNT2','WNT3A','WNT4','WNT5A','WNT5B','WNT6','WNT7A','WNT7B','WNT8A','WNT8B','WNT9A','WNT9B','AMER1','APC','AXIN1','AXIN2','DKK1','DKK2','DKK3','DKK4','GSK3B','RNF43','TCF7','TCF7L2','CHD4','ARRDC1','CNTN6','CREBBP','EP300','HES1','HES2','HES3','HES4','HES5','HEY1','HEY2','HEYL','KAT2B','KDM5A','NOTCH1','NOTCH2','NOTCH3','NOTCH4','NOV','NRARP','PSEN2','LFNG','ITCH','NCSTN','SPEN','JAG1','APH1A','FBXW7','FHL1','THBS2','HDAC2','MFAP2','CUL1','RFNG','NCOR1','NCOR2','MFAP5','HDAC1','NUMB','JAG2','MAML3','MFNG','CIR1','CNTN1','MAML1','MAML2','NUMBL','PSEN1','PSENEN','RBPJ','RBPJL','RBX1','SAP30','SKP1','SNW1','CTBP1','CTBP2','ADAM10','APH1B','ADAM17','DLK1','DLL1','DLL3','DLL4','DNER','DTX1','DTX2','DTX3','DTX3L','DTX4','EGFL7','STK4','STK3','SAV1','LATS1','LATS2','MOB1A','MOB1B','YAP1','WWTR1','TEAD1','TEAD2','TEAD3','TEAD4','PTPN14','NF2','WWC1','TAOK1','TAOK2','TAOK3','CRB1','CRB2','CRB3','LLGL1','LLGL2','HMCN1','HIPK2','FAT1','FAT2','FAT3','FAT4','DCHS1','DCHS2','CSNK1E','CSNK1D','AJUBA','LIMD1','WTIP','TGFBR1','TGFBR2','ACVR2A','ACVR1B','SMAD2','SMAD3','SMAD4','NFE2L2','KEAP1','CUL3')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+per AS (
+  SELECT members.set_name AS set_name, x.pid AS pid, max(x.is_alt) AS is_alt, max(x.is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, 0 AS is_alt, 0 AS is_drv FROM covered
+  ) x
+  INNER JOIN members ON members.gene = x.gene
+  GROUP BY set_name, pid
+)
+SELECT set_name, uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM per GROUP BY set_name
diff --git a/test/snapshots/pathways.svg b/test/snapshots/pathways.svg
new file mode 100644
index 00000000..34fa6adf
--- /dev/null
+++ b/test/snapshots/pathways.svg
@@ -0,0 +1,161 @@
+<!-- view 0 pathways -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 724" width="100%" role="img" aria-label="Oncogenic signalling pathways — Colorectal, Pancreatic" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Oncogenic signalling pathways — Colorectal, Pancreatic</title><rect width="960" height="724" fill="#ffffff"/><text x="8" y="12" font-size="10" fill="#5b6b76">pathway</text><text x="128" y="12" font-size="10" fill="#5b6b76">patients with ≥1 member driver</text><text x="324" y="12" font-size="10" fill="#5b6b76">most-altered members (% per cohort, ★ q &lt; 0.05)</text><line x1="128" y1="29" x2="128" y2="32" stroke="#5b6b76" stroke-width="1"/><text x="128" y="26" font-size="10" fill="#5b6b76">0%</text><line x1="185.5" y1="29" x2="185.5" y2="32" stroke="#5b6b76" stroke-width="1"/><text x="185.5" y="26" font-size="10" fill="#5b6b76" text-anchor="middle">50%</text><line x1="243" y1="29" x2="243" y2="32" stroke="#5b6b76" stroke-width="1"/><text x="243" y="26" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><rect x="0" y="34" width="960" height="58" fill="#dde4e8" opacity="0.35"/><text x="8" y="51" font-size="12" fill="#16232b" font-weight="600">RTK-RAS</text><text x="8" y="65" font-size="10" fill="#5b6b76">47 of 85 genes profiled</text><rect x="128" y="40" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="40" width="77.51" height="10" rx="2" fill="#0072B2"><title>Colorectal: 67.4% (3,738 of 5,543)</title></rect><text x="249" y="49" font-size="10" fill="#16232b">67.4%</text><rect x="128" y="54" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="54" width="96.14" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 83.6% (2,600 of 3,109)</title></rect><text x="249" y="63" font-size="10" fill="#16232b">83.6%</text><text x="249" y="78" font-size="10" fill="#5b6b76">q = 1.3e-62</text><g><title>KRAS (oncogene)
+Colorectal: 43.2% (2,395 of 5,543)
+Pancreatic: 81.0% (2,517 of 3,109)
+q = 7.7e-267</title><rect x="324" y="38" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="38" width="4" height="46" rx="2" fill="#ff0000" opacity="1"/><text x="333" y="52" font-size="11" fill="#16232b" font-weight="600">KRAS ★</text><rect x="333" y="59" width="26" height="6" fill="#dde4e8"/><rect x="333" y="59" width="11.23" height="6" fill="#0072B2"/><text x="363" y="65" font-size="10" fill="#16232b">43.2%</text><rect x="333" y="71" width="26" height="6" fill="#dde4e8"/><rect x="333" y="71" width="21.06" height="6" fill="#E69F00"/><text x="363" y="77" font-size="10" fill="#16232b">81.0%</text></g><g><title>BRAF (oncogene)
+Colorectal: 9.9% (547 of 5,543)
+Pancreatic: 1.1% (35 of 3,109)
+q = 7.4e-68</title><rect x="411" y="38" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="38" width="4" height="46" rx="2" fill="#ff0000" opacity="0.5"/><text x="420" y="52" font-size="11" fill="#16232b" font-weight="600">BRAF ★</text><rect x="420" y="59" width="26" height="6" fill="#dde4e8"/><rect x="420" y="59" width="2.57" height="6" fill="#0072B2"/><text x="450" y="65" font-size="10" fill="#16232b">9.9%</text><rect x="420" y="71" width="26" height="6" fill="#dde4e8"/><rect x="420" y="71" width="1" height="6" fill="#E69F00"/><text x="450" y="77" font-size="10" fill="#16232b">1.1%</text></g><g><title>ERBB2 (oncogene)
+Colorectal: 5.2% (291 of 5,543)
+Pancreatic: 1.2% (36 of 3,109)
+q = 2.4e-24</title><rect x="498" y="38" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="38" width="4" height="46" rx="2" fill="#ff0000" opacity="0.38"/><text x="507" y="52" font-size="11" fill="#16232b" font-weight="600">ERBB2 ★</text><rect x="507" y="59" width="26" height="6" fill="#dde4e8"/><rect x="507" y="59" width="1.35" height="6" fill="#0072B2"/><text x="537" y="65" font-size="10" fill="#16232b">5.2%</text><rect x="507" y="71" width="26" height="6" fill="#dde4e8"/><rect x="507" y="71" width="1" height="6" fill="#E69F00"/><text x="537" y="77" font-size="10" fill="#16232b">1.2%</text></g><g><title>NRAS (oncogene)
+Colorectal: 3.4% (189 of 5,543)
+Pancreatic: 0.5% (14 of 3,109)
+q = 1.6e-21</title><rect x="585" y="38" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="38" width="4" height="46" rx="2" fill="#ff0000" opacity="0.34"/><text x="594" y="52" font-size="11" fill="#16232b" font-weight="600">NRAS ★</text><rect x="594" y="59" width="26" height="6" fill="#dde4e8"/><rect x="594" y="59" width="1" height="6" fill="#0072B2"/><text x="624" y="65" font-size="10" fill="#16232b">3.4%</text><rect x="594" y="71" width="26" height="6" fill="#dde4e8"/><rect x="594" y="71" width="1" height="6" fill="#E69F00"/><text x="624" y="77" font-size="10" fill="#16232b">0.5%</text></g><g><title>FGFR1 (oncogene)
+Colorectal: 2.5% (136 of 5,543)
+Pancreatic: 1.7% (53 of 3,109)
+q = 0.037</title><rect x="672" y="38" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="38" width="4" height="46" rx="2" fill="#ff0000" opacity="0.31"/><text x="681" y="52" font-size="11" fill="#16232b" font-weight="600">FGFR1 ★</text><rect x="681" y="59" width="26" height="6" fill="#dde4e8"/><rect x="681" y="59" width="1" height="6" fill="#0072B2"/><text x="711" y="65" font-size="10" fill="#16232b">2.5%</text><rect x="681" y="71" width="26" height="6" fill="#dde4e8"/><rect x="681" y="71" width="1" height="6" fill="#E69F00"/><text x="711" y="77" font-size="10" fill="#16232b">1.7%</text></g><g><title>ERBB3 (oncogene)
+Colorectal: 2.3% (130 of 5,543)
+Pancreatic: 0.9% (28 of 3,109)
+q = 1.5e-6</title><rect x="759" y="38" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="759" y="38" width="4" height="46" rx="2" fill="#ff0000" opacity="0.31"/><text x="768" y="52" font-size="11" fill="#16232b" font-weight="600">ERBB3 ★</text><rect x="768" y="59" width="26" height="6" fill="#dde4e8"/><rect x="768" y="59" width="1" height="6" fill="#0072B2"/><text x="798" y="65" font-size="10" fill="#16232b">2.3%</text><rect x="768" y="71" width="26" height="6" fill="#dde4e8"/><rect x="768" y="71" width="1" height="6" fill="#E69F00"/><text x="798" y="77" font-size="10" fill="#16232b">0.9%</text></g><text x="846" y="51" font-size="10" fill="#5b6b76">+32 more altered</text><text x="846" y="63" font-size="10" fill="#5b6b76">38 never profiled</text><text x="8" y="109" font-size="12" fill="#16232b" font-weight="600">PI3K</text><text x="8" y="123" font-size="10" fill="#5b6b76">19 of 29 genes profiled</text><rect x="128" y="98" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="98" width="36.34" height="10" rx="2" fill="#0072B2"><title>Colorectal: 31.6% (1,754 of 5,543)</title></rect><text x="249" y="107" font-size="10" fill="#16232b">31.6%</text><rect x="128" y="112" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="112" width="11.5" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 10.0% (312 of 3,109)</title></rect><text x="249" y="121" font-size="10" fill="#16232b">10.0%</text><text x="249" y="136" font-size="10" fill="#5b6b76">q = 1.4e-124</text><g><title>PIK3CA (oncogene)
+Colorectal: 18.8% (1,043 of 5,543)
+Pancreatic: 2.0% (62 of 3,109)
+q = 1e-139</title><rect x="324" y="96" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="96" width="4" height="46" rx="2" fill="#ff0000" opacity="0.72"/><text x="333" y="110" font-size="11" fill="#16232b" font-weight="600">PIK3CA ★</text><rect x="333" y="117" width="26" height="6" fill="#dde4e8"/><rect x="333" y="117" width="4.89" height="6" fill="#0072B2"/><text x="363" y="123" font-size="10" fill="#16232b">18.8%</text><rect x="333" y="129" width="26" height="6" fill="#dde4e8"/><rect x="333" y="129" width="1" height="6" fill="#E69F00"/><text x="363" y="135" font-size="10" fill="#16232b">2.0%</text></g><g><title>PTEN (tumour suppressor)
+Colorectal: 6.8% (375 of 5,543)
+Pancreatic: 1.4% (45 of 3,109)
+q = 3e-32</title><rect x="411" y="96" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="96" width="4" height="46" rx="2" fill="#0000ff" opacity="0.42"/><text x="420" y="110" font-size="11" fill="#16232b" font-weight="600">PTEN ★</text><rect x="420" y="117" width="26" height="6" fill="#dde4e8"/><rect x="420" y="117" width="1.77" height="6" fill="#0072B2"/><text x="450" y="123" font-size="10" fill="#16232b">6.8%</text><rect x="420" y="129" width="26" height="6" fill="#dde4e8"/><rect x="420" y="129" width="1" height="6" fill="#E69F00"/><text x="450" y="135" font-size="10" fill="#16232b">1.4%</text></g><g><title>PIK3R1 (tumour suppressor)
+Colorectal: 4.3% (238 of 5,543)
+Pancreatic: 0.3% (10 of 3,109)
+q = 2.9e-33</title><rect x="498" y="96" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="96" width="4" height="46" rx="2" fill="#0000ff" opacity="0.36"/><text x="507" y="110" font-size="11" fill="#16232b" font-weight="600">PIK3R1 ★</text><rect x="507" y="117" width="26" height="6" fill="#dde4e8"/><rect x="507" y="117" width="1.12" height="6" fill="#0072B2"/><text x="537" y="123" font-size="10" fill="#16232b">4.3%</text><rect x="507" y="129" width="26" height="6" fill="#dde4e8"/><rect x="507" y="129" width="1" height="6" fill="#E69F00"/><text x="537" y="135" font-size="10" fill="#16232b">0.3%</text></g><g><title>AKT2 (oncogene)
+Colorectal: 0.7% (37 of 5,543)
+Pancreatic: 3.2% (98 of 3,109)
+q = 2.7e-17</title><rect x="585" y="96" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="96" width="4" height="46" rx="2" fill="#ff0000" opacity="0.33"/><text x="594" y="110" font-size="11" fill="#16232b" font-weight="600">AKT2 ★</text><rect x="594" y="117" width="26" height="6" fill="#dde4e8"/><rect x="594" y="117" width="1" height="6" fill="#0072B2"/><text x="624" y="123" font-size="10" fill="#16232b">0.7%</text><rect x="594" y="129" width="26" height="6" fill="#dde4e8"/><rect x="594" y="129" width="1" height="6" fill="#E69F00"/><text x="624" y="135" font-size="10" fill="#16232b">3.2%</text></g><g><title>STK11 (tumour suppressor)
+Colorectal: 0.8% (42 of 5,543)
+Pancreatic: 1.9% (58 of 3,109)
+q = 1.9e-5</title><rect x="672" y="96" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="96" width="4" height="46" rx="2" fill="#0000ff" opacity="0.3"/><text x="681" y="110" font-size="11" fill="#16232b" font-weight="600">STK11 ★</text><rect x="681" y="117" width="26" height="6" fill="#dde4e8"/><rect x="681" y="117" width="1" height="6" fill="#0072B2"/><text x="711" y="123" font-size="10" fill="#16232b">0.8%</text><rect x="681" y="129" width="26" height="6" fill="#dde4e8"/><rect x="681" y="129" width="1" height="6" fill="#E69F00"/><text x="711" y="135" font-size="10" fill="#16232b">1.9%</text></g><g><title>AKT1 (oncogene)
+Colorectal: 1.5% (81 of 5,543)
+Pancreatic: 0.3% (8 of 3,109)
+q = 3e-8</title><rect x="759" y="96" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="759" y="96" width="4" height="46" rx="2" fill="#ff0000" opacity="0.29"/><text x="768" y="110" font-size="11" fill="#16232b" font-weight="600">AKT1 ★</text><rect x="768" y="117" width="26" height="6" fill="#dde4e8"/><rect x="768" y="117" width="1" height="6" fill="#0072B2"/><text x="798" y="123" font-size="10" fill="#16232b">1.5%</text><rect x="768" y="129" width="26" height="6" fill="#dde4e8"/><rect x="768" y="129" width="1" height="6" fill="#E69F00"/><text x="798" y="135" font-size="10" fill="#16232b">0.3%</text></g><text x="846" y="109" font-size="10" fill="#5b6b76">+12 more altered</text><text x="846" y="121" font-size="10" fill="#5b6b76">10 never profiled</text><rect x="0" y="150" width="960" height="58" fill="#dde4e8" opacity="0.35"/><text x="8" y="167" font-size="12" fill="#16232b" font-weight="600">TP53</text><text x="8" y="181" font-size="10" fill="#5b6b76">5 of 6 genes profiled</text><rect x="128" y="156" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="156" width="88.55" height="10" rx="2" fill="#0072B2"><title>Colorectal: 77.0% (4,268 of 5,543)</title></rect><text x="249" y="165" font-size="10" fill="#16232b">77.0%</text><rect x="128" y="170" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="170" width="78.78" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 68.5% (2,129 of 3,109)</title></rect><text x="249" y="179" font-size="10" fill="#16232b">68.5%</text><text x="249" y="194" font-size="10" fill="#5b6b76">q = 1.3e-17</text><g><title>TP53 (tumour suppressor)
+Colorectal: 73.3% (4,064 of 5,543)
+Pancreatic: 66.5% (2,068 of 3,109)
+q = 1.4e-10</title><rect x="324" y="154" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="154" width="4" height="46" rx="2" fill="#0000ff" opacity="1"/><text x="333" y="168" font-size="11" fill="#16232b" font-weight="600">TP53 ★</text><rect x="333" y="175" width="26" height="6" fill="#dde4e8"/><rect x="333" y="175" width="19.06" height="6" fill="#0072B2"/><text x="363" y="181" font-size="10" fill="#16232b">73.3%</text><rect x="333" y="187" width="26" height="6" fill="#dde4e8"/><rect x="333" y="187" width="17.29" height="6" fill="#E69F00"/><text x="363" y="193" font-size="10" fill="#16232b">66.5%</text></g><g><title>ATM (tumour suppressor)
+Colorectal: 5.2% (287 of 5,543)
+Pancreatic: 1.8% (56 of 3,109)
+q = 2e-15</title><rect x="411" y="154" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="154" width="4" height="46" rx="2" fill="#0000ff" opacity="0.38"/><text x="420" y="168" font-size="11" fill="#16232b" font-weight="600">ATM ★</text><rect x="420" y="175" width="26" height="6" fill="#dde4e8"/><rect x="420" y="175" width="1.35" height="6" fill="#0072B2"/><text x="450" y="181" font-size="10" fill="#16232b">5.2%</text><rect x="420" y="187" width="26" height="6" fill="#dde4e8"/><rect x="420" y="187" width="1" height="6" fill="#E69F00"/><text x="450" y="193" font-size="10" fill="#16232b">1.8%</text></g><g><title>MDM2 (oncogene)
+Colorectal: 0.1% (7 of 5,543)
+Pancreatic: 1.1% (33 of 3,109)
+q = 6e-9</title><rect x="498" y="154" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="154" width="4" height="46" rx="2" fill="#ff0000" opacity="0.28"/><text x="507" y="168" font-size="11" fill="#16232b" font-weight="600">MDM2 ★</text><rect x="507" y="175" width="26" height="6" fill="#dde4e8"/><rect x="507" y="175" width="1" height="6" fill="#0072B2"/><text x="537" y="181" font-size="10" fill="#16232b">0.1%</text><rect x="507" y="187" width="26" height="6" fill="#dde4e8"/><rect x="507" y="187" width="1" height="6" fill="#E69F00"/><text x="537" y="193" font-size="10" fill="#16232b">1.1%</text></g><g><title>CHEK2 (tumour suppressor)
+Colorectal: 0.8% (42 of 5,543)
+Pancreatic: 0.2% (6 of 3,109)
+q = 8.8e-4</title><rect x="585" y="154" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="154" width="4" height="46" rx="2" fill="#0000ff" opacity="0.27"/><text x="594" y="168" font-size="11" fill="#16232b" font-weight="600">CHEK2 ★</text><rect x="594" y="175" width="26" height="6" fill="#dde4e8"/><rect x="594" y="175" width="1" height="6" fill="#0072B2"/><text x="624" y="181" font-size="10" fill="#16232b">0.8%</text><rect x="594" y="187" width="26" height="6" fill="#dde4e8"/><rect x="594" y="187" width="1" height="6" fill="#E69F00"/><text x="624" y="193" font-size="10" fill="#16232b">0.2%</text></g><g><title>MDM4 (oncogene)
+Colorectal: 0% (2 of 5,543)
+Pancreatic: 0.2% (5 of 3,109)
+q = 0.15</title><rect x="672" y="154" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="154" width="4" height="46" rx="2" fill="#ff0000" opacity="0.26"/><text x="681" y="168" font-size="11" fill="#16232b" font-weight="600">MDM4</text><rect x="681" y="175" width="26" height="6" fill="#dde4e8"/><text x="711" y="181" font-size="10" fill="#16232b">0%</text><rect x="681" y="187" width="26" height="6" fill="#dde4e8"/><rect x="681" y="187" width="1" height="6" fill="#E69F00"/><text x="711" y="193" font-size="10" fill="#16232b">0.2%</text></g><text x="759" y="167" font-size="10" fill="#5b6b76">1 never profiled</text><text x="8" y="225" font-size="12" fill="#16232b" font-weight="600">Cell cycle</text><text x="8" y="239" font-size="10" fill="#5b6b76">13 of 15 genes profiled</text><rect x="128" y="214" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="214" width="10.12" height="10" rx="2" fill="#0072B2"><title>Colorectal: 8.8% (489 of 5,543)</title></rect><text x="249" y="223" font-size="10" fill="#16232b">8.8%</text><rect x="128" y="228" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="228" width="44.97" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 39.1% (1,216 of 3,109)</title></rect><text x="249" y="237" font-size="10" fill="#16232b">39.1%</text><text x="249" y="252" font-size="10" fill="#5b6b76">q = 2.6e-244</text><g><title>CDKN2A (tumour suppressor)
+Colorectal: 2.0% (113 of 5,543)
+Pancreatic: 32.8% (1,020 of 3,109)
+q &lt; 1e-300</title><rect x="324" y="212" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="212" width="4" height="46" rx="2" fill="#0000ff" opacity="1"/><text x="333" y="226" font-size="11" fill="#16232b" font-weight="600">CDKN2A ★</text><rect x="333" y="233" width="26" height="6" fill="#dde4e8"/><rect x="333" y="233" width="1" height="6" fill="#0072B2"/><text x="363" y="239" font-size="10" fill="#16232b">2.0%</text><rect x="333" y="245" width="26" height="6" fill="#dde4e8"/><rect x="333" y="245" width="8.53" height="6" fill="#E69F00"/><text x="363" y="251" font-size="10" fill="#16232b">32.8%</text></g><g><title>CDKN2B (tumour suppressor)
+Colorectal: 1.0% (56 of 5,543)
+Pancreatic: 13.6% (422 of 3,109)
+q = 2.9e-131</title><rect x="411" y="212" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="212" width="4" height="46" rx="2" fill="#0000ff" opacity="0.59"/><text x="420" y="226" font-size="11" fill="#16232b" font-weight="600">CDKN2B ★</text><rect x="420" y="233" width="26" height="6" fill="#dde4e8"/><rect x="420" y="233" width="1" height="6" fill="#0072B2"/><text x="450" y="239" font-size="10" fill="#16232b">1.0%</text><rect x="420" y="245" width="26" height="6" fill="#dde4e8"/><rect x="420" y="245" width="3.54" height="6" fill="#E69F00"/><text x="450" y="251" font-size="10" fill="#16232b">13.6%</text></g><g><title>RB1 (tumour suppressor)
+Colorectal: 1.3% (74 of 5,543)
+Pancreatic: 2.5% (77 of 3,109)
+q = 3.3e-4</title><rect x="498" y="212" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="212" width="4" height="46" rx="2" fill="#0000ff" opacity="0.31"/><text x="507" y="226" font-size="11" fill="#16232b" font-weight="600">RB1 ★</text><rect x="507" y="233" width="26" height="6" fill="#dde4e8"/><rect x="507" y="233" width="1" height="6" fill="#0072B2"/><text x="537" y="239" font-size="10" fill="#16232b">1.3%</text><rect x="507" y="245" width="26" height="6" fill="#dde4e8"/><rect x="507" y="245" width="1" height="6" fill="#E69F00"/><text x="537" y="251" font-size="10" fill="#16232b">2.5%</text></g><g><title>CCND2 (oncogene)
+Colorectal: 2.2% (124 of 5,543)
+Pancreatic: 0.6% (18 of 3,109)
+q = 1.7e-9</title><rect x="585" y="212" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="212" width="4" height="46" rx="2" fill="#ff0000" opacity="0.31"/><text x="594" y="226" font-size="11" fill="#16232b" font-weight="600">CCND2 ★</text><rect x="594" y="233" width="26" height="6" fill="#dde4e8"/><rect x="594" y="233" width="1" height="6" fill="#0072B2"/><text x="624" y="239" font-size="10" fill="#16232b">2.2%</text><rect x="594" y="245" width="26" height="6" fill="#dde4e8"/><rect x="594" y="245" width="1" height="6" fill="#E69F00"/><text x="624" y="251" font-size="10" fill="#16232b">0.6%</text></g><g><title>CCNE1 (oncogene)
+Colorectal: 0.6% (33 of 5,543)
+Pancreatic: 1.9% (60 of 3,109)
+q = 7.1e-8</title><rect x="672" y="212" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="212" width="4" height="46" rx="2" fill="#ff0000" opacity="0.3"/><text x="681" y="226" font-size="11" fill="#16232b" font-weight="600">CCNE1 ★</text><rect x="681" y="233" width="26" height="6" fill="#dde4e8"/><rect x="681" y="233" width="1" height="6" fill="#0072B2"/><text x="711" y="239" font-size="10" fill="#16232b">0.6%</text><rect x="681" y="245" width="26" height="6" fill="#dde4e8"/><rect x="681" y="245" width="1" height="6" fill="#E69F00"/><text x="711" y="251" font-size="10" fill="#16232b">1.9%</text></g><g><title>CDK6 (oncogene)
+Colorectal: 0.2% (10 of 5,543)
+Pancreatic: 1.3% (41 of 3,109)
+q = 3.6e-10</title><rect x="759" y="212" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="759" y="212" width="4" height="46" rx="2" fill="#ff0000" opacity="0.28"/><text x="768" y="226" font-size="11" fill="#16232b" font-weight="600">CDK6 ★</text><rect x="768" y="233" width="26" height="6" fill="#dde4e8"/><rect x="768" y="233" width="1" height="6" fill="#0072B2"/><text x="798" y="239" font-size="10" fill="#16232b">0.2%</text><rect x="768" y="245" width="26" height="6" fill="#dde4e8"/><rect x="768" y="245" width="1" height="6" fill="#E69F00"/><text x="798" y="251" font-size="10" fill="#16232b">1.3%</text></g><text x="846" y="225" font-size="10" fill="#5b6b76">+6 more altered</text><text x="846" y="237" font-size="10" fill="#5b6b76">2 never profiled</text><rect x="0" y="266" width="960" height="58" fill="#dde4e8" opacity="0.35"/><text x="8" y="283" font-size="12" fill="#16232b" font-weight="600">MYC</text><text x="8" y="297" font-size="10" fill="#5b6b76">5 of 13 genes profiled</text><rect x="128" y="272" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="272" width="9.32" height="10" rx="2" fill="#0072B2"><title>Colorectal: 8.1% (451 of 5,543)</title></rect><text x="249" y="281" font-size="10" fill="#16232b">8.1%</text><rect x="128" y="286" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="286" width="5.75" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 5.0% (156 of 3,109)</title></rect><text x="249" y="295" font-size="10" fill="#16232b">5.0%</text><text x="249" y="310" font-size="10" fill="#5b6b76">q = 3.6e-8</text><g><title>MYC (oncogene)
+Colorectal: 4.8% (265 of 5,543)
+Pancreatic: 3.9% (120 of 3,109)
+q = 0.079</title><rect x="324" y="270" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="270" width="4" height="46" rx="2" fill="#ff0000" opacity="0.37"/><text x="333" y="284" font-size="11" fill="#16232b" font-weight="600">MYC</text><rect x="333" y="291" width="26" height="6" fill="#dde4e8"/><rect x="333" y="291" width="1.25" height="6" fill="#0072B2"/><text x="363" y="297" font-size="10" fill="#16232b">4.8%</text><rect x="333" y="303" width="26" height="6" fill="#dde4e8"/><rect x="333" y="303" width="1.01" height="6" fill="#E69F00"/><text x="363" y="309" font-size="10" fill="#16232b">3.9%</text></g><g><title>MGA (tumour suppressor)
+Colorectal: 2.9% (153 of 5,350)
+Pancreatic: 0.7% (22 of 3,076)
+q = 4.5e-12</title><rect x="411" y="270" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="270" width="4" height="46" rx="2" fill="#0000ff" opacity="0.32"/><text x="420" y="284" font-size="11" fill="#16232b" font-weight="600">MGA ★</text><rect x="420" y="291" width="26" height="6" fill="#dde4e8"/><rect x="420" y="291" width="1" height="6" fill="#0072B2"/><text x="450" y="297" font-size="10" fill="#16232b">2.9%</text><rect x="420" y="303" width="26" height="6" fill="#dde4e8"/><rect x="420" y="303" width="1" height="6" fill="#E69F00"/><text x="450" y="309" font-size="10" fill="#16232b">0.7%</text></g><g><title>MAX (tumour suppressor)
+Colorectal: 0.4% (22 of 5,543)
+Pancreatic: 0.2% (6 of 3,109)
+q = 0.17</title><rect x="498" y="270" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="270" width="4" height="46" rx="2" fill="#0000ff" opacity="0.26"/><text x="507" y="284" font-size="11" fill="#16232b" font-weight="600">MAX</text><rect x="507" y="291" width="26" height="6" fill="#dde4e8"/><rect x="507" y="291" width="1" height="6" fill="#0072B2"/><text x="537" y="297" font-size="10" fill="#16232b">0.4%</text><rect x="507" y="303" width="26" height="6" fill="#dde4e8"/><rect x="507" y="303" width="1" height="6" fill="#E69F00"/><text x="537" y="309" font-size="10" fill="#16232b">0.2%</text></g><g><title>MYCL (oncogene)
+Colorectal: 0.3% (16 of 5,543)
+Pancreatic: 0.3% (8 of 3,109)
+q = 1.00</title><rect x="585" y="270" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="270" width="4" height="46" rx="2" fill="#ff0000" opacity="0.26"/><text x="594" y="284" font-size="11" fill="#16232b" font-weight="600">MYCL</text><rect x="594" y="291" width="26" height="6" fill="#dde4e8"/><rect x="594" y="291" width="1" height="6" fill="#0072B2"/><text x="624" y="297" font-size="10" fill="#16232b">0.3%</text><rect x="594" y="303" width="26" height="6" fill="#dde4e8"/><rect x="594" y="303" width="1" height="6" fill="#E69F00"/><text x="624" y="309" font-size="10" fill="#16232b">0.3%</text></g><g><title>MYCN (oncogene)
+Colorectal: 0.1% (4 of 5,543)
+Pancreatic: 0.1% (2 of 3,109)
+q = 1.00</title><rect x="672" y="270" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="270" width="4" height="46" rx="2" fill="#ff0000" opacity="0.25"/><text x="681" y="284" font-size="11" fill="#16232b" font-weight="600">MYCN</text><rect x="681" y="291" width="26" height="6" fill="#dde4e8"/><rect x="681" y="291" width="1" height="6" fill="#0072B2"/><text x="711" y="297" font-size="10" fill="#16232b">0.1%</text><rect x="681" y="303" width="26" height="6" fill="#dde4e8"/><rect x="681" y="303" width="1" height="6" fill="#E69F00"/><text x="711" y="309" font-size="10" fill="#16232b">0.1%</text></g><text x="759" y="283" font-size="10" fill="#5b6b76">8 never profiled</text><text x="8" y="341" font-size="12" fill="#16232b" font-weight="600">WNT</text><text x="8" y="355" font-size="10" fill="#5b6b76">12 of 68 genes profiled</text><rect x="128" y="330" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="330" width="94.07" height="10" rx="2" fill="#0072B2"><title>Colorectal: 81.8% (4,536 of 5,543)</title></rect><text x="249" y="339" font-size="10" fill="#16232b">81.8%</text><rect x="128" y="344" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="344" width="8.97" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 7.8% (243 of 3,109)</title></rect><text x="249" y="353" font-size="10" fill="#16232b">7.8%</text><text x="249" y="368" font-size="10" fill="#5b6b76">q &lt; 1e-300</text><g><title>APC (tumour suppressor)
+Colorectal: 73.1% (4,050 of 5,543)
+Pancreatic: 1.0% (30 of 3,109)
+q &lt; 1e-300</title><rect x="324" y="328" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="328" width="4" height="46" rx="2" fill="#0000ff" opacity="1"/><text x="333" y="342" font-size="11" fill="#16232b" font-weight="600">APC ★</text><rect x="333" y="349" width="26" height="6" fill="#dde4e8"/><rect x="333" y="349" width="19.01" height="6" fill="#0072B2"/><text x="363" y="355" font-size="10" fill="#16232b">73.1%</text><rect x="333" y="361" width="26" height="6" fill="#dde4e8"/><rect x="333" y="361" width="1" height="6" fill="#E69F00"/><text x="363" y="367" font-size="10" fill="#16232b">1.0%</text></g><g><title>TCF7L2 (tumour suppressor)
+Colorectal: 12.1% (650 of 5,350)
+Pancreatic: 0.2% (5 of 3,076)
+q = 4.5e-124</title><rect x="411" y="328" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="328" width="4" height="46" rx="2" fill="#0000ff" opacity="0.55"/><text x="420" y="342" font-size="11" fill="#16232b" font-weight="600">TCF7L2 ★</text><rect x="420" y="349" width="26" height="6" fill="#dde4e8"/><rect x="420" y="349" width="3.15" height="6" fill="#0072B2"/><text x="450" y="355" font-size="10" fill="#16232b">12.1%</text><rect x="420" y="361" width="26" height="6" fill="#dde4e8"/><rect x="420" y="361" width="1" height="6" fill="#E69F00"/><text x="450" y="367" font-size="10" fill="#16232b">0.2%</text></g><g><title>AMER1 (tumour suppressor)
+Colorectal: 6.0% (335 of 5,543)
+Pancreatic: 0.9% (27 of 3,109)
+q = 9.3e-37</title><rect x="498" y="328" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="328" width="4" height="46" rx="2" fill="#0000ff" opacity="0.4"/><text x="507" y="342" font-size="11" fill="#16232b" font-weight="600">AMER1 ★</text><rect x="507" y="349" width="26" height="6" fill="#dde4e8"/><rect x="507" y="349" width="1.56" height="6" fill="#0072B2"/><text x="537" y="355" font-size="10" fill="#16232b">6.0%</text><rect x="507" y="361" width="26" height="6" fill="#dde4e8"/><rect x="507" y="361" width="1" height="6" fill="#E69F00"/><text x="537" y="367" font-size="10" fill="#16232b">0.9%</text></g><g><title>RNF43 (tumour suppressor)
+Colorectal: 5.1% (284 of 5,543)
+Pancreatic: 4.0% (124 of 3,109)
+q = 0.03</title><rect x="585" y="328" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="328" width="4" height="46" rx="2" fill="#0000ff" opacity="0.38"/><text x="594" y="342" font-size="11" fill="#16232b" font-weight="600">RNF43 ★</text><rect x="594" y="349" width="26" height="6" fill="#dde4e8"/><rect x="594" y="349" width="1.33" height="6" fill="#0072B2"/><text x="624" y="355" font-size="10" fill="#16232b">5.1%</text><rect x="594" y="361" width="26" height="6" fill="#dde4e8"/><rect x="594" y="361" width="1.04" height="6" fill="#E69F00"/><text x="624" y="367" font-size="10" fill="#16232b">4.0%</text></g><g><title>AXIN2 (tumour suppressor)
+Colorectal: 3.8% (212 of 5,543)
+Pancreatic: 0.5% (17 of 3,109)
+q = 2.6e-23</title><rect x="672" y="328" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="328" width="4" height="46" rx="2" fill="#0000ff" opacity="0.35"/><text x="681" y="342" font-size="11" fill="#16232b" font-weight="600">AXIN2 ★</text><rect x="681" y="349" width="26" height="6" fill="#dde4e8"/><rect x="681" y="349" width="1" height="6" fill="#0072B2"/><text x="711" y="355" font-size="10" fill="#16232b">3.8%</text><rect x="681" y="361" width="26" height="6" fill="#dde4e8"/><rect x="681" y="361" width="1" height="6" fill="#E69F00"/><text x="711" y="367" font-size="10" fill="#16232b">0.5%</text></g><g><title>ZNRF3 (tumour suppressor)
+Colorectal: 3.8% (68 of 1,798)
+Pancreatic: 0.3% (3 of 1,100)
+q = 1.1e-10</title><rect x="759" y="328" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="759" y="328" width="4" height="46" rx="2" fill="#0000ff" opacity="0.35"/><text x="768" y="342" font-size="11" fill="#16232b" font-weight="600">ZNRF3 ★</text><rect x="768" y="349" width="26" height="6" fill="#dde4e8"/><rect x="768" y="349" width="1" height="6" fill="#0072B2"/><text x="798" y="355" font-size="10" fill="#16232b">3.8%</text><rect x="768" y="361" width="26" height="6" fill="#dde4e8"/><rect x="768" y="361" width="1" height="6" fill="#E69F00"/><text x="798" y="367" font-size="10" fill="#16232b">0.3%</text></g><text x="846" y="341" font-size="10" fill="#5b6b76">+4 more altered</text><text x="846" y="353" font-size="10" fill="#5b6b76">56 never profiled</text><rect x="0" y="382" width="960" height="58" fill="#dde4e8" opacity="0.35"/><text x="8" y="399" font-size="12" fill="#16232b" font-weight="600">NOTCH</text><text x="8" y="413" font-size="10" fill="#5b6b76">14 of 71 genes profiled</text><rect x="128" y="388" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="388" width="25.19" height="10" rx="2" fill="#0072B2"><title>Colorectal: 21.9% (1,215 of 5,543)</title></rect><text x="249" y="397" font-size="10" fill="#16232b">21.9%</text><rect x="128" y="402" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="402" width="4.03" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 3.5% (108 of 3,109)</title></rect><text x="249" y="411" font-size="10" fill="#16232b">3.5%</text><text x="249" y="426" font-size="10" fill="#5b6b76">q = 8.7e-139</text><g><title>FBXW7 (tumour suppressor)
+Colorectal: 13.5% (750 of 5,543)
+Pancreatic: 1.0% (31 of 3,109)
+q = 6.2e-110</title><rect x="324" y="386" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="386" width="4" height="46" rx="2" fill="#0000ff" opacity="0.59"/><text x="333" y="400" font-size="11" fill="#16232b" font-weight="600">FBXW7 ★</text><rect x="333" y="407" width="26" height="6" fill="#dde4e8"/><rect x="333" y="407" width="3.51" height="6" fill="#0072B2"/><text x="363" y="413" font-size="10" fill="#16232b">13.5%</text><rect x="333" y="419" width="26" height="6" fill="#dde4e8"/><rect x="333" y="419" width="1" height="6" fill="#E69F00"/><text x="363" y="425" font-size="10" fill="#16232b">1.0%</text></g><g><title>CREBBP (tumour suppressor)
+Colorectal: 3.0% (166 of 5,543)
+Pancreatic: 0.5% (17 of 3,109)
+q = 6.6e-16</title><rect x="411" y="386" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="386" width="4" height="46" rx="2" fill="#0000ff" opacity="0.33"/><text x="420" y="400" font-size="11" fill="#16232b" font-weight="600">CREBBP ★</text><rect x="420" y="407" width="26" height="6" fill="#dde4e8"/><rect x="420" y="407" width="1" height="6" fill="#0072B2"/><text x="450" y="413" font-size="10" fill="#16232b">3.0%</text><rect x="420" y="419" width="26" height="6" fill="#dde4e8"/><rect x="420" y="419" width="1" height="6" fill="#E69F00"/><text x="450" y="425" font-size="10" fill="#16232b">0.5%</text></g><g><title>NCOR1 (tumour suppressor)
+Colorectal: 2.7% (149 of 5,543)
+Pancreatic: 0.2% (5 of 3,109)
+q = 3.7e-22</title><rect x="498" y="386" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="386" width="4" height="46" rx="2" fill="#0000ff" opacity="0.32"/><text x="507" y="400" font-size="11" fill="#16232b" font-weight="600">NCOR1 ★</text><rect x="507" y="407" width="26" height="6" fill="#dde4e8"/><rect x="507" y="407" width="1" height="6" fill="#0072B2"/><text x="537" y="413" font-size="10" fill="#16232b">2.7%</text><rect x="507" y="419" width="26" height="6" fill="#dde4e8"/><rect x="507" y="419" width="1" height="6" fill="#E69F00"/><text x="537" y="425" font-size="10" fill="#16232b">0.2%</text></g><g><title>NOTCH3 (tumour suppressor)
+Colorectal: 2.3% (128 of 5,543)
+Pancreatic: 0.2% (7 of 3,109)
+q = 1.7e-16</title><rect x="585" y="386" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="386" width="4" height="46" rx="2" fill="#0000ff" opacity="0.31"/><text x="594" y="400" font-size="11" fill="#16232b" font-weight="600">NOTCH3 ★</text><rect x="594" y="407" width="26" height="6" fill="#dde4e8"/><rect x="594" y="407" width="1" height="6" fill="#0072B2"/><text x="624" y="413" font-size="10" fill="#16232b">2.3%</text><rect x="594" y="419" width="26" height="6" fill="#dde4e8"/><rect x="594" y="419" width="1" height="6" fill="#E69F00"/><text x="624" y="425" font-size="10" fill="#16232b">0.2%</text></g><g><title>EP300 (tumour suppressor)
+Colorectal: 2.2% (122 of 5,543)
+Pancreatic: 0.4% (12 of 3,109)
+q = 2.9e-12</title><rect x="672" y="386" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="386" width="4" height="46" rx="2" fill="#0000ff" opacity="0.31"/><text x="681" y="400" font-size="11" fill="#16232b" font-weight="600">EP300 ★</text><rect x="681" y="407" width="26" height="6" fill="#dde4e8"/><rect x="681" y="407" width="1" height="6" fill="#0072B2"/><text x="711" y="413" font-size="10" fill="#16232b">2.2%</text><rect x="681" y="419" width="26" height="6" fill="#dde4e8"/><rect x="681" y="419" width="1" height="6" fill="#E69F00"/><text x="711" y="425" font-size="10" fill="#16232b">0.4%</text></g><g><title>SPEN (tumour suppressor)
+Colorectal: 1.8% (98 of 5,543)
+Pancreatic: 0.2% (5 of 3,109)
+q = 4e-13</title><rect x="759" y="386" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="759" y="386" width="4" height="46" rx="2" fill="#0000ff" opacity="0.3"/><text x="768" y="400" font-size="11" fill="#16232b" font-weight="600">SPEN ★</text><rect x="768" y="407" width="26" height="6" fill="#dde4e8"/><rect x="768" y="407" width="1" height="6" fill="#0072B2"/><text x="798" y="413" font-size="10" fill="#16232b">1.8%</text><rect x="768" y="419" width="26" height="6" fill="#dde4e8"/><rect x="768" y="419" width="1" height="6" fill="#E69F00"/><text x="798" y="425" font-size="10" fill="#16232b">0.2%</text></g><text x="846" y="399" font-size="10" fill="#5b6b76">+4 more altered</text><text x="846" y="411" font-size="10" fill="#5b6b76">57 never profiled</text><text x="8" y="457" font-size="12" fill="#16232b" font-weight="600">Hippo</text><text x="8" y="471" font-size="10" fill="#5b6b76">6 of 38 genes profiled</text><rect x="128" y="446" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="446" width="5.41" height="10" rx="2" fill="#0072B2"><title>Colorectal: 4.7% (260 of 5,543)</title></rect><text x="249" y="455" font-size="10" fill="#16232b">4.7%</text><rect x="128" y="460" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="460" width="1.5" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 1.3% (40 of 3,109)</title></rect><text x="249" y="469" font-size="10" fill="#16232b">1.3%</text><text x="249" y="484" font-size="10" fill="#5b6b76">q = 1e-18</text><g><title>FAT1 (tumour suppressor)
+Colorectal: 3.2% (177 of 5,543)
+Pancreatic: 0.5% (16 of 3,109)
+q = 2.4e-18</title><rect x="324" y="444" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="444" width="4" height="46" rx="2" fill="#0000ff" opacity="0.33"/><text x="333" y="458" font-size="11" fill="#16232b" font-weight="600">FAT1 ★</text><rect x="333" y="465" width="26" height="6" fill="#dde4e8"/><rect x="333" y="465" width="1" height="6" fill="#0072B2"/><text x="363" y="471" font-size="10" fill="#16232b">3.2%</text><rect x="333" y="477" width="26" height="6" fill="#dde4e8"/><rect x="333" y="477" width="1" height="6" fill="#E69F00"/><text x="363" y="483" font-size="10" fill="#16232b">0.5%</text></g><g><title>LATS1 (tumour suppressor)
+Colorectal: 0.9% (49 of 5,543)
+Pancreatic: 0.3% (10 of 3,109)
+q = 0.0033</title><rect x="411" y="444" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="444" width="4" height="46" rx="2" fill="#0000ff" opacity="0.27"/><text x="420" y="458" font-size="11" fill="#16232b" font-weight="600">LATS1 ★</text><rect x="420" y="465" width="26" height="6" fill="#dde4e8"/><rect x="420" y="465" width="1" height="6" fill="#0072B2"/><text x="450" y="471" font-size="10" fill="#16232b">0.9%</text><rect x="420" y="477" width="26" height="6" fill="#dde4e8"/><rect x="420" y="477" width="1" height="6" fill="#E69F00"/><text x="450" y="483" font-size="10" fill="#16232b">0.3%</text></g><g><title>LATS2 (tumour suppressor)
+Colorectal: 0.6% (34 of 5,543)
+Pancreatic: 0.2% (5 of 3,109)
+q = 0.0041</title><rect x="498" y="444" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="444" width="4" height="46" rx="2" fill="#0000ff" opacity="0.27"/><text x="507" y="458" font-size="11" fill="#16232b" font-weight="600">LATS2 ★</text><rect x="507" y="465" width="26" height="6" fill="#dde4e8"/><rect x="507" y="465" width="1" height="6" fill="#0072B2"/><text x="537" y="471" font-size="10" fill="#16232b">0.6%</text><rect x="507" y="477" width="26" height="6" fill="#dde4e8"/><rect x="507" y="477" width="1" height="6" fill="#E69F00"/><text x="537" y="483" font-size="10" fill="#16232b">0.2%</text></g><g><title>NF2 (tumour suppressor)
+Colorectal: 0.2% (9 of 5,543)
+Pancreatic: 0.3% (9 of 3,109)
+q = 0.31</title><rect x="585" y="444" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="444" width="4" height="46" rx="2" fill="#0000ff" opacity="0.26"/><text x="594" y="458" font-size="11" fill="#16232b" font-weight="600">NF2</text><rect x="594" y="465" width="26" height="6" fill="#dde4e8"/><rect x="594" y="465" width="1" height="6" fill="#0072B2"/><text x="624" y="471" font-size="10" fill="#16232b">0.2%</text><rect x="594" y="477" width="26" height="6" fill="#dde4e8"/><rect x="594" y="477" width="1" height="6" fill="#E69F00"/><text x="624" y="483" font-size="10" fill="#16232b">0.3%</text></g><g><title>YAP1 (oncogene)
+Colorectal: 0.2% (10 of 5,543)
+Pancreatic: 0% (1 of 3,109)
+q = 0.16</title><rect x="672" y="444" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="444" width="4" height="46" rx="2" fill="#ff0000" opacity="0.26"/><text x="681" y="458" font-size="11" fill="#16232b" font-weight="600">YAP1</text><rect x="681" y="465" width="26" height="6" fill="#dde4e8"/><rect x="681" y="465" width="1" height="6" fill="#0072B2"/><text x="711" y="471" font-size="10" fill="#16232b">0.2%</text><rect x="681" y="477" width="26" height="6" fill="#dde4e8"/><text x="711" y="483" font-size="10" fill="#16232b">0%</text></g><text x="759" y="457" font-size="10" fill="#5b6b76">32 never profiled</text><rect x="0" y="498" width="960" height="58" fill="#dde4e8" opacity="0.35"/><text x="8" y="515" font-size="12" fill="#16232b" font-weight="600">TGF-β</text><text x="8" y="529" font-size="10" fill="#5b6b76">5 of 7 genes profiled</text><rect x="128" y="504" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="504" width="24.73" height="10" rx="2" fill="#0072B2"><title>Colorectal: 21.5% (1,194 of 5,543)</title></rect><text x="249" y="513" font-size="10" fill="#16232b">21.5%</text><rect x="128" y="518" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="518" width="27.03" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 23.5% (732 of 3,109)</title></rect><text x="249" y="527" font-size="10" fill="#16232b">23.5%</text><text x="249" y="542" font-size="10" fill="#5b6b76">q = 0.037</text><g><title>SMAD4 (tumour suppressor)
+Colorectal: 15.5% (859 of 5,543)
+Pancreatic: 21.1% (655 of 3,109)
+q = 3.7e-10</title><rect x="324" y="502" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="502" width="4" height="46" rx="2" fill="#0000ff" opacity="0.78"/><text x="333" y="516" font-size="11" fill="#16232b" font-weight="600">SMAD4 ★</text><rect x="333" y="523" width="26" height="6" fill="#dde4e8"/><rect x="333" y="523" width="4.03" height="6" fill="#0072B2"/><text x="363" y="529" font-size="10" fill="#16232b">15.5%</text><rect x="333" y="535" width="26" height="6" fill="#dde4e8"/><rect x="333" y="535" width="5.49" height="6" fill="#E69F00"/><text x="363" y="541" font-size="10" fill="#16232b">21.1%</text></g><g><title>SMAD2 (tumour suppressor)
+Colorectal: 4.1% (225 of 5,543)
+Pancreatic: 0.4% (11 of 3,109)
+q = 4.1e-30</title><rect x="411" y="502" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="502" width="4" height="46" rx="2" fill="#0000ff" opacity="0.35"/><text x="420" y="516" font-size="11" fill="#16232b" font-weight="600">SMAD2 ★</text><rect x="420" y="523" width="26" height="6" fill="#dde4e8"/><rect x="420" y="523" width="1.07" height="6" fill="#0072B2"/><text x="450" y="529" font-size="10" fill="#16232b">4.1%</text><rect x="420" y="535" width="26" height="6" fill="#dde4e8"/><rect x="420" y="535" width="1" height="6" fill="#E69F00"/><text x="450" y="541" font-size="10" fill="#16232b">0.4%</text></g><g><title>SMAD3 (tumour suppressor)
+Colorectal: 3.2% (180 of 5,543)
+Pancreatic: 1.0% (31 of 3,109)
+q = 2.6e-11</title><rect x="498" y="502" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="502" width="4" height="46" rx="2" fill="#0000ff" opacity="0.33"/><text x="507" y="516" font-size="11" fill="#16232b" font-weight="600">SMAD3 ★</text><rect x="507" y="523" width="26" height="6" fill="#dde4e8"/><rect x="507" y="523" width="1" height="6" fill="#0072B2"/><text x="537" y="529" font-size="10" fill="#16232b">3.2%</text><rect x="507" y="535" width="26" height="6" fill="#dde4e8"/><rect x="507" y="535" width="1" height="6" fill="#E69F00"/><text x="537" y="541" font-size="10" fill="#16232b">1.0%</text></g><g><title>TGFBR1 (tumour suppressor)
+Colorectal: 0.9% (48 of 5,543)
+Pancreatic: 1.4% (42 of 3,109)
+q = 0.058</title><rect x="585" y="502" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="585" y="502" width="4" height="46" rx="2" fill="#0000ff" opacity="0.28"/><text x="594" y="516" font-size="11" fill="#16232b" font-weight="600">TGFBR1</text><rect x="594" y="523" width="26" height="6" fill="#dde4e8"/><rect x="594" y="523" width="1" height="6" fill="#0072B2"/><text x="624" y="529" font-size="10" fill="#16232b">0.9%</text><rect x="594" y="535" width="26" height="6" fill="#dde4e8"/><rect x="594" y="535" width="1" height="6" fill="#E69F00"/><text x="624" y="541" font-size="10" fill="#16232b">1.4%</text></g><g><title>TGFBR2 (tumour suppressor)
+Colorectal: 0.4% (22 of 5,543)
+Pancreatic: 0.7% (22 of 3,109)
+q = 0.091</title><rect x="672" y="502" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="672" y="502" width="4" height="46" rx="2" fill="#0000ff" opacity="0.27"/><text x="681" y="516" font-size="11" fill="#16232b" font-weight="600">TGFBR2</text><rect x="681" y="523" width="26" height="6" fill="#dde4e8"/><rect x="681" y="523" width="1" height="6" fill="#0072B2"/><text x="711" y="529" font-size="10" fill="#16232b">0.4%</text><rect x="681" y="535" width="26" height="6" fill="#dde4e8"/><rect x="681" y="535" width="1" height="6" fill="#E69F00"/><text x="711" y="541" font-size="10" fill="#16232b">0.7%</text></g><text x="759" y="515" font-size="10" fill="#5b6b76">2 never profiled</text><text x="8" y="573" font-size="12" fill="#16232b" font-weight="600">NRF2</text><text x="8" y="587" font-size="10" fill="#5b6b76">3 of 3 genes profiled</text><rect x="128" y="562" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="562" width="1.15" height="10" rx="2" fill="#0072B2"><title>Colorectal: 1.0% (54 of 5,543)</title></rect><text x="249" y="571" font-size="10" fill="#16232b">1.0%</text><rect x="128" y="576" width="115" height="10" rx="2" fill="#ffffff" stroke="#dde4e8"/><rect x="128" y="576" width="0.92" height="10" rx="2" fill="#E69F00"><title>Pancreatic: 0.8% (24 of 3,109)</title></rect><text x="249" y="585" font-size="10" fill="#16232b">0.8%</text><text x="249" y="600" font-size="10" fill="#5b6b76">q = 0.41</text><g><title>KEAP1 (tumour suppressor)
+Colorectal: 0.6% (33 of 5,543)
+Pancreatic: 0.6% (19 of 3,109)
+q = 1.00</title><rect x="324" y="560" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="324" y="560" width="4" height="46" rx="2" fill="#0000ff" opacity="0.27"/><text x="333" y="574" font-size="11" fill="#16232b" font-weight="600">KEAP1</text><rect x="333" y="581" width="26" height="6" fill="#dde4e8"/><rect x="333" y="581" width="1" height="6" fill="#0072B2"/><text x="363" y="587" font-size="10" fill="#16232b">0.6%</text><rect x="333" y="593" width="26" height="6" fill="#dde4e8"/><rect x="333" y="593" width="1" height="6" fill="#E69F00"/><text x="363" y="599" font-size="10" fill="#16232b">0.6%</text></g><g><title>CUL3 (tumour suppressor)
+Colorectal: 0.4% (20 of 5,543)
+Pancreatic: 0% (1 of 3,109)
+q = 0.0041</title><rect x="411" y="560" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="411" y="560" width="4" height="46" rx="2" fill="#0000ff" opacity="0.26"/><text x="420" y="574" font-size="11" fill="#16232b" font-weight="600">CUL3 ★</text><rect x="420" y="581" width="26" height="6" fill="#dde4e8"/><rect x="420" y="581" width="1" height="6" fill="#0072B2"/><text x="450" y="587" font-size="10" fill="#16232b">0.4%</text><rect x="420" y="593" width="26" height="6" fill="#dde4e8"/><text x="450" y="599" font-size="10" fill="#16232b">0%</text></g><g><title>NFE2L2 (oncogene)
+Colorectal: 0% (1 of 5,543)
+Pancreatic: 0.1% (4 of 3,109)
+q = 0.091</title><rect x="498" y="560" width="81" height="46" rx="4" fill="#ffffff" stroke="#dde4e8"/><rect x="498" y="560" width="4" height="46" rx="2" fill="#ff0000" opacity="0.25"/><text x="507" y="574" font-size="11" fill="#16232b" font-weight="600">NFE2L2</text><rect x="507" y="581" width="26" height="6" fill="#dde4e8"/><text x="537" y="587" font-size="10" fill="#16232b">0%</text><rect x="507" y="593" width="26" height="6" fill="#dde4e8"/><rect x="507" y="593" width="1" height="6" fill="#E69F00"/><text x="537" y="599" font-size="10" fill="#16232b">0.1%</text></g><rect x="8" y="623" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="631.9" font-size="11" fill="#16232b">Colorectal</text><rect x="89.479" y="623" width="10" height="10" rx="2" fill="#E69F00"/><text x="104.5" y="631.9" font-size="11" fill="#16232b">Pancreatic</text><line x1="172.894" y1="628" x2="190.894" y2="628" stroke="#ff0000" stroke-width="2"/><text x="195.9" y="631.9" font-size="11" fill="#16232b">oncogene</text><line x1="260.129" y1="628" x2="278.129" y2="628" stroke="#0000ff" stroke-width="2"/><text x="283.1" y="631.9" font-size="11" fill="#16232b">tumour suppressor (paper's role)</text><text x="8" y="650" font-size="10" fill="#5b6b76">Share of patients with an OncoKB driver (v7.5) in at least one member gene — drivers only, as Sanchez-Vega et al. counted curated oncogenic events. A pathway's</text><text x="8" y="664" font-size="10" fill="#5b6b76">denominator is the patients profiled for at least one of its genes, so a panel that covers few members makes a pathway look rarer than a genome-wide assay would; each</text><text x="8" y="678" font-size="10" fill="#5b6b76">member gene's own % is over the patients profiled for THAT gene. 206 member-gene slots were on no participating panel and are listed as not profiled rather than</text><text x="8" y="692" font-size="10" fill="#5b6b76">counted as unaltered. Pathway membership is the paper's Table S3; a gene's pathway role (oncogene / tumour suppressor) is not used in the count. Two-sided Fisher</text><text x="8" y="706" font-size="10" fill="#5b6b76">tests; q is Benjamini–Hochberg across the pathways drawn, and separately across every member gene tested.</text></svg>
diff --git a/test/snapshots/pathways.txt b/test/snapshots/pathways.txt
new file mode 100644
index 00000000..16ea356a
--- /dev/null
+++ b/test/snapshots/pathways.txt
@@ -0,0 +1,26 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Colorectal: 5,543 patients — CANCER_TYPE in [Colorectal Cancer] (any sample)
+- Pancreatic: 3,109 patients — CANCER_TYPE in [Pancreatic Cancer] (any sample)
+
+Oncogenic signalling pathways (% of patients with ≥1 member driver; union-of-panels denominator):
+  RTK-RAS (47/85 genes profiled): Colorectal 67.4% | Pancreatic 83.6% q=1.3e-62; top: KRAS 43.2%/81%, BRAF 9.9%/1.1%, ERBB2 5.2%/1.2%, NRAS 3.4%/0.5%, FGFR1 2.5%/1.7%, ERBB3 2.3%/0.9%, +32 more altered
+  PI3K (19/29 genes profiled): Colorectal 31.6% | Pancreatic 10% q=1.4e-124; top: PIK3CA 18.8%/2%, PTEN 6.8%/1.4%, PIK3R1 4.3%/0.3%, AKT2 0.7%/3.2%, STK11 0.8%/1.9%, AKT1 1.5%/0.3%, +12 more altered
+  TP53 (5/6 genes profiled): Colorectal 77% | Pancreatic 68.5% q=1.3e-17; top: TP53 73.3%/66.5%, ATM 5.2%/1.8%, MDM2 0.1%/1.1%, CHEK2 0.8%/0.2%, MDM4 0%/0.2%
+  Cell cycle (13/15 genes profiled): Colorectal 8.8% | Pancreatic 39.1% q=2.6e-244; top: CDKN2A 2%/32.8%, CDKN2B 1%/13.6%, RB1 1.3%/2.5%, CCND2 2.2%/0.6%, CCNE1 0.6%/1.9%, CDK6 0.2%/1.3%, +6 more altered
+  MYC (5/13 genes profiled): Colorectal 8.1% | Pancreatic 5% q=3.6e-8; top: MYC 4.8%/3.9%, MGA 2.9%/0.7%, MAX 0.4%/0.2%, MYCL 0.3%/0.3%, MYCN 0.1%/0.1%
+  WNT (12/68 genes profiled): Colorectal 81.8% | Pancreatic 7.8% q=<1e-300; top: APC 73.1%/1%, TCF7L2 12.1%/0.2%, AMER1 6%/0.9%, RNF43 5.1%/4%, AXIN2 3.8%/0.5%, ZNRF3 3.8%/0.3%, +4 more altered
+  NOTCH (14/71 genes profiled): Colorectal 21.9% | Pancreatic 3.5% q=8.7e-139; top: FBXW7 13.5%/1%, CREBBP 3%/0.5%, NCOR1 2.7%/0.2%, NOTCH3 2.3%/0.2%, EP300 2.2%/0.4%, SPEN 1.8%/0.2%, +4 more altered
+  Hippo (6/38 genes profiled): Colorectal 4.7% | Pancreatic 1.3% q=1.0e-18; top: FAT1 3.2%/0.5%, LATS1 0.9%/0.3%, LATS2 0.6%/0.2%, NF2 0.2%/0.3%, YAP1 0.2%/0%
+  TGF-β (5/7 genes profiled): Colorectal 21.5% | Pancreatic 23.5% q=0.0371; top: SMAD4 15.5%/21.1%, SMAD2 4.1%/0.4%, SMAD3 3.2%/1%, TGFBR1 0.9%/1.4%, TGFBR2 0.4%/0.7%
+  NRF2 (3/3 genes profiled): Colorectal 1% | Pancreatic 0.8% q=0.4068; top: KEAP1 0.6%/0.6%, CUL3 0.4%/0%, NFE2L2 0%/0.1%
+  CAVEAT (tell the user): Share of patients with an OncoKB driver (v7.5) in at least one member gene — drivers only, as Sanchez-Vega et al. counted curated oncogenic events. A pathway's denominator is the patients profiled for at least one of its genes, so a panel that covers few members makes a pathway look rarer than a genome-wide assay would; each member gene's own % is over the patients profiled for THAT gene. 206 member-gene slots were on no participating panel and are listed as not profiled rather than counted as unaltered. Pathway membership is the paper's Table S3; a gene's pathway role (oncogene / tumour suppressor) is not used in the count. Two-sided Fisher tests; q is Benjamini–Hochberg across the pathways drawn, and separately across every member gene tested.
+  METHOD:
+    - Count patients altered per pathway and per gene [Colorectal · msk_chord_2024]: The geneCounts and pathways:sets queries above. Per pathway: patients profiled for at least one member gene (the denominator) and of those, patients with an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in at least one member. Per member gene: the same over the patients profiled for that gene. Counts only.
+        pathways / sets: RTK-RAS (85), PI3K (29), TP53 (6), Cell cycle (15), MYC (13), WNT (68), NOTCH (71), Hippo (38), TGF-β (7), NRF2 (3)
+    - Count patients altered per pathway and per gene [Pancreatic · msk_chord_2024]: The geneCounts and pathways:sets queries above. Per pathway: patients profiled for at least one member gene (the denominator) and of those, patients with an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5) in at least one member. Per member gene: the same over the patients profiled for that gene. Counts only.
+        pathways / sets: RTK-RAS (85), PI3K (29), TP53 (6), Cell cycle (15), MYC (13), WNT (68), NOTCH (71), Hippo (38), TGF-β (7), NRF2 (3)
+    - Fisher's exact test per pathway and per member gene: Two-sided, altered vs not among the profiled, between the two cohorts.
+    - Benjamini–Hochberg: Across the pathways drawn, and separately across every member gene tested, each gene once even when it sits in two pathways.
+    Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg statsmodels multipletests, to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/plot.json b/test/snapshots/plot.json
new file mode 100644
index 00000000..24586d88
--- /dev/null
+++ b/test/snapshots/plot.json
@@ -0,0 +1,681 @@
+{
+  "study": "brca_tcga_pan_can_atlas_2018",
+  "cohorts": {
+    "all samples": {
+      "count": 1084,
+      "text": "all patients in the study",
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "plot",
+      "plot": {
+        "shape": "box",
+        "n": 1068,
+        "x": {
+          "label": "ERBB2: Putative copy-number alterations from GISTIC",
+          "kind": "cat"
+        },
+        "y": {
+          "label": "ERBB2: mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM)",
+          "kind": "num"
+        },
+        "groups": [
+          {
+            "g": "Shallow deletion",
+            "n": 260,
+            "lo": -3.8045,
+            "hi": 0.351,
+            "q1": -0.9513,
+            "median": -0.6594,
+            "q3": -0.4083,
+            "loWhisker": -1.7658,
+            "hiWhisker": 0.351
+          },
+          {
+            "g": "Diploid",
+            "n": 480,
+            "lo": -2.742,
+            "hi": 1.1073,
+            "q1": -0.4187,
+            "median": -0.1185,
+            "q3": 0.1519,
+            "loWhisker": -1.2746,
+            "hiWhisker": 1.0078
+          },
+          {
+            "g": "Gain",
+            "n": 206,
+            "lo": -1.5533000000000001,
+            "hi": 3.1795,
+            "q1": -0.23,
+            "median": 0.1592,
+            "q3": 0.415,
+            "loWhisker": -1.1975,
+            "hiWhisker": 1.3825
+          },
+          {
+            "g": "Amplification",
+            "n": 122,
+            "lo": -0.7721,
+            "hi": 3.7214,
+            "q1": 1.4466999999999999,
+            "median": 2.3303,
+            "q3": 2.8102,
+            "loWhisker": -0.5985500000000004,
+            "hiWhisker": 3.7214
+          }
+        ],
+        "violin": [
+          {
+            "g": "Gain",
+            "b": 15,
+            "n": 17
+          },
+          {
+            "g": "Diploid",
+            "b": 3,
+            "n": 2
+          },
+          {
+            "g": "Gain",
+            "b": 29,
+            "n": 1
+          },
+          {
+            "g": "Diploid",
+            "b": 16,
+            "n": 67
+          },
+          {
+            "g": "Shallow deletion",
+            "b": 9,
+            "n": 13
+          },
+          {
+            "g": "Amplification",
+            "b": 25,
+            "n": 2
+          },
+          {
+            "g": "Shallow deletion",
+            "b": 5,
+            "n": 4
+          },
+          {
+            "g": "Gain",
+            "b": 12,
+            "n": 6
+          },
+          {
+            "g": "Gain",
+            "b": 30,
+            "n": 1
+          },
+          {
+            "g": "Amplification",
+            "b": 36,
+            "n": 12
+          },
+          {
+            "g": "Diploid",
+            "b": 15,
+            "n": 65
+          },
+          {
+            "g": "Amplification",
+            "b": 21,
+            "n": 3
+          },
+          {
+            "g": "Gain",
+            "b": 9,
+            "n": 1
+          },
+          {
+            "g": "Amplification",
+            "b": 22,
+            "n": 3
+          },
+          {
+            "g": "Shallow deletion",
+            "b": 6,
+            "n": 6
+          },
+          {
+            "g": "Diploid",
+            "b": 12,
+            "n": 24
+          },
+          {
+            "g": "Amplification",
+            "b": 39,
+            "n": 12
+          },
+          {
+            "g": "Gain",
+            "b": 10,
+            "n": 2
+          },
+          {
+            "g": "Gain",
+            "b": 24,
+            "n": 1
+          },
+          {
+            "g": "Amplification",
+            "b": 26,
+            "n": 6
+          },
+          {
+            "…": "68 more",
+            "sha256": "6226d739d598cb9a"
+          }
+        ],
+        "violinBins": 40,
+        "overlay": "points",
+        "violinExtent": [
+          -2.435,
+          3.1795
+        ],
+        "coverage": {
+          "total": 1084,
+          "nx": 1070,
+          "ny": 1082
+        },
+        "points": [
+          {
+            "id": "TCGA-BH-A0BC-01",
+            "g": "Shallow deletion",
+            "v": -1.0313
+          },
+          {
+            "id": "TCGA-GM-A2DO-01",
+            "g": "Diploid",
+            "v": -0.5257
+          },
+          {
+            "id": "TCGA-D8-A27M-01",
+            "g": "Shallow deletion",
+            "v": -1.1943
+          },
+          {
+            "id": "TCGA-A2-A0YG-01",
+            "g": "Amplification",
+            "v": 2.6454
+          },
+          {
+            "id": "TCGA-AR-A1AY-01",
+            "g": "Shallow deletion",
+            "v": -0.8701
+          },
+          {
+            "id": "TCGA-A7-A3IZ-01",
+            "g": "Diploid",
+            "v": 0.2288
+          },
+          {
+            "id": "TCGA-C8-A1HN-01",
+            "g": "Gain",
+            "v": 0.221
+          },
+          {
+            "id": "TCGA-AR-A2LJ-01",
+            "g": "Amplification",
+            "v": 2.1318
+          },
+          {
+            "id": "TCGA-LL-A5YP-01",
+            "g": "Diploid",
+            "v": -0.9265
+          },
+          {
+            "id": "TCGA-OL-A6VR-01",
+            "g": "Diploid",
+            "v": -0.1528
+          },
+          {
+            "id": "TCGA-AR-A1AO-01",
+            "g": "Diploid",
+            "v": -0.9581
+          },
+          {
+            "id": "TCGA-A2-A3XV-01",
+            "g": "Amplification",
+            "v": 1.5072
+          },
+          {
+            "id": "TCGA-A2-A0ES-01",
+            "g": "Diploid",
+            "v": 0.0208
+          },
+          {
+            "id": "TCGA-AC-A3QP-01",
+            "g": "Diploid",
+            "v": 0.2131
+          },
+          {
+            "id": "TCGA-AR-A24P-01",
+            "g": "Gain",
+            "v": 0.2602
+          },
+          {
+            "id": "TCGA-B6-A0X0-01",
+            "g": "Diploid",
+            "v": 0.3066
+          },
+          {
+            "id": "TCGA-AR-A0U0-01",
+            "g": "Diploid",
+            "v": -0.5601
+          },
+          {
+            "id": "TCGA-BH-A1EU-01",
+            "g": "Shallow deletion",
+            "v": -1.0684
+          },
+          {
+            "id": "TCGA-AC-A2FF-01",
+            "g": "Diploid",
+            "v": 0.2463
+          },
+          {
+            "id": "TCGA-AN-A04C-01",
+            "g": "Amplification",
+            "v": 2.8872999999999998
+          },
+          {
+            "…": "1048 more",
+            "sha256": "ae5b2d782b1b6fdf"
+          }
+        ],
+        "pointSet": {
+          "rule": "all",
+          "drawn": 1068
+        },
+        "layers": [
+          {
+            "kind": "outliers",
+            "label": "outliers",
+            "color": "#D55E00",
+            "n": 0,
+            "shown": "none",
+            "why": "at or under 10,000 cases there is no density for them to stand out of"
+          }
+        ]
+      },
+      "cohorts": [
+        "all samples"
+      ],
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "brca_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "1,068 of 1,084 samples have a value on both axes (14 have no ERBB2: Putative copy-number alterations from GISTIC; 2 have no ERBB2: mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM)); only those are drawn. Point layers — outliers: not drawn, at or under 10,000 cases there is no density for them to stand out of."
+    },
+    {
+      "kind": "plot",
+      "plot": {
+        "shape": "box",
+        "n": 1068,
+        "x": {
+          "label": "TP53 mutation type",
+          "kind": "cat"
+        },
+        "y": {
+          "label": "FRACTION_GENOME_ALTERED",
+          "kind": "num"
+        },
+        "groups": [
+          {
+            "g": "Not mutated",
+            "n": 706,
+            "lo": 0,
+            "hi": 0.9971,
+            "q1": 0.1049,
+            "median": 0.1949,
+            "q3": 0.346,
+            "loWhisker": 0,
+            "hiWhisker": 0.7076499999999999
+          },
+          {
+            "g": "Missense",
+            "n": 209,
+            "lo": 0,
+            "hi": 0.9463,
+            "q1": 0.2622,
+            "median": 0.3797,
+            "q3": 0.5222,
+            "loWhisker": 0,
+            "hiWhisker": 0.9122
+          },
+          {
+            "g": "Truncating",
+            "n": 102,
+            "lo": 0.0002,
+            "hi": 0.947,
+            "q1": 0.2982,
+            "median": 0.4656,
+            "q3": 0.6182,
+            "loWhisker": 0.0002,
+            "hiWhisker": 0.947
+          },
+          {
+            "g": "Splice",
+            "n": 24,
+            "lo": 0.0027,
+            "hi": 0.8331,
+            "q1": 0.2373,
+            "median": 0.4675,
+            "q3": 0.5381,
+            "loWhisker": 0.0027,
+            "hiWhisker": 0.8331
+          },
+          {
+            "g": "Not profiled",
+            "n": 18,
+            "lo": 0,
+            "hi": 0.6502,
+            "q1": 0.0052,
+            "median": 0.1065,
+            "q3": 0.1675,
+            "loWhisker": 0,
+            "hiWhisker": 0.41095000000000004
+          },
+          {
+            "g": "Inframe",
+            "n": 5,
+            "lo": 0.2493,
+            "hi": 0.5605,
+            "q1": 0.2545,
+            "median": 0.2741,
+            "q3": 0.4806,
+            "loWhisker": 0.2493,
+            "hiWhisker": 0.5605
+          },
+          {
+            "g": "Multiple",
+            "n": 4,
+            "lo": 0.1011,
+            "hi": 0.6513,
+            "q1": 0.4622,
+            "median": 0.6276,
+            "q3": 0.6513,
+            "loWhisker": 0.17855000000000004,
+            "hiWhisker": 0.6513
+          }
+        ],
+        "violin": [
+          {
+            "g": "Not profiled",
+            "b": 0,
+            "n": 6
+          },
+          {
+            "g": "Missense",
+            "b": 34,
+            "n": 1
+          },
+          {
+            "g": "Not mutated",
+            "b": 30,
+            "n": 4
+          },
+          {
+            "g": "Missense",
+            "b": 28,
+            "n": 5
+          },
+          {
+            "g": "Not mutated",
+            "b": 8,
+            "n": 33
+          },
+          {
+            "g": "Truncating",
+            "b": 19,
+            "n": 1
+          },
+          {
+            "g": "Missense",
+            "b": 3,
+            "n": 1
+          },
+          {
+            "g": "Truncating",
+            "b": 12,
+            "n": 4
+          },
+          {
+            "g": "Truncating",
+            "b": 20,
+            "n": 6
+          },
+          {
+            "g": "Splice",
+            "b": 25,
+            "n": 1
+          },
+          {
+            "g": "Missense",
+            "b": 27,
+            "n": 8
+          },
+          {
+            "g": "Truncating",
+            "b": 11,
+            "n": 6
+          },
+          {
+            "g": "Missense",
+            "b": 4,
+            "n": 1
+          },
+          {
+            "g": "Not mutated",
+            "b": 23,
+            "n": 13
+          },
+          {
+            "g": "Not mutated",
+            "b": 1,
+            "n": 19
+          },
+          {
+            "g": "Truncating",
+            "b": 26,
+            "n": 5
+          },
+          {
+            "g": "Splice",
+            "b": 23,
+            "n": 2
+          },
+          {
+            "g": "Missense",
+            "b": 21,
+            "n": 9
+          },
+          {
+            "g": "Splice",
+            "b": 8,
+            "n": 1
+          },
+          {
+            "g": "Missense",
+            "b": 10,
+            "n": 6
+          },
+          {
+            "…": "123 more",
+            "sha256": "ddb05dbc992682e5"
+          }
+        ],
+        "violinBins": 40,
+        "overlay": "points",
+        "violinExtent": [
+          0,
+          0.9228
+        ],
+        "coverage": {
+          "total": 1084,
+          "nx": 1084,
+          "ny": 1068
+        },
+        "points": [
+          {
+            "id": "TCGA-AO-A0J6-01",
+            "g": "Truncating",
+            "v": 0.4668
+          },
+          {
+            "id": "TCGA-AO-A12B-01",
+            "g": "Not mutated",
+            "v": 0.1121
+          },
+          {
+            "id": "TCGA-BH-A0DI-01",
+            "g": "Missense",
+            "v": 0.2622
+          },
+          {
+            "id": "TCGA-A8-A07F-01",
+            "g": "Not mutated",
+            "v": 0.6103
+          },
+          {
+            "id": "TCGA-C8-A12P-01",
+            "g": "Missense",
+            "v": 0.3385
+          },
+          {
+            "id": "TCGA-A8-A091-01",
+            "g": "Not mutated",
+            "v": 0.3068
+          },
+          {
+            "id": "TCGA-E2-A1L8-01",
+            "g": "Not mutated",
+            "v": 0.1645
+          },
+          {
+            "id": "TCGA-A7-A26F-01",
+            "g": "Missense",
+            "v": 0.3177
+          },
+          {
+            "id": "TCGA-BH-A0W7-01",
+            "g": "Not mutated",
+            "v": 0.3048
+          },
+          {
+            "id": "TCGA-A2-A0YH-01",
+            "g": "Missense",
+            "v": 0.3154
+          },
+          {
+            "id": "TCGA-E9-A243-01",
+            "g": "Multiple",
+            "v": 0.1011
+          },
+          {
+            "id": "TCGA-E2-A150-01",
+            "g": "Truncating",
+            "v": 0.671
+          },
+          {
+            "id": "TCGA-AO-A1KQ-01",
+            "g": "Not mutated",
+            "v": 0.5026
+          },
+          {
+            "id": "TCGA-C8-A12K-01",
+            "g": "Missense",
+            "v": 0.4856
+          },
+          {
+            "id": "TCGA-C8-A9FZ-01",
+            "g": "Not mutated",
+            "v": 0.6245
+          },
+          {
+            "id": "TCGA-OL-A66N-01",
+            "g": "Not mutated",
+            "v": 0.2094
+          },
+          {
+            "id": "TCGA-BH-A28Q-01",
+            "g": "Not mutated",
+            "v": 0.1605
+          },
+          {
+            "id": "TCGA-A2-A04Y-01",
+            "g": "Not mutated",
+            "v": 0.147
+          },
+          {
+            "id": "TCGA-D8-A1XU-01",
+            "g": "Not mutated",
+            "v": 0.9465
+          },
+          {
+            "id": "TCGA-B6-A0I1-01",
+            "g": "Missense",
+            "v": 0.3742
+          },
+          {
+            "…": "1048 more",
+            "sha256": "720bf7719a5722b2"
+          }
+        ],
+        "pointSet": {
+          "rule": "all",
+          "drawn": 1068
+        },
+        "layers": [
+          {
+            "kind": "outliers",
+            "label": "outliers",
+            "color": "#D55E00",
+            "n": 0,
+            "shown": "none",
+            "why": "at or under 10,000 cases there is no density for them to stand out of"
+          }
+        ]
+      },
+      "cohorts": [
+        "all samples"
+      ],
+      "studies": [
+        "brca_tcga_pan_can_atlas_2018"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "brca_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "1,068 of 1,084 samples have a value on both axes (16 have no FRACTION_GENOME_ALTERED); only those are drawn. Point layers — outliers: not drawn, at or under 10,000 cases there is no density for them to stand out of."
+    }
+  ]
+}
diff --git a/test/snapshots/plot.sql b/test/snapshots/plot.sql
new file mode 100644
index 00000000..821743dc
--- /dev/null
+++ b/test/snapshots/plot.sql
@@ -0,0 +1,232 @@
+-- cohort all samples
+-- brca_tcga_pan_can_atlas_2018
+SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
+
+-- view 0 plot: plot:box · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id,
+       argMax(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL), 1) AS v
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY id),
+     gs AS (SELECT id, if(uniqExact(lv) > 1, 'Multiple', any(lv)) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, multiIf(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -2, 'Deep deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -1, 'Shallow deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 0, 'Diploid', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 1, 'Gain', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 2, 'Amplification', toString(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))) AS lv
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'gistic' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort))
+GROUP BY id)
+SELECT gs.v AS g, count() AS n, min(vs.v) AS lo, max(vs.v) AS hi,
+       quantilesExact(0.25, 0.5, 0.75)(vs.v) AS q
+FROM vs INNER JOIN gs ON gs.id = vs.id
+GROUP BY g ORDER BY n DESC LIMIT 151
+
+-- view 0 plot: plot:coverage · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     cases AS (SELECT DISTINCT replaceOne(sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)),
+     xs AS (SELECT id, if(uniqExact(lv) > 1, 'Multiple', any(lv)) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, multiIf(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -2, 'Deep deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -1, 'Shallow deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 0, 'Diploid', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 1, 'Gain', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 2, 'Amplification', toString(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))) AS lv
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'gistic' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort))
+GROUP BY id),
+     ys AS (SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id,
+       argMax(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL), 1) AS v
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY id)
+SELECT count() AS total, countIf(id IN (SELECT id FROM xs)) AS nx, countIf(id IN (SELECT id FROM ys)) AS ny FROM cases
+
+-- view 0 plot: plot:points · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id,
+       argMax(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL), 1) AS v
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY id),
+     gs AS (SELECT id, if(uniqExact(lv) > 1, 'Multiple', any(lv)) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, multiIf(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -2, 'Deep deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -1, 'Shallow deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 0, 'Diploid', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 1, 'Gain', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 2, 'Amplification', toString(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))) AS lv
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'gistic' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort))
+GROUP BY id)
+SELECT vs.id AS id, gs.v AS g, vs.v AS v
+FROM vs INNER JOIN gs ON gs.id = vs.id
+LIMIT 10001
+
+-- view 0 plot: plot:violin · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id,
+       argMax(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL), 1) AS v
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY id),
+     gs AS (SELECT id, if(uniqExact(lv) > 1, 'Multiple', any(lv)) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, multiIf(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -2, 'Deep deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = -1, 'Shallow deletion', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 0, 'Diploid', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 1, 'Gain', if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) = 2, 'Amplification', toString(if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL))) AS lv
+FROM snapshot_db.genetic_alteration_derived ga
+JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = ga.sample_unique_id
+WHERE ga.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND ga.profile_type = 'gistic' AND ga.hugo_gene_symbol = 'ERBB2'
+  AND if(isFinite(ifNull(toFloat64OrNull(ga.alteration_value), nan)), toFloat64OrNull(ga.alteration_value), NULL) IS NOT NULL AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort))
+GROUP BY id),
+     j AS (SELECT gs.v AS g, vs.v AS x FROM vs INNER JOIN gs ON gs.id = vs.id),
+     e AS (SELECT quantileExact(0.005)(x) x0, quantileExact(0.995)(x) x1 FROM j)
+SELECT g, least(39, toUInt32(floor((x - x0) / nullIf(x1 - x0, 0) * 40))) AS b, count() AS n, any(x0) AS vx0, any(x1) AS vx1
+FROM j, e GROUP BY g, b
+
+-- view 1 plot: plot:box · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND attribute_name = 'FRACTION_GENOME_ALTERED' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived cd
+  JOIN snapshot_db.sample_derived sd ON sd.patient_unique_id = cd.patient_unique_id
+  WHERE cd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND cd.attribute_name = 'FRACTION_GENOME_ALTERED' AND cd.attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) IS NOT NULL
+    AND cd.type = 'patient' AND replaceOne(cd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS alt_case, multiIf(countIf(g.variant_type = 'mutation') = 0, 'Not mutated', length(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation')) > 1, 'Multiple', arrayElement(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation'), 1)) AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol IN ('TP53')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case))
+SELECT gs.v AS g, count() AS n, min(vs.v) AS lo, max(vs.v) AS hi,
+       quantilesExact(0.25, 0.5, 0.75)(vs.v) AS q
+FROM vs INNER JOIN gs ON gs.id = vs.id
+GROUP BY g ORDER BY n DESC LIMIT 151
+
+-- view 1 plot: plot:coverage · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     cases AS (SELECT DISTINCT replaceOne(sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)),
+     xs AS (SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS alt_case, multiIf(countIf(g.variant_type = 'mutation') = 0, 'Not mutated', length(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation')) > 1, 'Multiple', arrayElement(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation'), 1)) AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol IN ('TP53')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case)),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND attribute_name = 'FRACTION_GENOME_ALTERED' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived cd
+  JOIN snapshot_db.sample_derived sd ON sd.patient_unique_id = cd.patient_unique_id
+  WHERE cd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND cd.attribute_name = 'FRACTION_GENOME_ALTERED' AND cd.attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) IS NOT NULL
+    AND cd.type = 'patient' AND replaceOne(cd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT count() AS total, countIf(id IN (SELECT id FROM xs)) AS nx, countIf(id IN (SELECT id FROM ys)) AS ny FROM cases
+
+-- view 1 plot: plot:points · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND attribute_name = 'FRACTION_GENOME_ALTERED' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived cd
+  JOIN snapshot_db.sample_derived sd ON sd.patient_unique_id = cd.patient_unique_id
+  WHERE cd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND cd.attribute_name = 'FRACTION_GENOME_ALTERED' AND cd.attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) IS NOT NULL
+    AND cd.type = 'patient' AND replaceOne(cd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS alt_case, multiIf(countIf(g.variant_type = 'mutation') = 0, 'Not mutated', length(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation')) > 1, 'Multiple', arrayElement(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation'), 1)) AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol IN ('TP53')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case))
+SELECT vs.id AS id, gs.v AS g, vs.v AS v
+FROM vs INNER JOIN gs ON gs.id = vs.id
+LIMIT 10001
+
+-- view 1 plot: plot:violin · brca_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND attribute_name = 'FRACTION_GENOME_ALTERED' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived cd
+  JOIN snapshot_db.sample_derived sd ON sd.patient_unique_id = cd.patient_unique_id
+  WHERE cd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND cd.attribute_name = 'FRACTION_GENOME_ALTERED' AND cd.attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(cd.attribute_value), nan)), toFloat64OrNull(cd.attribute_value), NULL) IS NOT NULL
+    AND cd.type = 'patient' AND replaceOne(cd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT alt_case AS id, if(hits = 0 AND NOT (alt_case IN (SELECT profiled_id FROM (SELECT DISTINCT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS profiled_id
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53'))))), 'Not profiled', v0) AS v FROM (
+SELECT replaceOne(sd.sample_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') AS alt_case, multiIf(countIf(g.variant_type = 'mutation') = 0, 'Not mutated', length(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation')) > 1, 'Multiple', arrayElement(groupUniqArrayIf(multiIf(lower(g.mutation_type) LIKE 'missense%', 'Missense', lower(g.mutation_type) LIKE 'nonsense%' OR lower(g.mutation_type) LIKE 'frame_shift%' OR lower(g.mutation_type) LIKE 'nonstop%', 'Truncating', lower(g.mutation_type) LIKE 'splice%', 'Splice', lower(g.mutation_type) LIKE 'in_frame%', 'Inframe', lower(g.mutation_type) LIKE '5''flank%' OR lower(g.mutation_type) LIKE 'promoter%', 'Promoter', 'Other'), g.variant_type = 'mutation'), 1)) AS v0, countIf(g.variant_type = 'mutation') AS hits
+FROM snapshot_db.sample_derived sd
+LEFT JOIN (
+     SELECT sample_unique_id, mutation_type, variant_type, cna_alteration
+     FROM snapshot_db.genomic_event_derived
+     WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol IN ('TP53')
+       AND mutation_status != 'UNCALLED'
+) g ON g.sample_unique_id = sd.sample_unique_id
+WHERE sd.cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND replaceOne(sd.patient_unique_id, 'brca_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+GROUP BY alt_case)),
+     j AS (SELECT gs.v AS g, vs.v AS x FROM vs INNER JOIN gs ON gs.id = vs.id),
+     e AS (SELECT quantileExact(0.005)(x) x0, quantileExact(0.995)(x) x1 FROM j)
+SELECT g, least(39, toUInt32(floor((x - x0) / nullIf(x1 - x0, 0) * 40))) AS b, count() AS n, any(x0) AS vx0, any(x1) AS vx1
+FROM j, e GROUP BY g, b
diff --git a/test/snapshots/plot.svg b/test/snapshots/plot.svg
new file mode 100644
index 00000000..6af087a0
--- /dev/null
+++ b/test/snapshots/plot.svg
@@ -0,0 +1,4 @@
+<!-- view 0 plot -->
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+<!-- view 1 plot -->
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cx="252.8" cy="140.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="260" cy="230.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="241.5" cy="218.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="239.4" cy="205" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="233.8" cy="272.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="260.4" cy="225.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="241.4" cy="234.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="232" cy="194.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="243.5" cy="192.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/></g><line x1="241.6" y1="348.2" x2="241.6" y2="49.6" stroke="#5b6b76" stroke-width="1"/><rect x="217.6" y="177.3" width="48" height="85.1" fill="#0072B2" fill-opacity="0.1" stroke="#0072B2"><title>Missense: median 0.38, quartiles 0.262–0.522, n = 209</title></rect><line x1="217.6" y1="223.9" x2="265.6" y2="223.9" stroke="#0072B2" stroke-width="2"/><g data-layer="base"><circle cx="378" cy="195.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="352" cy="128.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="361.4" cy="225.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="365.7" cy="239.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="358" cy="231.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="348.5" cy="260.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="377.2" cy="293.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="372.5" cy="293.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="388.7" cy="282.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="379.5" cy="128.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="367.6" cy="167.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="360.6" cy="118.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="372.1" cy="297.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="359.2" cy="123.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="350.9" cy="320.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="368.2" cy="250.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="377.9" cy="318.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="386.7" cy="317.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="365.1" cy="92.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="384.7" cy="146.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="378.3" cy="129.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="362" cy="61.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="363.6" cy="145.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="361.9" cy="270.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="350.8" cy="152.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="363.9" cy="108.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="349.6" cy="208.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="352.5" cy="209.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="378.6" cy="116.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="357.1" cy="68.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="371" cy="147.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="358.8" cy="172.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="388.5" cy="260.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="388.9" cy="145.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="375.8" cy="222.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="371.1" cy="181.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="354.3" cy="124.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="379" cy="348.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="385.7" cy="66.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="369.2" cy="231.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="376.3" cy="195.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="373" cy="174.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="370.6" cy="273.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="381.8" cy="207.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="361.5" cy="195.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="358.7" cy="238" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="388.3" cy="172.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="356" cy="249.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="355" cy="179.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="376.6" cy="322.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="362.5" cy="241.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="378.8" cy="221.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="382.5" cy="92.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="351.9" cy="117.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="366.2" cy="247.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="351.6" cy="185.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="348" cy="250.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="375" cy="145.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="360.5" cy="219.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="371.5" cy="262.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="384.3" cy="143.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="369.2" cy="253.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="379.5" cy="130.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="379.1" cy="161.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="371.6" cy="219.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="359" cy="214.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="389.9" cy="264.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="368.3" cy="190" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="350.1" cy="106" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="373.1" cy="252.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="353.6" cy="181.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="362.4" cy="110" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="369.4" cy="318.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="375.1" cy="262.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="349" cy="153.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="353.3" cy="203.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="359.4" cy="180.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="386.6" cy="197.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="354" cy="113.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="363.7" cy="194.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="356.8" cy="171.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="359.3" cy="294.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="366.8" cy="334.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="369.1" cy="249.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="368" cy="188.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="349.8" cy="206.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="358.3" cy="207.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="349.7" cy="222.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="353.1" cy="237.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="353.4" cy="223.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="371.5" cy="52.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="356.4" cy="183.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="352.3" cy="154.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="364.3" cy="338.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="371.6" cy="103.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="359.3" cy="260.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="384.6" cy="178.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="363.3" cy="38.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="357.2" cy="170.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="377.4" cy="348.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="389.3" cy="138.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="372.4" cy="209.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/></g><line x1="369.3" y1="348.1" x2="369.3" y2="38.2" stroke="#5b6b76" stroke-width="1"/><rect x="345.3" y="145.8" width="48" height="104.8" fill="#0072B2" fill-opacity="0.1" stroke="#0072B2"><title>Truncating: median 0.466, quartiles 0.298–0.618, n = 102</title></rect><line x1="345.3" y1="195.8" x2="393.3" y2="195.8" stroke="#0072B2" stroke-width="2"/><g data-layer="base"><circle cx="508.9" cy="195.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="509.1" cy="172.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="476" cy="75.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="510.9" cy="153.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="497.1" cy="234" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="493.1" cy="228" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="485.1" cy="247.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="505.3" cy="347.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="476.5" cy="284.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="486.3" cy="307.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="516.8" cy="270.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="491.5" cy="294.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="488.3" cy="218.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="495.2" cy="180.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="483.2" cy="165.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="484.3" cy="187.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="514.3" cy="180" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="488.7" cy="134.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="478.8" cy="275.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="488.6" cy="172.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="494.6" cy="94.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="488.1" cy="183.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="493.8" cy="290.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="477.7" cy="245.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/></g><line x1="497" y1="347.3" x2="497" y2="75.5" stroke="#5b6b76" stroke-width="1"/><rect x="473" y="172.1" width="48" height="98.5" fill="#0072B2" fill-opacity="0.1" stroke="#0072B2"><title>Splice: median 0.468, quartiles 0.237–0.538, n = 24</title></rect><line x1="473" y1="195.2" x2="521" y2="195.2" stroke="#0072B2" stroke-width="2"/><g data-layer="base"><circle cx="629.7" cy="216.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="627.6" cy="348.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="636.3" cy="313.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="628.2" cy="345.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="627.9" cy="286.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="645.9" cy="293.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="622.1" cy="308.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="611.2" cy="346.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="624.9" cy="135.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="639.3" cy="313.3" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="628.1" cy="348.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="622.6" cy="226" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="630" cy="308.4" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="631.2" cy="348.2" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="610.7" cy="348.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="623.9" cy="316" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="638.1" cy="317.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="617.3" cy="309.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/></g><line x1="624.7" y1="348.2" x2="624.7" y2="213.7" stroke="#5b6b76" stroke-width="1"/><rect x="600.7" y="293.4" width="48" height="53.1" fill="#0072B2" fill-opacity="0.1" stroke="#0072B2"><title>Not profiled: median 0.106, quartiles 0.0052–0.168, n = 18</title></rect><line x1="600.7" y1="313.3" x2="648.7" y2="313.3" stroke="#0072B2" stroke-width="2"/><g data-layer="base"><circle cx="764.2" cy="190.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="737.9" cy="264.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="747.3" cy="266.6" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="739.4" cy="164.7" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="765.9" cy="258.5" r="2.5" fill="#0072B2" fill-opacity="0.85"/></g><line x1="752.4" y1="266.6" x2="752.4" y2="164.7" stroke="#5b6b76" stroke-width="1"/><rect x="728.4" y="190.9" width="48" height="74" fill="#0072B2" fill-opacity="0.1" stroke="#0072B2"><title>Inframe: median 0.274, quartiles 0.255–0.481, n = 5</title></rect><line x1="728.4" y1="258.5" x2="776.4" y2="258.5" stroke="#0072B2" stroke-width="2"/><g data-layer="base"><circle cx="873.9" cy="315.1" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="896.1" cy="196.9" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="876.4" cy="142.8" r="2.5" fill="#0072B2" fill-opacity="0.85"/><circle cx="881.5" cy="135" r="2.5" fill="#0072B2" fill-opacity="0.85"/></g><line x1="880.1" y1="289.8" x2="880.1" y2="135" stroke="#5b6b76" stroke-width="1"/><rect x="856.1" y="135" width="48" height="61.9" fill="#0072B2" fill-opacity="0.1" stroke="#0072B2"><title>Multiple: median 0.628, quartiles 0.462–0.651, n = 4</title></rect><line x1="856.1" y1="142.8" x2="904.1" y2="142.8" stroke="#0072B2" stroke-width="2"/><line x1="50" y1="358" x2="944" y2="358" stroke="#5b6b76" stroke-width="1"/><text x="113.9" y="370" font-size="10" fill="#16232b" text-anchor="middle">Not mutated (706)</text><text x="241.6" y="370" font-size="10" fill="#16232b" text-anchor="middle">Missense (209)</text><text x="369.3" y="370" font-size="10" fill="#16232b" text-anchor="middle">Truncating (102)</text><text x="497" y="370" font-size="10" fill="#16232b" text-anchor="middle">Splice (24)</text><text x="624.7" y="370" font-size="10" fill="#16232b" text-anchor="middle">Not profiled (18)</text><text x="752.4" y="370" font-size="10" fill="#16232b" text-anchor="middle">Inframe (5)</text><text x="880.1" y="370" font-size="10" fill="#16232b" text-anchor="middle">Multiple (4)</text><text x="497" y="389" font-size="11" fill="#16232b" text-anchor="middle">TP53 mutation type</text><text x="8" y="417" font-size="10" fill="#5b6b76">1,068 of 1,084 samples have a value on both axes (16 have no FRACTION_GENOME_ALTERED); only those are drawn. Point layers — outliers: not drawn, at or under</text><text x="8" y="431" font-size="10" fill="#5b6b76">10,000 cases there is no density for them to stand out of. Box: quartiles with Tukey whiskers, computed over every case in the group; each name carries its count. Every</text><text x="8" y="445" font-size="10" fill="#5b6b76">value is drawn over its box; the jitter is fixed per case.</text></svg>
diff --git a/test/snapshots/plot.txt b/test/snapshots/plot.txt
new file mode 100644
index 00000000..c4a855b8
--- /dev/null
+++ b/test/snapshots/plot.txt
@@ -0,0 +1,23 @@
+Analysis over brca_tcga_pan_can_atlas_2018 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- all samples: 1,084 patients — all patients in the study
+
+Plot (box): ERBB2: mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM) by ERBB2: Putative copy-number alterations from GISTIC — 1,068 samples.
+  Shallow deletion: median -0.66 (IQR -0.95–-0.41, whiskers -1.77–0.35, n=260)
+  Diploid: median -0.12 (IQR -0.42–0.15, whiskers -1.27–1.01, n=480)
+  Gain: median 0.16 (IQR -0.23–0.41, whiskers -1.20–1.38, n=206)
+  Amplification: median 2.33 (IQR 1.45–2.81, whiskers -0.60–3.72, n=122)
+  Values are drawn as individual points.
+  1,068 of 1,084 samples have a value on both axes (14 have no ERBB2: Putative copy-number alterations from GISTIC; 2 have no ERBB2: mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM)); only those are drawn. Point layers — outliers: not drawn, at or under 10,000 cases there is no density for them to stand out of.
+
+Plot (box): FRACTION_GENOME_ALTERED by TP53 mutation type — 1,068 samples.
+  Not mutated: median 0.19 (IQR 0.10–0.35, whiskers 0.00–0.71, n=706)
+  Missense: median 0.38 (IQR 0.26–0.52, whiskers 0.00–0.91, n=209)
+  Truncating: median 0.47 (IQR 0.30–0.62, whiskers 0.00–0.95, n=102)
+  Splice: median 0.47 (IQR 0.24–0.54, whiskers 0.00–0.83, n=24)
+  Not profiled: median 0.11 (IQR 0.01–0.17, whiskers 0.00–0.41, n=18)
+  Inframe: median 0.27 (IQR 0.25–0.48, whiskers 0.25–0.56, n=5)
+  Multiple: median 0.63 (IQR 0.46–0.65, whiskers 0.18–0.65, n=4)
+  Values are drawn as individual points.
+  1,068 of 1,084 samples have a value on both axes (16 have no FRACTION_GENOME_ALTERED); only those are drawn. Point layers — outliers: not drawn, at or under 10,000 cases there is no density for them to stand out of.
diff --git a/test/snapshots/pooled-paired.json b/test/snapshots/pooled-paired.json
new file mode 100644
index 00000000..c1525ea3
--- /dev/null
+++ b/test/snapshots/pooled-paired.json
@@ -0,0 +1,431 @@
+{
+  "study": "egc_msk_2017",
+  "cohorts": {
+    "before and after treatment": {
+      "count": 481,
+      "text": "all patients in the study — across 5 studies",
+      "studies": [
+        "egc_msk_2017",
+        "breast_alpelisib_2020",
+        "egc_trap_ccr_msk_2023",
+        "blca_cornell_2016",
+        "brca_dldccc_2022"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "paired",
+      "cohorts": [
+        "before and after treatment"
+      ],
+      "paired": {
+        "pairBy": {
+          "attribute": "IMPACT_PRE_POST",
+          "a": [
+            "PRE-TX"
+          ],
+          "b": [
+            "POST-TX"
+          ]
+        },
+        "perStudy": {
+          "egc_msk_2017": {
+            "attribute": "IMPACT_PRE_POST",
+            "a": [
+              "PRE-TX"
+            ],
+            "b": [
+              "POST-TX"
+            ]
+          },
+          "breast_alpelisib_2020": {
+            "attribute": "SAMPLE_COLLECTION_TIMEPOINT",
+            "a": [
+              "pre-treatment"
+            ],
+            "b": [
+              "post-treatment"
+            ]
+          },
+          "egc_trap_ccr_msk_2023": {
+            "attribute": "PRE_OR_POST_TREATMENT",
+            "a": [
+              "Pre"
+            ],
+            "b": [
+              "Post"
+            ]
+          },
+          "blca_cornell_2016": {
+            "attribute": "SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO",
+            "a": [
+              "pre-chemotherapy"
+            ],
+            "b": [
+              "post-chemotherapy"
+            ]
+          },
+          "brca_dldccc_2022": {
+            "attribute": "TREATMENT_STATUS",
+            "a": [
+              "Pre-Treatment"
+            ],
+            "b": [
+              "Post-Treatment"
+            ]
+          }
+        },
+        "labels": {
+          "a": "before",
+          "b": "after"
+        },
+        "scan": {
+          "top": 12
+        },
+        "measure": "driver",
+        "panels": [
+          {
+            "cohort": "before and after treatment",
+            "patients": 111,
+            "aSamples": 148,
+            "bSamples": 130,
+            "genes": [
+              {
+                "gene": "CCND1",
+                "pairs": 95,
+                "both": 8,
+                "aOnly": 10,
+                "bOnly": 1,
+                "neither": 76,
+                "concordance": 0.421,
+                "p": 0.011718749999999981,
+                "q": 1
+              },
+              {
+                "gene": "MYC",
+                "pairs": 111,
+                "both": 5,
+                "aOnly": 10,
+                "bOnly": 2,
+                "neither": 94,
+                "concordance": 0.294,
+                "p": 0.03857421875000008,
+                "q": 1
+              },
+              {
+                "gene": "ERBB3",
+                "pairs": 40,
+                "both": 0,
+                "aOnly": 5,
+                "bOnly": 0,
+                "neither": 35,
+                "concordance": 0,
+                "p": 0.0625,
+                "q": 1
+              },
+              {
+                "gene": "MTAP",
+                "pairs": 26,
+                "both": 2,
+                "aOnly": 5,
+                "bOnly": 0,
+                "neither": 19,
+                "concordance": 0.286,
+                "p": 0.0625,
+                "q": 1
+              },
+              {
+                "gene": "TP53",
+                "pairs": 111,
+                "both": 52,
+                "aOnly": 10,
+                "bOnly": 3,
+                "neither": 46,
+                "concordance": 0.8,
+                "p": 0.09228515625000008,
+                "q": 1
+              },
+              {
+                "gene": "CDH1",
+                "pairs": 39,
+                "both": 2,
+                "aOnly": 4,
+                "bOnly": 0,
+                "neither": 33,
+                "concordance": 0.333,
+                "p": 0.125,
+                "q": 1
+              },
+              {
+                "gene": "FOXP1",
+                "pairs": 41,
+                "both": 0,
+                "aOnly": 0,
+                "bOnly": 4,
+                "neither": 37,
+                "concordance": 0,
+                "p": 0.125,
+                "q": 1
+              },
+              {
+                "gene": "FGFR1",
+                "pairs": 85,
+                "both": 3,
+                "aOnly": 6,
+                "bOnly": 1,
+                "neither": 75,
+                "concordance": 0.3,
+                "p": 0.12500000000000006,
+                "q": 1
+              },
+              {
+                "gene": "RSPO2",
+                "pairs": 32,
+                "both": 1,
+                "aOnly": 6,
+                "bOnly": 1,
+                "neither": 24,
+                "concordance": 0.125,
+                "p": 0.12500000000000006,
+                "q": 1
+              },
+              {
+                "gene": "LMNA",
+                "pairs": 42,
+                "both": 1,
+                "aOnly": 5,
+                "bOnly": 1,
+                "neither": 35,
+                "concordance": 0.143,
+                "p": 0.21875000000000017,
+                "q": 1
+              },
+              {
+                "gene": "BAALC",
+                "pairs": 42,
+                "both": 1,
+                "aOnly": 8,
+                "bOnly": 3,
+                "neither": 30,
+                "concordance": 0.083,
+                "p": 0.2265625000000001,
+                "q": 1
+              },
+              {
+                "gene": "UBR5",
+                "pairs": 42,
+                "both": 1,
+                "aOnly": 8,
+                "bOnly": 3,
+                "neither": 30,
+                "concordance": 0.083,
+                "p": 0.2265625000000001,
+                "q": 1
+              }
+            ],
+            "tested": 278
+          }
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "egc_msk_2017",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "breast_alpelisib_2020",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "egc_trap_ccr_msk_2023",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "blca_cornell_2016",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "brca_dldccc_2022",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "studies": [
+        "egc_msk_2017",
+        "breast_alpelisib_2020",
+        "egc_trap_ccr_msk_2023",
+        "blca_cornell_2016",
+        "brca_dldccc_2022"
+      ],
+      "caveat": "Within patients with samples on both sides, before and after (egc_msk_2017: IMPACT_PRE_POST PRE-TX vs POST-TX; breast_alpelisib_2020: SAMPLE_COLLECTION_TIMEPOINT pre-treatment vs post-treatment; egc_trap_ccr_msk_2023: PRE_OR_POST_TREATMENT Pre vs Post; blca_cornell_2016: SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO pre-chemotherapy vs post-chemotherapy; brca_dldccc_2022: TREATMENT_STATUS Pre-Treatment vs Post-Treatment; before and after treatment: 111 patients), per gene, among those profiled for it on BOTH sides by each side's own panels: an OncoKB driver (v7.5) on both sides, on the before side only, or the after side only; a patient with several samples on a side counts as altered there if any is. Concordance is both ÷ altered anywhere. The exact McNemar test asks whether discordance runs one way; q is Benjamini–Hochberg across the genes of each cohort. Genes were not chosen in advance: every gene with a driver in some paired patient was tested (before and after treatment: 278 genes with a discordant patient), q is taken across all of them, and the 12 most one-sided are drawn — at these sizes, read the counts, not only the q. Sides are as each study labels its samples; after-only means seen on that side and not the other, which is not proof it arose or vanished in between — a subclone one biopsy missed, or a lower-purity sample (common after treatment) under-calling its side, reads the same; and patients sampled twice are a selected group, typically with longer or harder disease courses. Counts are summed across 5 studies.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count each gene's state in paired samples",
+            "detail": "The paired:patients and paired:genes queries above. Patients with both a SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO = pre-chemotherapy sample (before) and a SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO = post-chemotherapy sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.",
+            "scope": "before and after treatment · blca_cornell_2016",
+            "rowsIn": 32
+          },
+          {
+            "kind": "fetch",
+            "title": "Count each gene's state in paired samples",
+            "detail": "The paired:patients and paired:genes queries above. Patients with both a TREATMENT_STATUS = Pre-Treatment sample (before) and a TREATMENT_STATUS = Post-Treatment sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.",
+            "scope": "before and after treatment · brca_dldccc_2022",
+            "rowsIn": 59
+          },
+          {
+            "kind": "fetch",
+            "title": "Count each gene's state in paired samples",
+            "detail": "The paired:patients and paired:genes queries above. Patients with both a SAMPLE_COLLECTION_TIMEPOINT = pre-treatment sample (before) and a SAMPLE_COLLECTION_TIMEPOINT = post-treatment sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.",
+            "scope": "before and after treatment · breast_alpelisib_2020",
+            "rowsIn": 51
+          },
+          {
+            "kind": "fetch",
+            "title": "Count each gene's state in paired samples",
+            "detail": "The paired:patients and paired:genes queries above. Patients with both a IMPACT_PRE_POST = PRE-TX sample (before) and a IMPACT_PRE_POST = POST-TX sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.",
+            "scope": "before and after treatment · egc_msk_2017",
+            "rowsIn": 305
+          },
+          {
+            "kind": "fetch",
+            "title": "Count each gene's state in paired samples",
+            "detail": "The paired:patients and paired:genes queries above. Patients with both a PRE_OR_POST_TREATMENT = Pre sample (before) and a PRE_OR_POST_TREATMENT = Post sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.",
+            "scope": "before and after treatment · egc_trap_ccr_msk_2023",
+            "rowsIn": 34
+          },
+          {
+            "kind": "transform",
+            "title": "Add each cohort's counts across its studies",
+            "detail": "The counts add exactly."
+          },
+          {
+            "kind": "statistic",
+            "title": "Exact McNemar test per gene",
+            "detail": "Only the discordant patients carry information about direction: of the before-only and after-only patients, each is a fair coin under the null, so p is twice the smaller binomial tail (capped at 1). Concordance is both ÷ altered anywhere.",
+            "params": [
+              [
+                "before and after treatment",
+                "111 patients with both kinds of sample"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across genes, per cohort",
+            "detail": "q-values over the genes with at least one discordant patient, separately for each cohort. In a scan that is every such gene, not only the ones drawn."
+          },
+          {
+            "kind": "transform",
+            "title": "Draw the 12 most one-sided genes",
+            "detail": "Of the genes with a discordant patient, ordered by McNemar p, then by the size of the imbalance between before-only and after-only patients, then by the number of discordant patients, then by name; the first 12 are drawn and reported.",
+            "params": [
+              [
+                "before and after treatment",
+                "278 genes tested"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "mcnemarExact",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "The exact McNemar test matches statsmodels 0.15.0 mcnemar(exact=True) and Benjamini–Hochberg multipletests, to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "paired:before and after treatment:CCND1:p": {
+            "v": 0.011718749999999981
+          },
+          "paired:before and after treatment:CCND1:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:MYC:p": {
+            "v": 0.03857421875000008
+          },
+          "paired:before and after treatment:MYC:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:ERBB3:p": {
+            "v": 0.0625
+          },
+          "paired:before and after treatment:ERBB3:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:MTAP:p": {
+            "v": 0.0625
+          },
+          "paired:before and after treatment:MTAP:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:TP53:p": {
+            "v": 0.09228515625000008
+          },
+          "paired:before and after treatment:TP53:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:CDH1:p": {
+            "v": 0.125
+          },
+          "paired:before and after treatment:CDH1:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:FOXP1:p": {
+            "v": 0.125
+          },
+          "paired:before and after treatment:FOXP1:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:FGFR1:p": {
+            "v": 0.12500000000000006
+          },
+          "paired:before and after treatment:FGFR1:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:RSPO2:p": {
+            "v": 0.12500000000000006
+          },
+          "paired:before and after treatment:RSPO2:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:LMNA:p": {
+            "v": 0.21875000000000017
+          },
+          "paired:before and after treatment:LMNA:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:BAALC:p": {
+            "v": 0.2265625000000001
+          },
+          "paired:before and after treatment:BAALC:q": {
+            "v": 1
+          },
+          "paired:before and after treatment:UBR5:p": {
+            "v": 0.2265625000000001
+          },
+          "paired:before and after treatment:UBR5:q": {
+            "v": 1
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/pooled-paired.sql b/test/snapshots/pooled-paired.sql
new file mode 100644
index 00000000..3fa2e520
--- /dev/null
+++ b/test/snapshots/pooled-paired.sql
@@ -0,0 +1,322 @@
+-- cohort before and after treatment
+-- egc_msk_2017
+SELECT DISTINCT replaceOne(patient_unique_id, 'egc_msk_2017_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'egc_msk_2017'
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- breast_alpelisib_2020
+SELECT DISTINCT replaceOne(patient_unique_id, 'breast_alpelisib_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'breast_alpelisib_2020'
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- egc_trap_ccr_msk_2023
+SELECT DISTINCT replaceOne(patient_unique_id, 'egc_trap_ccr_msk_2023_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'egc_trap_ccr_msk_2023'
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- blca_cornell_2016
+SELECT DISTINCT replaceOne(patient_unique_id, 'blca_cornell_2016_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'blca_cornell_2016'
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- brca_dldccc_2022
+SELECT DISTINCT replaceOne(patient_unique_id, 'brca_dldccc_2022_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_dldccc_2022'
+
+-- view 0 paired: paired:genes:before and after treatment · blca_cornell_2016
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'blca_cornell_2016_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'blca_cornell_2016'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'blca_cornell_2016_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('pre-chemotherapy'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('post-chemotherapy'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'blca_cornell_2016' AND type = 'sample' AND attribute_name = 'SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO'
+    AND replaceOne(patient_unique_id, 'blca_cornell_2016_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired)),
+alt AS (
+  SELECT g.hugo_gene_symbol AS gene, ps.pid AS pid, ps.side AS side, max(ann.is_driver) AS hit
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  INNER JOIN ps ON ps.suid = g.sample_unique_id
+  WHERE g.cancer_study_identifier = 'blca_cornell_2016' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND g.sample_unique_id IN (SELECT suid FROM ps)
+  GROUP BY gene, pid, side
+  HAVING hit > 0
+),
+cov AS (
+  SELECT DISTINCT pg.gene AS gene, ps.pid AS pid, ps.side AS side
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT DISTINCT gene FROM alt)
+  INNER JOIN ps ON ps.suid = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'blca_cornell_2016'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+),
+per AS (
+  SELECT gene, pid, max(side = 'a') AS pa, max(side = 'b') AS pb,
+         max(side = 'a' AND hit > 0) AS aa, max(side = 'b' AND hit > 0) AS ab
+  FROM (SELECT gene, pid, side, hit FROM alt UNION ALL SELECT gene, pid, side, 0 AS hit FROM cov)
+  GROUP BY gene, pid
+)
+SELECT gene, count() AS pairs, countIf(aa AND ab) AS both, countIf(aa AND NOT ab) AS a_only, countIf(ab AND NOT aa) AS b_only
+FROM per WHERE pa AND pb GROUP BY gene HAVING both + a_only + b_only > 0
+
+-- view 0 paired: paired:genes:before and after treatment · brca_dldccc_2022
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_dldccc_2022_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_dldccc_2022'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'brca_dldccc_2022_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pre-Treatment'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Post-Treatment'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_dldccc_2022' AND type = 'sample' AND attribute_name = 'TREATMENT_STATUS'
+    AND replaceOne(patient_unique_id, 'brca_dldccc_2022_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired)),
+alt AS (
+  SELECT g.hugo_gene_symbol AS gene, ps.pid AS pid, ps.side AS side, max(ann.is_driver) AS hit
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  INNER JOIN ps ON ps.suid = g.sample_unique_id
+  WHERE g.cancer_study_identifier = 'brca_dldccc_2022' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND g.sample_unique_id IN (SELECT suid FROM ps)
+  GROUP BY gene, pid, side
+  HAVING hit > 0
+),
+cov AS (
+  SELECT DISTINCT pg.gene AS gene, ps.pid AS pid, ps.side AS side
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT DISTINCT gene FROM alt)
+  INNER JOIN ps ON ps.suid = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'brca_dldccc_2022'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+),
+per AS (
+  SELECT gene, pid, max(side = 'a') AS pa, max(side = 'b') AS pb,
+         max(side = 'a' AND hit > 0) AS aa, max(side = 'b' AND hit > 0) AS ab
+  FROM (SELECT gene, pid, side, hit FROM alt UNION ALL SELECT gene, pid, side, 0 AS hit FROM cov)
+  GROUP BY gene, pid
+)
+SELECT gene, count() AS pairs, countIf(aa AND ab) AS both, countIf(aa AND NOT ab) AS a_only, countIf(ab AND NOT aa) AS b_only
+FROM per WHERE pa AND pb GROUP BY gene HAVING both + a_only + b_only > 0
+
+-- view 0 paired: paired:genes:before and after treatment · breast_alpelisib_2020
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'breast_alpelisib_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'breast_alpelisib_2020'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'breast_alpelisib_2020_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('pre-treatment'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('post-treatment'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'breast_alpelisib_2020' AND type = 'sample' AND attribute_name = 'SAMPLE_COLLECTION_TIMEPOINT'
+    AND replaceOne(patient_unique_id, 'breast_alpelisib_2020_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired)),
+alt AS (
+  SELECT g.hugo_gene_symbol AS gene, ps.pid AS pid, ps.side AS side, max(ann.is_driver) AS hit
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  INNER JOIN ps ON ps.suid = g.sample_unique_id
+  WHERE g.cancer_study_identifier = 'breast_alpelisib_2020' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND g.sample_unique_id IN (SELECT suid FROM ps)
+  GROUP BY gene, pid, side
+  HAVING hit > 0
+),
+cov AS (
+  SELECT DISTINCT pg.gene AS gene, ps.pid AS pid, ps.side AS side
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT DISTINCT gene FROM alt)
+  INNER JOIN ps ON ps.suid = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'breast_alpelisib_2020'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+),
+per AS (
+  SELECT gene, pid, max(side = 'a') AS pa, max(side = 'b') AS pb,
+         max(side = 'a' AND hit > 0) AS aa, max(side = 'b' AND hit > 0) AS ab
+  FROM (SELECT gene, pid, side, hit FROM alt UNION ALL SELECT gene, pid, side, 0 AS hit FROM cov)
+  GROUP BY gene, pid
+)
+SELECT gene, count() AS pairs, countIf(aa AND ab) AS both, countIf(aa AND NOT ab) AS a_only, countIf(ab AND NOT aa) AS b_only
+FROM per WHERE pa AND pb GROUP BY gene HAVING both + a_only + b_only > 0
+
+-- view 0 paired: paired:genes:before and after treatment · egc_msk_2017
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'egc_msk_2017_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'egc_msk_2017'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'egc_msk_2017_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('PRE-TX'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('POST-TX'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'egc_msk_2017' AND type = 'sample' AND attribute_name = 'IMPACT_PRE_POST'
+    AND replaceOne(patient_unique_id, 'egc_msk_2017_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired)),
+alt AS (
+  SELECT g.hugo_gene_symbol AS gene, ps.pid AS pid, ps.side AS side, max(ann.is_driver) AS hit
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  INNER JOIN ps ON ps.suid = g.sample_unique_id
+  WHERE g.cancer_study_identifier = 'egc_msk_2017' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND g.sample_unique_id IN (SELECT suid FROM ps)
+  GROUP BY gene, pid, side
+  HAVING hit > 0
+),
+cov AS (
+  SELECT DISTINCT pg.gene AS gene, ps.pid AS pid, ps.side AS side
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT DISTINCT gene FROM alt)
+  INNER JOIN ps ON ps.suid = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'egc_msk_2017'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+),
+per AS (
+  SELECT gene, pid, max(side = 'a') AS pa, max(side = 'b') AS pb,
+         max(side = 'a' AND hit > 0) AS aa, max(side = 'b' AND hit > 0) AS ab
+  FROM (SELECT gene, pid, side, hit FROM alt UNION ALL SELECT gene, pid, side, 0 AS hit FROM cov)
+  GROUP BY gene, pid
+)
+SELECT gene, count() AS pairs, countIf(aa AND ab) AS both, countIf(aa AND NOT ab) AS a_only, countIf(ab AND NOT aa) AS b_only
+FROM per WHERE pa AND pb GROUP BY gene HAVING both + a_only + b_only > 0
+
+-- view 0 paired: paired:genes:before and after treatment · egc_trap_ccr_msk_2023
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'egc_trap_ccr_msk_2023_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'egc_trap_ccr_msk_2023'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'egc_trap_ccr_msk_2023_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pre'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Post'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'egc_trap_ccr_msk_2023' AND type = 'sample' AND attribute_name = 'PRE_OR_POST_TREATMENT'
+    AND replaceOne(patient_unique_id, 'egc_trap_ccr_msk_2023_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired)),
+alt AS (
+  SELECT g.hugo_gene_symbol AS gene, ps.pid AS pid, ps.side AS side, max(ann.is_driver) AS hit
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  INNER JOIN ps ON ps.suid = g.sample_unique_id
+  WHERE g.cancer_study_identifier = 'egc_trap_ccr_msk_2023' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND g.sample_unique_id IN (SELECT suid FROM ps)
+  GROUP BY gene, pid, side
+  HAVING hit > 0
+),
+cov AS (
+  SELECT DISTINCT pg.gene AS gene, ps.pid AS pid, ps.side AS side
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT DISTINCT gene FROM alt)
+  INNER JOIN ps ON ps.suid = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'egc_trap_ccr_msk_2023'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+),
+per AS (
+  SELECT gene, pid, max(side = 'a') AS pa, max(side = 'b') AS pb,
+         max(side = 'a' AND hit > 0) AS aa, max(side = 'b' AND hit > 0) AS ab
+  FROM (SELECT gene, pid, side, hit FROM alt UNION ALL SELECT gene, pid, side, 0 AS hit FROM cov)
+  GROUP BY gene, pid
+)
+SELECT gene, count() AS pairs, countIf(aa AND ab) AS both, countIf(aa AND NOT ab) AS a_only, countIf(ab AND NOT aa) AS b_only
+FROM per WHERE pa AND pb GROUP BY gene HAVING both + a_only + b_only > 0
+
+-- view 0 paired: paired:patients:before and after treatment · blca_cornell_2016
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'blca_cornell_2016_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'blca_cornell_2016'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'blca_cornell_2016_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('pre-chemotherapy'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('post-chemotherapy'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'blca_cornell_2016' AND type = 'sample' AND attribute_name = 'SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO'
+    AND replaceOne(patient_unique_id, 'blca_cornell_2016_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired))
+SELECT uniqExact(pid) AS patients, countIf(side = 'a') AS a_samples, countIf(side = 'b') AS b_samples FROM ps
+
+-- view 0 paired: paired:patients:before and after treatment · brca_dldccc_2022
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'brca_dldccc_2022_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'brca_dldccc_2022'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'brca_dldccc_2022_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pre-Treatment'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Post-Treatment'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'brca_dldccc_2022' AND type = 'sample' AND attribute_name = 'TREATMENT_STATUS'
+    AND replaceOne(patient_unique_id, 'brca_dldccc_2022_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired))
+SELECT uniqExact(pid) AS patients, countIf(side = 'a') AS a_samples, countIf(side = 'b') AS b_samples FROM ps
+
+-- view 0 paired: paired:patients:before and after treatment · breast_alpelisib_2020
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'breast_alpelisib_2020_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'breast_alpelisib_2020'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'breast_alpelisib_2020_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('pre-treatment'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('post-treatment'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'breast_alpelisib_2020' AND type = 'sample' AND attribute_name = 'SAMPLE_COLLECTION_TIMEPOINT'
+    AND replaceOne(patient_unique_id, 'breast_alpelisib_2020_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired))
+SELECT uniqExact(pid) AS patients, countIf(side = 'a') AS a_samples, countIf(side = 'b') AS b_samples FROM ps
+
+-- view 0 paired: paired:patients:before and after treatment · egc_msk_2017
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'egc_msk_2017_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'egc_msk_2017'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'egc_msk_2017_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('PRE-TX'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('POST-TX'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'egc_msk_2017' AND type = 'sample' AND attribute_name = 'IMPACT_PRE_POST'
+    AND replaceOne(patient_unique_id, 'egc_msk_2017_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired))
+SELECT uniqExact(pid) AS patients, countIf(side = 'a') AS a_samples, countIf(side = 'b') AS b_samples FROM ps
+
+-- view 0 paired: paired:patients:before and after treatment · egc_trap_ccr_msk_2023
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'egc_trap_ccr_msk_2023_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'egc_trap_ccr_msk_2023'),
+sides AS (
+  SELECT sample_unique_id AS suid, replaceOne(patient_unique_id, 'egc_trap_ccr_msk_2023_', '') AS pid,
+         multiIf(lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pre'))), 'a', lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Post'))), 'b', '') AS side
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'egc_trap_ccr_msk_2023' AND type = 'sample' AND attribute_name = 'PRE_OR_POST_TREATMENT'
+    AND replaceOne(patient_unique_id, 'egc_trap_ccr_msk_2023_', '') IN (SELECT pid FROM cohort)
+),
+paired AS (SELECT pid FROM sides GROUP BY pid HAVING countIf(side = 'a') > 0 AND countIf(side = 'b') > 0),
+ps AS (SELECT suid, pid, side FROM sides WHERE side != '' AND pid IN (SELECT pid FROM paired))
+SELECT uniqExact(pid) AS patients, countIf(side = 'a') AS a_samples, countIf(side = 'b') AS b_samples FROM ps
diff --git a/test/snapshots/pooled-paired.svg b/test/snapshots/pooled-paired.svg
new file mode 100644
index 00000000..83ee2fe0
--- /dev/null
+++ b/test/snapshots/pooled-paired.svg
@@ -0,0 +1,2 @@
+<!-- view 0 paired -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 504" width="100%" role="img" aria-label="Paired samples — before vs after" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Paired samples — before vs after</title><rect width="960" height="504" fill="#ffffff"/><rect x="8" y="6.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="15" font-size="10" fill="#16232b">before only</text><rect x="89.64" y="6.5" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="104.6" y="15" font-size="10" fill="#16232b">both</text><rect x="141.23000000000002" y="6.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="156.2" y="15" font-size="10" fill="#16232b">after only</text><text x="8" y="39" font-size="12" fill="#16232b" font-weight="600">before and after treatment</text><text x="166.7" y="39" font-size="10" fill="#5b6b76">111 patients with both · 148 before and 130 after samples · 278 genes with a discordant patient, the 12 most one-sided drawn</text><text x="813" y="57" font-size="10" fill="#5b6b76" text-anchor="end">concordant</text><text x="886.4" y="57" font-size="10" fill="#5b6b76" text-anchor="end">profiled pairs</text><text x="952" y="57" font-size="10" fill="#5b6b76" text-anchor="end">McNemar q</text><line x1="58.6" y1="61" x2="58.6" y2="301" stroke="#dde4e8" stroke-width="0.75"/><line x1="157.1" y1="61" x2="157.1" y2="301" stroke="#dde4e8" stroke-width="0.75"/><line x1="255.6" y1="61" x2="255.6" y2="301" stroke="#dde4e8" stroke-width="0.75"/><line x1="354.1" y1="61" x2="354.1" y2="301" stroke="#dde4e8" stroke-width="0.75"/><line x1="452.6" y1="61" x2="452.6" y2="301" stroke="#dde4e8" stroke-width="0.75"/><line x1="551.1" y1="61" x2="551.1" y2="301" stroke="#dde4e8" stroke-width="0.75"/><line x1="649.6" y1="61" x2="649.6" y2="301" stroke="#dde4e8" stroke-width="0.75"/><line x1="748.1" y1="61" x2="748.1" y2="301" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="61" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="44.6" y="74.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">CCND1</text><rect x="58.6" y="64" width="98.5" height="14" fill="#0072B2"><title>CCND1 — 10 patients altered before only</title></rect><text x="107.8" y="74.5" font-size="10" fill="#ffffff" text-anchor="middle">10</text><rect x="157.1" y="64" width="78.8" height="14" fill="#9aa5ad"><title>CCND1 — 8 patients altered both</title></rect><text x="196.5" y="74.5" font-size="10" fill="#16232b" text-anchor="middle">8</text><rect x="235.9" y="64" width="9.9" height="14" fill="#E69F00"><title>CCND1 — 1 patients altered after only</title></rect><text x="813" y="74.5" font-size="10" fill="#5b6b76" text-anchor="end">42.1%</text><text x="886.4" y="74.5" font-size="10" fill="#5b6b76" text-anchor="end">95</text><text x="952" y="74.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><text x="44.6" y="94.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">MYC</text><rect x="58.6" y="84" width="98.5" height="14" fill="#0072B2"><title>MYC — 10 patients altered before only</title></rect><text x="107.8" y="94.5" font-size="10" fill="#ffffff" text-anchor="middle">10</text><rect x="157.1" y="84" width="49.3" height="14" fill="#9aa5ad"><title>MYC — 5 patients altered both</title></rect><text x="181.7" y="94.5" font-size="10" fill="#16232b" text-anchor="middle">5</text><rect x="206.3" y="84" width="19.7" height="14" fill="#E69F00"><title>MYC — 2 patients altered after only</title></rect><text x="216.2" y="94.5" font-size="10" fill="#16232b" text-anchor="middle">2</text><text x="813" y="94.5" font-size="10" fill="#5b6b76" text-anchor="end">29.4%</text><text x="886.4" y="94.5" font-size="10" fill="#5b6b76" text-anchor="end">111</text><text x="952" y="94.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><rect x="4" y="101" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="44.6" y="114.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">ERBB3</text><rect x="58.6" y="104" width="49.3" height="14" fill="#0072B2"><title>ERBB3 — 5 patients altered before only</title></rect><text x="83.2" y="114.5" font-size="10" fill="#ffffff" text-anchor="middle">5</text><text x="813" y="114.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><text x="886.4" y="114.5" font-size="10" fill="#5b6b76" text-anchor="end">40</text><text x="952" y="114.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><text x="44.6" y="134.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">MTAP</text><rect x="58.6" y="124" width="49.3" height="14" fill="#0072B2"><title>MTAP — 5 patients altered before only</title></rect><text x="83.2" y="134.5" font-size="10" fill="#ffffff" text-anchor="middle">5</text><rect x="107.8" y="124" width="19.7" height="14" fill="#9aa5ad"><title>MTAP — 2 patients altered both</title></rect><text x="117.7" y="134.5" font-size="10" fill="#16232b" text-anchor="middle">2</text><text x="813" y="134.5" font-size="10" fill="#5b6b76" text-anchor="end">28.6%</text><text x="886.4" y="134.5" font-size="10" fill="#5b6b76" text-anchor="end">26</text><text x="952" y="134.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><rect x="4" y="141" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="44.6" y="154.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">TP53</text><rect x="58.6" y="144" width="98.5" height="14" fill="#0072B2"><title>TP53 — 10 patients altered before only</title></rect><text x="107.8" y="154.5" font-size="10" fill="#ffffff" text-anchor="middle">10</text><rect x="157.1" y="144" width="512.2" height="14" fill="#9aa5ad"><title>TP53 — 52 patients altered both</title></rect><text x="413.2" y="154.5" font-size="10" fill="#16232b" text-anchor="middle">52</text><rect x="669.3" y="144" width="29.6" height="14" fill="#E69F00"><title>TP53 — 3 patients altered after only</title></rect><text x="684.1" y="154.5" font-size="10" fill="#16232b" text-anchor="middle">3</text><text x="813" y="154.5" font-size="10" fill="#5b6b76" text-anchor="end">80.0%</text><text x="886.4" y="154.5" font-size="10" fill="#5b6b76" text-anchor="end">111</text><text x="952" y="154.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><text x="44.6" y="174.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">CDH1</text><rect x="58.6" y="164" width="39.4" height="14" fill="#0072B2"><title>CDH1 — 4 patients altered before only</title></rect><text x="78.3" y="174.5" font-size="10" fill="#ffffff" text-anchor="middle">4</text><rect x="98" y="164" width="19.7" height="14" fill="#9aa5ad"><title>CDH1 — 2 patients altered both</title></rect><text x="107.8" y="174.5" font-size="10" fill="#16232b" text-anchor="middle">2</text><text x="813" y="174.5" font-size="10" fill="#5b6b76" text-anchor="end">33.3%</text><text x="886.4" y="174.5" font-size="10" fill="#5b6b76" text-anchor="end">39</text><text x="952" y="174.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><rect x="4" y="181" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="44.6" y="194.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">FOXP1</text><rect x="58.6" y="184" width="39.4" height="14" fill="#E69F00"><title>FOXP1 — 4 patients altered after only</title></rect><text x="78.3" y="194.5" font-size="10" fill="#16232b" text-anchor="middle">4</text><text x="813" y="194.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><text x="886.4" y="194.5" font-size="10" fill="#5b6b76" text-anchor="end">41</text><text x="952" y="194.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><text x="44.6" y="214.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">FGFR1</text><rect x="58.6" y="204" width="59.1" height="14" fill="#0072B2"><title>FGFR1 — 6 patients altered before only</title></rect><text x="88.1" y="214.5" font-size="10" fill="#ffffff" text-anchor="middle">6</text><rect x="117.7" y="204" width="29.6" height="14" fill="#9aa5ad"><title>FGFR1 — 3 patients altered both</title></rect><text x="132.4" y="214.5" font-size="10" fill="#16232b" text-anchor="middle">3</text><rect x="147.2" y="204" width="9.9" height="14" fill="#E69F00"><title>FGFR1 — 1 patients altered after only</title></rect><text x="813" y="214.5" font-size="10" fill="#5b6b76" text-anchor="end">30.0%</text><text x="886.4" y="214.5" font-size="10" fill="#5b6b76" text-anchor="end">85</text><text x="952" y="214.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><rect x="4" y="221" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="44.6" y="234.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">RSPO2</text><rect x="58.6" y="224" width="59.1" height="14" fill="#0072B2"><title>RSPO2 — 6 patients altered before only</title></rect><text x="88.1" y="234.5" font-size="10" fill="#ffffff" text-anchor="middle">6</text><rect x="117.7" y="224" width="9.9" height="14" fill="#9aa5ad"><title>RSPO2 — 1 patients altered both</title></rect><rect x="127.5" y="224" width="9.9" height="14" fill="#E69F00"><title>RSPO2 — 1 patients altered after only</title></rect><text x="813" y="234.5" font-size="10" fill="#5b6b76" text-anchor="end">12.5%</text><text x="886.4" y="234.5" font-size="10" fill="#5b6b76" text-anchor="end">32</text><text x="952" y="234.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><text x="44.6" y="254.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">LMNA</text><rect x="58.6" y="244" width="49.3" height="14" fill="#0072B2"><title>LMNA — 5 patients altered before only</title></rect><text x="83.2" y="254.5" font-size="10" fill="#ffffff" text-anchor="middle">5</text><rect x="107.8" y="244" width="9.9" height="14" fill="#9aa5ad"><title>LMNA — 1 patients altered both</title></rect><rect x="117.7" y="244" width="9.9" height="14" fill="#E69F00"><title>LMNA — 1 patients altered after only</title></rect><text x="813" y="254.5" font-size="10" fill="#5b6b76" text-anchor="end">14.3%</text><text x="886.4" y="254.5" font-size="10" fill="#5b6b76" text-anchor="end">42</text><text x="952" y="254.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><rect x="4" y="261" width="952" height="20" fill="#dde4e8" opacity="0.3"/><text x="44.6" y="274.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">BAALC</text><rect x="58.6" y="264" width="78.8" height="14" fill="#0072B2"><title>BAALC — 8 patients altered before only</title></rect><text x="98" y="274.5" font-size="10" fill="#ffffff" text-anchor="middle">8</text><rect x="137.4" y="264" width="9.9" height="14" fill="#9aa5ad"><title>BAALC — 1 patients altered both</title></rect><rect x="147.2" y="264" width="29.6" height="14" fill="#E69F00"><title>BAALC — 3 patients altered after only</title></rect><text x="162" y="274.5" font-size="10" fill="#16232b" text-anchor="middle">3</text><text x="813" y="274.5" font-size="10" fill="#5b6b76" text-anchor="end">8.3%</text><text x="886.4" y="274.5" font-size="10" fill="#5b6b76" text-anchor="end">42</text><text x="952" y="274.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><text x="44.6" y="294.9" font-size="11" fill="#16232b" text-anchor="end" font-weight="600">UBR5</text><rect x="58.6" y="284" width="78.8" height="14" fill="#0072B2"><title>UBR5 — 8 patients altered before only</title></rect><text x="98" y="294.5" font-size="10" fill="#ffffff" text-anchor="middle">8</text><rect x="137.4" y="284" width="9.9" height="14" fill="#9aa5ad"><title>UBR5 — 1 patients altered both</title></rect><rect x="147.2" y="284" width="29.6" height="14" fill="#E69F00"><title>UBR5 — 3 patients altered after only</title></rect><text x="162" y="294.5" font-size="10" fill="#16232b" text-anchor="middle">3</text><text x="813" y="294.5" font-size="10" fill="#5b6b76" text-anchor="end">8.3%</text><text x="886.4" y="294.5" font-size="10" fill="#5b6b76" text-anchor="end">42</text><text x="952" y="294.5" font-size="10" fill="#5b6b76" text-anchor="end">1.00</text><line x1="58.6" y1="301" x2="58.6" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="55.6" y="317" font-size="10" fill="#5b6b76">0</text><line x1="157.1" y1="301" x2="157.1" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="151.1" y="317" font-size="10" fill="#5b6b76">10</text><line x1="255.6" y1="301" x2="255.6" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="249.6" y="317" font-size="10" fill="#5b6b76">20</text><line x1="354.1" y1="301" x2="354.1" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="348.1" y="317" font-size="10" fill="#5b6b76">30</text><line x1="452.6" y1="301" x2="452.6" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="446.6" y="317" font-size="10" fill="#5b6b76">40</text><line x1="551.1" y1="301" x2="551.1" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="545.1" y="317" font-size="10" fill="#5b6b76">50</text><line x1="649.6" y1="301" x2="649.6" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="643.6" y="317" font-size="10" fill="#5b6b76">60</text><line x1="748.1" y1="301" x2="748.1" y2="304" stroke="#5b6b76" stroke-width="1"/><text x="742.1" y="317" font-size="10" fill="#5b6b76">70</text><line x1="58.553" y1="301" x2="748.0899999999999" y2="301" stroke="#5b6b76" stroke-width="1"/><text x="403.3" y="336" font-size="11" fill="#16232b" text-anchor="middle">patients altered on either side</text><text x="8" y="358" font-size="10" fill="#5b6b76">Within patients with samples on both sides, before and after (egc_msk_2017: IMPACT_PRE_POST PRE-TX vs POST-TX; breast_alpelisib_2020:</text><text x="8" y="372" font-size="10" fill="#5b6b76">SAMPLE_COLLECTION_TIMEPOINT pre-treatment vs post-treatment; egc_trap_ccr_msk_2023: PRE_OR_POST_TREATMENT Pre vs Post; blca_cornell_2016:</text><text x="8" y="386" font-size="10" fill="#5b6b76">SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO pre-chemotherapy vs post-chemotherapy; brca_dldccc_2022: TREATMENT_STATUS Pre-Treatment vs</text><text x="8" y="400" font-size="10" fill="#5b6b76">Post-Treatment; before and after treatment: 111 patients), per gene, among those profiled for it on BOTH sides by each side's own panels: an OncoKB driver (v7.5) on</text><text x="8" y="414" font-size="10" fill="#5b6b76">both sides, on the before side only, or the after side only; a patient with several samples on a side counts as altered there if any is. Concordance is both ÷ altered</text><text x="8" y="428" font-size="10" fill="#5b6b76">anywhere. The exact McNemar test asks whether discordance runs one way; q is Benjamini–Hochberg across the genes of each cohort. Genes were not chosen in</text><text x="8" y="442" font-size="10" fill="#5b6b76">advance: every gene with a driver in some paired patient was tested (before and after treatment: 278 genes with a discordant patient), q is taken across all of them, and</text><text x="8" y="456" font-size="10" fill="#5b6b76">the 12 most one-sided are drawn — at these sizes, read the counts, not only the q. Sides are as each study labels its samples; after-only means seen on that side and not</text><text x="8" y="470" font-size="10" fill="#5b6b76">the other, which is not proof it arose or vanished in between — a subclone one biopsy missed, or a lower-purity sample (common after treatment) under-calling its side,</text><text x="8" y="484" font-size="10" fill="#5b6b76">reads the same; and patients sampled twice are a selected group, typically with longer or harder disease courses. Counts are summed across 5 studies.</text></svg>
diff --git a/test/snapshots/pooled-paired.txt b/test/snapshots/pooled-paired.txt
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+Analysis over egc_msk_2017 + breast_alpelisib_2020 + egc_trap_ccr_msk_2023 + blca_cornell_2016 + brca_dldccc_2022 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- before and after treatment: 481 patients — all patients in the study — across 5 studies
+
+Paired samples, before vs after (egc_msk_2017: IMPACT_PRE_POST PRE-TX vs POST-TX; breast_alpelisib_2020: SAMPLE_COLLECTION_TIMEPOINT pre-treatment vs post-treatment; egc_trap_ccr_msk_2023: PRE_OR_POST_TREATMENT Pre vs Post; blca_cornell_2016: SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO pre-chemotherapy vs post-chemotherapy; brca_dldccc_2022: TREATMENT_STATUS Pre-Treatment vs Post-Treatment; drivers; profiled on both sides):
+  before and after treatment (111 patients with both; scanned 278 genes with a discordant patient, the 12 most one-sided listed):
+    CCND1: both 8, before-only 10, after-only 1 of 95 pairs, 42% concordant, McNemar q=1.0000
+    MYC: both 5, before-only 10, after-only 2 of 111 pairs, 29% concordant, McNemar q=1.0000
+    ERBB3: both 0, before-only 5, after-only 0 of 40 pairs, 0% concordant, McNemar q=1.0000
+    MTAP: both 2, before-only 5, after-only 0 of 26 pairs, 29% concordant, McNemar q=1.0000
+    TP53: both 52, before-only 10, after-only 3 of 111 pairs, 80% concordant, McNemar q=1.0000
+    CDH1: both 2, before-only 4, after-only 0 of 39 pairs, 33% concordant, McNemar q=1.0000
+    FOXP1: both 0, before-only 0, after-only 4 of 41 pairs, 0% concordant, McNemar q=1.0000
+    FGFR1: both 3, before-only 6, after-only 1 of 85 pairs, 30% concordant, McNemar q=1.0000
+    RSPO2: both 1, before-only 6, after-only 1 of 32 pairs, 13% concordant, McNemar q=1.0000
+    LMNA: both 1, before-only 5, after-only 1 of 42 pairs, 14% concordant, McNemar q=1.0000
+    BAALC: both 1, before-only 8, after-only 3 of 42 pairs, 8% concordant, McNemar q=1.0000
+    UBR5: both 1, before-only 8, after-only 3 of 42 pairs, 8% concordant, McNemar q=1.0000
+  CAVEAT (tell the user): Within patients with samples on both sides, before and after (egc_msk_2017: IMPACT_PRE_POST PRE-TX vs POST-TX; breast_alpelisib_2020: SAMPLE_COLLECTION_TIMEPOINT pre-treatment vs post-treatment; egc_trap_ccr_msk_2023: PRE_OR_POST_TREATMENT Pre vs Post; blca_cornell_2016: SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO pre-chemotherapy vs post-chemotherapy; brca_dldccc_2022: TREATMENT_STATUS Pre-Treatment vs Post-Treatment; before and after treatment: 111 patients), per gene, among those profiled for it on BOTH sides by each side's own panels: an OncoKB driver (v7.5) on both sides, on the before side only, or the after side only; a patient with several samples on a side counts as altered there if any is. Concordance is both ÷ altered anywhere. The exact McNemar test asks whether discordance runs one way; q is Benjamini–Hochberg across the genes of each cohort. Genes were not chosen in advance: every gene with a driver in some paired patient was tested (before and after treatment: 278 genes with a discordant patient), q is taken across all of them, and the 12 most one-sided are drawn — at these sizes, read the counts, not only the q. Sides are as each study labels its samples; after-only means seen on that side and not the other, which is not proof it arose or vanished in between — a subclone one biopsy missed, or a lower-purity sample (common after treatment) under-calling its side, reads the same; and patients sampled twice are a selected group, typically with longer or harder disease courses. Counts are summed across 5 studies.
+  METHOD:
+    - Count each gene's state in paired samples [before and after treatment · blca_cornell_2016]: The paired:patients and paired:genes queries above. Patients with both a SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO = pre-chemotherapy sample (before) and a SPECIMEN_COLLECTION_PRE_OR_POST_CHEMO = post-chemotherapy sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.
+    - Count each gene's state in paired samples [before and after treatment · brca_dldccc_2022]: The paired:patients and paired:genes queries above. Patients with both a TREATMENT_STATUS = Pre-Treatment sample (before) and a TREATMENT_STATUS = Post-Treatment sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.
+    - Count each gene's state in paired samples [before and after treatment · breast_alpelisib_2020]: The paired:patients and paired:genes queries above. Patients with both a SAMPLE_COLLECTION_TIMEPOINT = pre-treatment sample (before) and a SAMPLE_COLLECTION_TIMEPOINT = post-treatment sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.
+    - Count each gene's state in paired samples [before and after treatment · egc_msk_2017]: The paired:patients and paired:genes queries above. Patients with both a IMPACT_PRE_POST = PRE-TX sample (before) and a IMPACT_PRE_POST = POST-TX sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.
+    - Count each gene's state in paired samples [before and after treatment · egc_trap_ccr_msk_2023]: The paired:patients and paired:genes queries above. Patients with both a PRE_OR_POST_TREATMENT = Pre sample (before) and a PRE_OR_POST_TREATMENT = Post sample (after); per gene — every gene altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) in some such patient, among those profiled for it on both sides by each side's own panels: altered (an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5)) on both sides, on the before side only, or the after side only. A patient with several samples on one side counts as altered there if any is. Counts only.
+    - Add each cohort's counts across its studies: The counts add exactly.
+    - Exact McNemar test per gene: Only the discordant patients carry information about direction: of the before-only and after-only patients, each is a fair coin under the null, so p is twice the smaller binomial tail (capped at 1). Concordance is both ÷ altered anywhere.
+        before and after treatment: 111 patients with both kinds of sample
+    - Benjamini–Hochberg across genes, per cohort: q-values over the genes with at least one discordant patient, separately for each cohort. In a scan that is every such gene, not only the ones drawn.
+    - Draw the 12 most one-sided genes: Of the genes with a discordant patient, ordered by McNemar p, then by the size of the imbalance between before-only and after-only patients, then by the number of discordant patients, then by name; the first 12 are drawn and reported.
+        before and after treatment: 278 genes tested
+    The exact McNemar test matches statsmodels 0.15.0 mcnemar(exact=True) and Benjamini–Hochberg multipletests, to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/riskmodel.json b/test/snapshots/riskmodel.json
new file mode 100644
index 00000000..52279e3b
--- /dev/null
+++ b/test/snapshots/riskmodel.json
@@ -0,0 +1,1242 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
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+      "count": 7809,
+      "text": "CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
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+      "kind": "riskmodel",
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+        "NSCLC"
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+                },
+                {
+                  "value": "Never",
+                  "counts": [
+                    919,
+                    588,
+                    261
+                  ]
+                },
+                {
+                  "value": "Unknown",
+                  "counts": [
+                    93,
+                    83,
+                    131
+                  ]
+                }
+              ],
+              "missing": [
+                0,
+                0,
+                0
+              ],
+              "multiValued": 0,
+              "minExpected": 98.5,
+              "test": "chi-squared",
+              "statistic": 466.4278,
+              "df": 4,
+              "p": 1.2192483930603814e-99,
+              "q": 6.096241965301907e-99
+            },
+            {
+              "kind": "num",
+              "name": "CURRENT_AGE_DEID",
+              "per": "patient",
+              "role": null,
+              "perGroup": [
+                {
+                  "n": 2336,
+                  "median": 72,
+                  "q1": 64,
+                  "q3": 79,
+                  "missing": 1
+                },
+                {
+                  "n": 2175,
+                  "median": 71,
+                  "q1": 63,
+                  "q3": 77,
+                  "missing": 0
+                },
+                {
+                  "n": 2267,
+                  "median": 70,
+                  "q1": 63,
+                  "q3": 77,
+                  "missing": 0
+                }
+              ],
+              "multiValued": 0,
+              "test": "Kruskal-Wallis",
+              "statistic": 30.7119,
+              "df": 2,
+              "p": 2.1428781728255618e-7,
+              "q": 2.1428781728255618e-7
+            },
+            {
+              "kind": "gene",
+              "name": "KEAP1",
+              "role": "in the model",
+              "perGroup": [
+                {
+                  "altered": 1,
+                  "profiled": 2337,
+                  "notProfiled": 0
+                },
+                {
+                  "altered": 66,
+                  "profiled": 2175,
+                  "notProfiled": 0
+                },
+                {
+                  "altered": 827,
+                  "profiled": 2267,
+                  "notProfiled": 0
+                }
+              ],
+              "minExpected": 286.83,
+              "test": "chi-squared",
+              "statistic": 1622.8469,
+              "df": 2,
+              "p": 0,
+              "q": null
+            },
+            {
+              "kind": "gene",
+              "name": "TP53",
+              "role": "in the model",
+              "perGroup": [
+                {
+                  "altered": 14,
+                  "profiled": 2337,
+                  "notProfiled": 0
+                },
+                {
+                  "altered": 1615,
+                  "profiled": 2175,
+                  "notProfiled": 0
+                },
+                {
+                  "altered": 1851,
+                  "profiled": 2267,
+                  "notProfiled": 0
+                }
+              ],
+              "minExpected": 1058.46,
+              "test": "chi-squared",
+              "statistic": 3699.2236,
+              "df": 2,
+              "p": 0,
+              "q": null
+            },
+            {
+              "kind": "gene",
+              "name": "ZFHX3",
+              "role": null,
+              "perGroup": [
+                {
+                  "altered": 57,
+                  "profiled": 2249,
+                  "notProfiled": 88
+                },
+                {
+                  "altered": 108,
+                  "profiled": 2091,
+                  "notProfiled": 84
+                },
+                {
+                  "altered": 144,
+                  "profiled": 2162,
+                  "notProfiled": 105
+                }
+              ],
+              "minExpected": 99.37,
+              "test": "chi-squared",
+              "statistic": 42.6173,
+              "df": 2,
+              "p": 5.568821598887468e-10,
+              "q": 6.961026998609335e-10
+            }
+          ],
+          "omitted": [],
+          "tested": 5
+        }
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": true,
+        "aggregateOnly": []
+      },
+      "caveat": "Association, not cause: a gene's weight is its link to OS with the other candidates held fixed, in this cohort. Without strata, a gene that marks a cancer type carries that type's prognosis; on a cohort of several cancer types, stratify by CANCER_TYPE. Each patient is scored only by models that did not train on them. The group cuts are equal quantiles of the score, chosen without the outcome. Genes outside the panels every patient was sequenced on are not candidates, so a patient is never counted as wild-type for a gene they were not tested for. Table 1 describes the patients the curves draw; its rows marked “in the model” (the model's genes and covariates) and “outcome” differ between the groups by construction, so they get no q-value and are left out of the correction. The C-index ignores delayed entry. Seeded (seed 1): the same document gives the same model.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "One row per patient",
+            "scope": "NSCLC · msk_chord_2024",
+            "rowsIn": 7809,
+            "rowsOut": 7809,
+            "detail": "The riskmodel:rows query above: each patient's survival time and event, the panels their samples were sequenced on for mutations, the genes mutated in them, and their half of the engine's fixed held-out split. Patient-level: the rows never leave this server's engine, and no identifier is returned."
+          },
+          {
+            "kind": "transform",
+            "title": "Patients, candidate genes and covariates",
+            "detail": "A gene is a candidate only if every patient's panels cover it, so no patient is counted as wild-type for a gene they were never tested for, and only if at least 30 patients have it mutated.",
+            "params": [
+              [
+                "genes covered for every patient",
+                "341"
+              ],
+              [
+                "candidates",
+                "302"
+              ],
+              [
+                "left out: no mutation panel on record",
+                "0"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Elastic-net Cox, 5 random splits",
+            "detail": "Each run holds out a random 33% of the patients; on the rest, the penalty is chosen from a path of 25 by 3-fold cross-validated partial likelihood (Verweij & van Houwelingen), and the model at that penalty scores the held-out patients. Genes are scaled to unit variance before fitting; Breslow ties. A patient's risk score is the mean of the scores they got while held out (OncoCast's ensemble, Martin et al.).",
+            "params": [
+              [
+                "l1 share of the penalty (alpha)",
+                "0.5"
+              ],
+              [
+                "runs",
+                "5"
+              ],
+              [
+                "seed",
+                "1"
+              ],
+              [
+                "test-set C-index, median (IQR)",
+                "0.617 (0.615–0.618)"
+              ],
+              [
+                "never held out, so unscored",
+                "1030"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Risk groups, their survival and hazard ratios",
+            "detail": "Cuts at equal quantiles of the held-out scores, each moved to the nearer edge of any run of tied scores so equal scores share a group — no outcome is used to place them. Each group is drawn as a Kaplan–Meier curve and read at 12 and 36 months; the groups are compared with a log-rank test, and a Cox model of the outcome on the group indicators gives each group's hazard ratio against the lowest (Newton–Raphson, full information matrix, 95% Wald intervals). A second Cox model on the standardised score alone gives the hazard ratio per standard deviation. The score is also shown on OncoCast's 0–10 scale, (score − min) / (max − min) × 10, min and max the lowest and highest held-out score; the scale is linear, so it moves no patient between groups and changes no number above.",
+            "params": [
+              [
+                "0 on the 0–10 scale (raw score)",
+                "-0.4669"
+              ],
+              [
+                "10 on the 0–10 scale (raw score)",
+                "1.2761"
+              ],
+              [
+                "Low risk",
+                "2,337 patients, 888 events, median 68.2 months"
+              ],
+              [
+                "Intermediate risk",
+                "2,175 patients, 1,150 events, median 37.0 months, HR 1.58"
+              ],
+              [
+                "High risk",
+                "2,267 patients, 1,431 events, median 20.3 months, HR 2.26"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Table 1: the groups' patients again, with their clinical values",
+            "detail": "The riskmodel:table1 query above is the riskmodel:rows query with Table 1's attributes added: for each, the patient's value (a sample-level attribute: their first sample's), case and whitespace folded as everywhere in this app and shown in its commonest spelling, and how many values their samples hold. Its rows are kept exactly as the model's were and must match the model's patients one by one — survival time, entry and event — or the view is refused, so each group's column describes the patients its curve drew. riskmodel:table1-attributes gives each attribute's type (NUMBER is numeric), riskmodel:panels each mutation panel's genes. Patient-level: the rows never leave this server's engine; only the counts below do.",
+            "params": [
+              [
+                "Low risk",
+                "2,337 patients"
+              ],
+              [
+                "Intermediate risk",
+                "2,175 patients"
+              ],
+              [
+                "High risk",
+                "2,267 patients"
+              ]
+            ]
+          },
+          {
+            "kind": "transform",
+            "title": "Table 1's rows",
+            "detail": "Attributes as the view names them; a categorical one with more than 100 values among these patients is set aside. Genes as the view names them. A gene is counted as mutated the way the model counts it (any mutation), and only among the patients whose mutation panels cover it (a whole-exome patient covers every gene): a patient whose panels do not is not profiled for it, never unaltered, and is counted as such in each group. A row the groups differ in by construction is marked: a gene the model kept in any run and a covariate are \"in the model\" (the score is built from them), the endpoint's own columns are \"outcome\" (the groups are cut to differ in survival); the model's stratum is marked \"stratum\" but is a description, since the score has no stratum term.",
+            "params": []
+          },
+          {
+            "kind": "statistic",
+            "title": "Each row tested across the groups",
+            "detail": "A categorical attribute: Pearson χ² on the group × value table, no continuity correction, an all-zero row or column dropped first, patients with no value left out. A gene: Pearson χ² on the group × (mutated, not mutated) table of its profiled patients. A numeric attribute: Kruskal–Wallis on each group's values, ranks averaged within ties and tie-corrected; medians and quartiles interpolating linearly between ranks. The smallest expected count is reported, since χ² is poor below about 5.",
+            "params": [
+              [
+                "STAGE_HIGHEST_RECORDED",
+                "χ² 56.918 on 4 df · p 1.29e-11"
+              ],
+              [
+                "GENDER",
+                "χ² 167.47 on 4 df · p 3.65e-35"
+              ],
+              [
+                "SMOKING_PREDICTIONS_3_CLASSES",
+                "χ² 466.4278 on 4 df · p 1.22e-99"
+              ],
+              [
+                "CURRENT_AGE_DEID",
+                "H 30.7119 on 2 df · p 2.14e-7"
+              ],
+              [
+                "gene:KEAP1",
+                "χ² 1622.8469 on 2 df · p <1e-300 · in the model"
+              ],
+              [
+                "gene:TP53",
+                "χ² 3699.2236 on 2 df · p <1e-300 · in the model"
+              ],
+              [
+                "gene:ZFHX3",
+                "χ² 42.6173 on 2 df · p 5.57e-10"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across Table 1's open rows",
+            "detail": "q-values over the 5 tested rows that are not in the model and not its outcome. Those rows differ between the groups by construction, so they get a p-value but no q-value and do not enter the correction, where their p-values would shrink every other row's q."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "coxnetEnsemble",
+            "coxnetPath",
+            "cvLambda",
+            "concordance",
+            "coxFit",
+            "buildTable1",
+            "chiSquareTest",
+            "kruskalWallis",
+            "quantilesFromHistogram",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/coxnet.ts, lib/views/riskmodel-table1.ts, lib/stats.ts",
+          "commit": null
+        },
+        "verification": "lib/coxnet.ts's path is checked against scikit-survival's CoxnetSurvivalAnalysis, its delayed-entry, stratified fits (with standard errors) against statsmodels' PHReg and its C-index against lifelines, by test/coxnet-golden.test.ts (fixtures from python/golden/coxnet.py). The script below recomputes Table 1's tests and q-values from its counts (the model itself it does not refit); χ² matches scipy 1.18.1 chi2_contingency, Kruskal–Wallis and quantiles scipy/numpy, Benjamini–Hochberg statsmodels multipletests (test/tables-golden.test.ts, test/moments-golden.test.ts).",
+        "check": {
+          "table1:attribute:STAGE_HIGHEST_RECORDED:p": {
+            "v": 1.2871847901160984e-11
+          },
+          "table1:attribute:STAGE_HIGHEST_RECORDED:q": {
+            "v": 2.1453079835268308e-11
+          },
+          "table1:attribute:GENDER:p": {
+            "v": 3.651083887973751e-35
+          },
+          "table1:attribute:GENDER:q": {
+            "v": 9.127709719934377e-35
+          },
+          "table1:attribute:SMOKING_PREDICTIONS_3_CLASSES:p": {
+            "v": 1.2192483930603814e-99
+          },
+          "table1:attribute:SMOKING_PREDICTIONS_3_CLASSES:q": {
+            "v": 6.096241965301907e-99
+          },
+          "table1:attribute:CURRENT_AGE_DEID:p": {
+            "v": 2.1428781728255618e-7
+          },
+          "table1:attribute:CURRENT_AGE_DEID:q": {
+            "v": 2.1428781728255618e-7
+          },
+          "table1:gene:KEAP1:p": {
+            "v": 0
+          },
+          "table1:gene:TP53:p": {
+            "v": 0
+          },
+          "table1:gene:ZFHX3:p": {
+            "v": 5.568821598887468e-10
+          },
+          "table1:gene:ZFHX3:q": {
+            "v": 6.961026998609335e-10
+          },
+          "table1:attribute:CURRENT_AGE_DEID:Low risk:median": {
+            "v": 72,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:Low risk:q1": {
+            "v": 64,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:Low risk:q3": {
+            "v": 79,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:Intermediate risk:median": {
+            "v": 71,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:Intermediate risk:q1": {
+            "v": 63,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:Intermediate risk:q3": {
+            "v": 77,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:High risk:median": {
+            "v": 70,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:High risk:q1": {
+            "v": 63,
+            "abs": 0.000500001
+          },
+          "table1:attribute:CURRENT_AGE_DEID:High risk:q3": {
+            "v": 77,
+            "abs": 0.000500001
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/riskmodel.sql b/test/snapshots/riskmodel.sql
new file mode 100644
index 00000000..d6954549
--- /dev/null
+++ b/test/snapshots/riskmodel.sql
@@ -0,0 +1,115 @@
+-- cohort NSCLC
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))
+
+-- view 0 riskmodel: riskmodel:panels · msk_chord_2024
+SELECT toString(pg.gene_panel_id) AS panel, groupUniqArray(pg.gene) AS genes
+FROM snapshot_db.gene_panel_to_gene_derived pg
+WHERE pg.gene_panel_id IN (SELECT DISTINCT gene_panel_id FROM snapshot_db.sample_to_gene_panel_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND alteration_type = 'MUTATION_EXTENDED')
+GROUP BY panel
+
+-- view 0 riskmodel: riskmodel:panels · msk_chord_2024
+SELECT toString(pg.gene_panel_id) AS panel, groupUniqArray(pg.gene) AS genes
+FROM snapshot_db.gene_panel_to_gene_derived pg
+WHERE pg.gene_panel_id IN (SELECT DISTINCT gene_panel_id FROM snapshot_db.sample_to_gene_panel_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND alteration_type = 'MUTATION_EXTENDED')
+GROUP BY panel
+
+-- view 0 riskmodel: riskmodel:rows · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), NULL) AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status,
+         cityHash64(concat('cohort-lab-split-v1:msk_chord_2024:', replaceOne(patient_unique_id, 'msk_chord_2024_', ''))) % 2 AS half
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+panels AS (
+  SELECT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid, groupUniqArray(toString(sp.gene_panel_id)) AS panels
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+mut AS (
+  SELECT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid, groupUniqArray(g.hugo_gene_symbol) AS genes
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024' AND g.variant_type = 'mutation' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT os.months AS m, if(os.status LIKE '1%', 1, 0) AS ev, os.half AS half, panels.panels AS panels, mut.genes AS genes
+FROM os
+  LEFT JOIN panels ON panels.pid = os.pid
+  LEFT JOIN mut ON mut.pid = os.pid
+WHERE os.months IS NOT NULL AND os.months > 0
+ORDER BY os.pid
+
+-- view 0 riskmodel: riskmodel:table1 · msk_chord_2024
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Non-Small Cell Lung Cancer'))))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name='OS_MONTHS')), NULL) AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status,
+         cityHash64(concat('cohort-lab-split-v1:msk_chord_2024:', replaceOne(patient_unique_id, 'msk_chord_2024_', ''))) % 2 AS half
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+panels AS (
+  SELECT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid, groupUniqArray(toString(sp.gene_panel_id)) AS panels
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+mut AS (
+  SELECT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid, groupUniqArray(g.hugo_gene_symbol) AS genes
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024' AND g.variant_type = 'mutation' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+descr AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         argMinIf(lowerUTF8(trimBoth(attribute_value)), sample_unique_id, attribute_name = 'STAGE_HIGHEST_RECORDED') AS d0, argMinIf(attribute_value, sample_unique_id, attribute_name = 'STAGE_HIGHEST_RECORDED') AS r0, uniqExactIf(lowerUTF8(trimBoth(attribute_value)), attribute_name = 'STAGE_HIGHEST_RECORDED') AS u0,
+         argMinIf(lowerUTF8(trimBoth(attribute_value)), sample_unique_id, attribute_name = 'GENDER') AS d1, argMinIf(attribute_value, sample_unique_id, attribute_name = 'GENDER') AS r1, uniqExactIf(lowerUTF8(trimBoth(attribute_value)), attribute_name = 'GENDER') AS u1,
+         argMinIf(lowerUTF8(trimBoth(attribute_value)), sample_unique_id, attribute_name = 'SMOKING_PREDICTIONS_3_CLASSES') AS d2, argMinIf(attribute_value, sample_unique_id, attribute_name = 'SMOKING_PREDICTIONS_3_CLASSES') AS r2, uniqExactIf(lowerUTF8(trimBoth(attribute_value)), attribute_name = 'SMOKING_PREDICTIONS_3_CLASSES') AS u2,
+         argMinIf(lowerUTF8(trimBoth(attribute_value)), sample_unique_id, attribute_name = 'CURRENT_AGE_DEID') AS d3, argMinIf(attribute_value, sample_unique_id, attribute_name = 'CURRENT_AGE_DEID') AS r3, uniqExactIf(lowerUTF8(trimBoth(attribute_value)), attribute_name = 'CURRENT_AGE_DEID') AS u3
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name IN ('STAGE_HIGHEST_RECORDED','GENDER','SMOKING_PREDICTIONS_3_CLASSES','CURRENT_AGE_DEID') AND trimBoth(attribute_value) != ''
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT os.months AS m, if(os.status LIKE '1%', 1, 0) AS ev, os.half AS half, panels.panels AS panels, mut.genes AS genes, descr.d0 AS d0, descr.r0 AS r0, descr.u0 AS u0, descr.d1 AS d1, descr.r1 AS r1, descr.u1 AS u1, descr.d2 AS d2, descr.r2 AS r2, descr.u2 AS u2, descr.d3 AS d3, descr.r3 AS r3, descr.u3 AS u3
+FROM os
+  LEFT JOIN panels ON panels.pid = os.pid
+  LEFT JOIN mut ON mut.pid = os.pid
+  LEFT JOIN descr ON descr.pid = os.pid
+WHERE os.months IS NOT NULL AND os.months > 0
+ORDER BY os.pid
+
+-- view 0 riskmodel: riskmodel:table1-attributes · msk_chord_2024
+SELECT m.attr_id AS id, m.display_name AS name, m.datatype AS datatype, m.patient_attribute AS patient, toInt32OrZero(m.priority) AS priority
+FROM snapshot_db.clinical_attribute_meta m
+JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = m.cancer_study_id
+WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+ORDER BY priority DESC, name
diff --git a/test/snapshots/riskmodel.svg b/test/snapshots/riskmodel.svg
new file mode 100644
index 00000000..27809a5d
--- /dev/null
+++ b/test/snapshots/riskmodel.svg
@@ -0,0 +1,2 @@
+<!-- view 0 riskmodel -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 1578" width="100%" role="img" aria-label="Risk model — Low risk, Intermediate risk, High risk" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Risk model — Low risk, Intermediate risk, High risk</title><rect width="720" height="1578" fill="#ffffff"/><line x1="63" y1="242" x2="704" y2="242" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="245.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="63" y1="184.5" x2="704" y2="184.5" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="188" font-size="10" fill="#5b6b76" text-anchor="end">25%</text><line x1="63" y1="127" x2="704" y2="127" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="130.5" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><line x1="63" y1="69.5" x2="704" y2="69.5" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="73" font-size="10" fill="#5b6b76" text-anchor="end">75%</text><line x1="63" y1="12" x2="704" y2="12" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="15.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><text x="16.8" y="127" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 16.8 127)">Survival probability</text><line x1="63" y1="242" x2="63" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="60" y="258" font-size="10" fill="#5b6b76">0</text><line x1="201" y1="242" x2="201" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="195" y="258" font-size="10" fill="#5b6b76">24</text><line x1="338.9" y1="242" x2="338.9" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="332.9" y="258" font-size="10" fill="#5b6b76">48</text><line x1="476.9" y1="242" x2="476.9" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="470.9" y="258" font-size="10" fill="#5b6b76">72</text><line x1="614.8" y1="242" x2="614.8" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="608.8" y="258" font-size="10" 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fill="none" stroke="#009E73" stroke-width="2" stroke-linejoin="round"/><text x="32" y="299" font-size="10" fill="#5b6b76">group</text><text x="135.7" y="299" font-size="10" fill="#5b6b76">score (0–10)</text><text x="211.3" y="299" font-size="10" fill="#5b6b76">patients · events</text><text x="303.9" y="299" font-size="10" fill="#5b6b76">median</text><text x="363.1" y="299" font-size="10" fill="#5b6b76">12 mo</text><text x="409.7" y="299" font-size="10" fill="#5b6b76">36 mo</text><text x="456.4" y="299" font-size="10" fill="#5b6b76">HR vs lowest [95% CI]</text><rect x="4" y="303" width="640.8" height="20" fill="#dde4e8" opacity="0.35"/><line x1="8" y1="313" x2="26" y2="313" stroke="#0072B2" stroke-width="2"/><text x="32" y="316.9" font-size="11" fill="#16232b">Low risk</text><text x="135.7" y="316.9" font-size="11" fill="#16232b">0–2.7</text><text x="211.3" y="316.9" font-size="11" fill="#16232b">2,337 · 888</text><text x="303.9" y="316.9" font-size="11" fill="#16232b">68.2 mo</text><text x="363.1" y="316.9" font-size="11" fill="#16232b">86%</text><text x="409.7" y="316.9" font-size="11" fill="#16232b">66%</text><text x="456.4" y="316.9" font-size="11" fill="#16232b">reference</text><line x1="8" y1="333" x2="26" y2="333" stroke="#E69F00" stroke-width="2"/><text x="32" y="336.9" font-size="11" fill="#16232b">Intermediate risk</text><text x="135.7" y="336.9" font-size="11" fill="#16232b">2.7–4.4</text><text x="211.3" y="336.9" font-size="11" fill="#16232b">2,175 · 1,150</text><text x="303.9" y="336.9" font-size="11" fill="#16232b">37 mo</text><text x="363.1" y="336.9" font-size="11" fill="#16232b">76%</text><text x="409.7" y="336.9" font-size="11" fill="#16232b">51%</text><text x="456.4" y="336.9" font-size="11" fill="#16232b">1.58 [1.44–1.72]</text><text x="574.8" y="336.9" font-size="11" fill="#16232b">p = 2.7e-24</text><rect x="4" y="343" width="640.8" height="20" fill="#dde4e8" opacity="0.35"/><line x1="8" y1="353" x2="26" y2="353" stroke="#009E73" stroke-width="2"/><text x="32" y="356.9" font-size="11" fill="#16232b">High risk</text><text x="135.7" y="356.9" font-size="11" fill="#16232b">4.4–10</text><text x="211.3" y="356.9" font-size="11" fill="#16232b">2,267 · 1,431</text><text x="303.9" y="356.9" font-size="11" fill="#16232b">20.3 mo</text><text x="363.1" y="356.9" font-size="11" fill="#16232b">62%</text><text x="409.7" y="356.9" font-size="11" fill="#16232b">39%</text><text x="456.4" y="356.9" font-size="11" fill="#16232b">2.26 [2.08–2.46]</text><text x="574.8" y="356.9" font-size="11" fill="#16232b">p = 8.1e-81</text><text x="8" y="378" font-size="11" fill="#16232b">log-rank χ² = 380 (2 df) · p = 3.5e-83</text><text x="8" y="398" font-size="11" fill="#16232b">risk score alone: HR 1.39 per standard deviation [95% CI 1.35–1.44] · p = 4.7e-93</text><text x="8" y="418" font-size="11" fill="#16232b">held-out C-index 0.617 (IQR 0.615–0.618) over 5 runs</text><text x="8" y="443" font-size="12" fill="#16232b" font-weight="600">Risk score, 0–10 (held-out scores), with the group cuts</text><line x1="62" y1="529" x2="704" y2="529" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="532.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="62" y1="497" x2="704" y2="497" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="500.5" font-size="10" fill="#5b6b76" text-anchor="end">1,000</text><line x1="62" y1="465" x2="704" y2="465" stroke="#dde4e8" stroke-width="0.75"/><text x="56" y="468.5" font-size="10" fill="#5b6b76" text-anchor="end">2,000</text><text x="16.8" y="497" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 16.8 497)">patients</text><path d="M62.5 529V529H82.9V529ZM83.9 529V528.8H104.3V529ZM105.3 529V528.9H125.7V529ZM126.7 529V528.5H147.1V529ZM148.1 529V527.6H168.5V529ZM169.5 529V523.7H189.9V529ZM190.9 529V510.3H211.3V529ZM212.3 529V517.7H232.7V529ZM233.7 529V485.7H254.1V529ZM255.1 529V525.1H275.5V529ZM276.5 529V522.2H296.9V529ZM297.9 529V508.1H318.3V529ZM319.3 529V504.1H339.7V529ZM340.7 529V499.5H361.1V529ZM362.1 529V521.5H382.5V529ZM383.5 529V521.7H403.9V529ZM404.9 529V519.4H425.3V529ZM426.3 529V521.8H446.7V529ZM447.7 529V523.1H468.1V529ZM469.1 529V525.7H489.5V529ZM490.5 529V526.5H510.9V529ZM511.9 529V526.1H532.3V529ZM533.3 529V527.1H553.7V529ZM554.7 529V528.2H575.1V529ZM576.1 529V528.6H596.5V529ZM597.5 529V528.8H617.9V529ZM618.9 529V528.6H639.3V529ZM640.3 529V529H660.7V529ZM661.7 529V528.9H682.1V529ZM683.1 529V528.9H703.5V529Z" fill="#5b6b76" opacity="0.55"/><line x1="234" y1="463" x2="234" y2="529" stroke="#16232b" stroke-width="1" stroke-dasharray="3 2"/><text x="234" y="461" font-size="10" fill="#16232b" text-anchor="middle">2.7</text><line x1="345.1" y1="463" x2="345.1" y2="529" stroke="#16232b" stroke-width="1" stroke-dasharray="3 2"/><text x="345.1" y="461" font-size="10" fill="#16232b" text-anchor="middle">4.4</text><line x1="62" y1="529" x2="62" y2="532" stroke="#5b6b76" stroke-width="1"/><text x="59" y="545" font-size="10" fill="#5b6b76">0</text><line x1="190.4" y1="529" x2="190.4" y2="532" stroke="#5b6b76" stroke-width="1"/><text x="187.4" y="545" font-size="10" fill="#5b6b76">2</text><line x1="318.8" y1="529" x2="318.8" y2="532" stroke="#5b6b76" stroke-width="1"/><text x="315.8" y="545" font-size="10" fill="#5b6b76">4</text><line x1="447.2" y1="529" x2="447.2" y2="532" stroke="#5b6b76" stroke-width="1"/><text x="444.2" y="545" font-size="10" fill="#5b6b76">6</text><line x1="575.6" y1="529" x2="575.6" y2="532" stroke="#5b6b76" stroke-width="1"/><text x="572.6" y="545" font-size="10" fill="#5b6b76">8</text><line x1="704" y1="529" x2="704" y2="532" stroke="#5b6b76" stroke-width="1"/><text x="698" y="545" font-size="10" fill="#5b6b76">10</text><line x1="62" y1="529" x2="704" y2="529" stroke="#5b6b76" stroke-width="1"/><text x="383" y="564" font-size="11" fill="#16232b" text-anchor="middle">risk score, 0–10 — lower risk ← → higher risk</text><text x="8" y="584" font-size="12" fill="#16232b" font-weight="600">Selection frequency against mean hazard ratio (labelled: kept in ≥ 50% of runs)</text><line x1="63" y1="932" x2="708" y2="932" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="935.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="63" y1="856.3" x2="708" y2="856.3" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="859.8" font-size="10" fill="#5b6b76" text-anchor="end">25%</text><line x1="63" y1="780.5" x2="708" y2="780.5" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="784" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><line x1="63" y1="704.8" x2="708" y2="704.8" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="708.3" font-size="10" fill="#5b6b76" text-anchor="end">75%</text><line x1="63" y1="629" x2="708" y2="629" stroke="#dde4e8" stroke-width="0.75"/><text x="57" y="632.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><text x="16.8" y="780.5" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 16.8 780.5)">share of runs kept</text><line x1="245.4" y1="598" x2="245.4" y2="938" stroke="#dde4e8" stroke-width="0.75"/><line x1="245.4" y1="938" x2="245.4" y2="941" stroke="#5b6b76" stroke-width="1"/><text x="238" y="954" font-size="10" fill="#5b6b76">0.8</text><line x1="385.5" y1="598" x2="385.5" y2="938" stroke="#dde4e8" stroke-width="0.75"/><line x1="385.5" y1="938" x2="385.5" y2="941" stroke="#5b6b76" stroke-width="1"/><text x="382.5" y="954" font-size="10" fill="#5b6b76">1</text><line x1="500" y1="598" x2="500" y2="938" stroke="#dde4e8" stroke-width="0.75"/><line x1="500" y1="938" x2="500" y2="941" stroke="#5b6b76" stroke-width="1"/><text x="492.6" y="954" font-size="10" fill="#5b6b76">1.2</text><line x1="596.8" y1="598" x2="596.8" y2="938" stroke="#dde4e8" stroke-width="0.75"/><line x1="596.8" y1="938" x2="596.8" y2="941" stroke="#5b6b76" stroke-width="1"/><text x="589.5" y="954" font-size="10" fill="#5b6b76">1.4</text><line x1="680.7" y1="598" x2="680.7" y2="938" stroke="#dde4e8" stroke-width="0.75"/><line x1="680.7" y1="938" x2="680.7" y2="941" stroke="#5b6b76" stroke-width="1"/><text x="673.3" y="954" font-size="10" fill="#5b6b76">1.6</text><line x1="71" y1="938" x2="700" y2="938" stroke="#5b6b76" stroke-width="1"/><text x="385.5" y="973" font-size="11" fill="#16232b" text-anchor="middle">mean hazard ratio where kept (log scale) — favourable ← → adverse</text><line x1="385.5" y1="598" x2="385.5" y2="938" stroke="#5b6b76" stroke-width="0.75"/><line x1="63" y1="780.5" x2="708" y2="780.5" stroke="#5b6b76" stroke-width="1" stroke-dasharray="4 3"/><circle cx="583.1" cy="629" r="7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>TP53: kept in 100% of runs · mean HR 1.37 · 4,020 mutated</title></circle><circle cx="332.1" cy="629" r="5.5" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>EGFR: kept in 100% of runs · mean HR 0.92 · 1,893 mutated</title></circle><circle cx="582" cy="629" r="4.6" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>KEAP1: kept in 100% of runs · mean HR 1.37 · 1,034 mutated</title></circle><circle cx="509" cy="629" r="4.6" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>STK11: kept in 100% of runs · mean HR 1.22 · 1,013 mutated</title></circle><circle cx="343.4" cy="629" r="4.1" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>PTPRD: kept in 100% of runs · mean HR 0.94 · 644 mutated</title></circle><circle cx="392.4" cy="810.8" r="4.1" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>KMT2D: kept in 40% of runs · mean HR 1.01 · 629 mutated</title></circle><circle cx="536.1" cy="629" r="4" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SMARCA4: kept in 100% of runs · mean HR 1.27 · 580 mutated</title></circle><circle cx="396.9" cy="810.8" r="4" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>CDKN2A: kept in 40% of runs · mean HR 1.02 · 546 mutated</title></circle><circle cx="402" cy="871.4" r="4" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ATM: kept in 20% of runs · mean HR 1.03 · 541 mutated</title></circle><circle cx="351.9" cy="750.2" r="4" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>PTPRT: kept in 60% of runs · mean HR 0.95 · 539 mutated</title></circle><circle cx="343.2" cy="629" r="3.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>EPHA3: kept in 100% of runs · mean HR 0.93 · 426 mutated</title></circle><circle cx="400.7" cy="810.8" r="3.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>RB1: kept in 40% of runs · mean HR 1.02 · 403 mutated</title></circle><circle cx="402.4" cy="810.8" r="3.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>GRIN2A: kept in 40% of runs · mean HR 1.03 · 395 mutated</title></circle><circle cx="357.3" cy="629" r="3.6" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MED12: kept in 100% of runs · mean HR 0.96 · 351 mutated</title></circle><circle cx="341.3" cy="810.8" r="3.5" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>NOTCH4: kept in 40% of runs · mean HR 0.93 · 301 mutated</title></circle><circle cx="369.2" cy="871.4" r="3.5" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>AMER1: kept in 20% of runs · mean HR 0.97 · 287 mutated</title></circle><circle cx="368.6" cy="871.4" r="3.4" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>TBX3: kept in 20% of runs · mean HR 0.97 · 269 mutated</title></circle><circle cx="384.2" cy="871.4" r="3.4" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>BCOR: kept in 20% of runs · mean HR 1.00 · 268 mutated</title></circle><circle cx="351.3" cy="689.6" r="3.4" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>POLE: kept in 80% of runs · mean HR 0.95 · 265 mutated</title></circle><circle cx="466.3" cy="629" r="3.4" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>PTEN: kept in 100% of runs · mean HR 1.14 · 259 mutated</title></circle><circle cx="369.3" cy="871.4" r="3.4" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>BRCA2: kept in 20% of runs · mean HR 0.97 · 257 mutated</title></circle><circle cx="371.4" cy="871.4" r="3.4" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>PAK5: kept in 20% of runs · mean HR 0.98 · 252 mutated</title></circle><circle cx="482.7" cy="629" r="3.4" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>NFE2L2: kept in 100% of runs · mean HR 1.17 · 238 mutated</title></circle><circle cx="320.7" cy="689.6" r="3.4" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ARID1B: kept in 80% of runs · mean HR 0.90 · 232 mutated</title></circle><circle cx="372.6" cy="871.4" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>KMT2A: kept in 20% of runs · mean HR 0.98 · 227 mutated</title></circle><circle cx="335" cy="689.6" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MTOR: kept in 80% of runs · mean HR 0.92 · 225 mutated</title></circle><circle cx="354.3" cy="810.8" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>RET: kept in 40% of runs · mean HR 0.95 · 223 mutated</title></circle><circle cx="428.7" cy="629" r="3.3" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>EP300: kept in 100% of runs · mean HR 1.07 · 220 mutated</title></circle><circle cx="376.7" cy="871.4" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>FLT4: kept in 20% of runs · mean HR 0.99 · 218 mutated</title></circle><circle cx="370" cy="871.4" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>DICER1: kept in 20% of runs · mean HR 0.98 · 217 mutated</title></circle><circle cx="359.4" cy="871.4" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>AR: kept in 20% of runs · mean HR 0.96 · 210 mutated</title></circle><circle cx="418.1" cy="810.8" r="3.3" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>RASA1: kept in 40% of runs · mean HR 1.05 · 199 mutated</title></circle><circle cx="377.1" cy="871.4" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>NSD1: kept in 20% of runs · mean HR 0.99 · 199 mutated</title></circle><circle cx="352" cy="871.4" r="3.3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>STAG2: kept in 20% of runs · mean HR 0.95 · 194 mutated</title></circle><circle cx="428.4" cy="810.8" r="3.2" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MDC1: kept in 40% of runs · mean HR 1.07 · 180 mutated</title></circle><circle cx="370.6" cy="810.8" r="3.2" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>IL7R: kept in 40% of runs · mean HR 0.98 · 175 mutated</title></circle><circle cx="311" cy="689.6" r="3.2" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>DNMT3A: kept in 80% of runs · mean HR 0.89 · 166 mutated</title></circle><circle cx="364.5" cy="810.8" r="3.2" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SF3B1: kept in 40% of runs · mean HR 0.97 · 166 mutated</title></circle><circle cx="342.6" cy="871.4" r="3.2" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>U2AF1: kept in 20% of runs · mean HR 0.93 · 166 mutated</title></circle><circle cx="355.3" cy="689.6" r="3.1" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>JAK3: kept in 80% of runs · mean HR 0.95 · 154 mutated</title></circle><circle cx="448.5" cy="871.4" r="3.1" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>BRIP1: kept in 20% of runs · mean HR 1.11 · 152 mutated</title></circle><circle cx="298.7" cy="689.6" r="3.1" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>CDK12: kept in 80% of runs · mean HR 0.87 · 142 mutated</title></circle><circle cx="359.7" cy="750.2" r="3.1" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>GATA3: kept in 60% of runs · mean HR 0.96 · 140 mutated</title></circle><circle cx="378.1" cy="871.4" r="3.1" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MAP3K1: kept in 20% of runs · mean HR 0.99 · 140 mutated</title></circle><circle cx="383.4" cy="871.4" r="3.1" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>TP63: kept in 20% of runs · mean HR 1.00 · 137 mutated</title></circle><circle cx="621.4" cy="629" r="3" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>TGFBR2: kept in 100% of runs · mean HR 1.46 · 125 mutated</title></circle><circle cx="369.2" cy="750.2" r="3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>FGFR4: kept in 60% of runs · mean HR 0.97 · 122 mutated</title></circle><circle cx="390.9" cy="871.4" r="3" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>IGF1R: kept in 20% of runs · mean HR 1.01 · 119 mutated</title></circle><circle cx="320.9" cy="689.6" r="3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ARAF: kept in 80% of runs · mean HR 0.90 · 117 mutated</title></circle><circle cx="358.7" cy="871.4" r="3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ESR1: kept in 20% of runs · mean HR 0.96 · 109 mutated</title></circle><circle cx="374.1" cy="871.4" r="3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>GNAS: kept in 20% of runs · mean HR 0.98 · 108 mutated</title></circle><circle cx="428" cy="689.6" r="3" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>INPP4A: kept in 80% of runs · mean HR 1.07 · 107 mutated</title></circle><circle cx="430.2" cy="689.6" r="3" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>NF2: kept in 80% of runs · mean HR 1.07 · 106 mutated</title></circle><circle cx="311" cy="871.4" r="3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>TSC1: kept in 20% of runs · mean HR 0.89 · 105 mutated</title></circle><circle cx="437.1" cy="871.4" r="3" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>BAP1: kept in 20% of runs · mean HR 1.09 · 105 mutated</title></circle><circle cx="343.8" cy="810.8" r="3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ABL1: kept in 40% of runs · mean HR 0.94 · 104 mutated</title></circle><circle cx="329.9" cy="871.4" r="3" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MYOD1: kept in 20% of runs · mean HR 0.92 · 102 mutated</title></circle><circle cx="291.7" cy="871.4" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>FGFR1: kept in 20% of runs · mean HR 0.86 · 92 mutated</title></circle><circle cx="354.1" cy="871.4" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>PARP1: kept in 20% of runs · mean HR 0.95 · 91 mutated</title></circle><circle cx="323.7" cy="810.8" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SDHA: kept in 40% of runs · mean HR 0.91 · 90 mutated</title></circle><circle cx="463.3" cy="871.4" r="2.9" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>FH: kept in 20% of runs · mean HR 1.13 · 90 mutated</title></circle><circle cx="289.1" cy="750.2" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>BLM: kept in 60% of runs · mean HR 0.86 · 89 mutated</title></circle><circle cx="380.1" cy="871.4" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SOX9: kept in 20% of runs · mean HR 0.99 · 89 mutated</title></circle><circle cx="386.1" cy="871.4" r="2.9" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>AXL: kept in 20% of runs · mean HR 1.00 · 88 mutated</title></circle><circle cx="374" cy="871.4" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ERCC4: kept in 20% of runs · mean HR 0.98 · 83 mutated</title></circle><circle cx="344.7" cy="689.6" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>PAX5: kept in 80% of runs · mean HR 0.94 · 80 mutated</title></circle><circle cx="409.6" cy="871.4" r="2.9" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>XPO1: kept in 20% of runs · mean HR 1.04 · 80 mutated</title></circle><circle cx="378.6" cy="871.4" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>FOXP1: kept in 20% of runs · mean HR 0.99 · 80 mutated</title></circle><circle cx="371" cy="871.4" r="2.9" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>CTCF: kept in 20% of runs · mean HR 0.98 · 77 mutated</title></circle><circle cx="310.4" cy="810.8" r="2.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>RUNX1: kept in 40% of runs · mean HR 0.89 · 71 mutated</title></circle><circle cx="401" cy="871.4" r="2.8" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ETV6: kept in 20% of runs · mean HR 1.03 · 69 mutated</title></circle><circle cx="407.2" cy="810.8" r="2.8" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>ERCC3: kept in 40% of runs · mean HR 1.04 · 67 mutated</title></circle><circle cx="371" cy="810.8" r="2.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>TOP1: kept in 40% of runs · mean HR 0.98 · 67 mutated</title></circle><circle cx="385.1" cy="871.4" r="2.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>BCL6: kept in 20% of runs · mean HR 1.00 · 65 mutated</title></circle><circle cx="377.9" cy="871.4" r="2.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>FOXA1: kept in 20% of runs · mean HR 0.99 · 61 mutated</title></circle><circle cx="350.7" cy="750.2" r="2.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>TNFAIP3: kept in 60% of runs · mean HR 0.95 · 60 mutated</title></circle><circle cx="349.7" cy="750.2" r="2.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>FLCN: kept in 60% of runs · mean HR 0.94 · 55 mutated</title></circle><circle cx="316.2" cy="871.4" r="2.8" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>H3-5: kept in 20% of runs · mean HR 0.90 · 54 mutated</title></circle><circle cx="376.6" cy="810.8" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>EZH2: kept in 40% of runs · mean HR 0.99 · 52 mutated</title></circle><circle cx="410.6" cy="871.4" r="2.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SMARCB1: kept in 20% of runs · mean HR 1.04 · 51 mutated</title></circle><circle cx="330.5" cy="871.4" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MCL1: kept in 20% of runs · mean HR 0.92 · 50 mutated</title></circle><circle cx="387.9" cy="871.4" r="2.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SRC: kept in 20% of runs · mean HR 1.00 · 50 mutated</title></circle><circle cx="295.6" cy="810.8" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SPOP: kept in 40% of runs · mean HR 0.87 · 49 mutated</title></circle><circle cx="385.8" cy="871.4" r="2.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MDM4: kept in 20% of runs · mean HR 1.00 · 45 mutated</title></circle><circle cx="338.2" cy="629" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>CDK8: kept in 100% of runs · mean HR 0.93 · 44 mutated</title></circle><circle cx="326.8" cy="810.8" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>EED: kept in 40% of runs · mean HR 0.91 · 44 mutated</title></circle><circle cx="362.5" cy="871.4" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>GSK3B: kept in 20% of runs · mean HR 0.96 · 44 mutated</title></circle><circle cx="486.7" cy="871.4" r="2.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>AKT2: kept in 20% of runs · mean HR 1.17 · 43 mutated</title></circle><circle cx="323.2" cy="750.2" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>CDK6: kept in 60% of runs · mean HR 0.91 · 42 mutated</title></circle><circle cx="450.2" cy="871.4" r="2.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MYC: kept in 20% of runs · mean HR 1.11 · 42 mutated</title></circle><circle cx="393.7" cy="810.8" r="2.7" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>RAD51C: kept in 40% of runs · mean HR 1.01 · 38 mutated</title></circle><circle cx="358.9" cy="871.4" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MYCL: kept in 20% of runs · mean HR 0.96 · 37 mutated</title></circle><circle cx="333.5" cy="810.8" r="2.7" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>LMO1: kept in 40% of runs · mean HR 0.92 · 36 mutated</title></circle><circle cx="357.3" cy="810.8" r="2.6" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>CD79B: kept in 40% of runs · mean HR 0.96 · 35 mutated</title></circle><circle cx="305.1" cy="871.4" r="2.6" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>SH2D1A: kept in 20% of runs · mean HR 0.88 · 33 mutated</title></circle><circle cx="448.6" cy="871.4" r="2.6" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>RHOA: kept in 20% of runs · mean HR 1.11 · 30 mutated</title></circle><circle cx="372.7" cy="871.4" r="2.6" fill="#0072B2" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>MEF2B: kept in 20% of runs · mean HR 0.98 · 30 mutated</title></circle><circle cx="390.4" cy="871.4" r="2.6" fill="#D55E00" opacity="0.7" stroke="#ffffff" stroke-width="0.5"><title>NKX3-1: kept in 20% of runs · mean HR 1.01 · 30 mutated</title></circle><text x="627.4" y="631.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">TGFBR2</text><text x="589.1" y="631.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">TP53</text><text x="545.3" y="631.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">KEAP1</text><line x1="536.1" y1="624.5" x2="536.1" y2="616" stroke="#5b6b76" stroke-width="0.75"/><text x="513.4" y="612.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">SMARCA4</text><text x="494.1" y="642.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">STK11</text><text x="465" y="620.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">NFE2L2</text><text x="435.6" y="631.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">PTEN</text><text x="301.3" y="631.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">EGFR</text><line x1="338.2" y1="624.5" x2="338.2" y2="616" stroke="#5b6b76" stroke-width="0.75"/><text x="325.6" y="612.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">CDK8</text><text x="392.8" y="631.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">EP300</text><line x1="343.2" y1="633.5" x2="343.2" y2="642" stroke="#5b6b76" stroke-width="0.75"/><text x="327.6" y="650.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">EPHA3</text><line x1="346.6" y1="625.8" x2="359.5" y2="612.9" stroke="#5b6b76" stroke-width="0.75"/><text x="360.5" y="609.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">PTPRD</text><line x1="360.4" y1="632.2" x2="366.4" y2="638.1" stroke="#5b6b76" stroke-width="0.75"/><text x="367.4" y="646.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">MED12</text><text x="261.4" y="692.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">CDK12</text><line x1="311" y1="685.1" x2="311" y2="676.6" stroke="#5b6b76" stroke-width="0.75"/><text x="291" y="673.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">DNMT3A</text><text x="302.7" y="703.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">ARID1B</text><line x1="324.1" y1="686.4" x2="337" y2="673.5" stroke="#5b6b76" stroke-width="0.75"/><text x="338" y="670" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">ARAF</text><line x1="338.2" y1="692.8" x2="344.1" y2="698.7" stroke="#5b6b76" stroke-width="0.75"/><text x="345.1" y="707.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">MTOR</text><text x="436.2" y="692.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">NF2</text><text x="386.4" y="692.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">INPP4A</text><line x1="354.5" y1="686.4" x2="367.4" y2="673.5" stroke="#5b6b76" stroke-width="0.75"/><text x="368.4" y="670" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">POLE</text><text x="295.1" y="752.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">BLM</text><line x1="323.2" y1="745.7" x2="323.2" y2="737.2" stroke="#5b6b76" stroke-width="0.75"/><text x="310.5" y="733.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">CDK6</text><text x="337.8" y="763.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">FLCN</text><line x1="353.9" y1="747" x2="359.8" y2="741.1" stroke="#5b6b76" stroke-width="0.75"/><text x="360.8" y="737.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">TNFAIP3</text><line x1="356.4" y1="750.2" x2="374.9" y2="750.2" stroke="#5b6b76" stroke-width="0.75"/><text x="375.9" y="752.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">PTPRT</text><line x1="362.9" y1="753.4" x2="368.8" y2="759.3" stroke="#5b6b76" stroke-width="0.75"/><text x="369.8" y="767.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">GATA3</text><text x="8" y="989" font-size="10" fill="#5b6b76">Point size: patients mutated (largest 4,020). 30 of 98 genes kept in ≥ 50% of runs are named. 3 of 30 genes named on this volcano could not be labelled</text><text x="8" y="1004" font-size="10" fill="#5b6b76">without overprinting another label or point and are left unlabelled: PAX5, JAK3, FGFR4.</text><text x="8" y="1031" font-size="12" fill="#16232b" font-weight="600">Genes kept most often (share of runs · mean hazard ratio where kept)</text><text x="8" y="1051.9" font-size="11" fill="#16232b" font-weight="600">TGFBR2</text><text x="56.1" y="1051.9" font-size="10" fill="#5b6b76">100% · 125 mutated</text><rect x="383.8" y="1044" width="106.3" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1051.9" font-size="11" fill="#16232b">HR 1.46</text><text x="8" y="1069.9" font-size="11" fill="#16232b" font-weight="600">TP53</text><text x="40.8" y="1069.9" font-size="10" fill="#5b6b76">100% · 4,020 mutated</text><rect x="383.8" y="1062" width="89" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1069.9" font-size="11" fill="#16232b">HR 1.37</text><text x="8" y="1087.9" font-size="11" fill="#16232b" font-weight="600">KEAP1</text><text x="49.7" y="1087.9" font-size="10" fill="#5b6b76">100% · 1,034 mutated</text><rect x="383.8" y="1080" width="88.5" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1087.9" font-size="11" fill="#16232b">HR 1.37</text><text x="8" y="1105.9" font-size="11" fill="#16232b" font-weight="600">SMARCA4</text><text x="66.1" y="1105.9" font-size="10" fill="#5b6b76">100% · 580 mutated</text><rect x="383.8" y="1098" width="67.9" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1105.9" font-size="11" fill="#16232b">HR 1.27</text><text x="8" y="1123.9" font-size="11" fill="#16232b" font-weight="600">STK11</text><text x="48.1" y="1123.9" font-size="10" fill="#5b6b76">100% · 1,013 mutated</text><rect x="383.8" y="1116" width="55.7" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1123.9" font-size="11" fill="#16232b">HR 1.22</text><text x="8" y="1141.9" font-size="11" fill="#16232b" font-weight="600">NFE2L2</text><text x="54.1" y="1141.9" font-size="10" fill="#5b6b76">100% · 238 mutated</text><rect x="383.8" y="1134" width="43.8" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1141.9" font-size="11" fill="#16232b">HR 1.17</text><text x="8" y="1159.9" font-size="11" fill="#16232b" font-weight="600">PTEN</text><text x="42.3" y="1159.9" font-size="10" fill="#5b6b76">100% · 259 mutated</text><rect x="383.8" y="1152" width="36.4" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1159.9" font-size="11" fill="#16232b">HR 1.14</text><text x="8" y="1177.9" font-size="11" fill="#16232b" font-weight="600">EGFR</text><text x="42.4" y="1177.9" font-size="10" fill="#5b6b76">100% · 1,893 mutated</text><rect x="359.7" y="1170" width="24.1" height="8" rx="2" fill="#0072B2" opacity="0.85"/><text x="605.8" y="1177.9" font-size="11" fill="#16232b">HR 0.92</text><text x="8" y="1195.9" font-size="11" fill="#16232b" font-weight="600">CDK8</text><text x="43.1" y="1195.9" font-size="10" fill="#5b6b76">100% · 44 mutated</text><rect x="362.5" y="1188" width="21.3" height="8" rx="2" fill="#0072B2" opacity="0.85"/><text x="605.8" y="1195.9" font-size="11" fill="#16232b">HR 0.93</text><text x="8" y="1213.9" font-size="11" fill="#16232b" font-weight="600">EP300</text><text x="47.7" y="1213.9" font-size="10" fill="#5b6b76">100% · 220 mutated</text><rect x="383.8" y="1206" width="19.5" height="8" rx="2" fill="#D55E00" opacity="0.85"/><text x="605.8" y="1213.9" font-size="11" fill="#16232b">HR 1.07</text><text x="8" y="1231.9" font-size="11" fill="#16232b" font-weight="600">EPHA3</text><text x="50" y="1231.9" font-size="10" fill="#5b6b76">100% · 426 mutated</text><rect x="364.7" y="1224" width="19" height="8" rx="2" fill="#0072B2" opacity="0.85"/><text x="605.8" y="1231.9" font-size="11" fill="#16232b">HR 0.93</text><text x="8" y="1249.9" font-size="11" fill="#16232b" font-weight="600">PTPRD</text><text x="49.9" y="1249.9" font-size="10" fill="#5b6b76">100% · 644 mutated</text><rect x="364.8" y="1242" width="19" height="8" rx="2" fill="#0072B2" opacity="0.85"/><text x="605.8" y="1249.9" font-size="11" fill="#16232b">HR 0.94</text><text x="8" y="1267.9" font-size="11" fill="#16232b" font-weight="600">MED12</text><text x="50.6" y="1267.9" font-size="10" fill="#5b6b76">100% · 351 mutated</text><rect x="371.1" y="1260" width="12.7" height="8" rx="2" fill="#0072B2" opacity="0.85"/><text x="605.8" y="1267.9" font-size="11" fill="#16232b">HR 0.96</text><text x="8" y="1285.9" font-size="11" fill="#16232b" font-weight="600">CDK12</text><text x="49.7" y="1285.9" font-size="10" fill="#5b6b76">80% · 142 mutated</text><rect x="344.6" y="1278" width="39.1" height="8" rx="2" fill="#0072B2" opacity="0.85"/><text x="605.8" y="1285.9" font-size="11" fill="#16232b">HR 0.87</text><text x="8" y="1303.9" font-size="11" fill="#16232b" font-weight="600">DNMT3A</text><text x="59.2" y="1303.9" font-size="10" fill="#5b6b76">80% · 166 mutated</text><rect x="350.2" y="1296" width="33.6" height="8" rx="2" fill="#0072B2" opacity="0.85"/><text x="605.8" y="1303.9" font-size="11" fill="#16232b">HR 0.89</text><line x1="383.8" y1="1039" x2="383.8" y2="1309" stroke="#5b6b76" stroke-width="1" stroke-dasharray="3 3"/><line x1="187.6" y1="1311" x2="187.6" y2="1314" stroke="#5b6b76" stroke-width="1"/><text x="180.2" y="1327" font-size="10" fill="#5b6b76">0.5</text><line x1="383.8" y1="1311" x2="383.8" y2="1314" stroke="#5b6b76" stroke-width="1"/><text x="380.8" y="1327" font-size="10" fill="#5b6b76">1</text><line x1="498.6" y1="1311" x2="498.6" y2="1314" stroke="#5b6b76" stroke-width="1"/><text x="491.2" y="1327" font-size="10" fill="#5b6b76">1.5</text><line x1="580" y1="1311" x2="580" y2="1314" stroke="#5b6b76" stroke-width="1"/><text x="577" y="1327" font-size="10" fill="#5b6b76">2</text><line x1="173.788" y1="1311" x2="593.788" y2="1311" stroke="#5b6b76" stroke-width="1"/><text x="383.8" y="1346" font-size="11" fill="#16232b" text-anchor="middle">mean hazard ratio where kept (log scale)</text><text x="8" y="1364" font-size="10" fill="#5b6b76">7,809 patients, 3,975 events; 302 candidate genes (of 341 covered for every patient, mutated in ≥30); elastic net, alpha 0.5, seed 1. The risk score is</text><text x="8" y="1378" font-size="10" fill="#5b6b76">OncoCast's 0–10 scale: 0 and 10 are the lowest and highest held-out score (raw linear predictor −0.467 to 1.28). 204 of the 302 candidate genes were never</text><text x="8" y="1392" font-size="10" fill="#5b6b76">kept, so are not on the volcano. Left out: 1,030 never held out (so unscored and in no group). 83 more genes were kept in at least one run; they are in the</text><text x="8" y="1406" font-size="10" fill="#5b6b76">result, not drawn. Table 1 (not drawn; in the result) compares the groups (Low / Intermediate / High, 2,337 / 2,175 / 2,267 patients) on 7 rows, each tested</text><text x="8" y="1420" font-size="10" fill="#5b6b76">across groups (χ² or Kruskal–Wallis), q-values Benjamini–Hochberg over 5 of them; a category's share is of the patients with a value. Differ at q &lt; 0.05:</text><text x="8" y="1434" font-size="10" fill="#5b6b76">STAGE_HIGHEST_RECORDED Stage 1-3 in 61.5% / 50.7% / 54.9% (q = 2.1e-11); GENDER Female in 66.8% / 60.0% / 48.3% (q = 9.1e-35);</text><text x="8" y="1448" font-size="10" fill="#5b6b76">SMOKING_PREDICTIONS_3_CLASSES Former/Current Smoker in 56.7% / 69.1% / 82.7% (q = 6.1e-99); CURRENT_AGE_DEID median 72 / 71 / 70 (q =</text><text x="8" y="1462" font-size="10" fill="#5b6b76">2.1e-7); ZFHX3 mutated in 2.5% / 5.2% / 6.7% of patients whose panels cover it (q = 7e-10). Different by construction, so no q-value: KEAP1 mutated in</text><text x="8" y="1476" font-size="10" fill="#5b6b76">&lt;0.1% / 3.0% / 36.5% of patients whose panels cover it (in the model, p &lt; 1e-300); TP53 mutated in 0.6% / 74.3% / 81.6% of patients whose panels cover it</text><text x="8" y="1490" font-size="10" fill="#5b6b76">(in the model, p &lt; 1e-300). Association, not cause: a gene's weight is its link to OS with the other candidates held fixed, in this cohort. Without strata, a gene</text><text x="8" y="1504" font-size="10" fill="#5b6b76">that marks a cancer type carries that type's prognosis; on a cohort of several cancer types, stratify by CANCER_TYPE. Each patient is scored only by models</text><text x="8" y="1518" font-size="10" fill="#5b6b76">that did not train on them. The group cuts are equal quantiles of the score, chosen without the outcome. Genes outside the panels every patient was</text><text x="8" y="1532" font-size="10" fill="#5b6b76">sequenced on are not candidates, so a patient is never counted as wild-type for a gene they were not tested for. Table 1 describes the patients the curves</text><text x="8" y="1546" font-size="10" fill="#5b6b76">draw; its rows marked “in the model” (the model's genes and covariates) and “outcome” differ between the groups by construction, so they get no q-value</text><text x="8" y="1560" font-size="10" fill="#5b6b76">and are left out of the correction. The C-index ignores delayed entry. Seeded (seed 1): the same document gives the same model.</text></svg>
diff --git a/test/snapshots/riskmodel.txt b/test/snapshots/riskmodel.txt
new file mode 100644
index 00000000..a221439d
--- /dev/null
+++ b/test/snapshots/riskmodel.txt
@@ -0,0 +1,83 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- NSCLC: 7,809 patients — CANCER_TYPE in [Non-Small Cell Lung Cancer] (any sample)
+
+Risk model (elastic-net Cox, 5 held-out splits, alpha 0.5, seed 1; 7,809 patients, 3,975 events, 302 candidate genes):
+  held-out C-index 0.617 (IQR 0.615–0.618)
+  risk score on OncoCast's 0–10 scale (0 and 10 the lowest and highest held-out score; raw -0.467 to 1.276)
+  risk groups (held-out scores, equal quantiles):
+    Low risk (score 0–2.7): n=2337, 888 events, median 68.2mo, 12-month 86%, 36-month 66%
+    Intermediate risk (score 2.7–4.4): n=2175, 1150 events, median 37.0mo, 12-month 76%, 36-month 51%, HR vs lowest 1.58 (95% CI 1.44–1.72, 2.7e-24)
+    High risk (score 4.4–10): n=2267, 1431 events, median 20.3mo, 12-month 62%, 36-month 39%, HR vs lowest 2.26 (95% CI 2.08–2.46, 8.1e-81)
+  log-rank p=3.5e-83
+  risk score alone: HR 1.39 per SD (95% CI 1.35–1.44, 4.7e-93)
+  genes kept most often: TGFBR2 100% HR 1.46, TP53 100% HR 1.37, KEAP1 100% HR 1.37, SMARCA4 100% HR 1.27, STK11 100% HR 1.22, NFE2L2 100% HR 1.17, PTEN 100% HR 1.14, EGFR 100% HR 0.92, CDK8 100% HR 0.93, EP300 100% HR 1.07, EPHA3 100% HR 0.93, PTPRD 100% HR 0.94, MED12 100% HR 0.96, CDK12 80% HR 0.87, DNMT3A 80% HR 0.89
+  1030 patients were never held out, so have no score
+  CAVEAT (tell the user): Association, not cause: a gene's weight is its link to OS with the other candidates held fixed, in this cohort. Without strata, a gene that marks a cancer type carries that type's prognosis; on a cohort of several cancer types, stratify by CANCER_TYPE. Each patient is scored only by models that did not train on them. The group cuts are equal quantiles of the score, chosen without the outcome. Genes outside the panels every patient was sequenced on are not candidates, so a patient is never counted as wild-type for a gene they were not tested for. Table 1 describes the patients the curves draw; its rows marked “in the model” (the model's genes and covariates) and “outcome” differ between the groups by construction, so they get no q-value and are left out of the correction. The C-index ignores delayed entry. Seeded (seed 1): the same document gives the same model.
+  METHOD:
+    - One row per patient [NSCLC · msk_chord_2024] (7,809 → 7,809 rows): The riskmodel:rows query above: each patient's survival time and event, the panels their samples were sequenced on for mutations, the genes mutated in them, and their half of the engine's fixed held-out split. Patient-level: the rows never leave this server's engine, and no identifier is returned.
+    - Patients, candidate genes and covariates: A gene is a candidate only if every patient's panels cover it, so no patient is counted as wild-type for a gene they were never tested for, and only if at least 30 patients have it mutated.
+        genes covered for every patient: 341
+        candidates: 302
+        left out: no mutation panel on record: 0
+    - Elastic-net Cox, 5 random splits: Each run holds out a random 33% of the patients; on the rest, the penalty is chosen from a path of 25 by 3-fold cross-validated partial likelihood (Verweij & van Houwelingen), and the model at that penalty scores the held-out patients. Genes are scaled to unit variance before fitting; Breslow ties. A patient's risk score is the mean of the scores they got while held out (OncoCast's ensemble, Martin et al.).
+        l1 share of the penalty (alpha): 0.5
+        runs: 5
+        seed: 1
+        test-set C-index, median (IQR): 0.617 (0.615–0.618)
+        never held out, so unscored: 1030
+    - Risk groups, their survival and hazard ratios: Cuts at equal quantiles of the held-out scores, each moved to the nearer edge of any run of tied scores so equal scores share a group — no outcome is used to place them. Each group is drawn as a Kaplan–Meier curve and read at 12 and 36 months; the groups are compared with a log-rank test, and a Cox model of the outcome on the group indicators gives each group's hazard ratio against the lowest (Newton–Raphson, full information matrix, 95% Wald intervals). A second Cox model on the standardised score alone gives the hazard ratio per standard deviation. The score is also shown on OncoCast's 0–10 scale, (score − min) / (max − min) × 10, min and max the lowest and highest held-out score; the scale is linear, so it moves no patient between groups and changes no number above.
+        0 on the 0–10 scale (raw score): -0.4669
+        10 on the 0–10 scale (raw score): 1.2761
+        Low risk: 2,337 patients, 888 events, median 68.2 months
+        Intermediate risk: 2,175 patients, 1,150 events, median 37.0 months, HR 1.58
+        High risk: 2,267 patients, 1,431 events, median 20.3 months, HR 2.26
+    - Table 1: the groups' patients again, with their clinical values: The riskmodel:table1 query above is the riskmodel:rows query with Table 1's attributes added: for each, the patient's value (a sample-level attribute: their first sample's), case and whitespace folded as everywhere in this app and shown in its commonest spelling, and how many values their samples hold. Its rows are kept exactly as the model's were and must match the model's patients one by one — survival time, entry and event — or the view is refused, so each group's column describes the patients its curve drew. riskmodel:table1-attributes gives each attribute's type (NUMBER is numeric), riskmodel:panels each mutation panel's genes. Patient-level: the rows never leave this server's engine; only the counts below do.
+        Low risk: 2,337 patients
+        Intermediate risk: 2,175 patients
+        High risk: 2,267 patients
+    - Table 1's rows: Attributes as the view names them; a categorical one with more than 100 values among these patients is set aside. Genes as the view names them. A gene is counted as mutated the way the model counts it (any mutation), and only among the patients whose mutation panels cover it (a whole-exome patient covers every gene): a patient whose panels do not is not profiled for it, never unaltered, and is counted as such in each group. A row the groups differ in by construction is marked: a gene the model kept in any run and a covariate are "in the model" (the score is built from them), the endpoint's own columns are "outcome" (the groups are cut to differ in survival); the model's stratum is marked "stratum" but is a description, since the score has no stratum term.
+    - Each row tested across the groups: A categorical attribute: Pearson χ² on the group × value table, no continuity correction, an all-zero row or column dropped first, patients with no value left out. A gene: Pearson χ² on the group × (mutated, not mutated) table of its profiled patients. A numeric attribute: Kruskal–Wallis on each group's values, ranks averaged within ties and tie-corrected; medians and quartiles interpolating linearly between ranks. The smallest expected count is reported, since χ² is poor below about 5.
+        STAGE_HIGHEST_RECORDED: χ² 56.918 on 4 df · p 1.29e-11
+        GENDER: χ² 167.47 on 4 df · p 3.65e-35
+        SMOKING_PREDICTIONS_3_CLASSES: χ² 466.4278 on 4 df · p 1.22e-99
+        CURRENT_AGE_DEID: H 30.7119 on 2 df · p 2.14e-7
+        gene:KEAP1: χ² 1622.8469 on 2 df · p <1e-300 · in the model
+        gene:TP53: χ² 3699.2236 on 2 df · p <1e-300 · in the model
+        gene:ZFHX3: χ² 42.6173 on 2 df · p 5.57e-10
+    - Benjamini–Hochberg across Table 1's open rows: q-values over the 5 tested rows that are not in the model and not its outcome. Those rows differ between the groups by construction, so they get a p-value but no q-value and do not enter the correction, where their p-values would shrink every other row's q.
+    lib/coxnet.ts's path is checked against scikit-survival's CoxnetSurvivalAnalysis, its delayed-entry, stratified fits (with standard errors) against statsmodels' PHReg and its C-index against lifelines, by test/coxnet-golden.test.ts (fixtures from python/golden/coxnet.py). The script below recomputes Table 1's tests and q-values from its counts (the model itself it does not refit); χ² matches scipy 1.18.1 chi2_contingency, Kruskal–Wallis and quantiles scipy/numpy, Benjamini–Hochberg statsmodels multipletests (test/tables-golden.test.ts, test/moments-golden.test.ts).
+  METHOD:
+    - One row per patient [NSCLC · msk_chord_2024] (7,809 → 7,809 rows): The riskmodel:rows query above: each patient's survival time and event, the panels their samples were sequenced on for mutations, the genes mutated in them, and their half of the engine's fixed held-out split. Patient-level: the rows never leave this server's engine, and no identifier is returned.
+    - Patients, candidate genes and covariates: A gene is a candidate only if every patient's panels cover it, so no patient is counted as wild-type for a gene they were never tested for, and only if at least 30 patients have it mutated.
+        genes covered for every patient: 341
+        candidates: 302
+        left out: no mutation panel on record: 0
+    - Elastic-net Cox, 5 random splits: Each run holds out a random 33% of the patients; on the rest, the penalty is chosen from a path of 25 by 3-fold cross-validated partial likelihood (Verweij & van Houwelingen), and the model at that penalty scores the held-out patients. Genes are scaled to unit variance before fitting; Breslow ties. A patient's risk score is the mean of the scores they got while held out (OncoCast's ensemble, Martin et al.).
+        l1 share of the penalty (alpha): 0.5
+        runs: 5
+        seed: 1
+        test-set C-index, median (IQR): 0.617 (0.615–0.618)
+        never held out, so unscored: 1030
+    - Risk groups, their survival and hazard ratios: Cuts at equal quantiles of the held-out scores, each moved to the nearer edge of any run of tied scores so equal scores share a group — no outcome is used to place them. Each group is drawn as a Kaplan–Meier curve and read at 12 and 36 months; the groups are compared with a log-rank test, and a Cox model of the outcome on the group indicators gives each group's hazard ratio against the lowest (Newton–Raphson, full information matrix, 95% Wald intervals). A second Cox model on the standardised score alone gives the hazard ratio per standard deviation. The score is also shown on OncoCast's 0–10 scale, (score − min) / (max − min) × 10, min and max the lowest and highest held-out score; the scale is linear, so it moves no patient between groups and changes no number above.
+        0 on the 0–10 scale (raw score): -0.4669
+        10 on the 0–10 scale (raw score): 1.2761
+        Low risk: 2,337 patients, 888 events, median 68.2 months
+        Intermediate risk: 2,175 patients, 1,150 events, median 37.0 months, HR 1.58
+        High risk: 2,267 patients, 1,431 events, median 20.3 months, HR 2.26
+    - Table 1: the groups' patients again, with their clinical values: The riskmodel:table1 query above is the riskmodel:rows query with Table 1's attributes added: for each, the patient's value (a sample-level attribute: their first sample's), case and whitespace folded as everywhere in this app and shown in its commonest spelling, and how many values their samples hold. Its rows are kept exactly as the model's were and must match the model's patients one by one — survival time, entry and event — or the view is refused, so each group's column describes the patients its curve drew. riskmodel:table1-attributes gives each attribute's type (NUMBER is numeric), riskmodel:panels each mutation panel's genes. Patient-level: the rows never leave this server's engine; only the counts below do.
+        Low risk: 2,337 patients
+        Intermediate risk: 2,175 patients
+        High risk: 2,267 patients
+    - Table 1's rows: Attributes as the view names them; a categorical one with more than 100 values among these patients is set aside. Genes as the view names them. A gene is counted as mutated the way the model counts it (any mutation), and only among the patients whose mutation panels cover it (a whole-exome patient covers every gene): a patient whose panels do not is not profiled for it, never unaltered, and is counted as such in each group. A row the groups differ in by construction is marked: a gene the model kept in any run and a covariate are "in the model" (the score is built from them), the endpoint's own columns are "outcome" (the groups are cut to differ in survival); the model's stratum is marked "stratum" but is a description, since the score has no stratum term.
+    - Each row tested across the groups: A categorical attribute: Pearson χ² on the group × value table, no continuity correction, an all-zero row or column dropped first, patients with no value left out. A gene: Pearson χ² on the group × (mutated, not mutated) table of its profiled patients. A numeric attribute: Kruskal–Wallis on each group's values, ranks averaged within ties and tie-corrected; medians and quartiles interpolating linearly between ranks. The smallest expected count is reported, since χ² is poor below about 5.
+        STAGE_HIGHEST_RECORDED: χ² 56.918 on 4 df · p 1.29e-11
+        GENDER: χ² 167.47 on 4 df · p 3.65e-35
+        SMOKING_PREDICTIONS_3_CLASSES: χ² 466.4278 on 4 df · p 1.22e-99
+        CURRENT_AGE_DEID: H 30.7119 on 2 df · p 2.14e-7
+        gene:KEAP1: χ² 1622.8469 on 2 df · p <1e-300 · in the model
+        gene:TP53: χ² 3699.2236 on 2 df · p <1e-300 · in the model
+        gene:ZFHX3: χ² 42.6173 on 2 df · p 5.57e-10
+    - Benjamini–Hochberg across Table 1's open rows: q-values over the 5 tested rows that are not in the model and not its outcome. Those rows differ between the groups by construction, so they get a p-value but no q-value and do not enter the correction, where their p-values would shrink every other row's q.
+    lib/coxnet.ts's path is checked against scikit-survival's CoxnetSurvivalAnalysis, its delayed-entry, stratified fits (with standard errors) against statsmodels' PHReg and its C-index against lifelines, by test/coxnet-golden.test.ts (fixtures from python/golden/coxnet.py). The script below recomputes Table 1's tests and q-values from its counts (the model itself it does not refit); χ² matches scipy 1.18.1 chi2_contingency, Kruskal–Wallis and quantiles scipy/numpy, Benjamini–Hochberg statsmodels multipletests (test/tables-golden.test.ts, test/moments-golden.test.ts).
diff --git a/test/snapshots/sequence.json b/test/snapshots/sequence.json
new file mode 100644
index 00000000..796240a5
--- /dev/null
+++ b/test/snapshots/sequence.json
@@ -0,0 +1,810 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "TP53 mut": {
+      "count": 13105,
+      "text": "OQL[TP53: MUT]",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    },
+    "TP53 wt": {
+      "count": 11594,
+      "text": "NOT OQL[TP53:] AND profiled for TP53 MUTATION",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "sequence",
+      "cohorts": [
+        "TP53 mut",
+        "TP53 wt"
+      ],
+      "studies": [
+        "msk_chord_2024"
+      ],
+      "sequence": {
+        "labelKey": "SUBTYPE",
+        "lineGap": 90,
+        "steps": 3,
+        "foldedLabels": 51,
+        "truncated": false,
+        "keyOptions": [
+          {
+            "key": "subtype",
+            "label": "SUBTYPE",
+            "patients": 12738,
+            "values": 10
+          },
+          {
+            "key": "agent",
+            "label": "AGENT",
+            "patients": 11575,
+            "values": 220
+          },
+          {
+            "key": "flag_orotopical",
+            "label": "FLAG_OROTOPICAL",
+            "patients": 11575,
+            "values": 2
+          },
+          {
+            "key": "rx_investigative",
+            "label": "RX_INVESTIGATIVE",
+            "patients": 11575,
+            "values": 2
+          },
+          {
+            "key": "inferred_tx_prob",
+            "label": "INFERRED_TX_PROB",
+            "patients": 10877,
+            "values": 99
+          },
+          {
+            "key": "prior_med_to_msk",
+            "label": "PRIOR_MED_TO_MSK",
+            "patients": 10877,
+            "values": 2
+          },
+          {
+            "key": "style_color",
+            "label": "STYLE_COLOR",
+            "patients": 10877,
+            "values": 2
+          },
+          {
+            "key": "treatment_type",
+            "label": "Treatment_TYPE",
+            "patients": 10877,
+            "values": 1
+          }
+        ],
+        "panels": [
+          {
+            "cohort": "TP53 mut",
+            "nodes": [
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+                "label": "Chemo",
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+              {
+                "…": "22 more",
+                "sha256": "4b96ee2a5c623769"
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+              },
+              {
+                "column": 1,
+                "from": "Targeted",
+                "to": "",
+                "n": 136,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "Biologic + Chemo",
+                "to": "Biologic + Chemo",
+                "n": 135,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Hormone",
+                "to": "",
+                "n": 135,
+                "kind": "stopped"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Hormone + Targeted",
+                "to": "",
+                "n": 134,
+                "kind": "censored"
+              },
+              {
+                "…": "227 more",
+                "sha256": "0f4c33632dbcf0ee"
+              }
+            ],
+            "started": 11564,
+            "censored": 6417
+          },
+          {
+            "cohort": "TP53 wt",
+            "nodes": [
+              {
+                "column": 1,
+                "label": "Chemo",
+                "kind": "regimen",
+                "n": 2551
+              },
+              {
+                "column": 1,
+                "label": "other",
+                "kind": "regimen",
+                "n": 2434
+              },
+              {
+                "column": 1,
+                "label": "Hormone",
+                "kind": "regimen",
+                "n": 2209
+              },
+              {
+                "column": 1,
+                "label": "Chemo + Hormone",
+                "kind": "regimen",
+                "n": 781
+              },
+              {
+                "column": 1,
+                "label": "Chemo + Immuno",
+                "kind": "regimen",
+                "n": 365
+              },
+              {
+                "column": 1,
+                "label": "Biologic + Chemo",
+                "kind": "regimen",
+                "n": 351
+              },
+              {
+                "column": 1,
+                "label": "Chemo + Hormone + Targeted",
+                "kind": "regimen",
+                "n": 344
+              },
+              {
+                "column": 1,
+                "label": "Targeted",
+                "kind": "regimen",
+                "n": 320
+              },
+              {
+                "column": 1,
+                "label": "Investigational",
+                "kind": "regimen",
+                "n": 290
+              },
+              {
+                "column": 2,
+                "label": "",
+                "kind": "censored",
+                "n": 3577
+              },
+              {
+                "column": 2,
+                "label": "",
+                "kind": "stopped",
+                "n": 2399
+              },
+              {
+                "column": 2,
+                "label": "Hormone",
+                "kind": "regimen",
+                "n": 1172
+              },
+              {
+                "column": 2,
+                "label": "other",
+                "kind": "regimen",
+                "n": 910
+              },
+              {
+                "column": 2,
+                "label": "Chemo",
+                "kind": "regimen",
+                "n": 740
+              },
+              {
+                "column": 2,
+                "label": "Targeted",
+                "kind": "regimen",
+                "n": 212
+              },
+              {
+                "column": 2,
+                "label": "Biologic + Chemo",
+                "kind": "regimen",
+                "n": 164
+              },
+              {
+                "column": 2,
+                "label": "Investigational",
+                "kind": "regimen",
+                "n": 150
+              },
+              {
+                "column": 2,
+                "label": "Chemo + Hormone",
+                "kind": "regimen",
+                "n": 133
+              },
+              {
+                "column": 2,
+                "label": "Chemo + Hormone + Targeted",
+                "kind": "regimen",
+                "n": 106
+              },
+              {
+                "column": 2,
+                "label": "Chemo + Immuno",
+                "kind": "regimen",
+                "n": 82
+              },
+              {
+                "…": "22 more",
+                "sha256": "731466154026352e"
+              }
+            ],
+            "flows": [
+              {
+                "column": 1,
+                "from": "other",
+                "to": "",
+                "n": 1449,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "Chemo",
+                "to": "",
+                "n": 868,
+                "kind": "stopped"
+              },
+              {
+                "column": 1,
+                "from": "Hormone",
+                "to": "",
+                "n": 643,
+                "kind": "stopped"
+              },
+              {
+                "column": 1,
+                "from": "Hormone",
+                "to": "Hormone",
+                "n": 630,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Hormone",
+                "to": "",
+                "n": 612,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "Chemo",
+                "to": "Chemo",
+                "n": 479,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Hormone",
+                "to": "",
+                "n": 432,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "other",
+                "to": "",
+                "n": 393,
+                "kind": "stopped"
+              },
+              {
+                "column": 1,
+                "from": "Chemo",
+                "to": "",
+                "n": 363,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "Chemo",
+                "to": "other",
+                "n": 288,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Chemo",
+                "to": "Hormone",
+                "n": 270,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Hormone + Targeted",
+                "to": "",
+                "n": 269,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "other",
+                "to": "other",
+                "n": 223,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Hormone",
+                "to": "other",
+                "n": 171,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Targeted",
+                "to": "",
+                "n": 159,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Immuno",
+                "to": "",
+                "n": 155,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Hormone",
+                "to": "Hormone",
+                "n": 134,
+                "kind": "regimen"
+              },
+              {
+                "column": 1,
+                "from": "Biologic + Chemo",
+                "to": "",
+                "n": 119,
+                "kind": "censored"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Hormone",
+                "to": "",
+                "n": 115,
+                "kind": "stopped"
+              },
+              {
+                "column": 1,
+                "from": "Chemo + Immuno",
+                "to": "",
+                "n": 115,
+                "kind": "stopped"
+              },
+              {
+                "…": "234 more",
+                "sha256": "b7b472db490e48ad"
+              }
+            ],
+            "started": 9645,
+            "censored": 5474
+          }
+        ]
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "caveat": "Therapies are grouped into LINES before being sequenced, by the definition every line count here shares: systemic-therapy treatment records (class, from SUBTYPE or else TREATMENT_TYPE, on the systemic list: chemotherapy, immunotherapy, targeted, hormone, biologic, investigational and their spellings; radiation, surgery, bone and ancillary treatment are not lines). A record starting more than 90 days after every earlier one has stopped begins a new line; anything closer is the same line, and a line is labelled by its whole combination (sorted, so 'A + B' and 'B + A' are one regimen). That grouping is the point — a patient here has a median of 6 treatment records and 4 distinct agents, and combination therapy is several records describing ONE decision, not several lines. Lines are labelled by SUBTYPE. ENDING A PATH IS SPLIT IN TWO, because the difference matters more than anything else here: 'no further line' means the study kept watching for at least 90 days (one line gap, the same rule that defines a line) and saw no further therapy; 'record ends here' means it did not, so nothing can be concluded about that patient. 55.5% of paths end censored — reading those as patients who finished treatment would be a claim about the record-keeping dressed up as one about the patient. Neither terminal is an outcome measure. 51 less common regimens folded into 'other', which is 23.3% of first-line patients in the first panel — combination regimens have a genuinely long tail, so 'other' is real diversity rather than a rounding bucket. Raise topN to split it further. Both panels share one scale and one colour per regimen, so their heights are directly comparable. TP53 mut: 11,564 with a first line; TP53 wt: 9,645 with a first line.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Build lines and count what follows what",
+            "detail": "The treatmentSequence query above builds every patient's lines in the database and returns, per line number, how many patients went from one regimen to the next, stopped, or were lost to follow-up — counts only — plus how many records it set aside.",
+            "scope": "TP53 mut · msk_chord_2024",
+            "rowsIn": 13105
+          },
+          {
+            "kind": "fetch",
+            "title": "Build lines and count what follows what",
+            "detail": "The treatmentSequence query above builds every patient's lines in the database and returns, per line number, how many patients went from one regimen to the next, stopped, or were lost to follow-up — counts only — plus how many records it set aside.",
+            "scope": "TP53 wt · msk_chord_2024",
+            "rowsIn": 11594
+          },
+          {
+            "kind": "transform",
+            "title": "Choose the records that form lines",
+            "detail": "The one definition every line count shares (lib/treatment-lines.ts): treatment records whose class is systemic therapy (chemo, chemotherapy, immuno, immunotherapy, immunotherapy (including vaccines), vaccine, targeted, targeted therapy, targeted molecular therapy, targeted antibody therapy, hormone, hormone therapy, hormonal therapy, biologic, investigational, systemic therapy, medical therapy, pharmaceutical therapy, nos). A record's class is its SUBTYPE, or its TREATMENT_TYPE where it has none; names and values match ignoring case.",
+            "params": [
+              [
+                "Radiation Therapy",
+                "28,738 records set aside: not a line class"
+              ],
+              [
+                "Prior Medications to MSK",
+                "19,366 records set aside: not a line class"
+              ],
+              [
+                "Bone Treatment",
+                "5,548 records set aside: not a line class"
+              ],
+              [
+                "Other",
+                "12 records set aside: not a line class"
+              ]
+            ]
+          },
+          {
+            "kind": "transform",
+            "title": "Group records into lines",
+            "detail": "Per patient, by start date: a record starting more than 90 days after every earlier one has stopped begins a new line; anything closer — overlapping, or a switch within the gap — is the same line. A line is named by its sorted combination of classes ('A + B'); the label never decides which records form a line."
+          },
+          {
+            "kind": "transform",
+            "title": "Decide how each path ends",
+            "detail": "A patient's last line ends in 'no further line' only if they were observed for at least 90 days after it — their last contact being the later of their last recorded event of any type and their OS follow-up (OS_MONTHS × 30.4375 days) — and in 'record ends here' otherwise. Lines are followed up to line 3."
+          },
+          {
+            "kind": "transform",
+            "title": "Keep the 8 most common regimens",
+            "detail": "Flows are summed across studies, then regimens ranked by the patients leaving them (or, for one seen only as a destination, arriving), ties by name, across every panel so a regimen keeps one identity; the rest are drawn as 'other'.",
+            "params": [
+              [
+                "regimens folded into 'other'",
+                "51"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [],
+          "file": "lib/treatment-lines.ts, lib/views/sequence.ts",
+          "commit": null
+        },
+        "check": {
+          "sequence:TP53 mut:started": {
+            "v": 11564,
+            "abs": 0
+          },
+          "sequence:TP53 mut:censored": {
+            "v": 6417,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Chemo:n": {
+            "v": 5239,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:other:n": {
+            "v": 2689,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Biologic + Chemo:n": {
+            "v": 995,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Hormone:n": {
+            "v": 672,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Chemo + Hormone:n": {
+            "v": 644,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Chemo + Immuno:n": {
+            "v": 639,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Targeted:n": {
+            "v": 273,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Investigational:n": {
+            "v": 254,
+            "abs": 0
+          },
+          "sequence:TP53 mut:1:regimen:Chemo + Hormone + Targeted:n": {
+            "v": 159,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:censored::n": {
+            "v": 4206,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:stopped::n": {
+            "v": 3285,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:regimen:Chemo:n": {
+            "v": 1400,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:regimen:other:n": {
+            "v": 1038,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:regimen:Biologic + Chemo:n": {
+            "v": 538,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:regimen:Hormone:n": {
+            "v": 489,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:regimen:Targeted:n": {
+            "v": 174,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:regimen:Investigational:n": {
+            "v": 140,
+            "abs": 0
+          },
+          "sequence:TP53 mut:2:regimen:Chemo + Immuno:n": {
+            "v": 133,
+            "abs": 0
+          },
+          "…": {
+            "more": 68,
+            "sha256": "2abab4efd36844fb"
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/sequence.sql b/test/snapshots/sequence.sql
new file mode 100644
index 00000000..75d48920
--- /dev/null
+++ b/test/snapshots/sequence.sql
@@ -0,0 +1,262 @@
+-- cohort TP53 mut
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort TP53 wt
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))
+
+-- view 0 sequence: sequenceKeys · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))
+SELECT lowerUTF8(trimBoth(d.key)) AS k, arraySort((x) -> (-x.2, x.1), arrayMap((v) -> (v, countEqual(groupArray(trimBoth(d.key)), v)), groupUniqArray(trimBoth(d.key))))[1].1 AS label,
+       uniqExact(p.stable_id) AS pts, uniqExact(lowerUTF8(trimBoth(d.value))) AS values
+FROM snapshot_db.clinical_event ce
+JOIN snapshot_db.clinical_event_data d ON d.clinical_event_id = ce.clinical_event_id
+JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+  AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('treatment'))
+  AND p.stable_id IN (SELECT pid FROM cohort)
+GROUP BY k ORDER BY pts DESC, k LIMIT 25
+
+-- view 0 sequence: treatmentSequence:SUBTYPE/gap90 · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+txAll AS (
+  SELECT ce.clinical_event_id AS eid, ce.patient_id AS patient_id, p.stable_id AS pid,
+         ce.start_date AS s, coalesce(ce.stop_date, ce.start_date) AS e,
+         coalesce(cl.cls, '') AS cls, coalesce(cl.cls_raw, '') AS cls_raw
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  LEFT JOIN (
+    SELECT clinical_event_id, argMin(lowerUTF8(trimBoth(value)), (lowerUTF8(trimBoth(key)) != 'subtype', lowerUTF8(trimBoth(value)))) AS cls,
+           argMin(trimBoth(value), (lowerUTF8(trimBoth(key)) != 'subtype', lowerUTF8(trimBoth(value)))) AS cls_raw
+    FROM snapshot_db.clinical_event_data
+    WHERE lowerUTF8(trimBoth(key)) IN (lowerUTF8(trimBoth('subtype')), lowerUTF8(trimBoth('treatment_type'))) AND lowerUTF8(trimBoth(value)) != ''
+      AND clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_derived
+        WHERE cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(event_type)) = lowerUTF8(trimBoth('treatment')))
+    GROUP BY clinical_event_id
+  ) cl ON cl.clinical_event_id = ce.clinical_event_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('treatment'))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+),
+txLabelled AS (
+  SELECT t.eid AS eid, t.patient_id AS patient_id, t.pid AS pid, t.s AS s, t.e AS e, t.cls AS cls,
+         t.cls AS lk, t.cls_raw AS lraw
+  FROM txAll t
+  WHERE t.cls IN (lowerUTF8(trimBoth('chemo')), lowerUTF8(trimBoth('chemotherapy')), lowerUTF8(trimBoth('immuno')), lowerUTF8(trimBoth('immunotherapy')), lowerUTF8(trimBoth('immunotherapy (including vaccines)')), lowerUTF8(trimBoth('vaccine')), lowerUTF8(trimBoth('targeted')), lowerUTF8(trimBoth('targeted therapy')), lowerUTF8(trimBoth('targeted molecular therapy')), lowerUTF8(trimBoth('targeted antibody therapy')), lowerUTF8(trimBoth('hormone')), lowerUTF8(trimBoth('hormone therapy')), lowerUTF8(trimBoth('hormonal therapy')), lowerUTF8(trimBoth('biologic')), lowerUTF8(trimBoth('investigational')), lowerUTF8(trimBoth('systemic therapy')), lowerUTF8(trimBoth('medical therapy')), lowerUTF8(trimBoth('pharmaceutical therapy, nos')))
+),
+spell AS (
+  SELECT lk, arraySort((x) -> (-x.2, x.1), groupArray((lraw, c)))[1].1 AS lbl FROM (
+    SELECT lk, lraw, count() AS c FROM txLabelled WHERE lk != '' GROUP BY lk, lraw
+  ) GROUP BY lk
+),
+tx AS (
+  SELECT x.eid AS eid, x.patient_id AS patient_id, x.pid AS pid, x.s AS s, x.e AS e, x.cls AS cls,
+         if(x.lk = '', '', sp.lbl) AS lbl
+  FROM txLabelled x LEFT JOIN spell sp ON sp.lk = x.lk
+),
+flagged AS (
+  SELECT *, max(toNullable(e)) OVER (PARTITION BY patient_id
+    ORDER BY s, e, eid ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) AS pms
+  FROM tx
+),
+numbered AS (
+  SELECT *, sum(if(pms IS NULL OR s > pms + 90, 1, 0)) OVER (
+    PARTITION BY patient_id ORDER BY s, e, eid ROWS UNBOUNDED PRECEDING) AS line_no
+  FROM flagged
+),
+lines AS (
+  SELECT patient_id, any(pid) AS pid, line_no, min(s) AS ls, max(e) AS le,
+         if(countIf(lbl != '') = 0, '(no class)',
+            arrayStringConcat(arraySort(groupUniqArrayIf(lbl, lbl != '')), ' + ')) AS combo
+  FROM numbered GROUP BY patient_id, line_no
+),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), NULL) * 30.4375 AS os_day
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient'
+    AND attribute_name = 'OS_MONTHS'
+  GROUP BY pid
+),
+seen AS (
+  SELECT p.stable_id AS pid, max(coalesce(ce.stop_date, ce.start_date)) AS max_ev
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+obs AS (
+  SELECT sn.pid AS pid,
+         greatest(toFloat64(sn.max_ev), coalesce(os.os_day, toFloat64(sn.max_ev))) AS last_seen
+  FROM seen sn LEFT JOIN os ON os.pid = sn.pid
+),
+seq AS (
+  SELECT pid, line_no, combo, le AS line_end,
+         leadInFrame(combo) OVER (PARTITION BY pid ORDER BY line_no
+           ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS nxt
+  FROM lines
+),
+fated AS (
+  SELECT sq2.line_no AS line_no, sq2.combo AS combo, sq2.pid AS pid,
+         coalesce(sq2.nxt, '') AS nxt,
+         if(coalesce(sq2.nxt, '') != '', 0,
+            if(coalesce(o.last_seen, toFloat64(sq2.line_end)) - sq2.line_end >= 90, 1, 2)) AS fate
+  FROM seq sq2 LEFT JOIN obs o ON o.pid = sq2.pid
+)
+SELECT step, a, b, fate, n FROM (
+  SELECT toUInt32(line_no) AS step, toString(combo) AS a, toString(nxt) AS b, toUInt8(fate) AS fate, toUInt64(uniqExact(pid)) AS n
+  FROM fated WHERE line_no <= 3
+  GROUP BY step, a, b, fate ORDER BY n DESC, step, a, b, fate LIMIT 2000
+)
+UNION ALL
+SELECT toUInt32(0) AS step, toString(if(cls = '', '(no class)', any(cls_raw))) AS a,
+       toString(if(cls IN (lowerUTF8(trimBoth('chemo')), lowerUTF8(trimBoth('chemotherapy')), lowerUTF8(trimBoth('immuno')), lowerUTF8(trimBoth('immunotherapy')), lowerUTF8(trimBoth('immunotherapy (including vaccines)')), lowerUTF8(trimBoth('vaccine')), lowerUTF8(trimBoth('targeted')), lowerUTF8(trimBoth('targeted therapy')), lowerUTF8(trimBoth('targeted molecular therapy')), lowerUTF8(trimBoth('targeted antibody therapy')), lowerUTF8(trimBoth('hormone')), lowerUTF8(trimBoth('hormone therapy')), lowerUTF8(trimBoth('hormonal therapy')), lowerUTF8(trimBoth('biologic')), lowerUTF8(trimBoth('investigational')), lowerUTF8(trimBoth('systemic therapy')), lowerUTF8(trimBoth('medical therapy')), lowerUTF8(trimBoth('pharmaceutical therapy, nos'))), '#excluded by the document', '#not a line class')) AS b,
+       toUInt8(0) AS fate, toUInt64(count()) AS n
+FROM txAll WHERE eid NOT IN (SELECT eid FROM txLabelled)
+GROUP BY cls, b
+
+-- view 0 sequence: treatmentSequence:SUBTYPE/gap90 · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+txAll AS (
+  SELECT ce.clinical_event_id AS eid, ce.patient_id AS patient_id, p.stable_id AS pid,
+         ce.start_date AS s, coalesce(ce.stop_date, ce.start_date) AS e,
+         coalesce(cl.cls, '') AS cls, coalesce(cl.cls_raw, '') AS cls_raw
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  LEFT JOIN (
+    SELECT clinical_event_id, argMin(lowerUTF8(trimBoth(value)), (lowerUTF8(trimBoth(key)) != 'subtype', lowerUTF8(trimBoth(value)))) AS cls,
+           argMin(trimBoth(value), (lowerUTF8(trimBoth(key)) != 'subtype', lowerUTF8(trimBoth(value)))) AS cls_raw
+    FROM snapshot_db.clinical_event_data
+    WHERE lowerUTF8(trimBoth(key)) IN (lowerUTF8(trimBoth('subtype')), lowerUTF8(trimBoth('treatment_type'))) AND lowerUTF8(trimBoth(value)) != ''
+      AND clinical_event_id IN (SELECT clinical_event_id FROM snapshot_db.clinical_event_derived
+        WHERE cancer_study_identifier = 'msk_chord_2024' AND lowerUTF8(trimBoth(event_type)) = lowerUTF8(trimBoth('treatment')))
+    GROUP BY clinical_event_id
+  ) cl ON cl.clinical_event_id = ce.clinical_event_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND lowerUTF8(trimBoth(ce.event_type)) = lowerUTF8(trimBoth('treatment'))
+    AND p.stable_id IN (SELECT pid FROM cohort)
+),
+txLabelled AS (
+  SELECT t.eid AS eid, t.patient_id AS patient_id, t.pid AS pid, t.s AS s, t.e AS e, t.cls AS cls,
+         t.cls AS lk, t.cls_raw AS lraw
+  FROM txAll t
+  WHERE t.cls IN (lowerUTF8(trimBoth('chemo')), lowerUTF8(trimBoth('chemotherapy')), lowerUTF8(trimBoth('immuno')), lowerUTF8(trimBoth('immunotherapy')), lowerUTF8(trimBoth('immunotherapy (including vaccines)')), lowerUTF8(trimBoth('vaccine')), lowerUTF8(trimBoth('targeted')), lowerUTF8(trimBoth('targeted therapy')), lowerUTF8(trimBoth('targeted molecular therapy')), lowerUTF8(trimBoth('targeted antibody therapy')), lowerUTF8(trimBoth('hormone')), lowerUTF8(trimBoth('hormone therapy')), lowerUTF8(trimBoth('hormonal therapy')), lowerUTF8(trimBoth('biologic')), lowerUTF8(trimBoth('investigational')), lowerUTF8(trimBoth('systemic therapy')), lowerUTF8(trimBoth('medical therapy')), lowerUTF8(trimBoth('pharmaceutical therapy, nos')))
+),
+spell AS (
+  SELECT lk, arraySort((x) -> (-x.2, x.1), groupArray((lraw, c)))[1].1 AS lbl FROM (
+    SELECT lk, lraw, count() AS c FROM txLabelled WHERE lk != '' GROUP BY lk, lraw
+  ) GROUP BY lk
+),
+tx AS (
+  SELECT x.eid AS eid, x.patient_id AS patient_id, x.pid AS pid, x.s AS s, x.e AS e, x.cls AS cls,
+         if(x.lk = '', '', sp.lbl) AS lbl
+  FROM txLabelled x LEFT JOIN spell sp ON sp.lk = x.lk
+),
+flagged AS (
+  SELECT *, max(toNullable(e)) OVER (PARTITION BY patient_id
+    ORDER BY s, e, eid ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) AS pms
+  FROM tx
+),
+numbered AS (
+  SELECT *, sum(if(pms IS NULL OR s > pms + 90, 1, 0)) OVER (
+    PARTITION BY patient_id ORDER BY s, e, eid ROWS UNBOUNDED PRECEDING) AS line_no
+  FROM flagged
+),
+lines AS (
+  SELECT patient_id, any(pid) AS pid, line_no, min(s) AS ls, max(e) AS le,
+         if(countIf(lbl != '') = 0, '(no class)',
+            arrayStringConcat(arraySort(groupUniqArrayIf(lbl, lbl != '')), ' + ')) AS combo
+  FROM numbered GROUP BY patient_id, line_no
+),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         if(isFinite(ifNull(toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), nan)), toFloat64OrNull(anyIf(attribute_value, attribute_name = 'OS_MONTHS')), NULL) * 30.4375 AS os_day
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient'
+    AND attribute_name = 'OS_MONTHS'
+  GROUP BY pid
+),
+seen AS (
+  SELECT p.stable_id AS pid, max(coalesce(ce.stop_date, ce.start_date)) AS max_ev
+  FROM snapshot_db.clinical_event ce
+  JOIN snapshot_db.patient p ON p.internal_id = ce.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+    AND p.stable_id IN (SELECT pid FROM cohort)
+  GROUP BY pid
+),
+obs AS (
+  SELECT sn.pid AS pid,
+         greatest(toFloat64(sn.max_ev), coalesce(os.os_day, toFloat64(sn.max_ev))) AS last_seen
+  FROM seen sn LEFT JOIN os ON os.pid = sn.pid
+),
+seq AS (
+  SELECT pid, line_no, combo, le AS line_end,
+         leadInFrame(combo) OVER (PARTITION BY pid ORDER BY line_no
+           ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS nxt
+  FROM lines
+),
+fated AS (
+  SELECT sq2.line_no AS line_no, sq2.combo AS combo, sq2.pid AS pid,
+         coalesce(sq2.nxt, '') AS nxt,
+         if(coalesce(sq2.nxt, '') != '', 0,
+            if(coalesce(o.last_seen, toFloat64(sq2.line_end)) - sq2.line_end >= 90, 1, 2)) AS fate
+  FROM seq sq2 LEFT JOIN obs o ON o.pid = sq2.pid
+)
+SELECT step, a, b, fate, n FROM (
+  SELECT toUInt32(line_no) AS step, toString(combo) AS a, toString(nxt) AS b, toUInt8(fate) AS fate, toUInt64(uniqExact(pid)) AS n
+  FROM fated WHERE line_no <= 3
+  GROUP BY step, a, b, fate ORDER BY n DESC, step, a, b, fate LIMIT 2000
+)
+UNION ALL
+SELECT toUInt32(0) AS step, toString(if(cls = '', '(no class)', any(cls_raw))) AS a,
+       toString(if(cls IN (lowerUTF8(trimBoth('chemo')), lowerUTF8(trimBoth('chemotherapy')), lowerUTF8(trimBoth('immuno')), lowerUTF8(trimBoth('immunotherapy')), lowerUTF8(trimBoth('immunotherapy (including vaccines)')), lowerUTF8(trimBoth('vaccine')), lowerUTF8(trimBoth('targeted')), lowerUTF8(trimBoth('targeted therapy')), lowerUTF8(trimBoth('targeted molecular therapy')), lowerUTF8(trimBoth('targeted antibody therapy')), lowerUTF8(trimBoth('hormone')), lowerUTF8(trimBoth('hormone therapy')), lowerUTF8(trimBoth('hormonal therapy')), lowerUTF8(trimBoth('biologic')), lowerUTF8(trimBoth('investigational')), lowerUTF8(trimBoth('systemic therapy')), lowerUTF8(trimBoth('medical therapy')), lowerUTF8(trimBoth('pharmaceutical therapy, nos'))), '#excluded by the document', '#not a line class')) AS b,
+       toUInt8(0) AS fate, toUInt64(count()) AS n
+FROM txAll WHERE eid NOT IN (SELECT eid FROM txLabelled)
+GROUP BY cls, b
diff --git a/test/snapshots/sequence.svg b/test/snapshots/sequence.svg
new file mode 100644
index 00000000..918d66d4
--- /dev/null
+++ b/test/snapshots/sequence.svg
@@ -0,0 +1,2 @@
+<!-- view 0 sequence -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 879" width="100%" role="img" aria-label="Treatment sequence — TP53 mut, TP53 wt" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Treatment sequence — TP53 mut, TP53 wt</title><rect width="720" height="879" fill="#ffffff"/><rect x="8" y="7" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="15.9" font-size="11" fill="#16232b">Chemo</text><rect x="73.881" y="7" width="10" height="10" rx="2" fill="#E69F00"/><text x="88.9" y="15.9" font-size="11" fill="#16232b">Hormone</text><rect x="150.905" y="7" width="10" height="10" rx="2" fill="#009E73"/><text x="165.9" y="15.9" font-size="11" fill="#16232b">Biologic + Chemo</text><rect x="267.914" y="7" width="10" height="10" rx="2" fill="#CC79A7"/><text x="282.9" y="15.9" font-size="11" fill="#16232b">Chemo + Hormone</text><rect x="391.611" y="7" width="10" height="10" rx="2" fill="#D55E00"/><text x="406.6" y="15.9" font-size="11" fill="#16232b">Chemo + Immuno</text><rect x="511.546" y="7" width="10" height="10" rx="2" fill="#56B4E9"/><text x="526.5" y="15.9" font-size="11" fill="#16232b">Chemo + Hormone + Targeted</text><rect x="8" y="25" width="10" height="10" rx="2" fill="#0072B2" opacity="0.75"/><text x="23" y="33.9" font-size="11" fill="#16232b">Targeted</text><rect x="83.33" y="25" width="10" height="10" rx="2" fill="#E69F00" opacity="0.75"/><text x="98.3" y="33.9" font-size="11" fill="#16232b">Investigational</text><rect x="187.964" y="25" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="203" y="33.9" font-size="11" fill="#16232b">other<tspan dx="6" fill="#5b6b76">51 less common regimens</tspan></text><rect x="381.23" y="25" width="10" height="10" rx="2" fill="#62737d"/><text x="396.2" y="33.9" font-size="11" fill="#16232b">no further line<tspan dx="6" fill="#5b6b76">watched, none started</tspan></text><rect x="8" y="43" width="10" height="10" rx="2" fill="#c8ced2"/><text x="23" y="51.9" font-size="11" fill="#16232b">record ends here<tspan dx="6" fill="#5b6b76">nothing can be concluded</tspan></text><text x="8" y="82" font-size="11" fill="#16232b" font-weight="600">TP53 mut</text><text x="62" y="82" font-size="11" fill="#5b6b76">— 11,564 with a first line, 55.5% of paths censored</text><text x="8" y="95" font-size="10" fill="#5b6b76">line 1</text><text x="185" y="95" font-size="10" fill="#5b6b76">line 2</text><text x="362" y="95" font-size="10" fill="#5b6b76">line 3</text><text x="539" y="95" font-size="10" fill="#5b6b76">line 4+</text><path d="M 21 100 C 103 100, 103 100, 185 100 L 185 116.13 C 103 116.13, 103 116.13, 21 116.13 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo, line 1 to 2: 1,097</title></path><path d="M 21 116.13 C 103 116.13, 103 130.58, 185 130.58 L 185 137.76 C 103 137.76, 103 123.3, 21 123.3 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → other, line 1 to 2: 488</title></path><path d="M 21 123.3 C 103 123.3, 103 155.84, 185 155.84 L 185 160.68 C 103 160.68, 103 128.14, 21 128.14 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Biologic + Chemo, line 1 to 2: 329</title></path><path d="M 21 187.02 C 103 187.02, 103 137.76, 185 137.76 L 185 140.96 C 103 140.96, 103 190.22, 21 190.22 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → other, line 1 to 2: 218</title></path><path d="M 21 261.18 C 103 261.18, 103 173.75, 185 173.75 L 185 176.85 C 103 176.85, 103 264.28, 21 264.28 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Hormone, line 1 to 2: 211</title></path><path d="M 21 236.55 C 103 236.55, 103 160.68, 185 160.68 L 185 162.66 C 103 162.66, 103 238.53, 21 238.53 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Biologic + Chemo, line 1 to 2: 135</title></path><path d="M 21 128.14 C 103 128.14, 103 176.85, 185 176.85 L 185 178.7 C 103 178.7, 103 129.99, 21 129.99 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Hormone, line 1 to 2: 126</title></path><path d="M 21 190.22 C 103 190.22, 103 116.13, 185 116.13 L 185 117.92 C 103 117.92, 103 192.02, 21 192.02 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo, line 1 to 2: 122</title></path><path d="M 21 238.53 C 103 238.53, 103 140.96, 185 140.96 L 185 142.59 C 103 142.59, 103 240.16, 21 240.16 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → other, line 1 to 2: 111</title></path><path d="M 21 129.99 C 103 129.99, 103 215.55, 185 215.55 L 185 216.75 C 103 216.75, 103 131.18, 21 131.18 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Immuno, line 1 to 2: 81</title></path><path d="M 21 240.16 C 103 240.16, 103 117.92, 185 117.92 L 185 119.05 C 103 119.05, 103 241.3, 21 241.3 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo, line 1 to 2: 77</title></path><path d="M 21 281.05 C 103 281.05, 103 178.7, 185 178.7 L 185 179.84 C 103 179.84, 103 282.19, 21 282.19 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Hormone, line 1 to 2: 77</title></path><path d="M 21 131.18 C 103 131.18, 103 227.51, 185 227.51 L 185 228.35 C 103 228.35, 103 132.02, 21 132.02 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone, line 1 to 2: 57</title></path><path d="M 21 132.02 C 103 132.02, 103 203.5, 185 203.5 L 185 204.32 C 103 204.32, 103 132.84, 21 132.84 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Investigational, line 1 to 2: 56</title></path><path d="M 21 192.02 C 103 192.02, 103 179.84, 185 179.84 L 185 180.66 C 103 180.66, 103 192.84, 21 192.84 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Hormone, line 1 to 2: 56</title></path><path d="M 21 132.84 C 103 132.84, 103 190.94, 185 190.94 L 185 191.73 C 103 191.73, 103 133.64, 21 133.64 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Targeted, line 1 to 2: 54</title></path><path d="M 21 264.28 C 103 264.28, 103 142.59, 185 142.59 L 185 143.37 C 103 143.37, 103 265.06, 21 265.06 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → other, line 1 to 2: 53</title></path><path d="M 21 333.93 C 103 333.93, 103 143.37, 185 143.37 L 185 144.11 C 103 144.11, 103 334.66, 21 334.66 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → other, line 1 to 2: 50</title></path><path d="M 21 192.84 C 103 192.84, 103 191.73, 185 191.73 L 185 192.39 C 103 192.39, 103 193.5, 21 193.5 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Targeted, line 1 to 2: 45</title></path><path d="M 21 319.92 C 103 319.92, 103 192.39, 185 192.39 L 185 193.04 C 103 193.04, 103 320.56, 21 320.56 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Targeted, line 1 to 2: 44</title></path><path d="M 21 193.5 C 103 193.5, 103 162.66, 185 162.66 L 185 163.29 C 103 163.29, 103 194.13, 21 194.13 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Biologic + Chemo, line 1 to 2: 43</title></path><path d="M 21 282.19 C 103 282.19, 103 144.11, 185 144.11 L 185 144.71 C 103 144.71, 103 282.79, 21 282.79 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → other, line 1 to 2: 39</title></path><path d="M 21 300.52 C 103 300.52, 103 144.71, 185 144.71 L 185 145.31 C 103 145.31, 103 301.12, 21 301.12 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → other, line 1 to 2: 38</title></path><path d="M 21 194.13 C 103 194.13, 103 204.32, 185 204.32 L 185 204.92 C 103 204.92, 103 194.73, 21 194.73 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Investigational, line 1 to 2: 37</title></path><path d="M 21 320.56 C 103 320.56, 103 145.31, 185 145.31 L 185 145.91 C 103 145.91, 103 321.16, 21 321.16 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → other, line 1 to 2: 36</title></path><path d="M 21 265.06 C 103 265.06, 103 228.35, 185 228.35 L 185 228.95 C 103 228.95, 103 265.66, 21 265.66 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone, line 1 to 2: 35</title></path><path d="M 21 334.66 C 103 334.66, 103 119.05, 185 119.05 L 185 119.65 C 103 119.65, 103 335.26, 21 335.26 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo, line 1 to 2: 26</title></path><path d="M 21 282.79 C 103 282.79, 103 119.65, 185 119.65 L 185 120.25 C 103 120.25, 103 283.39, 21 283.39 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo, line 1 to 2: 25</title></path><path d="M 21 301.12 C 103 301.12, 103 120.25, 185 120.25 L 185 120.85 C 103 120.85, 103 301.72, 21 301.72 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo, line 1 to 2: 25</title></path><path d="M 21 265.66 C 103 265.66, 103 120.85, 185 120.85 L 185 121.45 C 103 121.45, 103 266.26, 21 266.26 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo, line 1 to 2: 22</title></path><path d="M 21 335.26 C 103 335.26, 103 204.92, 185 204.92 L 185 205.52 C 103 205.52, 103 335.86, 21 335.86 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Investigational, line 1 to 2: 22</title></path><path d="M 21 133.64 C 103 133.64, 103 239.2, 185 239.2 L 185 239.8 C 103 239.8, 103 134.24, 21 134.24 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone + Targeted, line 1 to 2: 21</title></path><path d="M 21 194.73 C 103 194.73, 103 216.75, 185 216.75 L 185 217.35 C 103 217.35, 103 195.33, 21 195.33 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Immuno, line 1 to 2: 18</title></path><path d="M 21 301.72 C 103 301.72, 103 217.35, 185 217.35 L 185 217.95 C 103 217.95, 103 302.32, 21 302.32 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Immuno, line 1 to 2: 17</title></path><path d="M 21 335.86 C 103 335.86, 103 193.04, 185 193.04 L 185 193.64 C 103 193.64, 103 336.46, 21 336.46 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Targeted, line 1 to 2: 11</title></path><path d="M 21 283.39 C 103 283.39, 103 228.95, 185 228.95 L 185 229.55 C 103 229.55, 103 283.99, 21 283.99 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone, line 1 to 2: 10</title></path><path d="M 21 283.99 C 103 283.99, 103 239.8, 185 239.8 L 185 240.4 C 103 240.4, 103 284.59, 21 284.59 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone + Targeted, line 1 to 2: 10</title></path><path d="M 21 302.32 C 103 302.32, 103 163.29, 185 163.29 L 185 163.89 C 103 163.89, 103 302.92, 21 302.92 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Biologic + Chemo, line 1 to 2: 10</title></path><path d="M 21 336.46 C 103 336.46, 103 217.95, 185 217.95 L 185 218.55 C 103 218.55, 103 337.06, 21 337.06 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Immuno, line 1 to 2: 10</title></path><path d="M 21 241.3 C 103 241.3, 103 193.64, 185 193.64 L 185 194.24 C 103 194.24, 103 241.9, 21 241.9 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Targeted, line 1 to 2: 8</title></path><path d="M 21 337.06 C 103 337.06, 103 180.66, 185 180.66 L 185 181.26 C 103 181.26, 103 337.66, 21 337.66 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Hormone, line 1 to 2: 8</title></path><path d="M 21 241.9 C 103 241.9, 103 205.52, 185 205.52 L 185 206.12 C 103 206.12, 103 242.5, 21 242.5 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Investigational, line 1 to 2: 7</title></path><path d="M 21 266.26 C 103 266.26, 103 240.4, 185 240.4 L 185 241 C 103 241, 103 266.86, 21 266.86 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone + Targeted, line 1 to 2: 7</title></path><path d="M 21 337.66 C 103 337.66, 103 163.89, 185 163.89 L 185 164.49 C 103 164.49, 103 338.26, 21 338.26 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Biologic + Chemo, line 1 to 2: 7</title></path><path d="M 21 321.16 C 103 321.16, 103 206.12, 185 206.12 L 185 206.72 C 103 206.72, 103 321.76, 21 321.76 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Investigational, line 1 to 2: 7</title></path><path d="M 21 195.33 C 103 195.33, 103 229.55, 185 229.55 L 185 230.15 C 103 230.15, 103 195.93, 21 195.93 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone, line 1 to 2: 7</title></path><path d="M 21 242.5 C 103 242.5, 103 181.26, 185 181.26 L 185 181.86 C 103 181.86, 103 243.1, 21 243.1 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Hormone, line 1 to 2: 6</title></path><path d="M 21 284.59 C 103 284.59, 103 164.49, 185 164.49 L 185 165.09 C 103 165.09, 103 285.19, 21 285.19 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Biologic + Chemo, line 1 to 2: 6</title></path><path d="M 21 195.93 C 103 195.93, 103 241, 185 241 L 185 241.6 C 103 241.6, 103 196.53, 21 196.53 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone + Targeted, line 1 to 2: 6</title></path><path d="M 21 347.66 C 103 347.66, 103 121.45, 185 121.45 L 185 122.05 C 103 122.05, 103 348.26, 21 348.26 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo, line 1 to 2: 5</title></path><path d="M 21 302.92 C 103 302.92, 103 194.24, 185 194.24 L 185 194.84 C 103 194.84, 103 303.52, 21 303.52 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Targeted, line 1 to 2: 5</title></path><path d="M 21 348.26 C 103 348.26, 103 145.91, 185 145.91 L 185 146.51 C 103 146.51, 103 348.86, 21 348.86 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → other, line 1 to 2: 5</title></path><path d="M 21 243.1 C 103 243.1, 103 230.15, 185 230.15 L 185 230.75 C 103 230.75, 103 243.7, 21 243.7 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo + Hormone, line 1 to 2: 4</title></path><path d="M 21 285.19 C 103 285.19, 103 194.84, 185 194.84 L 185 195.44 C 103 195.44, 103 285.79, 21 285.79 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Targeted, line 1 to 2: 4</title></path><path d="M 21 266.86 C 103 266.86, 103 165.09, 185 165.09 L 185 165.69 C 103 165.69, 103 267.46, 21 267.46 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Biologic + Chemo, line 1 to 2: 4</title></path><path d="M 21 267.46 C 103 267.46, 103 206.72, 185 206.72 L 185 207.32 C 103 207.32, 103 268.06, 21 268.06 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Investigational, line 1 to 2: 4</title></path><path d="M 21 321.76 C 103 321.76, 103 165.69, 185 165.69 L 185 166.29 C 103 166.29, 103 322.36, 21 322.36 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Biologic + Chemo, line 1 to 2: 4</title></path><path d="M 21 243.7 C 103 243.7, 103 218.55, 185 218.55 L 185 219.15 C 103 219.15, 103 244.3, 21 244.3 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo + Immuno, line 1 to 2: 3</title></path><path d="M 21 285.79 C 103 285.79, 103 207.32, 185 207.32 L 185 207.92 C 103 207.92, 103 286.39, 21 286.39 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Investigational, line 1 to 2: 3</title></path><path d="M 21 303.52 C 103 303.52, 103 207.92, 185 207.92 L 185 208.52 C 103 208.52, 103 304.12, 21 304.12 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Investigational, line 1 to 2: 3</title></path><path d="M 21 268.06 C 103 268.06, 103 219.15, 185 219.15 L 185 219.75 C 103 219.75, 103 268.66, 21 268.66 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Immuno, line 1 to 2: 3</title></path><path d="M 21 348.86 C 103 348.86, 103 181.86, 185 181.86 L 185 182.46 C 103 182.46, 103 349.46, 21 349.46 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Hormone, line 1 to 2: 2</title></path><path d="M 21 268.66 C 103 268.66, 103 195.44, 185 195.44 L 185 196.04 C 103 196.04, 103 269.26, 21 269.26 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Targeted, line 1 to 2: 2</title></path><path d="M 21 322.36 C 103 322.36, 103 182.46, 185 182.46 L 185 183.06 C 103 183.06, 103 322.96, 21 322.96 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Hormone, line 1 to 2: 2</title></path><path d="M 21 349.46 C 103 349.46, 103 208.52, 185 208.52 L 185 209.12 C 103 209.12, 103 350.06, 21 350.06 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Investigational, line 1 to 2: 1</title></path><path d="M 21 350.06 C 103 350.06, 103 196.04, 185 196.04 L 185 196.64 C 103 196.64, 103 350.66, 21 350.66 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Targeted, line 1 to 2: 1</title></path><path d="M 21 304.12 C 103 304.12, 103 230.75, 185 230.75 L 185 231.35 C 103 231.35, 103 304.72, 21 304.72 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Hormone, line 1 to 2: 1</title></path><path d="M 21 304.72 C 103 304.72, 103 183.06, 185 183.06 L 185 183.66 C 103 183.66, 103 305.32, 21 305.32 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Hormone, line 1 to 2: 1</title></path><path d="M 21 338.26 C 103 338.26, 103 231.35, 185 231.35 L 185 231.95 C 103 231.95, 103 338.86, 21 338.86 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone, line 1 to 2: 1</title></path><path d="M 21 338.86 C 103 338.86, 103 241.6, 185 241.6 L 185 242.2 C 103 242.2, 103 339.46, 21 339.46 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone + Targeted, line 1 to 2: 1</title></path><path d="M 21 322.96 C 103 322.96, 103 122.05, 185 122.05 L 185 122.65 C 103 122.65, 103 323.56, 21 323.56 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo, line 1 to 2: 1</title></path><path d="M 21 323.56 C 103 323.56, 103 242.2, 185 242.2 L 185 242.8 C 103 242.8, 103 324.16, 21 324.16 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Hormone + Targeted, line 1 to 2: 1</title></path><path d="M 21 324.16 C 103 324.16, 103 219.75, 185 219.75 L 185 220.35 C 103 220.35, 103 324.76, 21 324.76 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Immuno, line 1 to 2: 1</title></path><path d="M 21 134.24 C 103 134.24, 103 250.2, 185 250.2 L 185 279.1 C 103 279.1, 103 163.14, 21 163.14 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo → no further line, line 1 to 2: 1,966</title></path><path d="M 21 196.53 C 103 196.53, 103 279.1, 185 279.1 L 185 285.53 C 103 285.53, 103 202.96, 21 202.96 Z" fill="#62737d" fill-opacity="0.45"><title>other → no further line, line 1 to 2: 437</title></path><path d="M 21 305.32 C 103 305.32, 103 285.53, 185 285.53 L 185 288.79 C 103 288.79, 103 308.59, 21 308.59 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Immuno → no further line, line 1 to 2: 222</title></path><path d="M 21 244.3 C 103 244.3, 103 288.79, 185 288.79 L 185 291.97 C 103 291.97, 103 247.47, 21 247.47 Z" fill="#62737d" fill-opacity="0.45"><title>Biologic + Chemo → no further line, line 1 to 2: 216</title></path><path d="M 21 269.26 C 103 269.26, 103 291.97, 185 291.97 L 185 294.46 C 103 294.46, 103 271.76, 21 271.76 Z" fill="#62737d" fill-opacity="0.45"><title>Hormone → no further line, line 1 to 2: 170</title></path><path d="M 21 286.39 C 103 286.39, 103 294.46, 185 294.46 L 185 296.45 C 103 296.45, 103 288.37, 21 288.37 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone → no further line, line 1 to 2: 135</title></path><path d="M 21 339.46 C 103 339.46, 103 296.45, 185 296.45 L 185 297.73 C 103 297.73, 103 340.74, 21 340.74 Z" fill="#62737d" fill-opacity="0.45"><title>Investigational → no further line, line 1 to 2: 87</title></path><path d="M 21 324.76 C 103 324.76, 103 297.73, 185 297.73 L 185 298.33 C 103 298.33, 103 325.36, 21 325.36 Z" fill="#62737d" fill-opacity="0.45"><title>Targeted → no further line, line 1 to 2: 41</title></path><path d="M 21 350.66 C 103 350.66, 103 298.33, 185 298.33 L 185 298.93 C 103 298.93, 103 351.26, 21 351.26 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → no further line, line 1 to 2: 11</title></path><path d="M 21 202.96 C 103 202.96, 103 308.49, 185 308.49 L 185 333.48 C 103 333.48, 103 227.95, 21 227.95 Z" fill="#c8ced2" fill-opacity="0.45"><title>other → record ends here, line 1 to 2: 1,700</title></path><path d="M 21 163.14 C 103 163.14, 103 333.48, 185 333.48 L 185 347.66 C 103 347.66, 103 177.31, 21 177.31 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo → record ends here, line 1 to 2: 964</title></path><path d="M 21 247.47 C 103 247.47, 103 347.66, 185 347.66 L 185 353.95 C 103 353.95, 103 253.76, 21 253.76 Z" fill="#c8ced2" fill-opacity="0.45"><title>Biologic + Chemo → record ends here, line 1 to 2: 428</title></path><path d="M 21 288.37 C 103 288.37, 103 353.95, 185 353.95 L 185 358.87 C 103 358.87, 103 293.3, 21 293.3 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone → record ends here, line 1 to 2: 335</title></path><path d="M 21 308.59 C 103 308.59, 103 358.87, 185 358.87 L 185 363.53 C 103 363.53, 103 313.25, 21 313.25 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Immuno → record ends here, line 1 to 2: 317</title></path><path d="M 21 271.76 C 103 271.76, 103 363.53, 185 363.53 L 185 365.9 C 103 365.9, 103 274.12, 21 274.12 Z" fill="#c8ced2" fill-opacity="0.45"><title>Hormone → record ends here, line 1 to 2: 161</title></path><path d="M 21 325.36 C 103 325.36, 103 365.9, 185 365.9 L 185 367.9 C 103 367.9, 103 327.36, 21 327.36 Z" fill="#c8ced2" fill-opacity="0.45"><title>Targeted → record ends here, line 1 to 2: 136</title></path><path d="M 21 351.26 C 103 351.26, 103 367.9, 185 367.9 L 185 369.87 C 103 369.87, 103 353.23, 21 353.23 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → record ends here, line 1 to 2: 134</title></path><path d="M 21 340.74 C 103 340.74, 103 369.87, 185 369.87 L 185 370.47 C 103 370.47, 103 341.34, 21 341.34 Z" fill="#c8ced2" fill-opacity="0.45"><title>Investigational → record ends here, line 1 to 2: 31</title></path><path d="M 198 100 C 280 100, 280 100, 362 100 L 362 104.1 C 280 104.1, 280 104.1, 198 104.1 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo, line 2 to 3: 279</title></path><path d="M 198 173.75 C 280 173.75, 280 144.7, 362 144.7 L 362 147.51 C 280 147.51, 280 176.56, 198 176.56 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Hormone, line 2 to 3: 191</title></path><path d="M 198 130.58 C 280 130.58, 280 116.04, 362 116.04 L 362 117.54 C 280 117.54, 280 132.08, 198 132.08 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → other, line 2 to 3: 102</title></path><path d="M 198 155.84 C 280 155.84, 280 131.38, 362 131.38 L 362 132.77 C 280 132.77, 280 157.24, 198 157.24 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Biologic + Chemo, line 2 to 3: 95</title></path><path d="M 198 104.1 C 280 104.1, 280 117.54, 362 117.54 L 362 118.92 C 280 118.92, 280 105.48, 198 105.48 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → other, line 2 to 3: 94</title></path><path d="M 198 105.48 C 280 105.48, 280 132.77, 362 132.77 L 362 134.14 C 280 134.14, 280 106.85, 198 106.85 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Biologic + Chemo, line 2 to 3: 93</title></path><path d="M 198 132.08 C 280 132.08, 280 104.1, 362 104.1 L 362 104.81 C 280 104.81, 280 132.79, 198 132.79 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo, line 2 to 3: 48</title></path><path d="M 198 176.56 C 280 176.56, 280 118.92, 362 118.92 L 362 119.61 C 280 119.61, 280 177.25, 198 177.25 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → other, line 2 to 3: 47</title></path><path d="M 198 157.24 C 280 157.24, 280 119.61, 362 119.61 L 362 120.25 C 280 120.25, 280 157.87, 198 157.87 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → other, line 2 to 3: 43</title></path><path d="M 198 157.87 C 280 157.87, 280 104.81, 362 104.81 L 362 105.42 C 280 105.42, 280 158.49, 198 158.49 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo, line 2 to 3: 42</title></path><path d="M 198 190.94 C 280 190.94, 280 169.1, 362 169.1 L 362 169.7 C 280 169.7, 280 191.54, 198 191.54 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Targeted, line 2 to 3: 35</title></path><path d="M 198 203.5 C 280 203.5, 280 120.25, 362 120.25 L 362 120.85 C 280 120.85, 280 204.1, 198 204.1 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → other, line 2 to 3: 32</title></path><path d="M 198 191.54 C 280 191.54, 280 120.85, 362 120.85 L 362 121.45 C 280 121.45, 280 192.14, 198 192.14 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → other, line 2 to 3: 31</title></path><path d="M 198 132.79 C 280 132.79, 280 134.14, 362 134.14 L 362 134.74 C 280 134.74, 280 133.39, 198 133.39 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Biologic + Chemo, line 2 to 3: 29</title></path><path d="M 198 106.85 C 280 106.85, 280 157.98, 362 157.98 L 362 158.58 C 280 158.58, 280 107.45, 198 107.45 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Investigational, line 2 to 3: 27</title></path><path d="M 198 133.39 C 280 133.39, 280 169.7, 362 169.7 L 362 170.3 C 280 170.3, 280 133.99, 198 133.99 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Targeted, line 2 to 3: 21</title></path><path d="M 198 177.25 C 280 177.25, 280 191.2, 362 191.2 L 362 191.8 C 280 191.8, 280 177.85, 198 177.85 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone, line 2 to 3: 20</title></path><path d="M 198 107.45 C 280 107.45, 280 180.2, 362 180.2 L 362 180.8 C 280 180.8, 280 108.05, 198 108.05 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Immuno, line 2 to 3: 17</title></path><path d="M 198 204.1 C 280 204.1, 280 158.58, 362 158.58 L 362 159.18 C 280 159.18, 280 204.7, 198 204.7 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Investigational, line 2 to 3: 17</title></path><path d="M 198 133.99 C 280 133.99, 280 159.18, 362 159.18 L 362 159.78 C 280 159.78, 280 134.59, 198 134.59 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Investigational, line 2 to 3: 17</title></path><path d="M 198 177.85 C 280 177.85, 280 202.2, 362 202.2 L 362 202.8 C 280 202.8, 280 178.45, 198 178.45 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone + Targeted, line 2 to 3: 15</title></path><path d="M 198 134.59 C 280 134.59, 280 180.8, 362 180.8 L 362 181.4 C 280 181.4, 280 135.19, 198 135.19 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Immuno, line 2 to 3: 14</title></path><path d="M 198 204.7 C 280 204.7, 280 105.42, 362 105.42 L 362 106.02 C 280 106.02, 280 205.3, 198 205.3 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo, line 2 to 3: 12</title></path><path d="M 198 135.19 C 280 135.19, 280 147.51, 362 147.51 L 362 148.11 C 280 148.11, 280 135.79, 198 135.79 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Hormone, line 2 to 3: 12</title></path><path d="M 198 108.05 C 280 108.05, 280 191.8, 362 191.8 L 362 192.4 C 280 192.4, 280 108.65, 198 108.65 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone, line 2 to 3: 11</title></path><path d="M 198 178.45 C 280 178.45, 280 106.02, 362 106.02 L 362 106.62 C 280 106.62, 280 179.05, 198 179.05 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo, line 2 to 3: 11</title></path><path d="M 198 108.65 C 280 108.65, 280 148.11, 362 148.11 L 362 148.71 C 280 148.71, 280 109.25, 198 109.25 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Hormone, line 2 to 3: 10</title></path><path d="M 198 158.49 C 280 158.49, 280 159.78, 362 159.78 L 362 160.38 C 280 160.38, 280 159.09, 198 159.09 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Investigational, line 2 to 3: 9</title></path><path d="M 198 109.25 C 280 109.25, 280 170.3, 362 170.3 L 362 170.9 C 280 170.9, 280 109.85, 198 109.85 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Targeted, line 2 to 3: 8</title></path><path d="M 198 227.51 C 280 227.51, 280 106.62, 362 106.62 L 362 107.22 C 280 107.22, 280 228.11, 198 228.11 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo, line 2 to 3: 8</title></path><path d="M 198 215.55 C 280 215.55, 280 107.22, 362 107.22 L 362 107.82 C 280 107.82, 280 216.15, 198 216.15 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo, line 2 to 3: 8</title></path><path d="M 198 216.15 C 280 216.15, 280 121.45, 362 121.45 L 362 122.05 C 280 122.05, 280 216.75, 198 216.75 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → other, line 2 to 3: 7</title></path><path d="M 198 216.75 C 280 216.75, 280 181.4, 362 181.4 L 362 182 C 280 182, 280 217.35, 198 217.35 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Immuno, line 2 to 3: 6</title></path><path d="M 198 228.11 C 280 228.11, 280 122.05, 362 122.05 L 362 122.65 C 280 122.65, 280 228.71, 198 228.71 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → other, line 2 to 3: 6</title></path><path d="M 198 228.71 C 280 228.71, 280 148.71, 362 148.71 L 362 149.31 C 280 149.31, 280 229.31, 198 229.31 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Hormone, line 2 to 3: 5</title></path><path d="M 198 205.3 C 280 205.3, 280 134.74, 362 134.74 L 362 135.34 C 280 135.34, 280 205.9, 198 205.9 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Biologic + Chemo, line 2 to 3: 5</title></path><path d="M 198 205.9 C 280 205.9, 280 170.9, 362 170.9 L 362 171.5 C 280 171.5, 280 206.5, 198 206.5 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Targeted, line 2 to 3: 5</title></path><path d="M 198 206.5 C 280 206.5, 280 149.31, 362 149.31 L 362 149.91 C 280 149.91, 280 207.1, 198 207.1 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Hormone, line 2 to 3: 4</title></path><path d="M 198 159.09 C 280 159.09, 280 171.5, 362 171.5 L 362 172.1 C 280 172.1, 280 159.69, 198 159.69 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Targeted, line 2 to 3: 3</title></path><path d="M 198 192.14 C 280 192.14, 280 160.38, 362 160.38 L 362 160.98 C 280 160.98, 280 192.74, 198 192.74 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Investigational, line 2 to 3: 3</title></path><path d="M 198 109.85 C 280 109.85, 280 202.8, 362 202.8 L 362 203.4 C 280 203.4, 280 110.45, 198 110.45 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone + Targeted, line 2 to 3: 2</title></path><path d="M 198 229.31 C 280 229.31, 280 192.4, 362 192.4 L 362 193 C 280 193, 280 229.91, 198 229.91 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone, line 2 to 3: 2</title></path><path d="M 198 239.2 C 280 239.2, 280 107.82, 362 107.82 L 362 108.42 C 280 108.42, 280 239.8, 198 239.8 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo, line 2 to 3: 2</title></path><path d="M 198 217.35 C 280 217.35, 280 135.34, 362 135.34 L 362 135.94 C 280 135.94, 280 217.95, 198 217.95 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Biologic + Chemo, line 2 to 3: 2</title></path><path d="M 198 217.95 C 280 217.95, 280 160.98, 362 160.98 L 362 161.58 C 280 161.58, 280 218.55, 198 218.55 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Investigational, line 2 to 3: 2</title></path><path d="M 198 179.05 C 280 179.05, 280 182, 362 182 L 362 182.6 C 280 182.6, 280 179.65, 198 179.65 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Immuno, line 2 to 3: 2</title></path><path d="M 198 207.1 C 280 207.1, 280 193, 362 193 L 362 193.6 C 280 193.6, 280 207.7, 198 207.7 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone, line 2 to 3: 2</title></path><path d="M 198 192.74 C 280 192.74, 280 203.4, 362 203.4 L 362 204 C 280 204, 280 193.34, 198 193.34 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Hormone + Targeted, line 2 to 3: 2</title></path><path d="M 198 135.79 C 280 135.79, 280 204, 362 204 L 362 204.6 C 280 204.6, 280 136.39, 198 136.39 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone + Targeted, line 2 to 3: 2</title></path><path d="M 198 159.69 C 280 159.69, 280 182.6, 362 182.6 L 362 183.2 C 280 183.2, 280 160.29, 198 160.29 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo + Immuno, line 2 to 3: 1</title></path><path d="M 198 160.29 C 280 160.29, 280 149.91, 362 149.91 L 362 150.51 C 280 150.51, 280 160.89, 198 160.89 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Hormone, line 2 to 3: 1</title></path><path d="M 198 229.91 C 280 229.91, 280 172.1, 362 172.1 L 362 172.7 C 280 172.7, 280 230.51, 198 230.51 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Targeted, line 2 to 3: 1</title></path><path d="M 198 239.8 C 280 239.8, 280 204.6, 362 204.6 L 362 205.2 C 280 205.2, 280 240.4, 198 240.4 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo + Hormone + Targeted, line 2 to 3: 1</title></path><path d="M 198 240.4 C 280 240.4, 280 122.65, 362 122.65 L 362 123.25 C 280 123.25, 280 241, 198 241 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → other, line 2 to 3: 1</title></path><path d="M 198 218.55 C 280 218.55, 280 172.7, 362 172.7 L 362 173.3 C 280 173.3, 280 219.15, 198 219.15 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Targeted, line 2 to 3: 1</title></path><path d="M 198 179.65 C 280 179.65, 280 135.94, 362 135.94 L 362 136.54 C 280 136.54, 280 180.25, 198 180.25 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Biologic + Chemo, line 2 to 3: 1</title></path><path d="M 198 180.25 C 280 180.25, 280 161.58, 362 161.58 L 362 162.18 C 280 162.18, 280 180.85, 198 180.85 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Investigational, line 2 to 3: 1</title></path><path d="M 198 180.85 C 280 180.85, 280 173.3, 362 173.3 L 362 173.9 C 280 173.9, 280 181.45, 198 181.45 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Targeted, line 2 to 3: 1</title></path><path d="M 198 136.39 C 280 136.39, 280 193.6, 362 193.6 L 362 194.2 C 280 194.2, 280 136.99, 198 136.99 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone, line 2 to 3: 1</title></path><path d="M 198 207.7 C 280 207.7, 280 183.2, 362 183.2 L 362 183.8 C 280 183.8, 280 208.3, 198 208.3 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Immuno, line 2 to 3: 1</title></path><path d="M 198 193.34 C 280 193.34, 280 136.54, 362 136.54 L 362 137.14 C 280 137.14, 280 193.94, 198 193.94 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Biologic + Chemo, line 2 to 3: 1</title></path><path d="M 198 193.94 C 280 193.94, 280 108.42, 362 108.42 L 362 109.02 C 280 109.02, 280 194.54, 198 194.54 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo, line 2 to 3: 1</title></path><path d="M 198 110.45 C 280 110.45, 280 213.2, 362 213.2 L 362 219.49 C 280 219.49, 280 116.74, 198 116.74 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo → no further line, line 2 to 3: 428</title></path><path d="M 198 136.99 C 280 136.99, 280 219.49, 362 219.49 L 362 222.5 C 280 222.5, 280 140, 198 140 Z" fill="#62737d" fill-opacity="0.45"><title>other → no further line, line 2 to 3: 205</title></path><path d="M 198 160.89 C 280 160.89, 280 222.5, 362 222.5 L 362 224.2 C 280 224.2, 280 162.58, 198 162.58 Z" fill="#62737d" fill-opacity="0.45"><title>Biologic + Chemo → no further line, line 2 to 3: 115</title></path><path d="M 198 181.45 C 280 181.45, 280 224.2, 362 224.2 L 362 225.15 C 280 225.15, 280 182.4, 198 182.4 Z" fill="#62737d" fill-opacity="0.45"><title>Hormone → no further line, line 2 to 3: 65</title></path><path d="M 198 208.3 C 280 208.3, 280 225.15, 362 225.15 L 362 225.84 C 280 225.84, 280 208.99, 198 208.99 Z" fill="#62737d" fill-opacity="0.45"><title>Investigational → no further line, line 2 to 3: 47</title></path><path d="M 198 219.15 C 280 219.15, 280 225.84, 362 225.84 L 362 226.44 C 280 226.44, 280 219.75, 198 219.75 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Immuno → no further line, line 2 to 3: 31</title></path><path d="M 198 230.51 C 280 230.51, 280 226.44, 362 226.44 L 362 227.04 C 280 227.04, 280 231.11, 198 231.11 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone → no further line, line 2 to 3: 23</title></path><path d="M 198 194.54 C 280 194.54, 280 227.04, 362 227.04 L 362 227.64 C 280 227.64, 280 195.14, 198 195.14 Z" fill="#62737d" fill-opacity="0.45"><title>Targeted → no further line, line 2 to 3: 20</title></path><path d="M 198 241 C 280 241, 280 227.64, 362 227.64 L 362 228.24 C 280 228.24, 280 241.6, 198 241.6 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → no further line, line 2 to 3: 5</title></path><path d="M 198 140 C 280 140, 280 237, 362 237 L 362 245.63 C 280 245.63, 280 148.63, 198 148.63 Z" fill="#c8ced2" fill-opacity="0.45"><title>other → record ends here, line 2 to 3: 587</title></path><path d="M 198 116.74 C 280 116.74, 280 245.63, 362 245.63 L 362 251.97 C 280 251.97, 280 123.08, 198 123.08 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo → record ends here, line 2 to 3: 431</title></path><path d="M 198 162.58 C 280 162.58, 280 251.97, 362 251.97 L 362 255.33 C 280 255.33, 280 165.94, 198 165.94 Z" fill="#c8ced2" fill-opacity="0.45"><title>Biologic + Chemo → record ends here, line 2 to 3: 229</title></path><path d="M 198 182.4 C 280 182.4, 280 255.33, 362 255.33 L 362 257.32 C 280 257.32, 280 184.39, 198 184.39 Z" fill="#c8ced2" fill-opacity="0.45"><title>Hormone → record ends here, line 2 to 3: 135</title></path><path d="M 198 195.14 C 280 195.14, 280 257.32, 362 257.32 L 362 258.51 C 280 258.51, 280 196.33, 198 196.33 Z" fill="#c8ced2" fill-opacity="0.45"><title>Targeted → record ends here, line 2 to 3: 81</title></path><path d="M 198 219.75 C 280 219.75, 280 258.51, 362 258.51 L 362 259.63 C 280 259.63, 280 220.87, 198 220.87 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Immuno → record ends here, line 2 to 3: 76</title></path><path d="M 198 231.11 C 280 231.11, 280 259.63, 362 259.63 L 362 260.66 C 280 260.66, 280 232.14, 198 232.14 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone → record ends here, line 2 to 3: 70</title></path><path d="M 198 241.6 C 280 241.6, 280 260.66, 362 260.66 L 362 261.26 C 280 261.26, 280 242.2, 198 242.2 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → record ends here, line 2 to 3: 37</title></path><path d="M 198 208.99 C 280 208.99, 280 261.26, 362 261.26 L 362 261.86 C 280 261.86, 280 209.59, 198 209.59 Z" fill="#c8ced2" fill-opacity="0.45"><title>Investigational → record ends here, line 2 to 3: 15</title></path><path d="M 375 144.7 C 457 144.7, 457 124.29, 539 124.29 L 539 125.94 C 457 125.94, 457 146.35, 375 146.35 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Hormone, line 3 to 4: 112</title></path><path d="M 375 100 C 457 100, 457 112.31, 539 112.31 L 539 113.5 C 457 113.5, 457 101.19, 375 101.19 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo, line 3 to 4: 81</title></path><path d="M 375 131.38 C 457 131.38, 457 136.14, 539 136.14 L 539 136.88 C 457 136.88, 457 132.11, 375 132.11 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Biologic + Chemo, line 3 to 4: 50</title></path><path d="M 375 116.04 C 457 116.04, 457 100, 539 100 L 539 100.72 C 457 100.72, 457 116.76, 375 116.76 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → other, line 3 to 4: 49</title></path><path d="M 375 101.19 C 457 101.19, 457 136.88, 539 136.88 L 539 137.48 C 457 137.48, 457 101.79, 375 101.79 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Biologic + Chemo, line 3 to 4: 34</title></path><path d="M 375 146.35 C 457 146.35, 457 100.72, 539 100.72 L 539 101.32 C 457 101.32, 457 146.95, 375 146.95 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → other, line 3 to 4: 30</title></path><path d="M 375 101.79 C 457 101.79, 457 101.32, 539 101.32 L 539 101.92 C 457 101.92, 457 102.39, 375 102.39 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → other, line 3 to 4: 26</title></path><path d="M 375 116.76 C 457 116.76, 457 113.5, 539 113.5 L 539 114.1 C 457 114.1, 457 117.36, 375 117.36 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo, line 3 to 4: 24</title></path><path d="M 375 132.11 C 457 132.11, 457 101.92, 539 101.92 L 539 102.52 C 457 102.52, 457 132.71, 375 132.71 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → other, line 3 to 4: 16</title></path><path d="M 375 169.1 C 457 169.1, 457 147.72, 539 147.72 L 539 148.32 C 457 148.32, 457 169.7, 375 169.7 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Targeted, line 3 to 4: 15</title></path><path d="M 375 157.98 C 457 157.98, 457 102.52, 539 102.52 L 539 103.12 C 457 103.12, 457 158.58, 375 158.58 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → other, line 3 to 4: 15</title></path><path d="M 375 132.71 C 457 132.71, 457 114.1, 539 114.1 L 539 114.7 C 457 114.7, 457 133.31, 375 133.31 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo, line 3 to 4: 14</title></path><path d="M 375 146.95 C 457 146.95, 457 169.72, 539 169.72 L 539 170.32 C 457 170.32, 457 147.55, 375 147.55 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone, line 3 to 4: 12</title></path><path d="M 375 158.58 C 457 158.58, 457 158.72, 539 158.72 L 539 159.32 C 457 159.32, 457 159.18, 375 159.18 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Investigational, line 3 to 4: 12</title></path><path d="M 375 169.7 C 457 169.7, 457 103.12, 539 103.12 L 539 103.72 C 457 103.72, 457 170.3, 375 170.3 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → other, line 3 to 4: 12</title></path><path d="M 375 117.36 C 457 117.36, 457 137.48, 539 137.48 L 539 138.08 C 457 138.08, 457 117.96, 375 117.96 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Biologic + Chemo, line 3 to 4: 10</title></path><path d="M 375 147.55 C 457 147.55, 457 191.72, 539 191.72 L 539 192.32 C 457 192.32, 457 148.15, 375 148.15 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone + Targeted, line 3 to 4: 8</title></path><path d="M 375 159.18 C 457 159.18, 457 138.08, 539 138.08 L 539 138.68 C 457 138.68, 457 159.78, 375 159.78 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Biologic + Chemo, line 3 to 4: 8</title></path><path d="M 375 102.39 C 457 102.39, 457 180.72, 539 180.72 L 539 181.32 C 457 181.32, 457 102.99, 375 102.99 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Immuno, line 3 to 4: 6</title></path><path d="M 375 148.15 C 457 148.15, 457 114.7, 539 114.7 L 539 115.3 C 457 115.3, 457 148.75, 375 148.75 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo, line 3 to 4: 5</title></path><path d="M 375 159.78 C 457 159.78, 457 115.3, 539 115.3 L 539 115.9 C 457 115.9, 457 160.38, 375 160.38 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo, line 3 to 4: 5</title></path><path d="M 375 117.96 C 457 117.96, 457 148.32, 539 148.32 L 539 148.92 C 457 148.92, 457 118.56, 375 118.56 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Targeted, line 3 to 4: 5</title></path><path d="M 375 118.56 C 457 118.56, 457 125.94, 539 125.94 L 539 126.54 C 457 126.54, 457 119.16, 375 119.16 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Hormone, line 3 to 4: 5</title></path><path d="M 375 191.2 C 457 191.2, 457 126.54, 539 126.54 L 539 127.14 C 457 127.14, 457 191.8, 375 191.8 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Hormone, line 3 to 4: 4</title></path><path d="M 375 180.2 C 457 180.2, 457 103.72, 539 103.72 L 539 104.32 C 457 104.32, 457 180.8, 375 180.8 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → other, line 3 to 4: 4</title></path><path d="M 375 191.8 C 457 191.8, 457 104.32, 539 104.32 L 539 104.92 C 457 104.92, 457 192.4, 375 192.4 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → other, line 3 to 4: 4</title></path><path d="M 375 133.31 C 457 133.31, 457 181.32, 539 181.32 L 539 181.92 C 457 181.92, 457 133.91, 375 133.91 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo + Immuno, line 3 to 4: 3</title></path><path d="M 375 133.91 C 457 133.91, 457 159.32, 539 159.32 L 539 159.92 C 457 159.92, 457 134.51, 375 134.51 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Investigational, line 3 to 4: 3</title></path><path d="M 375 192.4 C 457 192.4, 457 115.9, 539 115.9 L 539 116.5 C 457 116.5, 457 193, 375 193 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo, line 3 to 4: 3</title></path><path d="M 375 148.75 C 457 148.75, 457 148.92, 539 148.92 L 539 149.52 C 457 149.52, 457 149.35, 375 149.35 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Targeted, line 3 to 4: 3</title></path><path d="M 375 170.3 C 457 170.3, 457 116.5, 539 116.5 L 539 117.1 C 457 117.1, 457 170.9, 375 170.9 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo, line 3 to 4: 3</title></path><path d="M 375 119.16 C 457 119.16, 457 181.92, 539 181.92 L 539 182.52 C 457 182.52, 457 119.76, 375 119.76 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Immuno, line 3 to 4: 3</title></path><path d="M 375 102.99 C 457 102.99, 457 170.32, 539 170.32 L 539 170.92 C 457 170.92, 457 103.59, 375 103.59 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone, line 3 to 4: 2</title></path><path d="M 375 103.59 C 457 103.59, 457 127.14, 539 127.14 L 539 127.74 C 457 127.74, 457 104.19, 375 104.19 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Hormone, line 3 to 4: 2</title></path><path d="M 375 104.19 C 457 104.19, 457 159.92, 539 159.92 L 539 160.52 C 457 160.52, 457 104.79, 375 104.79 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Investigational, line 3 to 4: 2</title></path><path d="M 375 149.35 C 457 149.35, 457 182.52, 539 182.52 L 539 183.12 C 457 183.12, 457 149.95, 375 149.95 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Immuno, line 3 to 4: 2</title></path><path d="M 375 160.38 C 457 160.38, 457 127.74, 539 127.74 L 539 128.34 C 457 128.34, 457 160.98, 375 160.98 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Hormone, line 3 to 4: 2</title></path><path d="M 375 170.9 C 457 170.9, 457 138.68, 539 138.68 L 539 139.28 C 457 139.28, 457 171.5, 375 171.5 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Biologic + Chemo, line 3 to 4: 2</title></path><path d="M 375 119.76 C 457 119.76, 457 160.52, 539 160.52 L 539 161.12 C 457 161.12, 457 120.36, 375 120.36 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Investigational, line 3 to 4: 2</title></path><path d="M 375 120.36 C 457 120.36, 457 170.92, 539 170.92 L 539 171.52 C 457 171.52, 457 120.96, 375 120.96 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone, line 3 to 4: 2</title></path><path d="M 375 134.51 C 457 134.51, 457 128.34, 539 128.34 L 539 128.94 C 457 128.94, 457 135.11, 375 135.11 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Hormone, line 3 to 4: 1</title></path><path d="M 375 135.11 C 457 135.11, 457 149.52, 539 149.52 L 539 150.12 C 457 150.12, 457 135.71, 375 135.71 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Targeted, line 3 to 4: 1</title></path><path d="M 375 120.96 C 457 120.96, 457 192.32, 539 192.32 L 539 192.92 C 457 192.92, 457 121.56, 375 121.56 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone + Targeted, line 3 to 4: 1</title></path><path d="M 375 104.79 C 457 104.79, 457 150.12, 539 150.12 L 539 150.72 C 457 150.72, 457 105.39, 375 105.39 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Targeted, line 3 to 4: 1</title></path><path d="M 375 193 C 457 193, 457 139.28, 539 139.28 L 539 139.88 C 457 139.88, 457 193.6, 375 193.6 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Biologic + Chemo, line 3 to 4: 1</title></path><path d="M 375 193.6 C 457 193.6, 457 171.52, 539 171.52 L 539 172.12 C 457 172.12, 457 194.2, 375 194.2 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone, line 3 to 4: 1</title></path><path d="M 375 194.2 C 457 194.2, 457 161.12, 539 161.12 L 539 161.72 C 457 161.72, 457 194.8, 375 194.8 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Investigational, line 3 to 4: 1</title></path><path d="M 375 202.2 C 457 202.2, 457 139.88, 539 139.88 L 539 140.48 C 457 140.48, 457 202.8, 375 202.8 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Biologic + Chemo, line 3 to 4: 1</title></path><path d="M 375 202.8 C 457 202.8, 457 104.92, 539 104.92 L 539 105.52 C 457 105.52, 457 203.4, 375 203.4 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → other, line 3 to 4: 1</title></path><path d="M 375 180.8 C 457 180.8, 457 140.48, 539 140.48 L 539 141.08 C 457 141.08, 457 181.4, 375 181.4 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Biologic + Chemo, line 3 to 4: 1</title></path><path d="M 375 181.4 C 457 181.4, 457 172.12, 539 172.12 L 539 172.72 C 457 172.72, 457 182, 375 182 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Hormone, line 3 to 4: 1</title></path><path d="M 375 182 C 457 182, 457 183.12, 539 183.12 L 539 183.72 C 457 183.72, 457 182.6, 375 182.6 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Immuno, line 3 to 4: 1</title></path><path d="M 375 182.6 C 457 182.6, 457 161.72, 539 161.72 L 539 162.32 C 457 162.32, 457 183.2, 375 183.2 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Investigational, line 3 to 4: 1</title></path><path d="M 375 149.95 C 457 149.95, 457 162.32, 539 162.32 L 539 162.92 C 457 162.92, 457 150.55, 375 150.55 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Investigational, line 3 to 4: 1</title></path><path d="M 375 160.98 C 457 160.98, 457 172.72, 539 172.72 L 539 173.32 C 457 173.32, 457 161.58, 375 161.58 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone, line 3 to 4: 1</title></path><path d="M 375 161.58 C 457 161.58, 457 150.72, 539 150.72 L 539 151.32 C 457 151.32, 457 162.18, 375 162.18 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Targeted, line 3 to 4: 1</title></path><path d="M 375 171.5 C 457 171.5, 457 192.92, 539 192.92 L 539 193.52 C 457 193.52, 457 172.1, 375 172.1 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Hormone + Targeted, line 3 to 4: 1</title></path><path d="M 375 172.1 C 457 172.1, 457 162.92, 539 162.92 L 539 163.52 C 457 163.52, 457 172.7, 375 172.7 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Investigational, line 3 to 4: 1</title></path><path d="M 375 105.39 C 457 105.39, 457 202.72, 539 202.72 L 539 204.39 C 457 204.39, 457 107.07, 375 107.07 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo → no further line, line 3 to 4: 114</title></path><path d="M 375 121.56 C 457 121.56, 457 204.39, 539 204.39 L 539 205.38 C 457 205.38, 457 122.55, 375 122.55 Z" fill="#62737d" fill-opacity="0.45"><title>other → no further line, line 3 to 4: 67</title></path><path d="M 375 135.71 C 457 135.71, 457 205.38, 539 205.38 L 539 206.04 C 457 206.04, 457 136.37, 375 136.37 Z" fill="#62737d" fill-opacity="0.45"><title>Biologic + Chemo → no further line, line 3 to 4: 45</title></path><path d="M 375 150.55 C 457 150.55, 457 206.04, 539 206.04 L 539 206.64 C 457 206.64, 457 151.15, 375 151.15 Z" fill="#62737d" fill-opacity="0.45"><title>Hormone → no further line, line 3 to 4: 26</title></path><path d="M 375 162.18 C 457 162.18, 457 206.64, 539 206.64 L 539 207.24 C 457 207.24, 457 162.78, 375 162.78 Z" fill="#62737d" fill-opacity="0.45"><title>Investigational → no further line, line 3 to 4: 20</title></path><path d="M 375 183.2 C 457 183.2, 457 207.24, 539 207.24 L 539 207.84 C 457 207.84, 457 183.8, 375 183.8 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Immuno → no further line, line 3 to 4: 16</title></path><path d="M 375 172.7 C 457 172.7, 457 207.84, 539 207.84 L 539 208.44 C 457 208.44, 457 173.3, 375 173.3 Z" fill="#62737d" fill-opacity="0.45"><title>Targeted → no further line, line 3 to 4: 13</title></path><path d="M 375 203.4 C 457 203.4, 457 208.44, 539 208.44 L 539 209.04 C 457 209.04, 457 204, 375 204 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → no further line, line 3 to 4: 3</title></path><path d="M 375 194.8 C 457 194.8, 457 209.04, 539 209.04 L 539 209.64 C 457 209.64, 457 195.4, 375 195.4 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone → no further line, line 3 to 4: 1</title></path><path d="M 375 122.55 C 457 122.55, 457 217.2, 539 217.2 L 539 220.07 C 457 220.07, 457 125.41, 375 125.41 Z" fill="#c8ced2" fill-opacity="0.45"><title>other → record ends here, line 3 to 4: 195</title></path><path d="M 375 107.07 C 457 107.07, 457 220.07, 539 220.07 L 539 222.17 C 457 222.17, 457 109.17, 375 109.17 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo → record ends here, line 3 to 4: 143</title></path><path d="M 375 136.37 C 457 136.37, 457 222.17, 539 222.17 L 539 223.54 C 457 223.54, 457 137.74, 375 137.74 Z" fill="#c8ced2" fill-opacity="0.45"><title>Biologic + Chemo → record ends here, line 3 to 4: 93</title></path><path d="M 375 173.3 C 457 173.3, 457 223.54, 539 223.54 L 539 224.14 C 457 224.14, 457 173.9, 375 173.9 Z" fill="#c8ced2" fill-opacity="0.45"><title>Targeted → record ends here, line 3 to 4: 28</title></path><path d="M 375 151.15 C 457 151.15, 457 224.14, 539 224.14 L 539 224.74 C 457 224.74, 457 151.75, 375 151.75 Z" fill="#c8ced2" fill-opacity="0.45"><title>Hormone → record ends here, line 3 to 4: 24</title></path><path d="M 375 195.4 C 457 195.4, 457 224.74, 539 224.74 L 539 225.34 C 457 225.34, 457 196, 375 196 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone → record ends here, line 3 to 4: 21</title></path><path d="M 375 204 C 457 204, 457 225.34, 539 225.34 L 539 225.94 C 457 225.94, 457 204.6, 375 204.6 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → record ends here, line 3 to 4: 17</title></path><path d="M 375 183.8 C 457 183.8, 457 225.94, 539 225.94 L 539 226.54 C 457 226.54, 457 184.4, 375 184.4 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Immuno → record ends here, line 3 to 4: 17</title></path><path d="M 375 162.78 C 457 162.78, 457 226.54, 539 226.54 L 539 227.14 C 457 227.14, 457 163.38, 375 163.38 Z" fill="#c8ced2" fill-opacity="0.45"><title>Investigational → record ends here, line 3 to 4: 12</title></path><rect x="8" y="100" width="13" height="77.02" rx="2" fill="#0072B2"><title>Chemo 5,239 (line 1)</title></rect><rect x="8" y="187.02" width="13" height="39.53" rx="2" fill="#9aa5ad"><title>other 2,689 (line 1)</title></rect><rect x="8" y="236.55" width="13" height="14.63" rx="2" fill="#009E73"><title>Biologic + Chemo 995 (line 1)</title></rect><rect x="8" y="261.18" width="13" height="9.88" rx="2" fill="#E69F00"><title>Hormone 672 (line 1)</title></rect><rect x="8" y="281.05" width="13" height="9.47" rx="2" fill="#CC79A7"><title>Chemo + Hormone 644 (line 1)</title></rect><rect x="8" y="300.52" width="13" height="9.39" rx="2" fill="#D55E00"><title>Chemo + Immuno 639 (line 1)</title></rect><rect x="8" y="319.92" width="13" height="4.01" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 273 (line 1)</title></rect><rect x="8" y="333.93" width="13" height="3.73" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 254 (line 1)</title></rect><rect x="8" y="347.66" width="13" height="2.34" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 159 (line 1)</title></rect><rect x="185" y="100" width="13" height="20.58" rx="2" fill="#0072B2"><title>Chemo 1,400 (line 2)</title></rect><rect x="185" y="130.58" width="13" height="15.26" rx="2" fill="#9aa5ad"><title>other 1,038 (line 2)</title></rect><rect x="185" y="155.84" width="13" height="7.91" rx="2" fill="#009E73"><title>Biologic + Chemo 538 (line 2)</title></rect><rect x="185" y="173.75" width="13" height="7.19" rx="2" fill="#E69F00"><title>Hormone 489 (line 2)</title></rect><rect x="185" y="190.94" width="13" height="2.56" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 174 (line 2)</title></rect><rect x="185" y="203.5" width="13" height="2.06" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 140 (line 2)</title></rect><rect x="185" y="215.55" width="13" height="1.96" rx="2" fill="#D55E00"><title>Chemo + Immuno 133 (line 2)</title></rect><rect x="185" y="227.51" width="13" height="1.69" rx="2" fill="#CC79A7"><title>Chemo + Hormone 115 (line 2)</title></rect><rect x="185" y="239.2" width="13" height="1" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 46 (line 2)</title></rect><rect x="185" y="250.2" width="13" height="48.29" rx="2" fill="#62737d"><title>no further line 3,285 (line 2)</title></rect><rect x="185" y="308.49" width="13" height="61.83" rx="2" fill="#c8ced2"><title>record ends here 4,206 (line 2)</title></rect><rect x="362" y="100" width="13" height="6.04" rx="2" fill="#0072B2"><title>Chemo 411 (line 3)</title></rect><rect x="362" y="116.04" width="13" height="5.34" rx="2" fill="#9aa5ad"><title>other 363 (line 3)</title></rect><rect x="362" y="131.38" width="13" height="3.32" rx="2" fill="#009E73"><title>Biologic + Chemo 226 (line 3)</title></rect><rect x="362" y="144.7" width="13" height="3.28" rx="2" fill="#E69F00"><title>Hormone 223 (line 3)</title></rect><rect x="362" y="157.98" width="13" height="1.12" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 76 (line 3)</title></rect><rect x="362" y="169.1" width="13" height="1.1" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 75 (line 3)</title></rect><rect x="362" y="180.2" width="13" height="1" rx="2" fill="#D55E00"><title>Chemo + Immuno 41 (line 3)</title></rect><rect x="362" y="191.2" width="13" height="1" rx="2" fill="#CC79A7"><title>Chemo + Hormone 36 (line 3)</title></rect><rect x="362" y="202.2" width="13" height="1" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 22 (line 3)</title></rect><rect x="362" y="213.2" width="13" height="13.8" rx="2" fill="#62737d"><title>no further line 939 (line 3)</title></rect><rect x="362" y="237" width="13" height="24.42" rx="2" fill="#c8ced2"><title>record ends here 1,661 (line 3)</title></rect><rect x="539" y="100" width="13" height="2.31" rx="2" fill="#9aa5ad"><title>other 157 (line 4)</title></rect><rect x="539" y="112.31" width="13" height="1.98" rx="2" fill="#0072B2"><title>Chemo 135 (line 4)</title></rect><rect x="539" y="124.29" width="13" height="1.85" rx="2" fill="#E69F00"><title>Hormone 126 (line 4)</title></rect><rect x="539" y="136.14" width="13" height="1.57" rx="2" fill="#009E73"><title>Biologic + Chemo 107 (line 4)</title></rect><rect x="539" y="147.72" width="13" height="1" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 26 (line 4)</title></rect><rect x="539" y="158.72" width="13" height="1" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 23 (line 4)</title></rect><rect x="539" y="169.72" width="13" height="1" rx="2" fill="#CC79A7"><title>Chemo + Hormone 19 (line 4)</title></rect><rect x="539" y="180.72" width="13" height="1" rx="2" fill="#D55E00"><title>Chemo + Immuno 15 (line 4)</title></rect><rect x="539" y="191.72" width="13" height="1" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 10 (line 4)</title></rect><rect x="539" y="202.72" width="13" height="4.48" rx="2" fill="#62737d"><title>no further line 305 (line 4)</title></rect><rect x="539" y="217.2" width="13" height="8.09" rx="2" fill="#c8ced2"><title>record ends here 550 (line 4)</title></rect><text x="25" y="142" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 5,239</text><text x="25" y="210.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 2,689</text><text x="25" y="247.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 995</text><text x="25" y="269.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 672</text><text x="25" y="289.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone 644</text><text x="25" y="308.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Immuno 639</text><text x="25" y="325.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Targeted 273</text><text x="25" y="339.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Investigational 254</text><text x="25" y="352.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone + Targeted 159</text><text x="202" y="113.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 1,400</text><text x="202" y="141.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 1,038</text><text x="202" y="163.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 538</text><text x="202" y="180.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 489</text><text x="202" y="195.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Targeted 174</text><text x="202" y="209" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Investigational 140</text><text x="202" y="222" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Immuno 133</text><text x="202" y="235.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone 115</text><text x="202" y="277.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">no further line 3,285</text><text x="202" y="342.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">record ends here 4,206</text><text x="379" y="106.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 411</text><text x="379" y="122.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 363</text><text x="379" y="136.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 226</text><text x="379" y="149.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 223</text><text x="379" y="163" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Investigational 76</text><text x="379" y="176.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Targeted 75</text><text x="379" y="189.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Immuno 41</text><text x="379" y="206.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone + Targeted 22</text><text x="379" y="223.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">no further line 939</text><text x="379" y="252.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">record ends here 1,661</text><text x="556" y="104.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 157</text><text x="556" y="117.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 135</text><text x="556" y="131.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 126</text><text x="556" y="144.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 107</text><text x="556" y="162.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Investigational 23</text><text x="556" y="175.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone 19</text><text x="556" y="188.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Immuno 15</text><text x="556" y="208.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">no further line 305</text><text x="556" y="224.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">record ends here 550</text><text x="8" y="408.3" font-size="11" fill="#16232b" font-weight="600">TP53 wt</text><text x="54.9" y="408.3" font-size="11" fill="#5b6b76">— 9,645 with a first line, 56.8% of paths censored</text><text x="8" y="421.3" font-size="10" fill="#5b6b76">line 1</text><text x="185" y="421.3" font-size="10" fill="#5b6b76">line 2</text><text x="362" y="421.3" font-size="10" fill="#5b6b76">line 3</text><text x="539" y="421.3" font-size="10" fill="#5b6b76">line 4+</text><path d="M 21 519.61 C 103 519.61, 103 426.32, 185 426.32 L 185 435.59 C 103 435.59, 103 528.87, 21 528.87 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Hormone, line 1 to 2: 630</title></path><path d="M 21 426.32 C 103 426.32, 103 476.93, 185 476.93 L 185 483.97 C 103 483.97, 103 433.37, 21 433.37 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo, line 1 to 2: 479</title></path><path d="M 21 433.37 C 103 433.37, 103 453.55, 185 453.55 L 185 457.79 C 103 457.79, 103 437.6, 21 437.6 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → other, line 1 to 2: 288</title></path><path d="M 21 437.6 C 103 437.6, 103 435.59, 185 435.59 L 185 439.55 C 103 439.55, 103 441.57, 21 441.57 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Hormone, line 1 to 2: 270</title></path><path d="M 21 473.83 C 103 473.83, 103 457.79, 185 457.79 L 185 461.07 C 103 461.07, 103 477.1, 21 477.1 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → other, line 1 to 2: 223</title></path><path d="M 21 528.87 C 103 528.87, 103 461.07, 185 461.07 L 185 463.58 C 103 463.58, 103 531.38, 21 531.38 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → other, line 1 to 2: 171</title></path><path d="M 21 562.08 C 103 562.08, 103 439.55, 185 439.55 L 185 441.52 C 103 441.52, 103 564.05, 21 564.05 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Hormone, line 1 to 2: 134</title></path><path d="M 21 477.1 C 103 477.1, 103 483.97, 185 483.97 L 185 485.5 C 103 485.5, 103 478.63, 21 478.63 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo, line 1 to 2: 104</title></path><path d="M 21 478.63 C 103 478.63, 103 441.52, 185 441.52 L 185 443.04 C 103 443.04, 103 480.15, 21 480.15 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Hormone, line 1 to 2: 103</title></path><path d="M 21 441.57 C 103 441.57, 103 510.93, 185 510.93 L 185 512 C 103 512, 103 442.64, 21 442.64 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Biologic + Chemo, line 1 to 2: 73</title></path><path d="M 21 531.38 C 103 531.38, 103 535.54, 185 535.54 L 185 536.5 C 103 536.5, 103 532.34, 21 532.34 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone, line 1 to 2: 65</title></path><path d="M 21 480.15 C 103 480.15, 103 497.81, 185 497.81 L 185 498.71 C 103 498.71, 103 481.04, 21 481.04 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Targeted, line 1 to 2: 61</title></path><path d="M 21 629.15 C 103 629.15, 103 498.71, 185 498.71 L 185 499.5 C 103 499.5, 103 629.94, 21 629.94 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Targeted, line 1 to 2: 54</title></path><path d="M 21 643.85 C 103 643.85, 103 463.58, 185 463.58 L 185 464.37 C 103 464.37, 103 644.64, 21 644.64 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → other, line 1 to 2: 54</title></path><path d="M 21 598.93 C 103 598.93, 103 464.37, 185 464.37 L 185 465.15 C 103 465.15, 103 599.71, 21 599.71 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → other, line 1 to 2: 53</title></path><path d="M 21 442.64 C 103 442.64, 103 499.5, 185 499.5 L 185 500.25 C 103 500.25, 103 443.39, 21 443.39 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Targeted, line 1 to 2: 51</title></path><path d="M 21 443.39 C 103 443.39, 103 559.06, 185 559.06 L 185 559.75 C 103 559.75, 103 444.08, 21 444.08 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Immuno, line 1 to 2: 47</title></path><path d="M 21 644.64 C 103 644.64, 103 523.34, 185 523.34 L 185 523.97 C 103 523.97, 103 645.28, 21 645.28 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Investigational, line 1 to 2: 43</title></path><path d="M 21 444.08 C 103 444.08, 103 536.5, 185 536.5 L 185 537.12 C 103 537.12, 103 444.7, 21 444.7 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone, line 1 to 2: 42</title></path><path d="M 21 444.7 C 103 444.7, 103 523.97, 185 523.97 L 185 524.59 C 103 524.59, 103 445.32, 21 445.32 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Investigational, line 1 to 2: 42</title></path><path d="M 21 629.94 C 103 629.94, 103 465.15, 185 465.15 L 185 465.75 C 103 465.75, 103 630.54, 21 630.54 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → other, line 1 to 2: 39</title></path><path d="M 21 599.71 C 103 599.71, 103 512, 185 512 L 185 512.6 C 103 512.6, 103 600.31, 21 600.31 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Biologic + Chemo, line 1 to 2: 38</title></path><path d="M 21 564.05 C 103 564.05, 103 465.75, 185 465.75 L 185 466.35 C 103 466.35, 103 564.65, 21 564.65 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → other, line 1 to 2: 38</title></path><path d="M 21 600.31 C 103 600.31, 103 485.5, 185 485.5 L 185 486.1 C 103 486.1, 103 600.91, 21 600.91 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo, line 1 to 2: 35</title></path><path d="M 21 481.04 C 103 481.04, 103 524.59, 185 524.59 L 185 525.19 C 103 525.19, 103 481.64, 21 481.64 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Investigational, line 1 to 2: 35</title></path><path d="M 21 532.34 C 103 532.34, 103 486.1, 185 486.1 L 185 486.7 C 103 486.7, 103 532.94, 21 532.94 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo, line 1 to 2: 33</title></path><path d="M 21 532.94 C 103 532.94, 103 547.5, 185 547.5 L 185 548.1 C 103 548.1, 103 533.54, 21 533.54 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone + Targeted, line 1 to 2: 32</title></path><path d="M 21 583.56 C 103 583.56, 103 466.35, 185 466.35 L 185 466.95 C 103 466.95, 103 584.16, 21 584.16 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → other, line 1 to 2: 31</title></path><path d="M 21 481.64 C 103 481.64, 103 512.6, 185 512.6 L 185 513.2 C 103 513.2, 103 482.24, 21 482.24 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Biologic + Chemo, line 1 to 2: 29</title></path><path d="M 21 445.32 C 103 445.32, 103 548.1, 185 548.1 L 185 548.7 C 103 548.7, 103 445.92, 21 445.92 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone + Targeted, line 1 to 2: 28</title></path><path d="M 21 564.65 C 103 564.65, 103 486.7, 185 486.7 L 185 487.3 C 103 487.3, 103 565.25, 21 565.25 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo, line 1 to 2: 24</title></path><path d="M 21 584.16 C 103 584.16, 103 487.3, 185 487.3 L 185 487.9 C 103 487.9, 103 584.76, 21 584.76 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo, line 1 to 2: 24</title></path><path d="M 21 645.28 C 103 645.28, 103 443.04, 185 443.04 L 185 443.64 C 103 443.64, 103 645.88, 21 645.88 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Hormone, line 1 to 2: 23</title></path><path d="M 21 482.24 C 103 482.24, 103 548.7, 185 548.7 L 185 549.3 C 103 549.3, 103 482.84, 21 482.84 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone + Targeted, line 1 to 2: 23</title></path><path d="M 21 645.88 C 103 645.88, 103 487.9, 185 487.9 L 185 488.5 C 103 488.5, 103 646.48, 21 646.48 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo, line 1 to 2: 21</title></path><path d="M 21 565.25 C 103 565.25, 103 549.3, 185 549.3 L 185 549.9 C 103 549.9, 103 565.85, 21 565.85 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone + Targeted, line 1 to 2: 17</title></path><path d="M 21 584.76 C 103 584.76, 103 559.75, 185 559.75 L 185 560.35 C 103 560.35, 103 585.36, 21 585.36 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Immuno, line 1 to 2: 16</title></path><path d="M 21 646.48 C 103 646.48, 103 500.25, 185 500.25 L 185 500.85 C 103 500.85, 103 647.08, 21 647.08 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Targeted, line 1 to 2: 16</title></path><path d="M 21 565.85 C 103 565.85, 103 537.12, 185 537.12 L 185 537.72 C 103 537.72, 103 566.45, 21 566.45 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone, line 1 to 2: 15</title></path><path d="M 21 614.09 C 103 614.09, 103 466.95, 185 466.95 L 185 467.55 C 103 467.55, 103 614.69, 21 614.69 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → other, line 1 to 2: 13</title></path><path d="M 21 614.69 C 103 614.69, 103 488.5, 185 488.5 L 185 489.1 C 103 489.1, 103 615.29, 21 615.29 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo, line 1 to 2: 11</title></path><path d="M 21 585.36 C 103 585.36, 103 500.85, 185 500.85 L 185 501.45 C 103 501.45, 103 585.96, 21 585.96 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Targeted, line 1 to 2: 10</title></path><path d="M 21 585.96 C 103 585.96, 103 513.2, 185 513.2 L 185 513.8 C 103 513.8, 103 586.56, 21 586.56 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Biologic + Chemo, line 1 to 2: 9</title></path><path d="M 21 630.54 C 103 630.54, 103 489.1, 185 489.1 L 185 489.7 C 103 489.7, 103 631.14, 21 631.14 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo, line 1 to 2: 9</title></path><path d="M 21 600.91 C 103 600.91, 103 525.19, 185 525.19 L 185 525.79 C 103 525.79, 103 601.51, 21 601.51 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Investigational, line 1 to 2: 8</title></path><path d="M 21 533.54 C 103 533.54, 103 525.79, 185 525.79 L 185 526.39 C 103 526.39, 103 534.14, 21 534.14 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Investigational, line 1 to 2: 8</title></path><path d="M 21 482.84 C 103 482.84, 103 560.35, 185 560.35 L 185 560.95 C 103 560.95, 103 483.44, 21 483.44 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Immuno, line 1 to 2: 8</title></path><path d="M 21 534.14 C 103 534.14, 103 501.45, 185 501.45 L 185 502.05 C 103 502.05, 103 534.74, 21 534.74 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Targeted, line 1 to 2: 7</title></path><path d="M 21 647.08 C 103 647.08, 103 513.8, 185 513.8 L 185 514.4 C 103 514.4, 103 647.68, 21 647.68 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Biologic + Chemo, line 1 to 2: 7</title></path><path d="M 21 601.51 C 103 601.51, 103 443.64, 185 443.64 L 185 444.24 C 103 444.24, 103 602.11, 21 602.11 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Hormone, line 1 to 2: 6</title></path><path d="M 21 602.11 C 103 602.11, 103 502.05, 185 502.05 L 185 502.65 C 103 502.65, 103 602.71, 21 602.71 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Targeted, line 1 to 2: 6</title></path><path d="M 21 615.29 C 103 615.29, 103 502.65, 185 502.65 L 185 503.25 C 103 503.25, 103 615.89, 21 615.89 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Targeted, line 1 to 2: 6</title></path><path d="M 21 483.44 C 103 483.44, 103 537.72, 185 537.72 L 185 538.32 C 103 538.32, 103 484.04, 21 484.04 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone, line 1 to 2: 6</title></path><path d="M 21 631.14 C 103 631.14, 103 526.39, 185 526.39 L 185 526.99 C 103 526.99, 103 631.74, 21 631.74 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Investigational, line 1 to 2: 5</title></path><path d="M 21 602.71 C 103 602.71, 103 560.95, 185 560.95 L 185 561.55 C 103 561.55, 103 603.31, 21 603.31 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo + Immuno, line 1 to 2: 4</title></path><path d="M 21 586.56 C 103 586.56, 103 526.99, 185 526.99 L 185 527.59 C 103 527.59, 103 587.16, 21 587.16 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Investigational, line 1 to 2: 4</title></path><path d="M 21 534.74 C 103 534.74, 103 514.4, 185 514.4 L 185 515 C 103 515, 103 535.34, 21 535.34 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Biologic + Chemo, line 1 to 2: 4</title></path><path d="M 21 535.34 C 103 535.34, 103 561.55, 185 561.55 L 185 562.15 C 103 562.15, 103 535.94, 21 535.94 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Immuno, line 1 to 2: 4</title></path><path d="M 21 647.68 C 103 647.68, 103 538.32, 185 538.32 L 185 538.92 C 103 538.92, 103 648.28, 21 648.28 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone, line 1 to 2: 4</title></path><path d="M 21 615.89 C 103 615.89, 103 549.9, 185 549.9 L 185 550.5 C 103 550.5, 103 616.49, 21 616.49 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo + Hormone + Targeted, line 1 to 2: 3</title></path><path d="M 21 616.49 C 103 616.49, 103 527.59, 185 527.59 L 185 528.19 C 103 528.19, 103 617.09, 21 617.09 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Investigational, line 1 to 2: 3</title></path><path d="M 21 631.74 C 103 631.74, 103 444.24, 185 444.24 L 185 444.84 C 103 444.84, 103 632.34, 21 632.34 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Hormone, line 1 to 2: 3</title></path><path d="M 21 566.45 C 103 566.45, 103 515, 185 515 L 185 515.6 C 103 515.6, 103 567.05, 21 567.05 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Biologic + Chemo, line 1 to 2: 2</title></path><path d="M 21 567.05 C 103 567.05, 103 528.19, 185 528.19 L 185 528.79 C 103 528.79, 103 567.65, 21 567.65 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Investigational, line 1 to 2: 2</title></path><path d="M 21 617.09 C 103 617.09, 103 444.84, 185 444.84 L 185 445.44 C 103 445.44, 103 617.69, 21 617.69 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Hormone, line 1 to 2: 2</title></path><path d="M 21 648.28 C 103 648.28, 103 550.5, 185 550.5 L 185 551.1 C 103 551.1, 103 648.88, 21 648.88 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone + Targeted, line 1 to 2: 2</title></path><path d="M 21 648.88 C 103 648.88, 103 562.15, 185 562.15 L 185 562.75 C 103 562.75, 103 649.48, 21 649.48 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Immuno, line 1 to 2: 2</title></path><path d="M 21 603.31 C 103 603.31, 103 538.92, 185 538.92 L 185 539.52 C 103 539.52, 103 603.91, 21 603.91 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo + Hormone, line 1 to 2: 1</title></path><path d="M 21 567.65 C 103 567.65, 103 562.75, 185 562.75 L 185 563.35 C 103 563.35, 103 568.25, 21 568.25 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Immuno, line 1 to 2: 1</title></path><path d="M 21 568.25 C 103 568.25, 103 503.25, 185 503.25 L 185 503.85 C 103 503.85, 103 568.85, 21 568.85 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Targeted, line 1 to 2: 1</title></path><path d="M 21 617.69 C 103 617.69, 103 515.6, 185 515.6 L 185 516.2 C 103 516.2, 103 618.29, 21 618.29 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Biologic + Chemo, line 1 to 2: 1</title></path><path d="M 21 587.16 C 103 587.16, 103 445.44, 185 445.44 L 185 446.04 C 103 446.04, 103 587.76, 21 587.76 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Hormone, line 1 to 2: 1</title></path><path d="M 21 632.34 C 103 632.34, 103 516.2, 185 516.2 L 185 516.8 C 103 516.8, 103 632.94, 21 632.94 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Biologic + Chemo, line 1 to 2: 1</title></path><path d="M 21 632.94 C 103 632.94, 103 551.1, 185 551.1 L 185 551.7 C 103 551.7, 103 633.54, 21 633.54 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Hormone + Targeted, line 1 to 2: 1</title></path><path d="M 21 445.92 C 103 445.92, 103 570.26, 185 570.26 L 185 583.02 C 103 583.02, 103 458.68, 21 458.68 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo → no further line, line 1 to 2: 868</title></path><path d="M 21 535.94 C 103 535.94, 103 583.02, 185 583.02 L 185 592.47 C 103 592.47, 103 545.39, 21 545.39 Z" fill="#62737d" fill-opacity="0.45"><title>Hormone → no further line, line 1 to 2: 643</title></path><path d="M 21 484.04 C 103 484.04, 103 592.47, 185 592.47 L 185 598.25 C 103 598.25, 103 489.82, 21 489.82 Z" fill="#62737d" fill-opacity="0.45"><title>other → no further line, line 1 to 2: 393</title></path><path d="M 21 568.85 C 103 568.85, 103 598.25, 185 598.25 L 185 599.94 C 103 599.94, 103 570.54, 21 570.54 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone → no further line, line 1 to 2: 115</title></path><path d="M 21 587.76 C 103 587.76, 103 599.94, 185 599.94 L 185 601.63 C 103 601.63, 103 589.45, 21 589.45 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Immuno → no further line, line 1 to 2: 115</title></path><path d="M 21 649.48 C 103 649.48, 103 601.63, 185 601.63 L 185 603.09 C 103 603.09, 103 650.93, 21 650.93 Z" fill="#62737d" fill-opacity="0.45"><title>Investigational → no further line, line 1 to 2: 99</title></path><path d="M 21 603.91 C 103 603.91, 103 603.09, 185 603.09 L 185 604.28 C 103 604.28, 103 605.1, 21 605.1 Z" fill="#62737d" fill-opacity="0.45"><title>Biologic + Chemo → no further line, line 1 to 2: 81</title></path><path d="M 21 633.54 C 103 633.54, 103 604.28, 185 604.28 L 185 605 C 103 605, 103 634.26, 21 634.26 Z" fill="#62737d" fill-opacity="0.45"><title>Targeted → no further line, line 1 to 2: 49</title></path><path d="M 21 618.29 C 103 618.29, 103 605, 185 605 L 185 605.6 C 103 605.6, 103 618.89, 21 618.89 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → no further line, line 1 to 2: 36</title></path><path d="M 21 489.82 C 103 489.82, 103 615.53, 185 615.53 L 185 636.83 C 103 636.83, 103 511.12, 21 511.12 Z" fill="#c8ced2" fill-opacity="0.45"><title>other → record ends here, line 1 to 2: 1,449</title></path><path d="M 21 545.39 C 103 545.39, 103 636.83, 185 636.83 L 185 645.83 C 103 645.83, 103 554.39, 21 554.39 Z" fill="#c8ced2" fill-opacity="0.45"><title>Hormone → record ends here, line 1 to 2: 612</title></path><path d="M 21 570.54 C 103 570.54, 103 645.83, 185 645.83 L 185 652.18 C 103 652.18, 103 576.89, 21 576.89 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone → record ends here, line 1 to 2: 432</title></path><path d="M 21 458.68 C 103 458.68, 103 652.18, 185 652.18 L 185 657.51 C 103 657.51, 103 464.01, 21 464.01 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo → record ends here, line 1 to 2: 363</title></path><path d="M 21 618.89 C 103 618.89, 103 657.51, 185 657.51 L 185 661.47 C 103 661.47, 103 622.84, 21 622.84 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → record ends here, line 1 to 2: 269</title></path><path d="M 21 634.26 C 103 634.26, 103 661.47, 185 661.47 L 185 663.81 C 103 663.81, 103 636.6, 21 636.6 Z" fill="#c8ced2" fill-opacity="0.45"><title>Targeted → record ends here, line 1 to 2: 159</title></path><path d="M 21 589.45 C 103 589.45, 103 663.81, 185 663.81 L 185 666.08 C 103 666.08, 103 591.73, 21 591.73 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Immuno → record ends here, line 1 to 2: 155</title></path><path d="M 21 605.1 C 103 605.1, 103 666.08, 185 666.08 L 185 667.83 C 103 667.83, 103 606.85, 21 606.85 Z" fill="#c8ced2" fill-opacity="0.45"><title>Biologic + Chemo → record ends here, line 1 to 2: 119</title></path><path d="M 21 650.93 C 103 650.93, 103 667.83, 185 667.83 L 185 668.43 C 103 668.43, 103 651.53, 21 651.53 Z" fill="#c8ced2" fill-opacity="0.45"><title>Investigational → record ends here, line 1 to 2: 19</title></path><path d="M 198 426.32 C 280 426.32, 280 426.32, 362 426.32 L 362 433.45 C 280 433.45, 280 433.45, 198 433.45 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Hormone, line 2 to 3: 485</title></path><path d="M 198 476.93 C 280 476.93, 280 460.02, 362 460.02 L 362 462.51 C 280 462.51, 280 479.42, 198 479.42 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo, line 2 to 3: 169</title></path><path d="M 198 433.45 C 280 433.45, 280 444.5, 362 444.5 L 362 446.23 C 280 446.23, 280 435.19, 198 435.19 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → other, line 2 to 3: 118</title></path><path d="M 198 453.55 C 280 453.55, 280 446.23, 362 446.23 L 362 447.76 C 280 447.76, 280 455.08, 198 455.08 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → other, line 2 to 3: 104</title></path><path d="M 198 479.42 C 280 479.42, 280 447.76, 362 447.76 L 362 448.57 C 280 448.57, 280 480.22, 198 480.22 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → other, line 2 to 3: 55</title></path><path d="M 198 455.08 C 280 455.08, 280 462.51, 362 462.51 L 362 463.27 C 280 463.27, 280 455.85, 198 455.85 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo, line 2 to 3: 52</title></path><path d="M 198 497.81 C 280 497.81, 280 474.2, 362 474.2 L 362 474.85 C 280 474.85, 280 498.46, 198 498.46 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Targeted, line 2 to 3: 44</title></path><path d="M 198 435.19 C 280 435.19, 280 507.73, 362 507.73 L 362 508.33 C 280 508.33, 280 435.79, 198 435.79 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone + Targeted, line 2 to 3: 33</title></path><path d="M 198 523.34 C 280 523.34, 280 448.57, 362 448.57 L 362 449.17 C 280 449.17, 280 523.94, 198 523.94 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → other, line 2 to 3: 32</title></path><path d="M 198 435.79 C 280 435.79, 280 518.73, 362 518.73 L 362 519.33 C 280 519.33, 280 436.39, 198 436.39 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone, line 2 to 3: 28</title></path><path d="M 198 498.46 C 280 498.46, 280 449.17, 362 449.17 L 362 449.77 C 280 449.77, 280 499.06, 198 499.06 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → other, line 2 to 3: 27</title></path><path d="M 198 523.94 C 280 523.94, 280 485.52, 362 485.52 L 362 486.12 C 280 486.12, 280 524.54, 198 524.54 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Investigational, line 2 to 3: 26</title></path><path d="M 198 510.93 C 280 510.93, 280 449.77, 362 449.77 L 362 450.37 C 280 450.37, 280 511.53, 198 511.53 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → other, line 2 to 3: 24</title></path><path d="M 198 455.85 C 280 455.85, 280 486.12, 362 486.12 L 362 486.72 C 280 486.72, 280 456.45, 198 456.45 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Investigational, line 2 to 3: 23</title></path><path d="M 198 456.45 C 280 456.45, 280 433.45, 362 433.45 L 362 434.05 C 280 434.05, 280 457.05, 198 457.05 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Hormone, line 2 to 3: 20</title></path><path d="M 198 511.53 C 280 511.53, 280 496.73, 362 496.73 L 362 497.33 C 280 497.33, 280 512.13, 198 512.13 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Biologic + Chemo, line 2 to 3: 19</title></path><path d="M 198 480.22 C 280 480.22, 280 434.05, 362 434.05 L 362 434.65 C 280 434.65, 280 480.82, 198 480.82 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Hormone, line 2 to 3: 19</title></path><path d="M 198 535.54 C 280 535.54, 280 434.65, 362 434.65 L 362 435.25 C 280 435.25, 280 536.14, 198 536.14 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Hormone, line 2 to 3: 17</title></path><path d="M 198 436.39 C 280 436.39, 280 463.27, 362 463.27 L 362 463.87 C 280 463.87, 280 436.99, 198 436.99 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo, line 2 to 3: 16</title></path><path d="M 198 524.54 C 280 524.54, 280 463.87, 362 463.87 L 362 464.47 C 280 464.47, 280 525.14, 198 525.14 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo, line 2 to 3: 16</title></path><path d="M 198 457.05 C 280 457.05, 280 474.85, 362 474.85 L 362 475.45 C 280 475.45, 280 457.65, 198 457.65 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Targeted, line 2 to 3: 16</title></path><path d="M 198 480.82 C 280 480.82, 280 497.33, 362 497.33 L 362 497.93 C 280 497.93, 280 481.42, 198 481.42 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Biologic + Chemo, line 2 to 3: 15</title></path><path d="M 198 457.65 C 280 457.65, 280 497.93, 362 497.93 L 362 498.53 C 280 498.53, 280 458.25, 198 458.25 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Biologic + Chemo, line 2 to 3: 14</title></path><path d="M 198 481.42 C 280 481.42, 280 529.73, 362 529.73 L 362 530.33 C 280 530.33, 280 482.02, 198 482.02 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Immuno, line 2 to 3: 13</title></path><path d="M 198 512.13 C 280 512.13, 280 464.47, 362 464.47 L 362 465.07 C 280 465.07, 280 512.73, 198 512.73 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo, line 2 to 3: 12</title></path><path d="M 198 482.02 C 280 482.02, 280 486.72, 362 486.72 L 362 487.32 C 280 487.32, 280 482.62, 198 482.62 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Investigational, line 2 to 3: 11</title></path><path d="M 198 482.62 C 280 482.62, 280 475.45, 362 475.45 L 362 476.05 C 280 476.05, 280 483.22, 198 483.22 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Targeted, line 2 to 3: 11</title></path><path d="M 198 525.14 C 280 525.14, 280 498.53, 362 498.53 L 362 499.13 C 280 499.13, 280 525.74, 198 525.74 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Biologic + Chemo, line 2 to 3: 11</title></path><path d="M 198 525.74 C 280 525.74, 280 476.05, 362 476.05 L 362 476.65 C 280 476.65, 280 526.34, 198 526.34 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Targeted, line 2 to 3: 10</title></path><path d="M 198 483.22 C 280 483.22, 280 508.33, 362 508.33 L 362 508.93 C 280 508.93, 280 483.82, 198 483.82 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone + Targeted, line 2 to 3: 9</title></path><path d="M 198 512.73 C 280 512.73, 280 487.32, 362 487.32 L 362 487.92 C 280 487.92, 280 513.33, 198 513.33 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Investigational, line 2 to 3: 8</title></path><path d="M 198 483.82 C 280 483.82, 280 519.33, 362 519.33 L 362 519.93 C 280 519.93, 280 484.42, 198 484.42 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone, line 2 to 3: 8</title></path><path d="M 198 436.99 C 280 436.99, 280 487.92, 362 487.92 L 362 488.52 C 280 488.52, 280 437.59, 198 437.59 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Investigational, line 2 to 3: 8</title></path><path d="M 198 526.34 C 280 526.34, 280 435.25, 362 435.25 L 362 435.85 C 280 435.85, 280 526.94, 198 526.94 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Hormone, line 2 to 3: 8</title></path><path d="M 198 536.14 C 280 536.14, 280 465.07, 362 465.07 L 362 465.67 C 280 465.67, 280 536.74, 198 536.74 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo, line 2 to 3: 7</title></path><path d="M 198 536.74 C 280 536.74, 280 450.37, 362 450.37 L 362 450.97 C 280 450.97, 280 537.34, 198 537.34 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → other, line 2 to 3: 7</title></path><path d="M 198 559.06 C 280 559.06, 280 450.97, 362 450.97 L 362 451.57 C 280 451.57, 280 559.66, 198 559.66 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → other, line 2 to 3: 7</title></path><path d="M 198 458.25 C 280 458.25, 280 508.93, 362 508.93 L 362 509.53 C 280 509.53, 280 458.85, 198 458.85 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone + Targeted, line 2 to 3: 7</title></path><path d="M 198 547.5 C 280 547.5, 280 435.85, 362 435.85 L 362 436.45 C 280 436.45, 280 548.1, 198 548.1 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Hormone, line 2 to 3: 6</title></path><path d="M 198 559.66 C 280 559.66, 280 530.33, 362 530.33 L 362 530.93 C 280 530.93, 280 560.26, 198 560.26 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Immuno, line 2 to 3: 6</title></path><path d="M 198 499.06 C 280 499.06, 280 465.67, 362 465.67 L 362 466.27 C 280 466.27, 280 499.66, 198 499.66 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo, line 2 to 3: 6</title></path><path d="M 198 560.26 C 280 560.26, 280 466.27, 362 466.27 L 362 466.87 C 280 466.87, 280 560.86, 198 560.86 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo, line 2 to 3: 5</title></path><path d="M 198 437.59 C 280 437.59, 280 476.65, 362 476.65 L 362 477.25 C 280 477.25, 280 438.19, 198 438.19 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Targeted, line 2 to 3: 5</title></path><path d="M 198 458.85 C 280 458.85, 280 519.93, 362 519.93 L 362 520.53 C 280 520.53, 280 459.45, 198 459.45 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone, line 2 to 3: 5</title></path><path d="M 198 459.45 C 280 459.45, 280 530.93, 362 530.93 L 362 531.53 C 280 531.53, 280 460.05, 198 460.05 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Immuno, line 2 to 3: 5</title></path><path d="M 198 513.33 C 280 513.33, 280 477.25, 362 477.25 L 362 477.85 C 280 477.85, 280 513.93, 198 513.93 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Targeted, line 2 to 3: 3</title></path><path d="M 198 537.34 C 280 537.34, 280 509.53, 362 509.53 L 362 510.13 C 280 510.13, 280 537.94, 198 537.94 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone + Targeted, line 2 to 3: 3</title></path><path d="M 198 499.66 C 280 499.66, 280 488.52, 362 488.52 L 362 489.12 C 280 489.12, 280 500.26, 198 500.26 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Investigational, line 2 to 3: 3</title></path><path d="M 198 513.93 C 280 513.93, 280 520.53, 362 520.53 L 362 521.13 C 280 521.13, 280 514.53, 198 514.53 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo + Hormone, line 2 to 3: 2</title></path><path d="M 198 548.1 C 280 548.1, 280 510.13, 362 510.13 L 362 510.73 C 280 510.73, 280 548.7, 198 548.7 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo + Hormone + Targeted, line 2 to 3: 2</title></path><path d="M 198 560.86 C 280 560.86, 280 499.13, 362 499.13 L 362 499.73 C 280 499.73, 280 561.46, 198 561.46 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Biologic + Chemo, line 2 to 3: 2</title></path><path d="M 198 438.19 C 280 438.19, 280 499.73, 362 499.73 L 362 500.33 C 280 500.33, 280 438.79, 198 438.79 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Biologic + Chemo, line 2 to 3: 2</title></path><path d="M 198 438.79 C 280 438.79, 280 531.53, 362 531.53 L 362 532.13 C 280 532.13, 280 439.39, 198 439.39 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Immuno, line 2 to 3: 2</title></path><path d="M 198 526.94 C 280 526.94, 280 521.13, 362 521.13 L 362 521.73 C 280 521.73, 280 527.54, 198 527.54 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone, line 2 to 3: 2</title></path><path d="M 198 500.26 C 280 500.26, 280 500.33, 362 500.33 L 362 500.93 C 280 500.93, 280 500.86, 198 500.86 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Biologic + Chemo, line 2 to 3: 2</title></path><path d="M 198 548.7 C 280 548.7, 280 451.57, 362 451.57 L 362 452.17 C 280 452.17, 280 549.3, 198 549.3 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → other, line 2 to 3: 2</title></path><path d="M 198 537.94 C 280 537.94, 280 521.73, 362 521.73 L 362 522.33 C 280 522.33, 280 538.54, 198 538.54 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone, line 2 to 3: 1</title></path><path d="M 198 538.54 C 280 538.54, 280 532.13, 362 532.13 L 362 532.73 C 280 532.73, 280 539.14, 198 539.14 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Immuno, line 2 to 3: 1</title></path><path d="M 198 539.14 C 280 539.14, 280 489.12, 362 489.12 L 362 489.72 C 280 489.72, 280 539.74, 198 539.74 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Investigational, line 2 to 3: 1</title></path><path d="M 198 549.3 C 280 549.3, 280 466.87, 362 466.87 L 362 467.47 C 280 467.47, 280 549.9, 198 549.9 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo, line 2 to 3: 1</title></path><path d="M 198 549.9 C 280 549.9, 280 522.33, 362 522.33 L 362 522.93 C 280 522.93, 280 550.5, 198 550.5 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo + Hormone, line 2 to 3: 1</title></path><path d="M 198 550.5 C 280 550.5, 280 489.72, 362 489.72 L 362 490.32 C 280 490.32, 280 551.1, 198 551.1 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Investigational, line 2 to 3: 1</title></path><path d="M 198 561.46 C 280 561.46, 280 490.32, 362 490.32 L 362 490.92 C 280 490.92, 280 562.06, 198 562.06 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Investigational, line 2 to 3: 1</title></path><path d="M 198 562.06 C 280 562.06, 280 477.85, 362 477.85 L 362 478.45 C 280 478.45, 280 562.66, 198 562.66 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Targeted, line 2 to 3: 1</title></path><path d="M 198 527.54 C 280 527.54, 280 510.73, 362 510.73 L 362 511.33 C 280 511.33, 280 528.14, 198 528.14 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone + Targeted, line 2 to 3: 1</title></path><path d="M 198 500.86 C 280 500.86, 280 522.93, 362 522.93 L 362 523.53 C 280 523.53, 280 501.46, 198 501.46 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Hormone, line 2 to 3: 1</title></path><path d="M 198 501.46 C 280 501.46, 280 511.33, 362 511.33 L 362 511.93 C 280 511.93, 280 502.06, 198 502.06 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Hormone + Targeted, line 2 to 3: 1</title></path><path d="M 198 502.06 C 280 502.06, 280 436.45, 362 436.45 L 362 437.05 C 280 437.05, 280 502.66, 198 502.66 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Hormone, line 2 to 3: 1</title></path><path d="M 198 484.42 C 280 484.42, 280 540.73, 362 540.73 L 362 543.74 C 280 543.74, 280 487.44, 198 487.44 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo → no further line, line 2 to 3: 205</title></path><path d="M 198 439.39 C 280 439.39, 280 543.74, 362 543.74 L 362 546.43 C 280 546.43, 280 442.08, 198 442.08 Z" fill="#62737d" fill-opacity="0.45"><title>Hormone → no further line, line 2 to 3: 183</title></path><path d="M 198 460.05 C 280 460.05, 280 546.43, 362 546.43 L 362 549.02 C 280 549.02, 280 462.63, 198 462.63 Z" fill="#62737d" fill-opacity="0.45"><title>other → no further line, line 2 to 3: 176</title></path><path d="M 198 528.14 C 280 528.14, 280 549.02, 362 549.02 L 362 549.62 C 280 549.62, 280 528.74, 198 528.74 Z" fill="#62737d" fill-opacity="0.45"><title>Investigational → no further line, line 2 to 3: 35</title></path><path d="M 198 514.53 C 280 514.53, 280 549.62, 362 549.62 L 362 550.22 C 280 550.22, 280 515.13, 198 515.13 Z" fill="#62737d" fill-opacity="0.45"><title>Biologic + Chemo → no further line, line 2 to 3: 32</title></path><path d="M 198 562.66 C 280 562.66, 280 550.22, 362 550.22 L 362 550.82 C 280 550.82, 280 563.26, 198 563.26 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Immuno → no further line, line 2 to 3: 27</title></path><path d="M 198 502.66 C 280 502.66, 280 550.82, 362 550.82 L 362 551.42 C 280 551.42, 280 503.26, 198 503.26 Z" fill="#62737d" fill-opacity="0.45"><title>Targeted → no further line, line 2 to 3: 25</title></path><path d="M 198 539.74 C 280 539.74, 280 551.42, 362 551.42 L 362 552.02 C 280 552.02, 280 540.34, 198 540.34 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone → no further line, line 2 to 3: 18</title></path><path d="M 198 551.1 C 280 551.1, 280 552.02, 362 552.02 L 362 552.62 C 280 552.62, 280 551.7, 198 551.7 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → no further line, line 2 to 3: 11</title></path><path d="M 198 462.63 C 280 462.63, 280 561.2, 362 561.2 L 362 568.37 C 280 568.37, 280 469.81, 198 469.81 Z" fill="#c8ced2" fill-opacity="0.45"><title>other → record ends here, line 2 to 3: 488</title></path><path d="M 198 442.08 C 280 442.08, 280 568.37, 362 568.37 L 362 572.66 C 280 572.66, 280 446.37, 198 446.37 Z" fill="#c8ced2" fill-opacity="0.45"><title>Hormone → record ends here, line 2 to 3: 292</title></path><path d="M 198 487.44 C 280 487.44, 280 572.66, 362 572.66 L 362 575.97 C 280 575.97, 280 490.75, 198 490.75 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo → record ends here, line 2 to 3: 225</title></path><path d="M 198 503.26 C 280 503.26, 280 575.97, 362 575.97 L 362 577.47 C 280 577.47, 280 504.76, 198 504.76 Z" fill="#c8ced2" fill-opacity="0.45"><title>Targeted → record ends here, line 2 to 3: 102</title></path><path d="M 198 551.7 C 280 551.7, 280 577.47, 362 577.47 L 362 578.67 C 280 578.67, 280 552.9, 198 552.9 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → record ends here, line 2 to 3: 82</title></path><path d="M 198 540.34 C 280 540.34, 280 578.67, 362 578.67 L 362 579.82 C 280 579.82, 280 541.49, 198 541.49 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone → record ends here, line 2 to 3: 78</title></path><path d="M 198 515.13 C 280 515.13, 280 579.82, 362 579.82 L 362 580.76 C 280 580.76, 280 516.07, 198 516.07 Z" fill="#c8ced2" fill-opacity="0.45"><title>Biologic + Chemo → record ends here, line 2 to 3: 64</title></path><path d="M 198 563.26 C 280 563.26, 280 580.76, 362 580.76 L 362 581.36 C 280 581.36, 280 563.86, 198 563.86 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Immuno → record ends here, line 2 to 3: 33</title></path><path d="M 198 528.74 C 280 528.74, 280 581.36, 362 581.36 L 362 581.96 C 280 581.96, 280 529.34, 198 529.34 Z" fill="#c8ced2" fill-opacity="0.45"><title>Investigational → record ends here, line 2 to 3: 9</title></path><path d="M 375 426.32 C 457 426.32, 457 426.32, 539 426.32 L 539 430.7 C 457 430.7, 457 430.7, 375 430.7 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Hormone, line 3 to 4: 298</title></path><path d="M 375 430.7 C 457 430.7, 457 441.12, 539 441.12 L 539 442.07 C 457 442.07, 457 431.66, 375 431.66 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → other, line 3 to 4: 65</title></path><path d="M 375 460.02 C 457 460.02, 457 453.6, 539 453.6 L 539 454.53 C 457 454.53, 457 460.95, 375 460.95 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo, line 3 to 4: 63</title></path><path d="M 375 444.5 C 457 444.5, 457 442.07, 539 442.07 L 539 442.72 C 457 442.72, 457 445.14, 375 445.14 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → other, line 3 to 4: 44</title></path><path d="M 375 460.95 C 457 460.95, 457 442.72, 539 442.72 L 539 443.32 C 457 443.32, 457 461.55, 375 461.55 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → other, line 3 to 4: 23</title></path><path d="M 375 431.66 C 457 431.66, 457 465.2, 539 465.2 L 539 465.8 C 457 465.8, 457 432.26, 375 432.26 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone + Targeted, line 3 to 4: 22</title></path><path d="M 375 432.26 C 457 432.26, 457 498.2, 539 498.2 L 539 498.8 C 457 498.8, 457 432.86, 375 432.86 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Hormone, line 3 to 4: 19</title></path><path d="M 375 474.2 C 457 474.2, 457 476.2, 539 476.2 L 539 476.8 C 457 476.8, 457 474.8, 375 474.8 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Targeted, line 3 to 4: 19</title></path><path d="M 375 485.52 C 457 485.52, 457 487.2, 539 487.2 L 539 487.8 C 457 487.8, 457 486.12, 375 486.12 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Investigational, line 3 to 4: 18</title></path><path d="M 375 486.12 C 457 486.12, 457 443.32, 539 443.32 L 539 443.92 C 457 443.92, 457 486.72, 375 486.72 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → other, line 3 to 4: 18</title></path><path d="M 375 445.14 C 457 445.14, 457 454.53, 539 454.53 L 539 455.13 C 457 455.13, 457 445.74, 375 445.74 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo, line 3 to 4: 14</title></path><path d="M 375 496.73 C 457 496.73, 457 509.2, 539 509.2 L 539 509.8 C 457 509.8, 457 497.33, 375 497.33 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Biologic + Chemo, line 3 to 4: 11</title></path><path d="M 375 445.74 C 457 445.74, 457 487.8, 539 487.8 L 539 488.4 C 457 488.4, 457 446.34, 375 446.34 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Investigational, line 3 to 4: 10</title></path><path d="M 375 474.8 C 457 474.8, 457 443.92, 539 443.92 L 539 444.52 C 457 444.52, 457 475.4, 375 475.4 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → other, line 3 to 4: 10</title></path><path d="M 375 461.55 C 457 461.55, 457 430.7, 539 430.7 L 539 431.3 C 457 431.3, 457 462.15, 375 462.15 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Hormone, line 3 to 4: 9</title></path><path d="M 375 462.15 C 457 462.15, 457 509.8, 539 509.8 L 539 510.4 C 457 510.4, 457 462.75, 375 462.75 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Biologic + Chemo, line 3 to 4: 8</title></path><path d="M 375 486.72 C 457 486.72, 457 455.13, 539 455.13 L 539 455.73 C 457 455.73, 457 487.32, 375 487.32 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo, line 3 to 4: 8</title></path><path d="M 375 462.75 C 457 462.75, 457 498.8, 539 498.8 L 539 499.4 C 457 499.4, 457 463.35, 375 463.35 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone, line 3 to 4: 7</title></path><path d="M 375 432.86 C 457 432.86, 457 455.73, 539 455.73 L 539 456.33 C 457 456.33, 457 433.46, 375 433.46 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo, line 3 to 4: 7</title></path><path d="M 375 487.32 C 457 487.32, 457 476.8, 539 476.8 L 539 477.4 C 457 477.4, 457 487.92, 375 487.92 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Targeted, line 3 to 4: 7</title></path><path d="M 375 507.73 C 457 507.73, 457 456.33, 539 456.33 L 539 456.93 C 457 456.93, 457 508.33, 375 508.33 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo, line 3 to 4: 6</title></path><path d="M 375 487.92 C 457 487.92, 457 431.3, 539 431.3 L 539 431.9 C 457 431.9, 457 488.52, 375 488.52 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Hormone, line 3 to 4: 6</title></path><path d="M 375 446.34 C 457 446.34, 457 431.9, 539 431.9 L 539 432.5 C 457 432.5, 457 446.94, 375 446.94 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Hormone, line 3 to 4: 6</title></path><path d="M 375 446.94 C 457 446.94, 457 465.8, 539 465.8 L 539 466.4 C 457 466.4, 457 447.54, 375 447.54 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone + Targeted, line 3 to 4: 6</title></path><path d="M 375 447.54 C 457 447.54, 457 510.4, 539 510.4 L 539 511 C 457 511, 457 448.14, 375 448.14 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Biologic + Chemo, line 3 to 4: 6</title></path><path d="M 375 497.33 C 457 497.33, 457 456.93, 539 456.93 L 539 457.53 C 457 457.53, 457 497.93, 375 497.93 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Chemo, line 3 to 4: 5</title></path><path d="M 375 518.73 C 457 518.73, 457 432.5, 539 432.5 L 539 433.1 C 457 433.1, 457 519.33, 375 519.33 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Hormone, line 3 to 4: 5</title></path><path d="M 375 448.14 C 457 448.14, 457 499.4, 539 499.4 L 539 500 C 457 500, 457 448.74, 375 448.74 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Hormone, line 3 to 4: 5</title></path><path d="M 375 519.33 C 457 519.33, 457 457.53, 539 457.53 L 539 458.13 C 457 458.13, 457 519.93, 375 519.93 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo, line 3 to 4: 4</title></path><path d="M 375 508.33 C 457 508.33, 457 466.4, 539 466.4 L 539 467 C 457 467, 457 508.93, 375 508.93 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo + Hormone + Targeted, line 3 to 4: 4</title></path><path d="M 375 488.52 C 457 488.52, 457 520.2, 539 520.2 L 539 520.8 C 457 520.8, 457 489.12, 375 489.12 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Immuno, line 3 to 4: 4</title></path><path d="M 375 508.93 C 457 508.93, 457 444.52, 539 444.52 L 539 445.12 C 457 445.12, 457 509.53, 375 509.53 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → other, line 3 to 4: 4</title></path><path d="M 375 448.74 C 457 448.74, 457 477.4, 539 477.4 L 539 478 C 457 478, 457 449.34, 375 449.34 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Targeted, line 3 to 4: 4</title></path><path d="M 375 463.35 C 457 463.35, 457 488.4, 539 488.4 L 539 489 C 457 489, 457 463.95, 375 463.95 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Investigational, line 3 to 4: 3</title></path><path d="M 375 463.95 C 457 463.95, 457 478, 539 478 L 539 478.6 C 457 478.6, 457 464.55, 375 464.55 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Targeted, line 3 to 4: 3</title></path><path d="M 375 489.12 C 457 489.12, 457 467, 539 467 L 539 467.6 C 457 467.6, 457 489.72, 375 489.72 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Chemo + Hormone + Targeted, line 3 to 4: 3</title></path><path d="M 375 497.93 C 457 497.93, 457 445.12, 539 445.12 L 539 445.72 C 457 445.72, 457 498.53, 375 498.53 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → other, line 3 to 4: 3</title></path><path d="M 375 464.55 C 457 464.55, 457 520.8, 539 520.8 L 539 521.4 C 457 521.4, 457 465.15, 375 465.15 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Immuno, line 3 to 4: 2</title></path><path d="M 375 519.93 C 457 519.93, 457 467.6, 539 467.6 L 539 468.2 C 457 468.2, 457 520.53, 375 520.53 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → Chemo + Hormone + Targeted, line 3 to 4: 2</title></path><path d="M 375 433.46 C 457 433.46, 457 489, 539 489 L 539 489.6 C 457 489.6, 457 434.06, 375 434.06 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Investigational, line 3 to 4: 2</title></path><path d="M 375 475.4 C 457 475.4, 457 489.6, 539 489.6 L 539 490.2 C 457 490.2, 457 476, 375 476 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Investigational, line 3 to 4: 2</title></path><path d="M 375 449.34 C 457 449.34, 457 521.4, 539 521.4 L 539 522 C 457 522, 457 449.94, 375 449.94 Z" fill="#9aa5ad" fill-opacity="0.32"><title>other → Chemo + Immuno, line 3 to 4: 2</title></path><path d="M 375 498.53 C 457 498.53, 457 478.6, 539 478.6 L 539 479.2 C 457 479.2, 457 499.13, 375 499.13 Z" fill="#009E73" fill-opacity="0.32"><title>Biologic + Chemo → Targeted, line 3 to 4: 1</title></path><path d="M 375 465.15 C 457 465.15, 457 468.2, 539 468.2 L 539 468.8 C 457 468.8, 457 465.75, 375 465.75 Z" fill="#0072B2" fill-opacity="0.32"><title>Chemo → Chemo + Hormone + Targeted, line 3 to 4: 1</title></path><path d="M 375 520.53 C 457 520.53, 457 445.72, 539 445.72 L 539 446.32 C 457 446.32, 457 521.13, 375 521.13 Z" fill="#CC79A7" fill-opacity="0.32"><title>Chemo + Hormone → other, line 3 to 4: 1</title></path><path d="M 375 509.53 C 457 509.53, 457 511, 539 511 L 539 511.6 C 457 511.6, 457 510.13, 375 510.13 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Biologic + Chemo, line 3 to 4: 1</title></path><path d="M 375 510.13 C 457 510.13, 457 500, 539 500 L 539 500.6 C 457 500.6, 457 510.73, 375 510.73 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Chemo + Hormone, line 3 to 4: 1</title></path><path d="M 375 510.73 C 457 510.73, 457 433.1, 539 433.1 L 539 433.7 C 457 433.7, 457 511.33, 375 511.33 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Hormone, line 3 to 4: 1</title></path><path d="M 375 511.33 C 457 511.33, 457 479.2, 539 479.2 L 539 479.8 C 457 479.8, 457 511.93, 375 511.93 Z" fill="#56B4E9" fill-opacity="0.32"><title>Chemo + Hormone + Targeted → Targeted, line 3 to 4: 1</title></path><path d="M 375 529.73 C 457 529.73, 457 458.13, 539 458.13 L 539 458.73 C 457 458.73, 457 530.33, 375 530.33 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo, line 3 to 4: 1</title></path><path d="M 375 530.33 C 457 530.33, 457 522, 539 522 L 539 522.6 C 457 522.6, 457 530.93, 375 530.93 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → Chemo + Immuno, line 3 to 4: 1</title></path><path d="M 375 530.93 C 457 530.93, 457 446.32, 539 446.32 L 539 446.92 C 457 446.92, 457 531.53, 375 531.53 Z" fill="#D55E00" fill-opacity="0.32"><title>Chemo + Immuno → other, line 3 to 4: 1</title></path><path d="M 375 434.06 C 457 434.06, 457 522.6, 539 522.6 L 539 523.2 C 457 523.2, 457 434.66, 375 434.66 Z" fill="#E69F00" fill-opacity="0.32"><title>Hormone → Chemo + Immuno, line 3 to 4: 1</title></path><path d="M 375 489.72 C 457 489.72, 457 511.6, 539 511.6 L 539 512.2 C 457 512.2, 457 490.32, 375 490.32 Z" fill="#E69F00" fill-opacity="0.24"><title>Investigational → Biologic + Chemo, line 3 to 4: 1</title></path><path d="M 375 476 C 457 476, 457 512.2, 539 512.2 L 539 512.8 C 457 512.8, 457 476.6, 375 476.6 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Biologic + Chemo, line 3 to 4: 1</title></path><path d="M 375 476.6 C 457 476.6, 457 458.73, 539 458.73 L 539 459.33 C 457 459.33, 457 477.2, 375 477.2 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo, line 3 to 4: 1</title></path><path d="M 375 477.2 C 457 477.2, 457 468.8, 539 468.8 L 539 469.4 C 457 469.4, 457 477.8, 375 477.8 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Chemo + Hormone + Targeted, line 3 to 4: 1</title></path><path d="M 375 477.8 C 457 477.8, 457 433.7, 539 433.7 L 539 434.3 C 457 434.3, 457 478.4, 375 478.4 Z" fill="#0072B2" fill-opacity="0.24"><title>Targeted → Hormone, line 3 to 4: 1</title></path><path d="M 375 449.94 C 457 449.94, 457 531.2, 539 531.2 L 539 532.35 C 457 532.35, 457 451.09, 375 451.09 Z" fill="#62737d" fill-opacity="0.45"><title>other → no further line, line 3 to 4: 78</title></path><path d="M 375 465.75 C 457 465.75, 457 532.35, 539 532.35 L 539 533.42 C 457 533.42, 457 466.82, 375 466.82 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo → no further line, line 3 to 4: 73</title></path><path d="M 375 434.66 C 457 434.66, 457 533.42, 539 533.42 L 539 534.3 C 457 534.3, 457 435.54, 375 435.54 Z" fill="#62737d" fill-opacity="0.45"><title>Hormone → no further line, line 3 to 4: 60</title></path><path d="M 375 499.13 C 457 499.13, 457 534.3, 539 534.3 L 539 534.9 C 457 534.9, 457 499.73, 375 499.73 Z" fill="#62737d" fill-opacity="0.45"><title>Biologic + Chemo → no further line, line 3 to 4: 16</title></path><path d="M 375 478.4 C 457 478.4, 457 534.9, 539 534.9 L 539 535.5 C 457 535.5, 457 479, 375 479 Z" fill="#62737d" fill-opacity="0.45"><title>Targeted → no further line, line 3 to 4: 15</title></path><path d="M 375 490.32 C 457 490.32, 457 535.5, 539 535.5 L 539 536.1 C 457 536.1, 457 490.92, 375 490.92 Z" fill="#62737d" fill-opacity="0.45"><title>Investigational → no further line, line 3 to 4: 12</title></path><path d="M 375 521.13 C 457 521.13, 457 536.1, 539 536.1 L 539 536.7 C 457 536.7, 457 521.73, 375 521.73 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone → no further line, line 3 to 4: 11</title></path><path d="M 375 531.53 C 457 531.53, 457 536.7, 539 536.7 L 539 537.3 C 457 537.3, 457 532.13, 375 532.13 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Immuno → no further line, line 3 to 4: 8</title></path><path d="M 375 511.93 C 457 511.93, 457 537.3, 539 537.3 L 539 537.9 C 457 537.9, 457 512.53, 375 512.53 Z" fill="#62737d" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → no further line, line 3 to 4: 4</title></path><path d="M 375 451.09 C 457 451.09, 457 545.28, 539 545.28 L 539 548.23 C 457 548.23, 457 454.05, 375 454.05 Z" fill="#c8ced2" fill-opacity="0.45"><title>other → record ends here, line 3 to 4: 201</title></path><path d="M 375 466.82 C 457 466.82, 457 548.23, 539 548.23 L 539 549.58 C 457 549.58, 457 468.18, 375 468.18 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo → record ends here, line 3 to 4: 92</title></path><path d="M 375 435.54 C 457 435.54, 457 549.58, 539 549.58 L 539 550.79 C 457 550.79, 457 436.75, 375 436.75 Z" fill="#c8ced2" fill-opacity="0.45"><title>Hormone → record ends here, line 3 to 4: 82</title></path><path d="M 375 479 C 457 479, 457 550.79, 539 550.79 L 539 551.39 C 457 551.39, 457 479.6, 375 479.6 Z" fill="#c8ced2" fill-opacity="0.45"><title>Targeted → record ends here, line 3 to 4: 40</title></path><path d="M 375 512.53 C 457 512.53, 457 551.39, 539 551.39 L 539 551.99 C 457 551.99, 457 513.13, 375 513.13 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone + Targeted → record ends here, line 3 to 4: 34</title></path><path d="M 375 499.73 C 457 499.73, 457 551.99, 539 551.99 L 539 552.59 C 457 552.59, 457 500.33, 375 500.33 Z" fill="#c8ced2" fill-opacity="0.45"><title>Biologic + Chemo → record ends here, line 3 to 4: 29</title></path><path d="M 375 521.73 C 457 521.73, 457 552.59, 539 552.59 L 539 553.19 C 457 553.19, 457 522.33, 375 522.33 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Hormone → record ends here, line 3 to 4: 25</title></path><path d="M 375 532.13 C 457 532.13, 457 553.19, 539 553.19 L 539 553.79 C 457 553.79, 457 532.73, 375 532.73 Z" fill="#c8ced2" fill-opacity="0.45"><title>Chemo + Immuno → record ends here, line 3 to 4: 16</title></path><path d="M 375 490.92 C 457 490.92, 457 553.79, 539 553.79 L 539 554.39 C 457 554.39, 457 491.52, 375 491.52 Z" fill="#c8ced2" fill-opacity="0.45"><title>Investigational → record ends here, line 3 to 4: 5</title></path><rect x="8" y="426.32" width="13" height="37.5" rx="2" fill="#0072B2"><title>Chemo 2,551 (line 1)</title></rect><rect x="8" y="473.83" width="13" height="35.78" rx="2" fill="#9aa5ad"><title>other 2,434 (line 1)</title></rect><rect x="8" y="519.61" width="13" height="32.47" rx="2" fill="#E69F00"><title>Hormone 2,209 (line 1)</title></rect><rect x="8" y="562.08" width="13" height="11.48" rx="2" fill="#CC79A7"><title>Chemo + Hormone 781 (line 1)</title></rect><rect x="8" y="583.56" width="13" height="5.37" rx="2" fill="#D55E00"><title>Chemo + Immuno 365 (line 1)</title></rect><rect x="8" y="598.93" width="13" height="5.16" rx="2" fill="#009E73"><title>Biologic + Chemo 351 (line 1)</title></rect><rect x="8" y="614.09" width="13" height="5.06" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 344 (line 1)</title></rect><rect x="8" y="629.15" width="13" height="4.7" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 320 (line 1)</title></rect><rect x="8" y="643.85" width="13" height="4.26" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 290 (line 1)</title></rect><rect x="185" y="426.32" width="13" height="17.23" rx="2" fill="#E69F00"><title>Hormone 1,172 (line 2)</title></rect><rect x="185" y="453.55" width="13" height="13.38" rx="2" fill="#9aa5ad"><title>other 910 (line 2)</title></rect><rect x="185" y="476.93" width="13" height="10.88" rx="2" fill="#0072B2"><title>Chemo 740 (line 2)</title></rect><rect x="185" y="497.81" width="13" height="3.12" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 212 (line 2)</title></rect><rect x="185" y="510.93" width="13" height="2.41" rx="2" fill="#009E73"><title>Biologic + Chemo 164 (line 2)</title></rect><rect x="185" y="523.34" width="13" height="2.21" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 150 (line 2)</title></rect><rect x="185" y="535.54" width="13" height="1.96" rx="2" fill="#CC79A7"><title>Chemo + Hormone 133 (line 2)</title></rect><rect x="185" y="547.5" width="13" height="1.56" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 106 (line 2)</title></rect><rect x="185" y="559.06" width="13" height="1.21" rx="2" fill="#D55E00"><title>Chemo + Immuno 82 (line 2)</title></rect><rect x="185" y="570.26" width="13" height="35.27" rx="2" fill="#62737d"><title>no further line 2,399 (line 2)</title></rect><rect x="185" y="615.53" width="13" height="52.58" rx="2" fill="#c8ced2"><title>record ends here 3,577 (line 2)</title></rect><rect x="362" y="426.32" width="13" height="8.17" rx="2" fill="#E69F00"><title>Hormone 556 (line 3)</title></rect><rect x="362" y="444.5" width="13" height="5.53" rx="2" fill="#9aa5ad"><title>other 376 (line 3)</title></rect><rect x="362" y="460.02" width="13" height="4.18" rx="2" fill="#0072B2"><title>Chemo 284 (line 3)</title></rect><rect x="362" y="474.2" width="13" height="1.32" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 90 (line 3)</title></rect><rect x="362" y="485.52" width="13" height="1.21" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 82 (line 3)</title></rect><rect x="362" y="496.73" width="13" height="1" rx="2" fill="#009E73"><title>Biologic + Chemo 65 (line 3)</title></rect><rect x="362" y="507.73" width="13" height="1" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 56 (line 3)</title></rect><rect x="362" y="518.73" width="13" height="1" rx="2" fill="#CC79A7"><title>Chemo + Hormone 48 (line 3)</title></rect><rect x="362" y="529.73" width="13" height="1" rx="2" fill="#D55E00"><title>Chemo + Immuno 27 (line 3)</title></rect><rect x="362" y="540.73" width="13" height="10.47" rx="2" fill="#62737d"><title>no further line 712 (line 3)</title></rect><rect x="362" y="561.2" width="13" height="20.18" rx="2" fill="#c8ced2"><title>record ends here 1,373 (line 3)</title></rect><rect x="539" y="426.32" width="13" height="4.79" rx="2" fill="#E69F00"><title>Hormone 326 (line 4)</title></rect><rect x="539" y="441.12" width="13" height="2.48" rx="2" fill="#9aa5ad"><title>other 169 (line 4)</title></rect><rect x="539" y="453.6" width="13" height="1.6" rx="2" fill="#0072B2"><title>Chemo 109 (line 4)</title></rect><rect x="539" y="465.2" width="13" height="1" rx="2" fill="#56B4E9"><title>Chemo + Hormone + Targeted 39 (line 4)</title></rect><rect x="539" y="476.2" width="13" height="1" rx="2" fill="#0072B2" opacity="0.75"><title>Targeted 35 (line 4)</title></rect><rect x="539" y="487.2" width="13" height="1" rx="2" fill="#E69F00" opacity="0.75"><title>Investigational 35 (line 4)</title></rect><rect x="539" y="498.2" width="13" height="1" rx="2" fill="#CC79A7"><title>Chemo + Hormone 32 (line 4)</title></rect><rect x="539" y="509.2" width="13" height="1" rx="2" fill="#009E73"><title>Biologic + Chemo 28 (line 4)</title></rect><rect x="539" y="520.2" width="13" height="1" rx="2" fill="#D55E00"><title>Chemo + Immuno 10 (line 4)</title></rect><rect x="539" y="531.2" width="13" height="4.07" rx="2" fill="#62737d"><title>no further line 277 (line 4)</title></rect><rect x="539" y="545.28" width="13" height="7.7" rx="2" fill="#c8ced2"><title>record ends here 524 (line 4)</title></rect><text x="25" y="448.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 2,551</text><text x="25" y="495.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 2,434</text><text x="25" y="539.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 2,209</text><text x="25" y="571.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone 781</text><text x="25" y="589.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Immuno 365</text><text x="25" y="605" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 351</text><text x="25" y="620.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone + Targeted 344</text><text x="25" y="635" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Targeted 320</text><text x="25" y="649.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Investigational 290</text><text x="202" y="438.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 1,172</text><text x="202" y="463.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 910</text><text x="202" y="485.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 740</text><text x="202" y="502.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Targeted 212</text><text x="202" y="516.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 164</text><text x="202" y="529.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Investigational 150</text><text x="202" y="542.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone 133</text><text x="202" y="555.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone + Targeted 106</text><text x="202" y="591.4" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">no further line 2,399</text><text x="202" y="645.3" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">record ends here 3,577</text><text x="379" y="433.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 556</text><text x="379" y="450.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 376</text><text x="379" y="465.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 284</text><text x="379" y="478.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Targeted 90</text><text x="379" y="492.1" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Investigational 82</text><text x="379" y="505.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 65</text><text x="379" y="522.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone 48</text><text x="379" y="535.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Immuno 27</text><text x="379" y="549.5" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">no further line 712</text><text x="379" y="574.8" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">record ends here 1,373</text><text x="556" y="432.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Hormone 326</text><text x="556" y="445.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">other 169</text><text x="556" y="458.9" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo 109</text><text x="556" y="472.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone + Targeted 39</text><text x="556" y="485.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Targeted 35</text><text x="556" y="502.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Chemo + Hormone 32</text><text x="556" y="515.2" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">Biologic + Chemo 28</text><text x="556" y="536.7" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">no further line 277</text><text x="556" y="552.6" font-size="10" fill="#16232b" stroke="#ffffff" stroke-width="3" stroke-linejoin="round" paint-order="stroke">record ends here 524</text><text x="8" y="692.1129367001039" font-size="10" fill="#5b6b76">8 of 84 nodes are too small to label here; each names itself and its count on hover, and its colour is in the key above. Therapies are grouped into LINES</text><text x="8" y="706.1129367001039" font-size="10" fill="#5b6b76">before being sequenced, by the definition every line count here shares: systemic-therapy treatment records (class, from SUBTYPE or else</text><text x="8" y="720.1129367001039" font-size="10" fill="#5b6b76">TREATMENT_TYPE, on the systemic list: chemotherapy, immunotherapy, targeted, hormone, biologic, investigational and their spellings; radiation, surgery,</text><text x="8" y="734.1129367001039" font-size="10" fill="#5b6b76">bone and ancillary treatment are not lines). A record starting more than 90 days after every earlier one has stopped begins a new line; anything closer is the</text><text x="8" y="748.1129367001039" font-size="10" fill="#5b6b76">same line, and a line is labelled by its whole combination (sorted, so 'A + B' and 'B + A' are one regimen). That grouping is the point — a patient here has a</text><text x="8" y="762.1129367001039" font-size="10" fill="#5b6b76">median of 6 treatment records and 4 distinct agents, and combination therapy is several records describing ONE decision, not several lines. Lines are</text><text x="8" y="776.1129367001039" font-size="10" fill="#5b6b76">labelled by SUBTYPE. ENDING A PATH IS SPLIT IN TWO, because the difference matters more than anything else here: 'no further line' means the study kept</text><text x="8" y="790.1129367001039" font-size="10" fill="#5b6b76">watching for at least 90 days (one line gap, the same rule that defines a line) and saw no further therapy; 'record ends here' means it did not, so nothing can</text><text x="8" y="804.1129367001039" font-size="10" fill="#5b6b76">be concluded about that patient. 55.5% of paths end censored — reading those as patients who finished treatment would be a claim about the</text><text x="8" y="818.1129367001039" font-size="10" fill="#5b6b76">record-keeping dressed up as one about the patient. Neither terminal is an outcome measure. 51 less common regimens folded into 'other', which is 23.3%</text><text x="8" y="832.1129367001039" font-size="10" fill="#5b6b76">of first-line patients in the first panel — combination regimens have a genuinely long tail, so 'other' is real diversity rather than a rounding bucket. Raise topN</text><text x="8" y="846.1129367001039" font-size="10" fill="#5b6b76">to split it further. Both panels share one scale and one colour per regimen, so their heights are directly comparable. TP53 mut: 11,564 with a first line; TP53</text><text x="8" y="860.1129367001039" font-size="10" fill="#5b6b76">wt: 9,645 with a first line.</text></svg>
diff --git a/test/snapshots/sequence.txt b/test/snapshots/sequence.txt
new file mode 100644
index 00000000..cebf0521
--- /dev/null
+++ b/test/snapshots/sequence.txt
@@ -0,0 +1,24 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- TP53 mut: 13,105 patients — OQL[TP53: MUT]
+- TP53 wt: 11,594 patients — NOT OQL[TP53:] AND profiled for TP53 MUTATION
+
+Treatment sequence (lines of therapy by SUBTYPE, 90-day line gap):
+  TP53 mut: 11,564 with a first line — Chemo 5,239, other 2,689, Biologic + Chemo 995, Hormone 672, Chemo + Hormone 644
+    of all paths, 4,529 were WATCHED and began no further line; 6,417 (55%) end because the RECORD stops — nothing can be concluded about those
+  TP53 wt: 9,645 with a first line — Chemo 2,551, other 2,434, Hormone 2,209, Chemo + Hormone 781, Chemo + Immuno 365
+    of all paths, 3,388 were WATCHED and began no further line; 5,474 (57%) end because the RECORD stops — nothing can be concluded about those
+  CAVEAT (tell the user): Therapies are grouped into LINES before being sequenced, by the definition every line count here shares: systemic-therapy treatment records (class, from SUBTYPE or else TREATMENT_TYPE, on the systemic list: chemotherapy, immunotherapy, targeted, hormone, biologic, investigational and their spellings; radiation, surgery, bone and ancillary treatment are not lines). A record starting more than 90 days after every earlier one has stopped begins a new line; anything closer is the same line, and a line is labelled by its whole combination (sorted, so 'A + B' and 'B + A' are one regimen). That grouping is the point — a patient here has a median of 6 treatment records and 4 distinct agents, and combination therapy is several records describing ONE decision, not several lines. Lines are labelled by SUBTYPE. ENDING A PATH IS SPLIT IN TWO, because the difference matters more than anything else here: 'no further line' means the study kept watching for at least 90 days (one line gap, the same rule that defines a line) and saw no further therapy; 'record ends here' means it did not, so nothing can be concluded about that patient. 55.5% of paths end censored — reading those as patients who finished treatment would be a claim about the record-keeping dressed up as one about the patient. Neither terminal is an outcome measure. 51 less common regimens folded into 'other', which is 23.3% of first-line patients in the first panel — combination regimens have a genuinely long tail, so 'other' is real diversity rather than a rounding bucket. Raise topN to split it further. Both panels share one scale and one colour per regimen, so their heights are directly comparable. TP53 mut: 11,564 with a first line; TP53 wt: 9,645 with a first line.
+  METHOD:
+    - Build lines and count what follows what [TP53 mut · msk_chord_2024]: The treatmentSequence query above builds every patient's lines in the database and returns, per line number, how many patients went from one regimen to the next, stopped, or were lost to follow-up — counts only — plus how many records it set aside.
+    - Build lines and count what follows what [TP53 wt · msk_chord_2024]: The treatmentSequence query above builds every patient's lines in the database and returns, per line number, how many patients went from one regimen to the next, stopped, or were lost to follow-up — counts only — plus how many records it set aside.
+    - Choose the records that form lines: The one definition every line count shares (lib/treatment-lines.ts): treatment records whose class is systemic therapy (chemo, chemotherapy, immuno, immunotherapy, immunotherapy (including vaccines), vaccine, targeted, targeted therapy, targeted molecular therapy, targeted antibody therapy, hormone, hormone therapy, hormonal therapy, biologic, investigational, systemic therapy, medical therapy, pharmaceutical therapy, nos). A record's class is its SUBTYPE, or its TREATMENT_TYPE where it has none; names and values match ignoring case.
+        Radiation Therapy: 28,738 records set aside: not a line class
+        Prior Medications to MSK: 19,366 records set aside: not a line class
+        Bone Treatment: 5,548 records set aside: not a line class
+        Other: 12 records set aside: not a line class
+    - Group records into lines: Per patient, by start date: a record starting more than 90 days after every earlier one has stopped begins a new line; anything closer — overlapping, or a switch within the gap — is the same line. A line is named by its sorted combination of classes ('A + B'); the label never decides which records form a line.
+    - Decide how each path ends: A patient's last line ends in 'no further line' only if they were observed for at least 90 days after it — their last contact being the later of their last recorded event of any type and their OS follow-up (OS_MONTHS × 30.4375 days) — and in 'record ends here' otherwise. Lines are followed up to line 3.
+    - Keep the 8 most common regimens: Flows are summed across studies, then regimens ranked by the patients leaving them (or, for one seen only as a destination, arriving), ties by name, across every panel so a regimen keeps one identity; the rest are drawn as 'other'.
+        regimens folded into 'other': 51
diff --git a/test/snapshots/survival.json b/test/snapshots/survival.json
new file mode 100644
index 00000000..f6ccbb2e
--- /dev/null
+++ b/test/snapshots/survival.json
@@ -0,0 +1,399 @@
+{
+  "study": "ucec_msk_2018",
+  "cohorts": {
+    "MSI-H": {
+      "count": 28,
+      "text": "MSI_STATUS in [MSI-H] (any sample)",
+      "studies": [
+        "ucec_msk_2018"
+      ]
+    },
+    "MSS": {
+      "count": 148,
+      "text": "MSI_STATUS in [MSS] (any sample)",
+      "studies": [
+        "ucec_msk_2018"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "survival",
+      "cohorts": [
+        "MSI-H",
+        "MSS"
+      ],
+      "curves": [
+        {
+          "cohort": "MSI-H",
+          "points": [
+            {
+              "t": 0,
+              "s": 1
+            },
+            {
+              "t": 1.67,
+              "s": 0.9643
+            },
+            {
+              "t": 3.3,
+              "s": 0.9286
+            },
+            {
+              "t": 5.5,
+              "s": 0.8929
+            },
+            {
+              "t": 5.63,
+              "s": 0.8571
+            },
+            {
+              "t": 8.8,
+              "s": 0.8182
+            },
+            {
+              "t": 9.77,
+              "s": 0.7773
+            },
+            {
+              "t": 10.57,
+              "s": 0.7364
+            },
+            {
+              "t": 12.47,
+              "s": 0.693
+            },
+            {
+              "t": 13.37,
+              "s": 0.6497
+            },
+            {
+              "t": 13.77,
+              "s": 0.6064
+            },
+            {
+              "t": 15.03,
+              "s": 0.5631
+            },
+            {
+              "t": 18.3,
+              "s": 0.5162
+            },
+            {
+              "t": 18.37,
+              "s": 0.4693
+            },
+            {
+              "t": 20.9,
+              "s": 0.4223
+            },
+            {
+              "t": 21.63,
+              "s": 0.3754
+            },
+            {
+              "t": 24.9,
+              "s": 0.3285
+            },
+            {
+              "t": 29.6,
+              "s": 0.2816
+            },
+            {
+              "t": 30.5,
+              "s": 0.2346
+            },
+            {
+              "t": 32.57,
+              "s": 0.1877
+            },
+            {
+              "…": "4 more",
+              "sha256": "ae4e15c7574d45c3"
+            }
+          ],
+          "median": 18.37,
+          "n": 28,
+          "events": 23,
+          "atRisk": [
+            28,
+            28,
+            27,
+            27,
+            26,
+            26,
+            24,
+            23,
+            22,
+            20,
+            19,
+            18,
+            17,
+            16,
+            14,
+            14,
+            13,
+            13,
+            12,
+            10,
+            {
+              "…": "107 more",
+              "sha256": "ce019366da5426a4"
+            }
+          ],
+          "missing": 0
+        },
+        {
+          "cohort": "MSS",
+          "points": [
+            {
+              "t": 0,
+              "s": 1
+            },
+            {
+              "t": 1.9,
+              "s": 0.9932
+            },
+            {
+              "t": 2.57,
+              "s": 0.9865
+            },
+            {
+              "t": 2.6,
+              "s": 0.9797
+            },
+            {
+              "t": 2.9,
+              "s": 0.973
+            },
+            {
+              "t": 3.5,
+              "s": 0.9662
+            },
+            {
+              "t": 3.83,
+              "s": 0.9595
+            },
+            {
+              "t": 4.4,
+              "s": 0.9527
+            },
+            {
+              "t": 4.43,
+              "s": 0.9459
+            },
+            {
+              "t": 4.47,
+              "s": 0.9392
+            },
+            {
+              "t": 4.8,
+              "s": 0.9324
+            },
+            {
+              "t": 5.03,
+              "s": 0.9257
+            },
+            {
+              "t": 5.1,
+              "s": 0.9189
+            },
+            {
+              "t": 5.27,
+              "s": 0.9122
+            },
+            {
+              "t": 5.37,
+              "s": 0.9054
+            },
+            {
+              "t": 5.4,
+              "s": 0.8986
+            },
+            {
+              "t": 5.57,
+              "s": 0.8919
+            },
+            {
+              "t": 5.6,
+              "s": 0.8851
+            },
+            {
+              "t": 6.03,
+              "s": 0.8784
+            },
+            {
+              "t": 6.73,
+              "s": 0.8716
+            },
+            {
+              "…": "113 more",
+              "sha256": "d929e8ddbccaad19"
+            }
+          ],
+          "median": 15.3,
+          "n": 148,
+          "events": 142,
+          "atRisk": [
+            148,
+            148,
+            147,
+            144,
+            142,
+            138,
+            131,
+            128,
+            122,
+            115,
+            106,
+            96,
+            91,
+            83,
+            76,
+            75,
+            69,
+            66,
+            60,
+            57,
+            {
+              "…": "167 more",
+              "sha256": "348a13dfb556251a"
+            }
+          ],
+          "missing": 0
+        }
+      ],
+      "endpoint": "PFS_1L",
+      "participation": {
+        "studies": [
+          {
+            "study": "ucec_msk_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "logrank": {
+        "chi2": 0.3942,
+        "p": 0.5301191634922409,
+        "df": 1,
+        "observed": [
+          23,
+          141
+        ],
+        "expected": [
+          25.9,
+          138.1
+        ],
+        "variance": 21.5713,
+        "strata": 1
+      },
+      "caveat": "PFS_1L from each study's own baseline (left truncation applies) Status '0:...' treated as censored. Log-rank on a single study, so no stratification applies.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:PFS_1L query above: patients grouped by identical PFS_MONTHS_FIRST_LINE_TX, event, returned as counts — no patient identifier leaves the database. Event: PROGRESSION_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.",
+            "scope": "MSI-H · ucec_msk_2018",
+            "rowsIn": 28,
+            "rowsOut": 28,
+            "params": [
+              [
+                "count rows",
+                "28"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:PFS_1L query above: patients grouped by identical PFS_MONTHS_FIRST_LINE_TX, event, returned as counts — no patient identifier leaves the database. Event: PROGRESSION_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.",
+            "scope": "MSS · ucec_msk_2018",
+            "rowsIn": 148,
+            "rowsOut": 148,
+            "params": [
+              [
+                "count rows",
+                "138"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Estimate survival by Kaplan–Meier",
+            "detail": "The product-limit estimator, one curve per cohort, pooled over its studies. The median is the first time the curve is at or below 0.5.",
+            "params": [
+              [
+                "MSI-H",
+                "28 patients, 23 events, median 18.4 months"
+              ],
+              [
+                "MSS",
+                "148 patients, 142 events, median 15.3 months"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Two-group log-rank test",
+            "detail": "At each event time, every cohort's observed events against its share of the risk set, with the hypergeometric variance; χ² on 1 df.",
+            "params": [
+              [
+                "χ² / df / p",
+                "0.3942 / 1 / 0.530"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "kaplanMeier",
+            "logRank"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Kaplan–Meier matches lifelines 0.30.3 KaplanMeierFitter (curves are reported to 4 decimals); the log-rank matches statsmodels 0.15.0 survdiff to a relative 1e-6, pooled, stratified, with delayed entry and for k groups (test/survival-golden.test.ts).",
+        "check": {
+          "km:MSI-H:n": {
+            "v": 28,
+            "abs": 0
+          },
+          "km:MSI-H:events": {
+            "v": 23,
+            "abs": 0
+          },
+          "km:MSI-H:median": {
+            "v": 18.37,
+            "abs": 1e-9
+          },
+          "km:MSS:n": {
+            "v": 148,
+            "abs": 0
+          },
+          "km:MSS:events": {
+            "v": 142,
+            "abs": 0
+          },
+          "km:MSS:median": {
+            "v": 15.3,
+            "abs": 1e-9
+          },
+          "logrank:chi2": {
+            "v": 0.3942,
+            "abs": 0.00005
+          },
+          "logrank:p": {
+            "v": 0.5301191634922409,
+            "rel": 0.00001,
+            "abs": 1e-300
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/survival.sql b/test/snapshots/survival.sql
new file mode 100644
index 00000000..3a1529d3
--- /dev/null
+++ b/test/snapshots/survival.sql
@@ -0,0 +1,51 @@
+-- cohort MSI-H
+-- ucec_msk_2018
+(SELECT DISTINCT replaceOne(patient_unique_id, 'ucec_msk_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'ucec_msk_2018' AND type = 'sample'
+    AND attribute_name = 'MSI_STATUS' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('MSI-H'))))
+
+-- cohort MSS
+-- ucec_msk_2018
+(SELECT DISTINCT replaceOne(patient_unique_id, 'ucec_msk_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'ucec_msk_2018' AND type = 'sample'
+    AND attribute_name = 'MSI_STATUS' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('MSS'))))
+
+-- view 0 survival: survivalCounts:PFS_1L · ucec_msk_2018
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'ucec_msk_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'ucec_msk_2018' AND type = 'sample'
+    AND attribute_name = 'MSI_STATUS' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('MSI-H'))))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'ucec_msk_2018_', '') AS pid,
+         anyIf(attribute_value, attribute_name='PFS_MONTHS_FIRST_LINE_TX') AS months,
+         anyIf(attribute_value, attribute_name='PROGRESSION_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'ucec_msk_2018' AND type='patient'
+    AND attribute_name IN ('PFS_MONTHS_FIRST_LINE_TX','PROGRESSION_STATUS')
+    AND replaceOne(patient_unique_id, 'ucec_msk_2018_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 0 survival: survivalCounts:PFS_1L · ucec_msk_2018
+WITH cohort AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'ucec_msk_2018_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'ucec_msk_2018' AND type = 'sample'
+    AND attribute_name = 'MSI_STATUS' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('MSS'))))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'ucec_msk_2018_', '') AS pid,
+         anyIf(attribute_value, attribute_name='PFS_MONTHS_FIRST_LINE_TX') AS months,
+         anyIf(attribute_value, attribute_name='PROGRESSION_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'ucec_msk_2018' AND type='patient'
+    AND attribute_name IN ('PFS_MONTHS_FIRST_LINE_TX','PROGRESSION_STATUS')
+    AND replaceOne(patient_unique_id, 'ucec_msk_2018_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
diff --git a/test/snapshots/survival.svg b/test/snapshots/survival.svg
new file mode 100644
index 00000000..619358fe
--- /dev/null
+++ b/test/snapshots/survival.svg
@@ -0,0 +1,2 @@
+<!-- view 0 survival -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 415" width="100%" role="img" aria-label="Kaplan–Meier — MSI-H, MSS" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Kaplan–Meier — MSI-H, MSS</title><rect width="720" height="415" fill="#ffffff"/><line x1="66.99" y1="242" x2="704" y2="242" stroke="#dde4e8" stroke-width="0.75"/><text x="61" y="245.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="66.99" y1="184.5" x2="704" y2="184.5" stroke="#dde4e8" stroke-width="0.75"/><text x="61" y="188" font-size="10" fill="#5b6b76" text-anchor="end">25%</text><line x1="66.99" y1="127" x2="704" y2="127" stroke="#dde4e8" stroke-width="0.75"/><text x="61" y="130.5" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><line x1="66.99" y1="69.5" x2="704" y2="69.5" stroke="#dde4e8" stroke-width="0.75"/><text x="61" y="73" font-size="10" fill="#5b6b76" text-anchor="end">75%</text><line x1="66.99" y1="12" x2="704" y2="12" stroke="#dde4e8" stroke-width="0.75"/><text x="61" y="15.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><text x="20.8" y="127" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 20.8 127)">Survival probability</text><line x1="67" y1="242" x2="67" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="64" y="258" font-size="10" fill="#5b6b76">0</text><line x1="149" y1="242" x2="149" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="143" y="258" font-size="10" fill="#5b6b76">24</text><line x1="231" y1="242" x2="231" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="225" y="258" font-size="10" fill="#5b6b76">48</text><line x1="313" y1="242" x2="313" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="307" y="258" font-size="10" fill="#5b6b76">72</text><line x1="395.1" y1="242" x2="395.1" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="389.1" y="258" font-size="10" fill="#5b6b76">96</text><line x1="477.1" y1="242" x2="477.1" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="468.1" y="258" font-size="10" fill="#5b6b76">120</text><line x1="559.1" y1="242" x2="559.1" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="550.1" y="258" font-size="10" fill="#5b6b76">144</text><line x1="641.1" y1="242" x2="641.1" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="632.1" y="258" font-size="10" fill="#5b6b76">168</text><line x1="66.99" y1="242" x2="704" y2="242" stroke="#5b6b76" stroke-width="1"/><text x="385.5" y="277" font-size="11" fill="#16232b" text-anchor="middle">Months</text><path d="M67 12H67V12H72.7V20.2H78.3V28.4H85.8V44.9H97.1V53.8H100.4V63.2H103.1V72.6H109.6V82.6H112.7V92.6H114V102.5H118.4V112.5H129.5V134.1H138.4V144.9H140.9V155.7H152.1V166.4H168.1V177.2H171.2V188H178.3V198.8H190.4V209.6H194.7V220.4H269.4V231.2H500.1V242H704" fill="none" stroke="#0072B2" stroke-width="2" stroke-linejoin="round"/><path d="M67 12H67V12H73.5V13.6H75.8V16.7H76.9V18.2H79V19.8H80.1V21.3H82V26H83.4V27.5H84.2V30.7H85V35.3H86V38.4H87.6V40H90V41.5H90.8V43.1H91.5V44.6H92.2V49.3H93.2V50.8H94.4V54H95.4V57.1H96.7V60.3H97.3V61.8H97.8V64.9H98.4V68.1H99.8V69.7H100.4V74.4H100.9V77.5H102.1V82.2H102.8V85.3H103.6V91.6H105.4V96.3H107.7V99.4H108.8V102.6H109.4V107.3H110V110.5H112.9V116.9H113.8V118.4H114.4V121.6H117.7V123.2H118.5V126.4H119.3V128H121V131.2H121.7V132.8H123.7V134.4H124.4V136H125.2V139.2H126V142.4H126.6V145.6H128.6V147.2H130.8V148.9H131.5V150.4H132.9V152.1H133.5V155.4H135.2V157H136.6V160.3H139.2V161.9H140V163.5H143.4V168.4H147.1V170.1H148V171.7H149.9V173.3H151.3V179.9H154.9V181.5H156.3V183.1H161.3V186.5H162.2V188.2H162.8V189.9H166.1V191.6H168.1V193.2H168.9V194.9H172.3V198.3H173.9V200H175.9V201.6H182.2V203.3H185.5V205H191.5V208.4H197.6V210.1H206.8V211.7H209.8V213.4H212.1V215.1H217.6V216.8H220.8V218.6H221.4V220.4H244.9V222.2H257.8V224H282.3V225.8H289.5V227.6H295.5V229.4H304.9V231.2H319.5V233H324.4V234.8H345.5V236.6H358.6V238.4H400.5V240.2H704V242" fill="none" stroke="#E69F00" stroke-width="2" stroke-linejoin="round"/><text x="8" y="299" font-size="10" fill="#5b6b76" font-weight="600">Number at risk</text><line x1="8" y1="313" x2="24" y2="313" stroke="#0072B2" stroke-width="2"/><text x="28" y="316.5" font-size="10" fill="#16232b">MSI-H</text><text x="63" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 28 at risk at 0 months</title>28</text><text x="146" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 8 at risk at 24 months</title>8</text><text x="228" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 2 at risk at 48 months</title>2</text><text x="310" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 1 at risk at 72 months</title>1</text><text x="392.1" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 1 at risk at 96 months</title>1</text><text x="474.1" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 1 at risk at 120 months</title>1</text><text x="556.1" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 0 at risk at 144 months</title>0</text><text x="638.1" y="316.5" font-size="10" fill="#5b6b76"><title>MSI-H: 0 at risk at 168 months</title>0</text><line x1="8" y1="329" x2="24" y2="329" stroke="#E69F00" stroke-width="2"/><text x="28" y="332.5" font-size="10" fill="#16232b">MSS</text><text x="63" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 148 at risk at 0 months</title>148</text><text x="143" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 43 at risk at 24 months</title>43</text><text x="225" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 12 at risk at 48 months</title>12</text><text x="310" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 6 at risk at 72 months</title>6</text><text x="392.1" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 2 at risk at 96 months</title>2</text><text x="474.1" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 1 at risk at 120 months</title>1</text><text x="556.1" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 1 at risk at 144 months</title>1</text><text x="638.1" y="332.5" font-size="10" fill="#5b6b76"><title>MSS: 1 at risk at 168 months</title>1</text><line x1="8" y1="353" x2="26" y2="353" stroke="#0072B2" stroke-width="2"/><text x="31" y="356.9" font-size="11" fill="#16232b">MSI-H<tspan dx="6" fill="#5b6b76">median 18.4 mo · n = 28 · 23 events</tspan></text><line x1="368" y1="353" x2="386" y2="353" stroke="#E69F00" stroke-width="2"/><text x="391" y="356.9" font-size="11" fill="#16232b">MSS<tspan dx="6" fill="#5b6b76">median 15.3 mo · n = 148 · 142 events</tspan></text><text x="8" y="378" font-size="11" fill="#16232b" class="logrank">log-rank χ² = 0.394 (1 df) · p = 0.53</text><text x="8" y="397" font-size="10" fill="#5b6b76">PFS_1L from each study's own baseline (left truncation applies) Status '0:...' treated as censored. Log-rank on a single study, so no stratification applies.</text></svg>
diff --git a/test/snapshots/survival.txt b/test/snapshots/survival.txt
new file mode 100644
index 00000000..c05c35ca
--- /dev/null
+++ b/test/snapshots/survival.txt
@@ -0,0 +1,22 @@
+Analysis over ucec_msk_2018 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- MSI-H: 28 patients — MSI_STATUS in [MSI-H] (any sample)
+- MSS: 148 patients — MSI_STATUS in [MSS] (any sample)
+
+Survival (KM, PFS_1L):
+  MSI-H: median 18.4mo (n=28, 23 events)
+  MSS: median 15.3mo (n=148, 142 events)
+  log-rank p=0.5301
+CAVEAT (tell the user): PFS_1L from each study's own baseline (left truncation applies) Status '0:...' treated as censored. Log-rank on a single study, so no stratification applies.
+  METHOD:
+    - Count patients by time and status [MSI-H · ucec_msk_2018] (28 → 28 rows): The survivalCounts:PFS_1L query above: patients grouped by identical PFS_MONTHS_FIRST_LINE_TX, event, returned as counts — no patient identifier leaves the database. Event: PROGRESSION_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.
+        count rows: 28
+    - Count patients by time and status [MSS · ucec_msk_2018] (148 → 148 rows): The survivalCounts:PFS_1L query above: patients grouped by identical PFS_MONTHS_FIRST_LINE_TX, event, returned as counts — no patient identifier leaves the database. Event: PROGRESSION_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.
+        count rows: 138
+    - Estimate survival by Kaplan–Meier: The product-limit estimator, one curve per cohort, pooled over its studies. The median is the first time the curve is at or below 0.5.
+        MSI-H: 28 patients, 23 events, median 18.4 months
+        MSS: 148 patients, 142 events, median 15.3 months
+    - Two-group log-rank test: At each event time, every cohort's observed events against its share of the risk set, with the hypergeometric variance; χ² on 1 df.
+        χ² / df / p: 0.3942 / 1 / 0.530
+    Kaplan–Meier matches lifelines 0.30.3 KaplanMeierFitter (curves are reported to 4 decimals); the log-rank matches statsmodels 0.15.0 survdiff to a relative 1e-6, pooled, stratified, with delayed entry and for k groups (test/survival-golden.test.ts).
diff --git a/test/snapshots/table.json b/test/snapshots/table.json
new file mode 100644
index 00000000..a5728b81
--- /dev/null
+++ b/test/snapshots/table.json
@@ -0,0 +1,888 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "Pancreatic": {
+      "count": 3109,
+      "text": "CANCER_TYPE in [Pancreatic Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "table",
+      "cohorts": [
+        "Pancreatic"
+      ],
+      "table": {
+        "unit": "sample",
+        "columns": [
+          {
+            "id": "patient",
+            "name": "Patient ID",
+            "numeric": false
+          },
+          {
+            "id": "sample",
+            "name": "Sample ID",
+            "numeric": false
+          },
+          {
+            "id": "CANCER_TYPE",
+            "name": "Cancer Type",
+            "numeric": false
+          },
+          {
+            "id": "CLINICAL_GROUP",
+            "name": "Clinical Group",
+            "numeric": false
+          },
+          {
+            "id": "CLINICAL_SUMMARY",
+            "name": "Clinical Summary",
+            "numeric": false
+          },
+          {
+            "id": "DIAGNOSIS_DESCRIPTION",
+            "name": "Diagnosis Description",
+            "numeric": false
+          },
+          {
+            "id": "ICD_O_HISTOLOGY_DESCRIPTION",
+            "name": "ICD-O Histology Description",
+            "numeric": false
+          },
+          {
+            "id": "PATHOLOGICAL_GROUP",
+            "name": "Pathological Group",
+            "numeric": false
+          },
+          {
+            "id": "CANCER_TYPE_DETAILED",
+            "name": "Cancer Type Detailed",
+            "numeric": false
+          },
+          {
+            "id": "OS_STATUS",
+            "name": "Overall Survival Status",
+            "numeric": false
+          },
+          {
+            "id": "SAMPLE_TYPE",
+            "name": "Sample Type",
+            "numeric": false
+          },
+          {
+            "id": "RACE",
+            "name": "Race",
+            "numeric": false
+          },
+          {
+            "id": "GENDER",
+            "name": "Sex",
+            "numeric": false
+          },
+          {
+            "id": "STAGE_HIGHEST_RECORDED",
+            "name": "Stage (Highest Recorded)",
+            "numeric": false
+          },
+          {
+            "id": "ETHNICITY",
+            "name": "Ethnicity",
+            "numeric": false
+          },
+          {
+            "id": "MSI_TYPE",
+            "name": "MSI Type",
+            "numeric": false
+          },
+          {
+            "id": "GENE_PANEL",
+            "name": "Gene Panel",
+            "numeric": false
+          },
+          {
+            "id": "CURRENT_AGE_DEID",
+            "name": "Current Age",
+            "numeric": true
+          },
+          {
+            "id": "SMOKING_PREDICTIONS_3_CLASSES",
+            "name": "Smoking History (NLP)",
+            "numeric": false
+          },
+          {
+            "id": "SOMATIC_STATUS",
+            "name": "Somatic Status",
+            "numeric": false
+          },
+          {
+            "…": "32 more",
+            "sha256": "0faca185ba41eeac"
+          }
+        ],
+        "shown": [
+          "patient",
+          "sample",
+          "CANCER_TYPE",
+          "CLINICAL_GROUP",
+          "CLINICAL_SUMMARY",
+          "DIAGNOSIS_DESCRIPTION",
+          "ICD_O_HISTOLOGY_DESCRIPTION",
+          "PATHOLOGICAL_GROUP",
+          "CANCER_TYPE_DETAILED",
+          "OS_STATUS",
+          "SAMPLE_TYPE",
+          "RACE",
+          "GENDER",
+          "STAGE_HIGHEST_RECORDED",
+          "ETHNICITY",
+          "MSI_TYPE",
+          "GENE_PANEL",
+          "CURRENT_AGE_DEID",
+          "SMOKING_PREDICTIONS_3_CLASSES",
+          "SOMATIC_STATUS",
+          {
+            "…": "32 more",
+            "sha256": "5b17d65b1df7f010"
+          }
+        ],
+        "defaults": [
+          "patient",
+          "sample",
+          "CANCER_TYPE",
+          "CLINICAL_GROUP",
+          "CLINICAL_SUMMARY",
+          "DIAGNOSIS_DESCRIPTION",
+          "ICD_O_HISTOLOGY_DESCRIPTION",
+          "PATHOLOGICAL_GROUP",
+          "CANCER_TYPE_DETAILED",
+          "OS_STATUS",
+          "SAMPLE_TYPE",
+          "RACE",
+          "GENDER",
+          "STAGE_HIGHEST_RECORDED",
+          "ETHNICITY",
+          "MSI_TYPE",
+          "GENE_PANEL",
+          "CURRENT_AGE_DEID",
+          "SMOKING_PREDICTIONS_3_CLASSES",
+          "SOMATIC_STATUS",
+          {
+            "…": "32 more",
+            "sha256": "5b17d65b1df7f010"
+          }
+        ],
+        "query": {
+          "sort": {
+            "column": "CURRENT_AGE_DEID",
+            "desc": true
+          }
+        },
+        "rows": [
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0006018",
+            "sample": "P-0006018-T01-IM5",
+            "values": [
+              "P-0006018",
+              "P-0006018-T01-IM5",
+              "Pancreatic Cancer",
+              "2A",
+              "Regional,Extension and Nodes",
+              "Pancreas",
+              "Blastoma, Nos",
+              "2B",
+              "Pancreatic Neuroendocrine Tumor",
+              "0:LIVING",
+              "Metastasis",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Unknown",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "afc13182844dc3f9"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0006930",
+            "sample": "P-0006930-T01-IM5",
+            "values": [
+              "P-0006930",
+              "P-0006930-T01-IM5",
+              "Pancreatic Cancer",
+              "1A",
+              "Regional,Direct Extension",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "2A",
+              "Pancreatic Adenocarcinoma",
+              "0:LIVING",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Unknown whether Spanish or not",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "8020c700bdb025bc"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0007791",
+            "sample": "P-0007791-T01-IM5",
+            "values": [
+              "P-0007791",
+              "P-0007791-T01-IM5",
+              "Pancreatic Cancer",
+              "2A",
+              "Regional,Extension and Nodes",
+              "Pancreas",
+              "Tubular Adenocarcinoma",
+              "2B",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Male",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "e8e76e157522be59"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0009404",
+            "sample": "P-0009404-T01-IM5",
+            "values": [
+              "P-0009404",
+              "P-0009404-T01-IM5",
+              "Pancreatic Cancer",
+              "1A",
+              "Regional,Direct Extension",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "2A",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "eb82e2189938f90b"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0010195",
+            "sample": "P-0010195-T01-IM5",
+            "values": [
+              "P-0010195",
+              "P-0010195-T01-IM5",
+              "Pancreatic Cancer",
+              "3",
+              "Regional,Direct Extension",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "0",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Spanish  NOS; Hispanic NOS, Latino NOS",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "7df155ddfc1b5f7e"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0011111",
+            "sample": "P-0011111-T01-IM5",
+            "values": [
+              "P-0011111",
+              "P-0011111-T01-IM5",
+              "Pancreatic Cancer",
+              "1B",
+              "Regional Both 2 and 3",
+              "Pancreas",
+              "Infiltrating Duct Carcinoma",
+              "2B",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Male",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "efbfdff8d7af63df"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0012579",
+            "sample": "P-0012579-T01-IM5",
+            "values": [
+              "P-0012579",
+              "P-0012579-T01-IM5",
+              "Pancreatic Cancer",
+              "1B",
+              "Regional by Direct Extension",
+              "Pancreas",
+              "Infiltrating Duct Carcinoma",
+              "2A",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "10f4e2e8ae1fcfda"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0013009",
+            "sample": "P-0013009-T01-IM5",
+            "values": [
+              "P-0013009",
+              "P-0013009-T01-IM5",
+              "Pancreatic Cancer",
+              "4",
+              "Distant Metastases/Systemic Disease",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 4",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT410",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "1fea999693130f2a"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0018639",
+            "sample": "P-0018639-T01-IM6",
+            "values": [
+              "P-0018639",
+              "P-0018639-T01-IM6",
+              "Pancreatic Cancer",
+              "1A",
+              "Regional by Direct Extension",
+              "Pancreas",
+              "Infiltrating Duct Carcinoma",
+              "2A",
+              "Pancreatic Adenocarcinoma",
+              "0:LIVING",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Unknown whether Spanish or not",
+              "Do not report",
+              "IMPACT468",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "2c220bf4e83f8721"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0023044",
+            "sample": "P-0023044-T01-IM6",
+            "values": [
+              "P-0023044",
+              "P-0023044-T01-IM6",
+              "Pancreatic Cancer",
+              "2A",
+              "Regional by Direct Extension",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "2364ccef24263596"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0024552",
+            "sample": "P-0024552-T01-IM6",
+            "values": [
+              "P-0024552",
+              "P-0024552-T01-IM6",
+              "Pancreatic Cancer",
+              "4",
+              "Distant Metastases/Systemic Disease",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "4",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Metastasis",
+              "White",
+              "Male",
+              "Stage 4",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "1edffbaf7be9de84"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0028084",
+            "sample": "P-0028084-T01-IM6",
+            "values": [
+              "P-0028084",
+              "P-0028084-T01-IM6",
+              "Pancreatic Cancer",
+              "1B",
+              "Regional Both 2 and 3",
+              "Pancreas",
+              "Infiltrating Duct Carcinoma",
+              "2B",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "a37511482894836f"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0028158",
+            "sample": "P-0028158-T01-IM6",
+            "values": [
+              "P-0028158",
+              "P-0028158-T01-IM6",
+              "Pancreatic Cancer",
+              "2A",
+              "Regional by Direct Extension",
+              "Pancreas",
+              "Carcinoma, Undifferentiated Type, Nos",
+              "",
+              "Undifferentiated Carcinoma of the Pancreas",
+              "1:DECEASED",
+              "Metastasis",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "bf27641e9a00df42"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0028249",
+            "sample": "P-0028249-T01-IM6",
+            "values": [
+              "P-0028249",
+              "P-0028249-T01-IM6",
+              "Pancreatic Cancer",
+              "1A",
+              "Regional Both 2 and 3",
+              "Pancreas",
+              "Infiltrating Duct Carcinoma",
+              "3",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Male",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "268d734a7e163257"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0029109",
+            "sample": "P-0029109-T01-IM6",
+            "values": [
+              "P-0029109",
+              "P-0029109-T01-IM6",
+              "Pancreatic Cancer",
+              "",
+              "Localized",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "1A",
+              "Pancreatic Adenocarcinoma",
+              "0:LIVING",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 1-3",
+              "Spanish  NOS; Hispanic NOS, Latino NOS",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "a2854bf8c1d89271"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0030506",
+            "sample": "P-0030506-T01-IM6",
+            "values": [
+              "P-0030506",
+              "P-0030506-T01-IM6",
+              "Pancreatic Cancer",
+              "1B",
+              "Regional,Extension and Nodes",
+              "Pancreas",
+              "Infiltrating Duct Carcinoma",
+              "2B",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Male",
+              "Stage 1-3",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "b9188b83e59a6f8e"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0031431",
+            "sample": "P-0031431-T01-IM6",
+            "values": [
+              "P-0031431",
+              "P-0031431-T01-IM6",
+              "Pancreatic Cancer",
+              "4",
+              "Distant Metastases/Systemic Disease",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "4",
+              "Pancreatic Adenocarcinoma",
+              "0:LIVING",
+              "Primary",
+              "White",
+              "Male",
+              "Stage 4",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "323df33aeef91a2c"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0032403",
+            "sample": "P-0032403-T01-IM6",
+            "values": [
+              "P-0032403",
+              "P-0032403-T01-IM6",
+              "Pancreatic Cancer",
+              "4",
+              "Distant Metastases/Systemic Disease",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "4",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Female",
+              "Stage 4",
+              "Non-Spanish; Non-Hispanic",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "3c6e36eaac9ab4c3"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0037989",
+            "sample": "P-0037989-T01-IM6",
+            "values": [
+              "P-0037989",
+              "P-0037989-T01-IM6",
+              "Pancreatic Cancer",
+              "1B",
+              "Regional To Lymph Nodes",
+              "Pancreas",
+              "Infiltrating Duct Carcinoma",
+              "2B",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Male",
+              "Stage 1-3",
+              "Unknown whether Spanish or not",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Never",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "c0d401658333a605"
+              }
+            ]
+          },
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "patient": "P-0038511",
+            "sample": "P-0038511-T01-IM6",
+            "values": [
+              "P-0038511",
+              "P-0038511-T01-IM6",
+              "Pancreatic Cancer",
+              "1B",
+              "Localized",
+              "Pancreas",
+              "Adenocarcinoma, Nos",
+              "",
+              "Pancreatic Adenocarcinoma",
+              "1:DECEASED",
+              "Primary",
+              "White",
+              "Male",
+              "Stage 1-3",
+              "Unknown whether Spanish or not",
+              "Stable",
+              "IMPACT468",
+              "89",
+              "Former/Current Smoker",
+              "Matched",
+              {
+                "…": "32 more",
+                "sha256": "15bf9c98fecb5f7c"
+              }
+            ]
+          },
+          {
+            "…": "5 more",
+            "sha256": "627de08f5ae47ff2"
+          }
+        ],
+        "offset": 0,
+        "pageSize": 25,
+        "lists": [
+          {
+            "cohort": "Pancreatic",
+            "study": "msk_chord_2024",
+            "total": 3134,
+            "all": 3134
+          }
+        ],
+        "total": 3134,
+        "all": 3134
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": true,
+        "aggregateOnly": []
+      },
+      "caveat": "One row per sample of each cohort's patients, carrying its patient's attributes too, with 50 attributes as each study recorded them — vocabularies are not harmonized across studies. 3,134 samples. Sorted by Current Age (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count the samples per cohort and study",
+            "detail": "The table:counts query above: every sample of each cohort's patients (from the study's sample list, so a sample with no sample attribute still has a row), each with the value of each of the 50 attributes as recorded — blank where none was.",
+            "params": [
+              [
+                "Pancreatic · msk_chord_2024",
+                "3,134"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Sort them together and take one page",
+            "detail": "The table:rows query above: the samples of every cohort and study combined in one query, sorted by Current Age descending with missing values last, then patient and sample id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "clinicalRowSource",
+            "pageSql",
+            "countsSql"
+          ],
+          "file": "lib/clinical-table.ts, lib/table-query.ts",
+          "commit": null
+        },
+        "verification": "A listing: no statistic is computed, so there is no script — the queries are the numbers."
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/table.sql b/test/snapshots/table.sql
new file mode 100644
index 00000000..e94d6e0c
--- /dev/null
+++ b/test/snapshots/table.sql
@@ -0,0 +1,80 @@
+-- cohort Pancreatic
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))
+
+-- view 0 table: table:attributes · msk_chord_2024
+SELECT m.attr_id AS id, m.display_name AS name, m.datatype AS datatype, m.patient_attribute AS patient, toInt32OrZero(m.priority) AS priority
+FROM snapshot_db.clinical_attribute_meta m
+JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = m.cancer_study_id
+WHERE cs.cancer_study_identifier = 'msk_chord_2024'
+ORDER BY priority DESC, name
+
+-- view 0 table: table:counts · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))),
+u0 AS (
+  SELECT patient_unique_id AS puid, sample_unique_id AS suid, 1 AS samples FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+),
+pv0 AS (
+  SELECT patient_unique_id AS k,
+         CAST((groupArray(attribute_name), groupArray(attribute_value)), 'Map(String, String)') AS m
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient' AND attribute_name IN ('CURRENT_AGE_DEID')
+    AND attribute_value != '' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY k
+),
+sv0 AS (
+  SELECT sample_unique_id AS k,
+         CAST((groupArray(attribute_name), groupArray(attribute_value)), 'Map(String, String)') AS m
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample' AND 0
+    AND attribute_value != '' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY k
+),
+rows AS (
+  SELECT 'Pancreatic' AS cohort, 'msk_chord_2024' AS study, 0 AS ord,
+         replaceOne(u.puid, 'msk_chord_2024_', '') AS patient, replaceOne(u.suid, 'msk_chord_2024_', '') AS sample, u.samples AS samples, mapUpdate(pv.m, sv.m) AS m
+  FROM u0 AS u LEFT JOIN pv0 AS pv ON pv.k = u.puid LEFT JOIN sv0 AS sv ON sv.k = u.suid
+)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 table: table:rows · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Pancreatic Cancer'))))),
+u0 AS (
+  SELECT patient_unique_id AS puid, sample_unique_id AS suid, 1 AS samples FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+),
+pv0 AS (
+  SELECT patient_unique_id AS k,
+         CAST((groupArray(attribute_name), groupArray(attribute_value)), 'Map(String, String)') AS m
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'patient' AND attribute_name IN ('OS_STATUS','RACE','GENDER','STAGE_HIGHEST_RECORDED','ETHNICITY','CURRENT_AGE_DEID','SMOKING_PREDICTIONS_3_CLASSES','PRIOR_MED_TO_MSK','GLEASON_HIGHEST_REPORTED','HISTORY_OF_PDL1','NUM_ICDO_DX','SAMPLE_COUNT','GLEASON_FIRST_REPORTED','HER2','HR','OS_MONTHS','ADRENAL_GLANDS','BONE','CNS_BRAIN','INTRA_ABDOMINAL','LIVER','LUNG','LYMPH_NODES','OTHER','PLEURA','REPRODUCTIVE_ORGANS')
+    AND attribute_value != '' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY k
+),
+sv0 AS (
+  SELECT sample_unique_id AS k,
+         CAST((groupArray(attribute_name), groupArray(attribute_value)), 'Map(String, String)') AS m
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample' AND attribute_name IN ('CANCER_TYPE','CLINICAL_GROUP','CLINICAL_SUMMARY','DIAGNOSIS_DESCRIPTION','ICD_O_HISTOLOGY_DESCRIPTION','PATHOLOGICAL_GROUP','CANCER_TYPE_DETAILED','SAMPLE_TYPE','MSI_TYPE','GENE_PANEL','SOMATIC_STATUS','SAMPLE_CLASS','MUTATION_COUNT','FRACTION_GENOME_ALTERED','TMB_NONSYNONYMOUS','GLEASON_SAMPLE_LEVEL','MSI_COMMENT','MSI_SCORE','METASTATIC_SITE','ONCOTREE_CODE','PRIMARY_SITE','PDL1_POSITIVE','SAMPLE_COVERAGE','TUMOR_PURITY')
+    AND attribute_value != '' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY k
+),
+rows AS (
+  SELECT 'Pancreatic' AS cohort, 'msk_chord_2024' AS study, 0 AS ord,
+         replaceOne(u.puid, 'msk_chord_2024_', '') AS patient, replaceOne(u.suid, 'msk_chord_2024_', '') AS sample, u.samples AS samples, mapUpdate(pv.m, sv.m) AS m
+  FROM u0 AS u LEFT JOIN pv0 AS pv ON pv.k = u.puid LEFT JOIN sv0 AS sv ON sv.k = u.suid
+)
+SELECT cohort, study, patient, sample, samples, m, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY if(isFinite(ifNull(toFloat64OrNull(m['CURRENT_AGE_DEID']), nan)), toFloat64OrNull(m['CURRENT_AGE_DEID']), NULL) DESC NULLS LAST, patient, sample, ord
+LIMIT 25 OFFSET 0
diff --git a/test/snapshots/table.svg b/test/snapshots/table.svg
new file mode 100644
index 00000000..7e001dcb
--- /dev/null
+++ b/test/snapshots/table.svg
@@ -0,0 +1,2 @@
+<!-- view 0 table -->
+(no figure)
diff --git a/test/snapshots/table.txt b/test/snapshots/table.txt
new file mode 100644
index 00000000..1bb6ae1b
--- /dev/null
+++ b/test/snapshots/table.txt
@@ -0,0 +1,39 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Pancreatic: 3,109 patients — CANCER_TYPE in [Pancreatic Cancer] (any sample)
+
+Clinical data table (one row per sample): 3,134 rows; the first 25, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	patient	sample	CANCER_TYPE	CLINICAL_GROUP	CLINICAL_SUMMARY	DIAGNOSIS_DESCRIPTION	ICD_O_HISTOLOGY_DESCRIPTION	PATHOLOGICAL_GROUP	CANCER_TYPE_DETAILED	OS_STATUS	SAMPLE_TYPE	RACE	GENDER	STAGE_HIGHEST_RECORDED	ETHNICITY	MSI_TYPE	GENE_PANEL	CURRENT_AGE_DEID	SMOKING_PREDICTIONS_3_CLASSES	SOMATIC_STATUS	SAMPLE_CLASS	PRIOR_MED_TO_MSK	GLEASON_HIGHEST_REPORTED	HISTORY_OF_PDL1	NUM_ICDO_DX	MUTATION_COUNT	FRACTION_GENOME_ALTERED	SAMPLE_COUNT	TMB_NONSYNONYMOUS	GLEASON_SAMPLE_LEVEL	GLEASON_FIRST_REPORTED	HER2	HR	MSI_COMMENT	MSI_SCORE	METASTATIC_SITE	ONCOTREE_CODE	OS_MONTHS	PRIMARY_SITE	PDL1_POSITIVE	SAMPLE_COVERAGE	TUMOR_PURITY	ADRENAL_GLANDS	BONE	CNS_BRAIN	INTRA_ABDOMINAL	LIVER	LUNG	LYMPH_NODES	OTHER	PLEURA	REPRODUCTIVE_ORGANS
+  Pancreatic	P-0006018	P-0006018-T01-IM5	Pancreatic Cancer	2A	Regional,Extension and Nodes	Pancreas	Blastoma, Nos	2B	Pancreatic Neuroendocrine Tumor	0:LIVING	Metastasis	White	Female	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT410	89	Unknown	Matched	Tumor	Unknown			2	2	0.7416	1	1.957439395						0.6	Liver	PANET	0.032876676	Pancreas		564	90	Yes	No	No	Yes	Yes	No	Yes	Yes	No	No
+  Pancreatic	P-0006930	P-0006930-T01-IM5	Pancreatic Cancer	1A	Regional,Direct Extension	Pancreas	Adenocarcinoma, Nos	2A	Pancreatic Adenocarcinoma	0:LIVING	Primary	White	Female	Stage 1-3	Unknown whether Spanish or not	Stable	IMPACT410	89	Never	Matched	Tumor	Unknown			1	5	0.1124	1	6.851037883						0		PAAD	11.99998685	Pancreas		1030	10	No	No	No	No	No	Yes	No	Yes	No	No
+  Pancreatic	P-0007791	P-0007791-T01-IM5	Pancreatic Cancer	2A	Regional,Extension and Nodes	Pancreas	Tubular Adenocarcinoma	2B	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Male	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT410	89	Former/Current Smoker	Matched	Tumor	No prior medications			1	1	0.0169	1	0.978719698					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	80.90950037	Pancreas		717	30	No	No	No	No	Yes	No	Yes	Yes	No	No
+  Pancreatic	P-0009404	P-0009404-T01-IM5	Pancreatic Cancer	1A	Regional,Direct Extension	Pancreas	Adenocarcinoma, Nos	2A	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT410	89	Never	Matched	Tumor	Unknown			1	5	0.0692	1	4.893598488					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	8.021909017	Pancreas		566	10	No	Yes	No	Yes	Yes	No	Yes	Yes	No	Yes
+  Pancreatic	P-0010195	P-0010195-T01-IM5	Pancreatic Cancer	3	Regional,Direct Extension	Pancreas	Adenocarcinoma, Nos	0	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 1-3	Spanish  NOS; Hispanic NOS, Latino NOS	Stable	IMPACT410	89	Former/Current Smoker	Matched	Tumor	No prior medications			1	3	0.131	1	2.936159093					MICROSATELLITE STABLE (MSS). See MSI note below.	0.44		PAAD	3.978077832	Pancreas		236	30	No	No	No	No	No	No	No	Yes	No	No
+  Pancreatic	P-0011111	P-0011111-T01-IM5	Pancreatic Cancer	1B	Regional Both 2 and 3	Pancreas	Infiltrating Duct Carcinoma	2B	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Male	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT410	89	Never	Matched	Tumor	No prior medications			2	4	0.0	1	3.91487879					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	26.33421772	Pancreas		379	20	No	No	No	No	No	Yes	Yes	Yes	No	No
+  Pancreatic	P-0012579	P-0012579-T01-IM5	Pancreatic Cancer	1B	Regional by Direct Extension	Pancreas	Infiltrating Duct Carcinoma	2A	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT410	89	Former/Current Smoker	Matched	Tumor	No prior medications			1	2	0.0618	1	1.957439395						0.07		PAAD	28.30681829	Pancreas		785	20	No	No	No	No	No	No	No	Yes	No	No
+  Pancreatic	P-0013009	P-0013009-T01-IM5	Pancreatic Cancer	4	Distant Metastases/Systemic Disease	Pancreas	Adenocarcinoma, Nos		Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 4	Non-Spanish; Non-Hispanic	Stable	IMPACT410	89	Never	Matched	Tumor	No prior medications			2	5	0.1705	1	4.893598488						0.14		PAAD	8.580812514	Pancreas		909	10	No	No	No	Yes	Yes	Yes	Yes	Yes	No	No
+  Pancreatic	P-0018639	P-0018639-T01-IM6	Pancreatic Cancer	1A	Regional by Direct Extension	Pancreas	Infiltrating Duct Carcinoma	2A	Pancreatic Adenocarcinoma	0:LIVING	Primary	White	Female	Stage 1-3	Unknown whether Spanish or not	Do not report	IMPACT468	89	Former/Current Smoker	Matched	Tumor	No prior medications			3	5	0.0	1	4.323490475						0		PAAD	76.50402575	Pancreas		873	20	No	No	No	No	Yes	Yes	No	Yes	No	No
+  Pancreatic	P-0023044	P-0023044-T01-IM6	Pancreatic Cancer	2A	Regional by Direct Extension	Pancreas	Adenocarcinoma, Nos		Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Never	Matched	Tumor	Prior medications to MSK			1	8	0.004	1	6.91758476					MICROSATELLITE STABLE (MSS). See MSI note below.	0.88		PAAD	6.246568497	Pancreas		174	30	No	Yes	No	No	Yes	Yes	Yes	No	No	No
+  Pancreatic	P-0024552	P-0024552-T01-IM6	Pancreatic Cancer	4	Distant Metastases/Systemic Disease	Pancreas	Adenocarcinoma, Nos	4	Pancreatic Adenocarcinoma	1:DECEASED	Metastasis	White	Male	Stage 4	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Former/Current Smoker	Matched	Tumor	No prior medications	6		3	3	0.2175	1	2.594094285		6			MICROSATELLITE STABLE (MSS). See MSI note below.	0.29	Liver	PAAD	6.082185115	Pancreas		829	40	Yes	No	No	Yes	Yes	No	No	Yes	No	No
+  Pancreatic	P-0028084	P-0028084-T01-IM6	Pancreatic Cancer	1B	Regional Both 2 and 3	Pancreas	Infiltrating Duct Carcinoma	2B	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Never	Matched	Tumor	No prior medications			2	3	0.0121	1	2.594094285					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	52.27391532	Pancreas		696	10	No	No	No	No	No	No	No	Yes	No	No
+  Pancreatic	P-0028158	P-0028158-T01-IM6	Pancreatic Cancer	2A	Regional by Direct Extension	Pancreas	Carcinoma, Undifferentiated Type, Nos		Undifferentiated Carcinoma of the Pancreas	1:DECEASED	Metastasis	White	Female	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Never	Matched	Tumor	No prior medications		Yes	2	9	0.2976	1	7.782282855					MICROSATELLITE STABLE (MSS). See MSI note below.	0.61		UCP	16.53696818	Pancreas	Yes	656	50	No	No	No	No	No	Yes	No	Yes	No	No
+  Pancreatic	P-0028249	P-0028249-T01-IM6	Pancreatic Cancer	1A	Regional Both 2 and 3	Pancreas	Infiltrating Duct Carcinoma	3	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Male	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Never	Matched	Tumor	No prior medications			1	6	0.0	1	5.18818857					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	31.69311595	Pancreas		758	10	No	No	No	No	No	No	Yes	Yes	No	No
+  Pancreatic	P-0029109	P-0029109-T01-IM6	Pancreatic Cancer		Localized	Pancreas	Adenocarcinoma, Nos	1A	Pancreatic Adenocarcinoma	0:LIVING	Primary	White	Female	Stage 1-3	Spanish  NOS; Hispanic NOS, Latino NOS	Stable	IMPACT468	89	Former/Current Smoker	Matched	Tumor	No prior medications			1	5	0.0	1	6.052886665					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	59.60541413	Pancreas		533	20	No	No	No	No	No	No	Yes	Yes	No	No
+  Pancreatic	P-0030506	P-0030506-T01-IM6	Pancreatic Cancer	1B	Regional,Extension and Nodes	Pancreas	Infiltrating Duct Carcinoma	2B	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Male	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Never	Matched	Tumor	Unknown	7		3	3	0.0549	1	2.594094285		7			MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	6.016431763	Pancreas		525	40	No	No	No	Yes	Yes	No	No	Yes	No	Yes
+  Pancreatic	P-0031431	P-0031431-T01-IM6	Pancreatic Cancer	4	Distant Metastases/Systemic Disease	Pancreas	Adenocarcinoma, Nos	4	Pancreatic Adenocarcinoma	0:LIVING	Primary	White	Male	Stage 4	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Former/Current Smoker	Matched	Tumor	No prior medications			1	10	0.2258	1	8.64698095					MICROSATELLITE STABLE (MSS). See MSI note below.	0.19		PAAD	47.93419404	Pancreas		530	30	No	No	No	No	No	No	No	No	No	No
+  Pancreatic	P-0032403	P-0032403-T01-IM6	Pancreatic Cancer	4	Distant Metastases/Systemic Disease	Pancreas	Adenocarcinoma, Nos	4	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 4	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Never	Matched	Tumor	No prior medications			1	6	0.235	1	5.18818857					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	43.59447277	Pancreas		645	30	No	No	No	No	Yes	No	No	Yes	No	No
+  Pancreatic	P-0037989	P-0037989-T01-IM6	Pancreatic Cancer	1B	Regional To Lymph Nodes	Pancreas	Infiltrating Duct Carcinoma	2B	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Male	Stage 1-3	Unknown whether Spanish or not	Stable	IMPACT468	89	Never	Matched	Tumor	No prior medications			2	7	0.0349	1	6.052886665					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	19.0027189	Pancreas		652	40	No	No	No	Yes	No	Yes	No	Yes	No	No
+  Pancreatic	P-0038511	P-0038511-T01-IM6	Pancreatic Cancer	1B	Localized	Pancreas	Adenocarcinoma, Nos		Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Male	Stage 1-3	Unknown whether Spanish or not	Stable	IMPACT468	89	Former/Current Smoker	Matched	Tumor	No prior medications	7		2	6	0.3097	1	5.18818857		7			MICROSATELLITE STABLE (MSS). See MSI note below.	0.26		PAAD	12.95341046	Pancreas		587	40	No	No	No	No	Yes	No	No	Yes	No	No
+  Pancreatic	P-0039568	P-0039568-T01-IM6	Pancreatic Cancer	2A	Regional Both 2 and 3	Pancreas	Adenocarcinoma, Nos	3	Pancreatic Adenocarcinoma	0:LIVING	Metastasis	White	Female	Stage 1-3	Unknown whether Spanish or not	Stable	IMPACT468	89	Never	Matched	Tumor	No prior medications			3	5	0.2215	1	4.323490475					MICROSATELLITE STABLE (MSS). See MSI note below.	0.06	Duodenum	PAAD	35.60544043	Pancreas		592	30	No	No	No	Yes	No	No	Yes	Yes	No	No
+  Pancreatic	P-0040808	P-0040808-T01-IM6	Pancreatic Cancer	2A	Localized	Pancreas	Adenocarcinoma, Nos		Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Male	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Former/Current Smoker	Matched	Tumor	No prior medications			2	8	0.016	1	6.91758476					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	8.284922427	Pancreas		763	20	Yes	No	No	No	Yes	No	Yes	Yes	No	No
+  Pancreatic	P-0044512	P-0044512-T01-IM6	Pancreatic Cancer	1B	Regional Both 2 and 3	Pancreas	Infiltrating Duct Carcinoma	2B	Pancreatic Adenocarcinoma	1:DECEASED	Primary	White	Female	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Never	Matched	Tumor	No prior medications			1	3	0.044	1	2.594094285					MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	45.00816985	Pancreas		632	10	No	No	No	Yes	Yes	Yes	No	Yes	No	No
+  Pancreatic	P-0046434	P-0046434-T01-IM6	Pancreatic Cancer	3	Regional,Direct Extension	Pancreas	Adenocarcinoma, Nos		Pancreatic Adenocarcinoma	1:DECEASED	Metastasis	White	Male	Stage 1-3	Unknown whether Spanish or not	Stable	IMPACT468	89	Never	Matched	Tumor	Unknown			1	5	0.0461	1	4.323490475					MICROSATELLITE STABLE (MSS). See MSI note below.	0.06	Liver	PAAD	7.956155664	Pancreas		474	10	No	No	No	No	Yes	Yes	No	Yes	No	No
+  Pancreatic	P-0048987	P-0048987-T01-IM6	Pancreatic Cancer		Localized	Pancreas	Infiltrating Duct Carcinoma	1A	Pancreatic Adenocarcinoma	0:LIVING	Primary	White	Male	Stage 1-3	Non-Spanish; Non-Hispanic	Stable	IMPACT468	89	Former/Current Smoker	Matched	Tumor	Unknown	6		2	4	0.002	1	3.45879238		6			MICROSATELLITE STABLE (MSS). See MSI note below.	0		PAAD	46.8821404	Pancreas		626	20	No	No	No	No	Yes	No	No	Yes	No	No
+  … 3,109 more rows
+  CAVEAT (tell the user): One row per sample of each cohort's patients, carrying its patient's attributes too, with 50 attributes as each study recorded them — vocabularies are not harmonized across studies. 3,134 samples. Sorted by Current Age (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. 
+  METHOD:
+    - Count the samples per cohort and study: The table:counts query above: every sample of each cohort's patients (from the study's sample list, so a sample with no sample attribute still has a row), each with the value of each of the 50 attributes as recorded — blank where none was.
+        Pancreatic · msk_chord_2024: 3,134
+    - Sort them together and take one page: The table:rows query above: the samples of every cohort and study combined in one query, sorted by Current Age descending with missing values last, then patient and sample id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset.
+    A listing: no statistic is computed, so there is no script — the queries are the numbers.
diff --git a/test/snapshots/timeline.json b/test/snapshots/timeline.json
new file mode 100644
index 00000000..8d62a60a
--- /dev/null
+++ b/test/snapshots/timeline.json
@@ -0,0 +1,2243 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "Breast": {
+      "count": 5368,
+      "text": "CANCER_TYPE in [Breast Cancer] (any sample)",
+      "studies": [
+        "msk_chord_2024"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "timeline",
+      "cohorts": [
+        "Breast"
+      ],
+      "svg": "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 1000 522\" width=\"100%\" role=\"img\" aria-label=\"Timeline of P-0000012\" style=\"max-width:1000px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif\"><title>Timeline of P-0000012</title><rect width=\"1000\" height=\"522\" fill=\"var(--bg, #ffffff)\"/><line x1=\"164\" y1=\"25.5\" x2=\"986\" y2=\"25.5\" stroke=\"var(--muted, #5b6b76)\" stroke-width=\"1\"/><line x1=\"178.2\" x2=\"178.2\" y1=\"22\" y2=\"468\" stroke=\"var(--line, #dde4e8)\" stroke-width=\"0.75\"/><text x=\"178.2\" y=\"18\" font-size=\"10\" fill=\"var(--muted, #5b6b76)\" text-anchor=\"middle\">-26y</text><g transform=\"translate(213.5 0)\"><title>9 years with no events, cut</title><path d=\"M-6 17 q3 -6 6 0 t6 0\" fill=\"none\" stroke=\"var(--muted, #5b6b76)\" stroke-width=\"1.2\"/><line x1=\"0\" x2=\"0\" y1=\"26\" y2=\"468\" stroke=\"var(--muted, #5b6b76)\" stroke-width=\"0.75\" stroke-dasharray=\"2 3\"/></g><line 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transform=\"translate(706.4 0)\"><rect x=\"0\" y=\"113.5\" width=\"8.0\" height=\"5\" rx=\"2\" ry=\"2\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(708.8 0)\"><rect x=\"0\" y=\"113.5\" width=\"8.0\" height=\"5\" rx=\"2\" ry=\"2\" fill=\"rgb(31, 119, 180)\"/></g><g><title>Radiation Therapy</title><text x=\"19\" y=\"139.9\" font-size=\"11\" fill=\"var(--ink, #16232b)\">Radiation Therapy</text></g><line x1=\"0\" x2=\"1000\" y1=\"145.5\" y2=\"145.5\" stroke=\"var(--line, #dde4e8)\" stroke-width=\"0.75\" opacity=\"0.6\"/><g transform=\"translate(706.4 0)\"><circle cx=\"0\" cy=\"136\" r=\"4\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(846.7 0)\"><circle cx=\"0\" cy=\"136\" r=\"4\" fill=\"rgb(31, 119, 180)\"/></g><g><title>Investigational</title><text x=\"19\" y=\"159.9\" font-size=\"11\" fill=\"var(--ink, #16232b)\">▾ Investigational</text></g><line x1=\"0\" x2=\"1000\" y1=\"165.5\" y2=\"165.5\" stroke=\"var(--line, #dde4e8)\" stroke-width=\"0.75\" 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stroke-width=\"0.2\"/><ellipse cx=\"0\" cy=\"275\" rx=\"4.5\" ry=\"2.34\" fill=\"var(--bg, #ffffff)\"/><ellipse cx=\"0\" cy=\"273.75\" rx=\"4.5\" ry=\"2.34\" fill=\"#D62727\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/></g><g transform=\"translate(716.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#C0C0C0\"/></g><g transform=\"translate(724.5 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(727.0 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(727.7 0)\"><ellipse cx=\"0\" cy=\"277.125\" rx=\"4.5\" ry=\"2.34\" fill=\"#C0C0C0\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/><ellipse cx=\"0\" cy=\"276.125\" rx=\"4.5\" ry=\"2.34\" fill=\"var(--bg, #ffffff)\"/><ellipse cx=\"0\" cy=\"274.875\" rx=\"4.5\" ry=\"2.34\" fill=\"#2AA02B\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/></g><g transform=\"translate(728.4 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(729.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(731.4 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(731.5 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(734.2 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(737.7 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(745.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(745.4 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(752.5 0)\"><ellipse cx=\"0\" cy=\"277.125\" rx=\"4.5\" ry=\"2.34\" fill=\"#2AA02B\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/><ellipse cx=\"0\" cy=\"276.125\" rx=\"4.5\" ry=\"2.34\" fill=\"var(--bg, #ffffff)\"/><ellipse cx=\"0\" cy=\"274.875\" rx=\"4.5\" ry=\"2.34\" fill=\"#2AA02B\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/></g><g transform=\"translate(761.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(770.4 0)\"><ellipse cx=\"0\" cy=\"277.125\" rx=\"4.5\" ry=\"2.34\" fill=\"#D62727\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/><ellipse cx=\"0\" cy=\"276.125\" rx=\"4.5\" ry=\"2.34\" fill=\"var(--bg, #ffffff)\"/><ellipse cx=\"0\" cy=\"274.875\" rx=\"4.5\" ry=\"2.34\" fill=\"#2AA02B\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/></g><g transform=\"translate(785.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#C0C0C0\"/></g><g transform=\"translate(798.1 0)\"><ellipse cx=\"0\" cy=\"277.125\" rx=\"4.5\" ry=\"2.34\" fill=\"#2AA02B\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/><ellipse cx=\"0\" cy=\"276.125\" rx=\"4.5\" ry=\"2.34\" fill=\"var(--bg, #ffffff)\"/><ellipse cx=\"0\" cy=\"274.875\" rx=\"4.5\" ry=\"2.34\" fill=\"#C0C0C0\" stroke=\"var(--ink, #16232b)\" stroke-width=\"0.2\"/></g><g transform=\"translate(813.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(828.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(843.4 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(844.9 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(845.1 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(858.4 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(867.7 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(877.0 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(892.2 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#D62727\"/></g><g transform=\"translate(906.7 0)\"><circle cx=\"0\" cy=\"276\" r=\"4\" fill=\"#2AA02B\"/></g><g transform=\"translate(906.8 0)\"><circle cx=\"0\" cy=\"276\" 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495.4,456.2 519.2,459.0 546.6,456.2 586.7,450.7 646.8,453.5 676.9,439.6 696.8,445.2\"/><g transform=\"translate(262.8 0)\"><circle cx=\"0\" cy=\"423\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(273.5 0)\"><circle cx=\"0\" cy=\"436.84615384615387\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(288.4 0)\"><circle cx=\"0\" cy=\"434.0769230769231\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(300.8 0)\"><circle cx=\"0\" cy=\"442.38461538461536\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(314.5 0)\"><circle cx=\"0\" cy=\"442.38461538461536\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(327.3 0)\"><circle cx=\"0\" cy=\"434.0769230769231\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(341.3 0)\"><circle cx=\"0\" cy=\"445.15384615384613\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(375.9 0)\"><circle cx=\"0\" cy=\"442.38461538461536\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(404.4 0)\"><circle cx=\"0\" cy=\"439.61538461538464\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(422.7 0)\"><circle cx=\"0\" cy=\"442.38461538461536\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(440.7 0)\"><circle cx=\"0\" cy=\"447.9230769230769\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(455.3 0)\"><circle cx=\"0\" cy=\"447.9230769230769\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(478.0 0)\"><circle cx=\"0\" cy=\"445.15384615384613\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(495.4 0)\"><circle cx=\"0\" cy=\"456.2307692307692\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(519.2 0)\"><circle cx=\"0\" cy=\"459\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(546.6 0)\"><circle cx=\"0\" cy=\"456.2307692307692\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(586.7 0)\"><circle cx=\"0\" cy=\"450.6923076923077\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(646.8 0)\"><circle cx=\"0\" cy=\"453.46153846153845\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(676.9 0)\"><circle cx=\"0\" cy=\"439.61538461538464\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><g transform=\"translate(696.8 0)\"><circle cx=\"0\" cy=\"445.15384615384613\" r=\"3\" fill=\"rgb(31, 119, 180)\"/></g><text x=\"8\" y=\"484\" font-size=\"10\" fill=\"var(--muted, #5b6b76)\">P-0000012 (msk_chord_2024): 172 events in 6 event types, from -26 years, 5 months, 1 day to 9 years, 3 months, 2 days relative to diagnosis (day 0). Samples,</text><text x=\"8\" y=\"498\" font-size=\"10\" fill=\"var(--muted, #5b6b76)\">numbered in collection order: 1 P-0000012-T02-IM3 (Primary), 2 P-0000012-T03-IM3 (Metastasis). 9 years with no events are cut from the axis (dashed line);</text><text x=\"8\" y=\"512\" font-size=\"10\" fill=\"var(--muted, #5b6b76)\">expandGaps draws every year. Events of one type nest by attribute as on cBioPortal (treatment by subtype and agent, lab tests by test); vocabularies are the study's own.</text></svg>",
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+          {
+            "id": "patient",
+            "name": "Patient ID",
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+            "name": "Samples",
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+            "id": "events",
+            "name": "Events",
+            "numeric": true
+          },
+          {
+            "id": "first",
+            "name": "First event (day)",
+            "numeric": true
+          },
+          {
+            "id": "last",
+            "name": "Last event (day)",
+            "numeric": true
+          },
+          {
+            "id": "span",
+            "name": "Span (days)",
+            "numeric": true
+          },
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+            "id": "types",
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+          }
+        },
+        "rows": [
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0014149",
+            "sample": "",
+            "values": [
+              "P-0014149",
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+          {
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+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
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+            "sample": "",
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+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0041672",
+            "sample": "",
+            "values": [
+              "P-0041672",
+              "this portal (primary)",
+              1,
+              708,
+              -3542,
+              1133,
+              4675,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0047755",
+            "sample": "",
+            "values": [
+              "P-0047755",
+              "this portal (primary)",
+              1,
+              694,
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+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0016734",
+            "sample": "",
+            "values": [
+              "P-0016734",
+              "this portal (primary)",
+              1,
+              677,
+              -6200,
+              565,
+              6765,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0057382",
+            "sample": "",
+            "values": [
+              "P-0057382",
+              "this portal (primary)",
+              1,
+              663,
+              -6967,
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+              7893,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0003958",
+            "sample": "",
+            "values": [
+              "P-0003958",
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+              655,
+              -3390,
+              3493,
+              6883,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0010993",
+            "sample": "",
+            "values": [
+              "P-0010993",
+              "this portal (primary)",
+              1,
+              652,
+              -4350,
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+            ]
+          },
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+            "study": "msk_chord_2024",
+            "patient": "P-0011420",
+            "sample": "",
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+              "P-0011420",
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+              646,
+              -5572,
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+            ]
+          },
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+            "patient": "P-0015568",
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+            ]
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+            "patient": "P-0018146",
+            "sample": "",
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+              "P-0018146",
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+              638,
+              -7821,
+              1762,
+              9583,
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+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0008398",
+            "sample": "",
+            "values": [
+              "P-0008398",
+              "this portal (primary)",
+              1,
+              635,
+              -847,
+              2605,
+              3452,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0029771",
+            "sample": "",
+            "values": [
+              "P-0029771",
+              "this portal (primary)",
+              1,
+              632,
+              -2639,
+              1995,
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+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0001990",
+            "sample": "",
+            "values": [
+              "P-0001990",
+              "this portal (primary)",
+              1,
+              621,
+              -3820,
+              1008,
+              4828,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0007045",
+            "sample": "",
+            "values": [
+              "P-0007045",
+              "this portal (primary)",
+              1,
+              612,
+              -3459,
+              464,
+              3923,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0013915",
+            "sample": "",
+            "values": [
+              "P-0013915",
+              "this portal (primary)",
+              1,
+              609,
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+              2168,
+              7004,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0001939",
+            "sample": "",
+            "values": [
+              "P-0001939",
+              "this portal (primary)",
+              1,
+              574,
+              -3575,
+              2723,
+              6298,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0025567",
+            "sample": "",
+            "values": [
+              "P-0025567",
+              "this portal (primary)",
+              1,
+              573,
+              -3303,
+              2061,
+              5364,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0002531",
+            "sample": "",
+            "values": [
+              "P-0002531",
+              "this portal (primary)",
+              1,
+              571,
+              -4323,
+              2181,
+              6504,
+              "Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment"
+            ]
+          },
+          {
+            "…": "5 more",
+            "sha256": "43eea4588ff30354"
+          }
+        ],
+        "offset": 0,
+        "pageSize": 25,
+        "lists": [
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "total": 5368,
+            "all": 5368
+          }
+        ],
+        "total": 5368,
+        "all": 5368,
+        "patient": {
+          "study": "msk_chord_2024",
+          "patient": "P-0000012",
+          "events": [
+            {
+              "type": "Diagnosis",
+              "start": -9641,
+              "end": null,
+              "attrs": [
+                [
+                  "SUBTYPE",
+                  "Primary"
+                ],
+                [
+                  "CLINICAL_GROUP",
+                  "99"
+                ],
+                [
+                  "SUMMARY",
+                  "N/A"
+                ],
+                [
+                  "PATH_GROUP",
+                  "99"
+                ],
+                [
+                  "SOURCE",
+                  "Tumor Registry"
+                ],
+                [
+                  "DX_DESCRIPTION",
+                  "INFILTRATING DUCT CARCINOMA | BREAST, NOS (M8500/3 | C509)"
+                ],
+                [
+                  "STAGE_CDM_DERIVED",
+                  "Stage 1-3"
+                ]
+              ]
+            },
+            {
+              "type": "Sample acquisition",
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+              "end": null,
+              "attrs": [
+                [
+                  "SEQ_DATE",
+                  "48"
+                ],
+                [
+                  "SAMPLE_ID",
+                  "P-0000012-T02-IM3"
+                ]
+              ]
+            },
+            {
+              "type": "Treatment",
+              "start": -5437,
+              "end": -5369,
+              "attrs": [
+                [
+                  "SUBTYPE",
+                  "Chemo"
+                ],
+                [
+                  "AGENT",
+                  "CYCLOPHOSPHAMIDE"
+                ],
+                [
+                  "RX_INVESTIGATIVE",
+                  "N"
+                ],
+                [
+                  "FLAG_OROTOPICAL",
+                  "0"
+                ]
+              ]
+            },
+            {
+              "type": "Treatment",
+              "start": -5437,
+              "end": -5326,
+              "attrs": [
+                [
+                  "SUBTYPE",
+                  "Chemo"
+                ],
+                [
+                  "AGENT",
+                  "FLUOROURACIL"
+                ],
+                [
+                  "RX_INVESTIGATIVE",
+                  "N"
+                ],
+                [
+                  "FLAG_OROTOPICAL",
+                  "0"
+                ]
+              ]
+            },
+            {
+              "type": "Treatment",
+              "start": -5437,
+              "end": -5327,
+              "attrs": [
+                [
+                  "SUBTYPE",
+                  "Chemo"
+                ],
+                [
+                  "AGENT",
+                  "METHOTREXATE"
+                ],
+                [
+                  "RX_INVESTIGATIVE",
+                  "N"
+                ],
+                [
+                  "FLAG_OROTOPICAL",
+                  "0"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -5437,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.8"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -5307,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CA_15-3"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "Units/ml"
+                ],
+                [
+                  "RESULT",
+                  "21"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -5307,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.9"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -5178,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CA_15-3"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "Units/ml"
+                ],
+                [
+                  "RESULT",
+                  "16"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -5178,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.4"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4999,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CA_15-3"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "Units/ml"
+                ],
+                [
+                  "RESULT",
+                  "17"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4999,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.5"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4849,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CA_15-3"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "Units/ml"
+                ],
+                [
+                  "RESULT",
+                  "14"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4849,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.4"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4684,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CA_15-3"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "Units/ml"
+                ],
+                [
+                  "RESULT",
+                  "14"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4684,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.7"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4530,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CA_15-3"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "Units/ml"
+                ],
+                [
+                  "RESULT",
+                  "17"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4530,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.2"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4362,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CA_15-3"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "Units/ml"
+                ],
+                [
+                  "RESULT",
+                  "13"
+                ]
+              ]
+            },
+            {
+              "type": "Lab_Test",
+              "start": -4362,
+              "end": null,
+              "attrs": [
+                [
+                  "TEST",
+                  "CEA"
+                ],
+                [
+                  "LR_UNIT_MEASURE",
+                  "ng/ml"
+                ],
+                [
+                  "RESULT",
+                  "1.3"
+                ]
+              ]
+            },
+            {
+              "…": "152 more",
+              "sha256": "5d06e726a3a25f5f"
+            }
+          ],
+          "samples": [
+            {
+              "id": "P-0000012-T02-IM3",
+              "type": "Primary"
+            },
+            {
+              "id": "P-0000012-T03-IM3",
+              "type": "Metastasis"
+            }
+          ],
+          "source": {
+            "label": "primary"
+          }
+        },
+        "position": {
+          "index": 1251,
+          "prev": {
+            "study": "msk_chord_2024",
+            "id": "P-0082770"
+          },
+          "next": {
+            "study": "msk_chord_2024",
+            "id": "P-0000478"
+          }
+        }
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": true,
+        "aggregateOnly": []
+      },
+      "caveat": "The list is one row per patient of each cohort; event counts and days come from the study's timeline, in days from diagnosis as the study records them. 5,368 patients. Sorted by Events (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count the patients per cohort and study",
+            "detail": "The timeline:counts query above: every patient of each cohort, with their sample count and, from the study's timeline, the number of events, the first and last event day (a range counted to its stop) and the event types — a patient with no timeline is listed with none.",
+            "params": [
+              [
+                "Breast · msk_chord_2024",
+                "5,368"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Sort them together and take one page",
+            "detail": "The timeline:rows query above: the patients of every cohort and study combined in one query, sorted by Events descending with missing values last, then patient id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset."
+          },
+          {
+            "kind": "fetch",
+            "title": "Read P-0000012's timeline",
+            "detail": "The timeline:events and timeline:samples queries above: every clinical event recorded for P-0000012 in msk_chord_2024 with all its attributes, and the patient's samples with their SAMPLE_TYPE. Events are drawn as stored, in days from diagnosis; tracks group them by event type (case-folded) and nest them by attribute as cBioPortal's patient view does.",
+            "params": [
+              [
+                "events",
+                "172"
+              ],
+              [
+                "samples",
+                "2"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "runTimeline",
+            "timelinePatientRowSource",
+            "patientEventsSql",
+            "buildTracks",
+            "pageSql",
+            "countsSql",
+            "rankSql"
+          ],
+          "file": "lib/views/timeline.ts, lib/patient-timeline.ts, lib/timeline-tracks.ts, lib/table-query.ts",
+          "commit": null
+        },
+        "verification": "A listing and one patient's record: no statistic is computed, so there is no script — the queries are the numbers."
+      }
+    },
+    {
+      "kind": "alterations",
+      "cohorts": [
+        "Breast"
+      ],
+      "alterations": {
+        "columns": [
+          {
+            "id": "patient",
+            "name": "Patient ID",
+            "numeric": false
+          },
+          {
+            "id": "source",
+            "name": "Source",
+            "numeric": false
+          },
+          {
+            "id": "samples",
+            "name": "Samples",
+            "numeric": true
+          },
+          {
+            "id": "mutations",
+            "name": "Mutations",
+            "numeric": true
+          },
+          {
+            "id": "cna",
+            "name": "CNAs",
+            "numeric": true
+          },
+          {
+            "id": "sv",
+            "name": "Structural Variants",
+            "numeric": true
+          }
+        ],
+        "shown": [
+          "patient",
+          "source",
+          "samples",
+          "mutations",
+          "cna",
+          "sv"
+        ],
+        "defaults": [
+          "patient",
+          "source",
+          "samples",
+          "mutations",
+          "cna",
+          "sv"
+        ],
+        "query": {},
+        "rows": [
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0000012",
+            "sample": "",
+            "values": [
+              "P-0000012",
+              "this portal (primary)",
+              2,
+              31,
+              3,
+              0
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0000015",
+            "sample": "",
+            "values": [
+              "P-0000015",
+              "this portal (primary)",
+              1,
+              7,
+              11,
+              0
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0000041",
+            "sample": "",
+            "values": [
+              "P-0000041",
+              "this portal (primary)",
+              1,
+              10,
+              0,
+              0
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0000057",
+            "sample": "",
+            "values": [
+              "P-0000057",
+              "this portal (primary)",
+              1,
+              5,
+              0,
+              0
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0000058",
+            "sample": "",
+            "values": [
+              "P-0000058",
+              "this portal (primary)",
+              1,
+              1,
+              7,
+              1
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0000066",
+            "sample": "",
+            "values": [
+              "P-0000066",
+              "this portal (primary)",
+              1,
+              4,
+              5,
+              0
+            ]
+          },
+          {
+            "cohort": "Breast",
+            "study": "msk_chord_2024",
+            "patient": "P-0000077",
+            "sample": "",
+            "values": [
+              "P-0000077",
+              "this portal (primary)",
+              1,
+              2,
+              8,
+              0
+            ]
+          },
+          {
+            "cohort": "Breast",
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+                "study": "msk_chord_2024",
+                "patient": "P-0000012",
+                "sample": "P-0000012-T02-IM3",
+                "values": [
+                  "P-0000012-T02-IM3",
+                  "RIT1",
+                  "AMP",
+                  "Unknown",
+                  "1q22",
+                  0.7
+                ]
+              }
+            ],
+            "offset": 0,
+            "pageSize": 25,
+            "lists": [
+              {
+                "cohort": "Breast",
+                "study": "msk_chord_2024",
+                "total": 3,
+                "all": 3
+              }
+            ],
+            "total": 3,
+            "all": 3
+          },
+          "sv": {
+            "columns": [
+              {
+                "id": "samples",
+                "name": "Samples",
+                "numeric": false
+              },
+              {
+                "id": "gene1",
+                "name": "Gene 1",
+                "numeric": false
+              },
+              {
+                "id": "gene2",
+                "name": "Gene 2",
+                "numeric": false
+              },
+              {
+                "id": "class",
+                "name": "Variant Class",
+                "numeric": false
+              },
+              {
+                "id": "eventInfo",
+                "name": "Event Info",
+                "numeric": false
+              },
+              {
+                "id": "breakpointType",
+                "name": "Breakpoint Type",
+                "numeric": false
+              },
+              {
+                "id": "connectionType",
+                "name": "Connection Type",
+                "numeric": false
+              },
+              {
+                "id": "frame",
+                "name": "Effect on Frame",
+                "numeric": false
+              },
+              {
+                "id": "site1",
+                "name": "Site 1 Description",
+                "numeric": false
+              },
+              {
+                "id": "site2",
+                "name": "Site 2 Description",
+                "numeric": false
+              },
+              {
+                "id": "pos1",
+                "name": "Site 1 Position",
+                "numeric": false
+              },
+              {
+                "id": "pos2",
+                "name": "Site 2 Position",
+                "numeric": false
+              },
+              {
+                "id": "length",
+                "name": "Length",
+                "numeric": true
+              },
+              {
+                "id": "tumorVariantCount",
+                "name": "Tumor Variant Count",
+                "numeric": true
+              },
+              {
+                "id": "tumorReadCount",
+                "name": "Tumor Read Count",
+                "numeric": true
+              },
+              {
+                "id": "dnaSupport",
+                "name": "DNA Support",
+                "numeric": false
+              },
+              {
+                "id": "rnaSupport",
+                "name": "RNA Support",
+                "numeric": false
+              },
+              {
+                "id": "status",
+                "name": "Status",
+                "numeric": false
+              },
+              {
+                "id": "svAnnotation",
+                "name": "Study Annotation",
+                "numeric": false
+              },
+              {
+                "id": "comments",
+                "name": "Comments",
+                "numeric": false
+              }
+            ],
+            "shown": [
+              "samples",
+              "gene1",
+              "gene2",
+              "class",
+              "eventInfo",
+              "breakpointType",
+              "connectionType"
+            ],
+            "defaults": [
+              "samples",
+              "gene1",
+              "gene2",
+              "class",
+              "eventInfo",
+              "breakpointType",
+              "connectionType"
+            ],
+            "query": {},
+            "rows": [],
+            "offset": 0,
+            "pageSize": 25,
+            "lists": [
+              {
+                "cohort": "Breast",
+                "study": "msk_chord_2024",
+                "total": 0,
+                "all": 0
+              }
+            ],
+            "total": 0,
+            "all": 0
+          }
+        },
+        "oncokbPin": "v7.5"
+      },
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": true,
+        "aggregateOnly": []
+      },
+      "caveat": "P-0000012 — Mutations: 31 rows; Copy Number Alterations: 3 rows; Structural Variants: 0 rows. Each table lists a variant, a gene's call or an event once however many of the patient's samples carry it (Samples names them); allele frequency and read counts are the highest among those samples. Copy number is the deep calls only (AMP, HOMDEL), as the portal's patient view lists them; Copy # on a mutation is the gene's deep calls anywhere in the patient. Cohort % is the share of the study's samples profiled for the gene by their panel with a mutation in it, or with the same copy-number call. Annotation is OncoKB v7.5 (oncogenicity and its highest sensitivity level); structural variants carry the study's own annotation, not OncoKB's. The patient list: 5,368 patients. Sorted with patients who have any alteration first, then by patient id across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. ",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count the patients per cohort and study",
+            "detail": "The alterations:counts query above: every patient of each cohort, with their sample count and how many rows each of the three tables lists for them (a mutation, a gene's deep copy-number call, a structural variant — each counted once however many samples carry it).",
+            "params": [
+              [
+                "Breast · msk_chord_2024",
+                "5,368"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Sort them together and take one page",
+            "detail": "The alterations:rows query above: the patients of every cohort and study combined in one query, sorted patients with any alteration first, then by patient id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset."
+          },
+          {
+            "kind": "fetch",
+            "title": "Read P-0000012's three tables",
+            "detail": "The alterations:mutations, alterations:cna and alterations:sv queries above, each paged, searched, filtered and sorted in the database like any table view. Mutations: every called mutation of the patient's samples (uncalled calls left out), one row per variant with the samples carrying it, the highest allele frequency among them (variant / (variant + reference) reads) with that sample's reads, and the gene's deep copy-number calls in the patient (Copy #). Copy-number alterations: the deep calls (AMP, HOMDEL), one row per gene and call. Structural variants: one row per event (both genes and breakpoints, class, event info), the highest read counts among its samples. Cohort % is the share of the study's samples profiled for the gene (by their gene panel) with any mutation in it, or with the same copy-number call; the annotation is the OncoKB v7.5 call for the protein change, or for AMPLIFICATION / DELETION.",
+            "params": [
+              [
+                "Mutations",
+                "31 rows"
+              ],
+              [
+                "Copy Number Alterations",
+                "3 rows"
+              ],
+              [
+                "Structural Variants",
+                "0 rows"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "runAlterations",
+            "alterationPatientRowSource",
+            "patientMutationsSource",
+            "patientCnaSource",
+            "patientSvSource",
+            "pageSql",
+            "countsSql",
+            "rankSql"
+          ],
+          "file": "lib/views/alterations.ts, lib/patient-alterations.ts, lib/table-query.ts",
+          "commit": null
+        },
+        "verification": "A listing: no statistic is computed, so there is no script — the queries are the numbers."
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/timeline.sql b/test/snapshots/timeline.sql
new file mode 100644
index 00000000..1d5090f0
--- /dev/null
+++ b/test/snapshots/timeline.sql
@@ -0,0 +1,511 @@
+-- cohort Breast
+-- msk_chord_2024
+(SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))
+
+-- view 0 timeline: timeline:counts · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+pt0 AS (
+  SELECT p.internal_id AS iid, p.stable_id AS pid FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+),
+ev0 AS (
+  SELECT ce.patient_id AS iid, count() AS events, min(ce.start_date) AS first,
+         max(greatest(ce.start_date, ifNull(ce.stop_date, ce.start_date))) AS last,
+         arrayStringConcat(arraySort(groupUniqArray(trimBoth(ce.event_type))), ', ') AS types
+  FROM snapshot_db.clinical_event ce WHERE ce.patient_id IN (SELECT iid FROM pt0)
+  GROUP BY iid
+),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS samples FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, pt.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.samples, 0)) AS samples, toUInt64(ifNull(ev.events, 0)) AS events,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.first)), NULL) AS first,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.last)), NULL) AS last,
+         ifNull(ev.types, '') AS types
+  FROM pt0 AS pt LEFT JOIN ev0 AS ev ON ev.iid = pt.iid LEFT JOIN sm0 AS sm ON sm.pid = pt.pid
+)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 0 timeline: timeline:events · msk_chord_2024
+WITH pt AS (
+  SELECT p.internal_id FROM snapshot_db.patient p JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id = 'P-0000012'
+), ev AS (
+  SELECT clinical_event_id, event_type, start_date, stop_date FROM snapshot_db.clinical_event WHERE patient_id IN (SELECT internal_id FROM pt)
+)
+SELECT ev.clinical_event_id AS id, ev.event_type AS type, ev.start_date AS start, ev.stop_date AS stop,
+       groupArrayIf((d.key, d.value), d.key != '') AS attrs
+FROM ev LEFT JOIN (
+  SELECT clinical_event_id, key, value FROM snapshot_db.clinical_event_data WHERE clinical_event_id IN (SELECT clinical_event_id FROM ev)
+) d ON d.clinical_event_id = ev.clinical_event_id
+GROUP BY id, type, start, stop
+ORDER BY start, id
+
+-- view 0 timeline: timeline:rank · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+pt0 AS (
+  SELECT p.internal_id AS iid, p.stable_id AS pid FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+),
+ev0 AS (
+  SELECT ce.patient_id AS iid, count() AS events, min(ce.start_date) AS first,
+         max(greatest(ce.start_date, ifNull(ce.stop_date, ce.start_date))) AS last,
+         arrayStringConcat(arraySort(groupUniqArray(trimBoth(ce.event_type))), ', ') AS types
+  FROM snapshot_db.clinical_event ce WHERE ce.patient_id IN (SELECT iid FROM pt0)
+  GROUP BY iid
+),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS samples FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, pt.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.samples, 0)) AS samples, toUInt64(ifNull(ev.events, 0)) AS events,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.first)), NULL) AS first,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.last)), NULL) AS last,
+         ifNull(ev.types, '') AS types
+  FROM pt0 AS pt LEFT JOIN ev0 AS ev ON ev.iid = pt.iid LEFT JOIN sm0 AS sm ON sm.pid = pt.pid
+)
+SELECT idx FROM (SELECT study, patient, row_number() OVER (ORDER BY toFloat64(events) DESC NULLS LAST, patient, ord) - 1 AS idx FROM rows
+WHERE 1)
+WHERE study = 'msk_chord_2024' AND patient = 'P-0000012' LIMIT 1
+
+-- view 0 timeline: timeline:rows · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+pt0 AS (
+  SELECT p.internal_id AS iid, p.stable_id AS pid FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+),
+ev0 AS (
+  SELECT ce.patient_id AS iid, count() AS events, min(ce.start_date) AS first,
+         max(greatest(ce.start_date, ifNull(ce.stop_date, ce.start_date))) AS last,
+         arrayStringConcat(arraySort(groupUniqArray(trimBoth(ce.event_type))), ', ') AS types
+  FROM snapshot_db.clinical_event ce WHERE ce.patient_id IN (SELECT iid FROM pt0)
+  GROUP BY iid
+),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS samples FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, pt.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.samples, 0)) AS samples, toUInt64(ifNull(ev.events, 0)) AS events,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.first)), NULL) AS first,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.last)), NULL) AS last,
+         ifNull(ev.types, '') AS types
+  FROM pt0 AS pt LEFT JOIN ev0 AS ev ON ev.iid = pt.iid LEFT JOIN sm0 AS sm ON sm.pid = pt.pid
+)
+SELECT cohort, study, patient, sample, source, samples, events, first, last, types, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY toFloat64(events) DESC NULLS LAST, patient, ord
+LIMIT 25 OFFSET 0
+
+-- view 0 timeline: timeline:rows · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+pt0 AS (
+  SELECT p.internal_id AS iid, p.stable_id AS pid FROM snapshot_db.patient p
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+),
+ev0 AS (
+  SELECT ce.patient_id AS iid, count() AS events, min(ce.start_date) AS first,
+         max(greatest(ce.start_date, ifNull(ce.stop_date, ce.start_date))) AS last,
+         arrayStringConcat(arraySort(groupUniqArray(trimBoth(ce.event_type))), ', ') AS types
+  FROM snapshot_db.clinical_event ce WHERE ce.patient_id IN (SELECT iid FROM pt0)
+  GROUP BY iid
+),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS samples FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, pt.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.samples, 0)) AS samples, toUInt64(ifNull(ev.events, 0)) AS events,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.first)), NULL) AS first,
+         if(ifNull(ev.events, 0) > 0, toNullable(toInt64(ev.last)), NULL) AS last,
+         ifNull(ev.types, '') AS types
+  FROM pt0 AS pt LEFT JOIN ev0 AS ev ON ev.iid = pt.iid LEFT JOIN sm0 AS sm ON sm.pid = pt.pid
+)
+SELECT cohort, study, patient, sample, source, samples, events, first, last, types, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY toFloat64(events) DESC NULLS LAST, patient, ord
+LIMIT 3 OFFSET 1250
+
+-- view 0 timeline: timeline:samples · msk_chord_2024
+SELECT replaceOne(s.sample_unique_id, 'msk_chord_2024_', '') AS id, any(c.attribute_value) AS type
+FROM snapshot_db.sample_derived s
+LEFT JOIN snapshot_db.clinical_data_derived c ON c.sample_unique_id = s.sample_unique_id AND c.type = 'sample'
+  AND c.cancer_study_identifier = 'msk_chord_2024' AND c.attribute_name = 'SAMPLE_TYPE'
+WHERE s.cancer_study_identifier = 'msk_chord_2024' AND s.patient_unique_id = 'msk_chord_2024_P-0000012'
+GROUP BY id ORDER BY id
+
+-- view 1 alterations: alterations:cna:counts · msk_chord_2024
+WITH c AS (
+  SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, hugo_gene_symbol AS gene, cna_alteration AS alt, cna_cytoband AS cytoband
+  FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0000012' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+),
+g AS (SELECT gene, alt, any(cytoband) AS cytoband, arrayStringConcat(arraySort(x -> if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid))) AS so FROM c GROUP BY gene, alt),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'CNA' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+fa AS (SELECT hugo_gene_symbol AS gene, cna_alteration AS alt, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alt),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'COPY_NUMBER_ALTERATION' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0000012' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, if(g.alt > 0, 'AMP', 'HOMDEL') AS cna, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.cytoband AS cytoband, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = if(g.alt > 0, 'AMPLIFICATION', 'DELETION')
+  LEFT JOIN fa ON fa.gene = g.gene AND fa.alt = g.alt LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 1 alterations: alterations:cna:rows · msk_chord_2024
+WITH c AS (
+  SELECT replaceOne(sample_unique_id, 'msk_chord_2024_', '') AS sid, hugo_gene_symbol AS gene, cna_alteration AS alt, cna_cytoband AS cytoband
+  FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0000012' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+),
+g AS (SELECT gene, alt, any(cytoband) AS cytoband, arrayStringConcat(arraySort(x -> if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid))) AS so FROM c GROUP BY gene, alt),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'CNA' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+fa AS (SELECT hugo_gene_symbol AS gene, cna_alteration AS alt, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alt),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'COPY_NUMBER_ALTERATION' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0000012' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, if(g.alt > 0, 'AMP', 'HOMDEL') AS cna, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.cytoband AS cytoband, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = if(g.alt > 0, 'AMPLIFICATION', 'DELETION')
+  LEFT JOIN fa ON fa.gene = g.gene AND fa.alt = g.alt LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT *, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY gdrv DESC, gene, so, gene, cna
+LIMIT 25 OFFSET 0
+
+-- view 1 alterations: alterations:counts · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 1 alterations: alterations:mutations:counts · msk_chord_2024
+WITH m AS (
+  SELECT sampleId AS sid, `GENE.hugoGeneSymbol` AS gene, proteinChange, mutationType, variantType, chr, startPosition AS start, endPosition AS end,
+         referenceAllele AS ref, tumorSeqAllele AS var, tumorAltCount AS ac, tumorRefCount AS rc, mutationStatus AS ms, proteinPosStart AS codon,
+         if(tumorAltCount + tumorRefCount > 0, tumorAltCount / (tumorAltCount + tumorRefCount), NULL) AS f
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND patientId = 'P-0000012' AND molecularProfileId NOT LIKE '%uncalled%'
+),
+g AS (
+  SELECT gene, proteinChange, mutationType, variantType, chr, start, end, ref, var, any(ms) AS status, min(codon) AS pos, arrayStringConcat(arraySort(x -> if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid))) AS so,
+         max(f) AS vaf, argMax(ac, ifNull(f, -1)) AS altCount, argMax(rc, ifNull(f, -1)) AS refCount
+  FROM m GROUP BY gene, proteinChange, mutationType, variantType, chr, start, end, ref, var
+),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'MUTATION' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+cn AS (SELECT hugo_gene_symbol AS gene, arrayStringConcat(arraySort(groupUniqArray(if(cna_alteration > 0, 'AMP', 'HOMDEL'))), ', ') AS copy
+  FROM snapshot_db.genomic_event_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0000012'
+    AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fa AS (SELECT hugo_gene_symbol AS gene, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'mutation' AND genetic_profile_stable_id NOT LIKE '%uncalled%'
+    AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0000012' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, g.proteinChange AS proteinChange, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.mutationType AS mutationType, ifNull(cn.copy, '') AS copy, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq,
+         g.vaf AS vaf, g.altCount AS altCount, g.refCount AS refCount, g.variantType AS variantType, g.chr AS chr, g.start AS start, g.end AS end,
+         g.ref AS ref, g.var AS var, g.status AS status, g.pos AS pos
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = g.proteinChange
+  LEFT JOIN cn ON cn.gene = g.gene LEFT JOIN fa ON fa.gene = g.gene LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 1 alterations: alterations:mutations:rows · msk_chord_2024
+WITH m AS (
+  SELECT sampleId AS sid, `GENE.hugoGeneSymbol` AS gene, proteinChange, mutationType, variantType, chr, startPosition AS start, endPosition AS end,
+         referenceAllele AS ref, tumorSeqAllele AS var, tumorAltCount AS ac, tumorRefCount AS rc, mutationStatus AS ms, proteinPosStart AS codon,
+         if(tumorAltCount + tumorRefCount > 0, tumorAltCount / (tumorAltCount + tumorRefCount), NULL) AS f
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND patientId = 'P-0000012' AND molecularProfileId NOT LIKE '%uncalled%'
+),
+g AS (
+  SELECT gene, proteinChange, mutationType, variantType, chr, start, end, ref, var, any(ms) AS status, min(codon) AS pos, arrayStringConcat(arraySort(x -> if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid))) AS so,
+         max(f) AS vaf, argMax(ac, ifNull(f, -1)) AS altCount, argMax(rc, ifNull(f, -1)) AS refCount
+  FROM m GROUP BY gene, proteinChange, mutationType, variantType, chr, start, end, ref, var
+),
+ann AS (SELECT hugo_gene_symbol AS gene, alteration, any(oncogenicity) AS oncogenicity, any(highest_sensitive_level) AS level, max(is_driver) AS driver
+  FROM oncokb_annotation.variant_annotation WHERE oncokb_version = 'v7.5' AND alteration_type = 'MUTATION' AND hugo_gene_symbol IN (SELECT gene FROM g)
+  GROUP BY gene, alteration),
+cn AS (SELECT hugo_gene_symbol AS gene, arrayStringConcat(arraySort(groupUniqArray(if(cna_alteration > 0, 'AMP', 'HOMDEL'))), ', ') AS copy
+  FROM snapshot_db.genomic_event_derived WHERE cancer_study_identifier = 'msk_chord_2024' AND patient_unique_id = 'msk_chord_2024_P-0000012'
+    AND variant_type = 'cna' AND abs(cna_alteration) >= 2 AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fa AS (SELECT hugo_gene_symbol AS gene, uniqExact(sample_unique_id) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'mutation' AND genetic_profile_stable_id NOT LIKE '%uncalled%'
+    AND hugo_gene_symbol IN (SELECT gene FROM g) GROUP BY gene),
+fp AS (SELECT pg.gene AS gene, uniqExact(sp.sample_unique_id) AS n FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN (SELECT gene FROM g)
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED' GROUP BY gene),
+rows AS (SELECT *, max(drv) OVER (PARTITION BY gene) AS gdrv FROM (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0000012' AS patient, splitByString(', ', g.samples)[1] AS sid, g.samples AS samples, g.so AS so, g.gene AS gene, g.proteinChange AS proteinChange, ifNull(ann.oncogenicity, '') AS oncogenicity, ifNull(ann.level, '') AS level, toUInt8(ifNull(ann.driver, 0)) AS drv,
+         g.mutationType AS mutationType, ifNull(cn.copy, '') AS copy, if(ifNull(fp.n, 0) > 0, 100 * ifNull(fa.n, 0) / fp.n, NULL) AS freq,
+         g.vaf AS vaf, g.altCount AS altCount, g.refCount AS refCount, g.variantType AS variantType, g.chr AS chr, g.start AS start, g.end AS end,
+         g.ref AS ref, g.var AS var, g.status AS status, g.pos AS pos
+  FROM g
+  LEFT JOIN ann ON ann.gene = g.gene AND ann.alteration = g.proteinChange
+  LEFT JOIN cn ON cn.gene = g.gene LEFT JOIN fa ON fa.gene = g.gene LEFT JOIN fp ON fp.gene = g.gene
+))
+SELECT *, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY gdrv DESC, gene, so, pos ASC NULLS LAST, gene, chr, start, end, ref, var, proteinChange, mutationType
+LIMIT 25 OFFSET 0
+
+-- view 1 alterations: alterations:rank · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT idx FROM (SELECT study, patient, row_number() OVER (ORDER BY mutations + cna + sv = 0, patient, patient, ord) - 1 AS idx FROM rows
+WHERE 1)
+WHERE study = 'msk_chord_2024' AND patient = 'P-0000012' LIMIT 1
+
+-- view 1 alterations: alterations:rows · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT cohort, study, patient, sample, source, samples, mutations, cna, sv, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY mutations + cna + sv = 0, patient, patient, ord
+LIMIT 2 OFFSET 0
+
+-- view 1 alterations: alterations:rows · msk_chord_2024
+WITH c0 AS ((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type = 'sample'
+    AND attribute_name = 'CANCER_TYPE' AND lowerUTF8(trimBoth(attribute_value)) IN (lowerUTF8(trimBoth('Breast Cancer'))))),
+sm0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(sample_unique_id) AS n FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+mu0 AS (
+  SELECT patientId AS pid, uniqExact(tuple(`GENE.hugoGeneSymbol`, proteinChange, mutationType, variantType, chr, startPosition, endPosition, referenceAllele, tumorSeqAllele)) AS n
+  FROM snapshot_db.mutation_derived
+  WHERE studyId = 'msk_chord_2024' AND molecularProfileId NOT LIKE '%uncalled%' AND patientId IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+cn0 AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid, uniqExact(hugo_gene_symbol, cna_alteration) AS n FROM snapshot_db.genomic_event_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND variant_type = 'cna' AND abs(cna_alteration) >= 2
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+sv0 AS (
+  SELECT p.stable_id AS pid, uniqExact(tuple(sv.site1_entrez_gene_id, sv.site2_entrez_gene_id, sv.class, sv.event_info, sv.site1_chromosome, sv.site1_position, sv.site2_chromosome, sv.site2_position)) AS n
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample s ON s.internal_id = sv.sample_id
+  JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+  JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+  WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id IN (SELECT pid FROM c0)
+  GROUP BY pid
+),
+rows AS (
+  SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, c.pid AS patient, '' AS sample, 'this portal (primary)' AS source,
+         toUInt64(ifNull(sm.n, 0)) AS samples, toUInt64(ifNull(mu.n, 0)) AS mutations, toUInt64(ifNull(cn.n, 0)) AS cna, toUInt64(ifNull(sv.n, 0)) AS sv
+  FROM (SELECT DISTINCT pid FROM c0) AS c
+  LEFT JOIN sm0 AS sm ON sm.pid = c.pid LEFT JOIN mu0 AS mu ON mu.pid = c.pid
+  LEFT JOIN cn0 AS cn ON cn.pid = c.pid LEFT JOIN sv0 AS sv ON sv.pid = c.pid
+)
+SELECT cohort, study, patient, sample, source, samples, mutations, cna, sv, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY mutations + cna + sv = 0, patient, patient, ord
+LIMIT 25 OFFSET 0
+
+-- view 1 alterations: alterations:samples:counts · msk_chord_2024
+WITH s AS (SELECT replaceOne(s.sample_unique_id, 'msk_chord_2024_', '') AS id, any(c.attribute_value) AS type
+FROM snapshot_db.sample_derived s
+LEFT JOIN snapshot_db.clinical_data_derived c ON c.sample_unique_id = s.sample_unique_id AND c.type = 'sample'
+  AND c.cancer_study_identifier = 'msk_chord_2024' AND c.attribute_name = 'SAMPLE_TYPE'
+WHERE s.cancer_study_identifier = 'msk_chord_2024' AND s.patient_unique_id = 'msk_chord_2024_P-0000012'
+GROUP BY id ORDER BY id),
+rows AS (SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0000012' AS patient, s.id AS sid, ifNull(s.type, '') AS type FROM s)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
+
+-- view 1 alterations: alterations:samples:rows · msk_chord_2024
+WITH s AS (SELECT replaceOne(s.sample_unique_id, 'msk_chord_2024_', '') AS id, any(c.attribute_value) AS type
+FROM snapshot_db.sample_derived s
+LEFT JOIN snapshot_db.clinical_data_derived c ON c.sample_unique_id = s.sample_unique_id AND c.type = 'sample'
+  AND c.cancer_study_identifier = 'msk_chord_2024' AND c.attribute_name = 'SAMPLE_TYPE'
+WHERE s.cancer_study_identifier = 'msk_chord_2024' AND s.patient_unique_id = 'msk_chord_2024_P-0000012'
+GROUP BY id ORDER BY id),
+rows AS (SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0000012' AS patient, s.id AS sid, ifNull(s.type, '') AS type FROM s)
+SELECT *, count() OVER () AS total FROM rows
+WHERE 1
+ORDER BY sid, sid
+LIMIT 1000 OFFSET 0
+
+-- view 1 alterations: alterations:sv:counts · msk_chord_2024
+WITH s AS (
+  SELECT smp.stable_id AS sid, ifNull(g1.hugo_gene_symbol, '') AS gene1, ifNull(g2.hugo_gene_symbol, '') AS gene2,
+         ifNull(sv.class, '') AS class, ifNull(sv.event_info, '') AS eventInfo, sv.breakpoint_type AS breakpointType, sv.connection_type AS connectionType,
+         sv.site2_effect_on_frame AS frame, sv.site1_description AS site1, sv.site2_description AS site2,
+         if(sv.site1_position IS NULL, '', concat(ifNull(sv.site1_chromosome, ''), ':', toString(sv.site1_position))) AS pos1,
+         if(sv.site2_position IS NULL, '', concat(ifNull(sv.site2_chromosome, ''), ':', toString(sv.site2_position))) AS pos2,
+         sv.length AS length, sv.tumor_variant_count AS tumorVariantCount, sv.tumor_read_count AS tumorReadCount,
+         sv.dna_support AS dnaSupport, sv.rna_support AS rnaSupport, sv.sv_status AS status, sv.annotation AS svAnnotation, sv.comments AS comments
+  FROM snapshot_db.structural_variant sv
+  JOIN snapshot_db.sample smp ON smp.internal_id = sv.sample_id
+  LEFT JOIN snapshot_db.gene g1 ON g1.entrez_gene_id = sv.site1_entrez_gene_id
+  LEFT JOIN snapshot_db.gene g2 ON g2.entrez_gene_id = sv.site2_entrez_gene_id
+  WHERE sv.sample_id IN (SELECT s.internal_id FROM snapshot_db.sample s JOIN snapshot_db.patient p ON p.internal_id = s.patient_id
+    JOIN snapshot_db.cancer_study cs ON cs.cancer_study_id = p.cancer_study_id
+    WHERE cs.cancer_study_identifier = 'msk_chord_2024' AND p.stable_id = 'P-0000012')
+),
+g AS (
+  SELECT gene1, gene2, class, eventInfo, pos1, pos2, arrayStringConcat(arraySort(x -> if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], x)), groupUniqArray(sid)), ', ') AS samples, min(if(indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid) = 0, 1000000, indexOf(['P-0000012-T02-IM3', 'P-0000012-T03-IM3'], sid))) AS so,
+         any(breakpointType) AS breakpointType, any(connectionType) AS connectionType, any(frame) AS frame, any(site1) AS site1, any(site2) AS site2,
+         any(length) AS length, max(tumorVariantCount) AS tumorVariantCount, max(tumorReadCount) AS tumorReadCount,
+         any(dnaSupport) AS dnaSupport, any(rnaSupport) AS rnaSupport, any(status) AS status, any(svAnnotation) AS svAnnotation, any(comments) AS comments
+  FROM s GROUP BY gene1, gene2, class, eventInfo, pos1, pos2
+),
+rows AS (SELECT 'Breast' AS cohort, 'msk_chord_2024' AS study, 0 AS ord, 'P-0000012' AS patient, splitByString(', ', g.samples)[1] AS sid, g.* FROM g)
+SELECT cohort, study, countIf(1) AS n, count() AS all FROM rows GROUP BY cohort, study
diff --git a/test/snapshots/timeline.svg b/test/snapshots/timeline.svg
new file mode 100644
index 00000000..8cb6f463
--- /dev/null
+++ b/test/snapshots/timeline.svg
@@ -0,0 +1,4 @@
+<!-- view 0 timeline -->
+(no figure)
+<!-- view 1 alterations -->
+(no figure)
diff --git a/test/snapshots/timeline.txt b/test/snapshots/timeline.txt
new file mode 100644
index 00000000..bc5ff2f5
--- /dev/null
+++ b/test/snapshots/timeline.txt
@@ -0,0 +1,290 @@
+Analysis over msk_chord_2024 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Breast: 5,368 patients — CANCER_TYPE in [Breast Cancer] (any sample)
+
+Patient timeline — patients to choose from: 5,368; the first 25, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	patient	source	samples	events	span	types
+  Breast	P-0014149	this portal (primary)	1	925	6137	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0007146	this portal (primary)	1	800	5211	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0015681	this portal (primary)	1	717	6495	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0041672	this portal (primary)	1	708	4675	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0047755	this portal (primary)	1	694	5302	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0016734	this portal (primary)	1	677	6765	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0057382	this portal (primary)	1	663	7893	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0003958	this portal (primary)	1	655	6883	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0010993	this portal (primary)	1	652	6492	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0011420	this portal (primary)	1	646	7755	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0015568	this portal (primary)	1	642	9729	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0018146	this portal (primary)	1	638	9583	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0008398	this portal (primary)	1	635	3452	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0029771	this portal (primary)	1	632	4634	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0001990	this portal (primary)	1	621	4828	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0007045	this portal (primary)	1	612	3923	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0013915	this portal (primary)	1	609	7004	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0001939	this portal (primary)	1	574	6298	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0025567	this portal (primary)	1	573	5364	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0002531	this portal (primary)	1	571	6504	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0062207	this portal (primary)	1	569	5832	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0022066	this portal (primary)	1	565	3851	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0020130	this portal (primary)	1	561	6233	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0004381	this portal (primary)	1	555	8571	Diagnosis, Lab_Test, Sample acquisition, Sequencing, Surgery, Treatment
+  Breast	P-0020707	this portal (primary)	1	555	11415	Diagnosis, Lab_Test, Pathology, Sample acquisition, Sequencing, Surgery, Treatment
+  … 5,343 more rows
+P-0000012 (msk_chord_2024): 172 events, days from diagnosis; samples: P-0000012-T02-IM3 (Primary), P-0000012-T03-IM3 (Metastasis)
+  day -9641: Diagnosis — SUBTYPE=Primary, CLINICAL_GROUP=99, SUMMARY=N/A, PATH_GROUP=99, SOURCE=Tumor Registry, DX_DESCRIPTION=INFILTRATING DUCT CARCINOMA | BREAST, NOS (M8500/3 | C509), STAGE_CDM_DERIVED=Stage 1-3
+  day -5453: Sample acquisition — SEQ_DATE=48, SAMPLE_ID=P-0000012-T02-IM3
+  day -5437–-5369: Treatment — SUBTYPE=Chemo, AGENT=CYCLOPHOSPHAMIDE, RX_INVESTIGATIVE=N, FLAG_OROTOPICAL=0
+  day -5437–-5326: Treatment — SUBTYPE=Chemo, AGENT=FLUOROURACIL, RX_INVESTIGATIVE=N, FLAG_OROTOPICAL=0
+  day -5437–-5327: Treatment — SUBTYPE=Chemo, AGENT=METHOTREXATE, RX_INVESTIGATIVE=N, FLAG_OROTOPICAL=0
+  day -5437: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.8
+  day -5307: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=21
+  day -5307: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.9
+  day -5178: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=16
+  day -5178: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.4
+  day -4999: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=17
+  day -4999: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.5
+  day -4849: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=14
+  day -4849: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.4
+  day -4684: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=14
+  day -4684: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.7
+  day -4530: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=17
+  day -4530: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.2
+  day -4362: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=13
+  day -4362: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.3
+  day -3946: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=14
+  day -3946: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.8
+  day -3603: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=15
+  day -3603: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=2.2
+  day -3382: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=14
+  day -3382: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.9
+  day -3165: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=12
+  day -3165: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=2.2
+  day -2990: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=12
+  day -2990: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.9
+  day -2717: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=13
+  day -2717: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=2.1
+  day -2634: Surgery — SUBTYPE=PROCEDURE
+  day -2507: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=9
+  day -2507: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=2
+  day -2221: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=8
+  day -2221: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=1.8
+  day -1891: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=9
+  day -1891: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=2.4
+  day -1408: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=11
+  day -1408: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=2.2
+  day -695: Surgery — SUBTYPE=PROCEDURE
+  day -685: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=10
+  day -685: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=2.9
+  day -323: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=15
+  day -323: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=3.2
+  day -255: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.037178118, PROCEDURE_TYPE=MR, PROGRESSION=N
+  day -255: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0010828, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=MR
+  day -253: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.38813508, PROCEDURE_TYPE=MR, PROGRESSION=Indeterminate
+  day -253: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0004522, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=MR
+  day -83: Lab_Test — TEST=CA_15-3, LR_UNIT_MEASURE=Units/ml, RESULT=13
+  day -83: Lab_Test — TEST=CEA, LR_UNIT_MEASURE=ng/ml, RESULT=4.6
+  day -68: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.284787, PROCEDURE_TYPE=PET, PROGRESSION=Indeterminate
+  day -68: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.99975103, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=PET
+  day -68: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=PET, TUMOR_SITE=Lymph Nodes, CHEST=0, OTHER=1, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day -42: Diagnosis — SUBTYPE=Primary, CLINICAL_GROUP=3B, SUMMARY=Distant, AJCC=IIIB, PATH_GROUP=99, SOURCE=Tumor Registry, DX_DESCRIPTION=ADENOCARCINOMA, NOS | LUNG, NOS (M8140/3 | C349), STAGE_CDM_DERIVED=Stage 1-3
+  day 0: Sequencing — SAMPLE_ID=P-0000012-T03-IM3
+  day 0: Sample acquisition — SEQ_DATE=0, SAMPLE_ID=P-0000012-T03-IM3
+  day 0: Surgery — SUBTYPE=SAMPLE
+  day 10: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.90068585, PROCEDURE_TYPE=CT, PROGRESSION=Y
+  day 10: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.8226569, PROCEDURE_TYPE=PET, PROGRESSION=Y
+  day 10: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.3279463, PROCEDURE_TYPE=MR, PROGRESSION=Indeterminate
+  day 10: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.9997148, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=PET
+  day 10: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.0003389, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=MR
+  day 10: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.99968517, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 10: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lymph Nodes, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 10: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=PET, TUMOR_SITE=Lymph Nodes, CHEST=0, OTHER=1, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 10: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Other, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 33–40: Treatment — SUBTYPE=Chemo, AGENT=CISPLATIN, RX_INVESTIGATIVE=N, FLAG_OROTOPICAL=0
+  day 33–65: Treatment — SUBTYPE=Chemo, AGENT=ETOPOSIDE, RX_INVESTIGATIVE=N, FLAG_OROTOPICAL=0
+  day 33: Treatment — SUBTYPE=Radiation Therapy
+  day 48: Sequencing — SAMPLE_ID=P-0000012-T02-IM3
+  day 61–68: Treatment — SUBTYPE=Chemo, AGENT=CARBOPLATIN, RX_INVESTIGATIVE=N, FLAG_OROTOPICAL=0
+  day 149: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.18018433, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 149: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.6276817, ABDOMEN=FALSE, HAS_CANCER=Indeterminate, PROCEDURE_TYPE=CT
+  day 149: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lymph Nodes, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 159: Diagnosis — SUBTYPE=Performance Status, ECOG=0
+  day 251: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.17210542, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 251: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.04371244, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 281: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.8392186, PROCEDURE_TYPE=PET, PROGRESSION=Y
+  day 281: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.99964726, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=PET
+  day 281: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=PET, TUMOR_SITE=Intra-Abdominal, CHEST=0, OTHER=1, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 281: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=PET, TUMOR_SITE=Lymph Nodes, CHEST=0, OTHER=1, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 289: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.2349452, PROCEDURE_TYPE=PET, PROGRESSION=Indeterminate
+  day 289: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.18626133, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 289: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.1163108, ABDOMEN=TRUE, HAS_CANCER=N, PROCEDURE_TYPE=PET
+  day 289: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.3926556, ABDOMEN=FALSE, HAS_CANCER=Indeterminate, PROCEDURE_TYPE=PET
+  day 289: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=PET, TUMOR_SITE=Lymph Nodes, CHEST=0, OTHER=0, HEAD=0, ABDOMEN=1, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 289: Surgery — SUBTYPE=SAMPLE
+  day 297: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.043223906, PROCEDURE_TYPE=MR, PROGRESSION=N
+  day 297: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.0004539, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=MR
+  day 306: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.00981985, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 306: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.99856204, ABDOMEN=TRUE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 306: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Intra-Abdominal, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=1, SOURCE=Radiology Reports (NLP), PELVIS=1
+  day 306: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Other, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=1, SOURCE=Radiology Reports (NLP), PELVIS=1
+  day 320: Treatment — SUBTYPE=Investigational, AGENT=INVESTIGATIONAL, RX_INVESTIGATIVE=Y, FLAG_OROTOPICAL=0
+  day 320: Treatment — SUBTYPE=Investigational, AGENT=INVESTIGATIONAL, RX_INVESTIGATIVE=Y, FLAG_OROTOPICAL=0
+  day 334: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.18738739, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 334: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.001281, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 335: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.1852476, PROCEDURE_TYPE=MR, PROGRESSION=N
+  day 335: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.00104094, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=MR
+  day 367: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.002592743, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 367: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.97957146, ABDOMEN=TRUE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 367: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lymph Nodes, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=1, SOURCE=Radiology Reports (NLP), PELVIS=1
+  day 409: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.2979155, PROCEDURE_TYPE=CT, PROGRESSION=Indeterminate
+  day 409: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.9997031, ABDOMEN=TRUE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 409: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lymph Nodes, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=1, SOURCE=Radiology Reports (NLP), PELVIS=1
+  day 499: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.001804908, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 499: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.05136776, ABDOMEN=TRUE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 502: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.01994526, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 502: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.970199, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=PET
+  day 502: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=PET, TUMOR_SITE=Lymph Nodes, CHEST=0, OTHER=1, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 587: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.13669538, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 587: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.09822476, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 587: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0006456, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=PET
+  day 587: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0010181, ABDOMEN=TRUE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 691: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.001298996, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 691: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.00719047, ABDOMEN=TRUE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 803: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.002349537, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 803: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.033495385, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 803: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0007946, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=PET
+  day 803: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.927494, ABDOMEN=TRUE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 980: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.001755151, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 980: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.3707743, ABDOMEN=TRUE, HAS_CANCER=Indeterminate, PROCEDURE_TYPE=CT
+  day 1136: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.07828353, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 1136: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.002298566, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 1136: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.21760744, ABDOMEN=TRUE, HAS_CANCER=Indeterminate, PROCEDURE_TYPE=CT
+  day 1136: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0052873, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=PET
+  day 1317: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.034641445, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 1317: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0012883, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=PET
+  day 1498: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.008373962, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 1498: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.00069153, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=PET
+  day 1681: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.028722772, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 1681: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.91513604, ABDOMEN=TRUE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 1681: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lung, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=1, SOURCE=Radiology Reports (NLP), PELVIS=1
+  day 1688: Surgery — SUBTYPE=SAMPLE
+  day 1700: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.42995712, PROCEDURE_TYPE=MR, PROGRESSION=Indeterminate
+  day 1700: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.0015915, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=MR
+  day 1702: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.002305086, PROCEDURE_TYPE=PET, PROGRESSION=N
+  day 1702: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.99966264, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=PET
+  day 1702: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=PET, TUMOR_SITE=Lung, CHEST=0, OTHER=0, HEAD=1, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 1721: Treatment — SUBTYPE=Radiation Therapy
+  day 1734–1814: Treatment — SUBTYPE=Immuno, AGENT=NIVOLUMAB, RX_INVESTIGATIVE=N, FLAG_OROTOPICAL=0
+  day 1862: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.12809414, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 1862: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.1993236, ABDOMEN=TRUE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 1974: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.0156624, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 1974: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.9815399, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 1974: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lung, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 2086: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.002995548, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 2086: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0402322, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 2269: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.088399045, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 2269: Diagnosis — SUBTYPE=HasCancer, PELVIS=TRUE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.99789196, ABDOMEN=TRUE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 2269: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lung, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=1, SOURCE=Radiology Reports (NLP), PELVIS=1
+  day 2444: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.111479566, PROCEDURE_TYPE=MR, PROGRESSION=N
+  day 2444: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=FALSE, HEAD=TRUE, NLP_HAS_CANCER_PROBABILITY=0.0043055, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=MR
+  day 2445: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.001614616, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 2445: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.99383426, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 2627: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.001661092, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 2627: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.20430684, ABDOMEN=FALSE, HAS_CANCER=Indeterminate, PROCEDURE_TYPE=CT
+  day 2809: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.003464612, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 2809: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.9919733, ABDOMEN=FALSE, HAS_CANCER=Y, PROCEDURE_TYPE=CT
+  day 2809: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=CT, TUMOR_SITE=Lung, CHEST=1, OTHER=0, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  day 2998: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.004493397, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 2998: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0110871, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 3216: Surgery — SUBTYPE=PROCEDURE
+  day 3366: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.021393053, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 3366: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=TRUE, OTHER=FALSE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.0114738, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 3367: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.17969903, PROCEDURE_TYPE=CT, PROGRESSION=N
+  day 3367: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.00155187, ABDOMEN=FALSE, HAS_CANCER=N, PROCEDURE_TYPE=CT
+  day 3377: Diagnosis — SUBTYPE=Progression, NLP_PROGRESSION_PROBABILITY=0.17046168, PROCEDURE_TYPE=MR, PROGRESSION=N
+  day 3377: Diagnosis — SUBTYPE=HasCancer, PELVIS=FALSE, CHEST=FALSE, OTHER=TRUE, HEAD=FALSE, NLP_HAS_CANCER_PROBABILITY=0.4474247, ABDOMEN=FALSE, HAS_CANCER=Indeterminate, PROCEDURE_TYPE=MR
+  day 3377: Diagnosis — SUBTYPE=Tumor Sites, SOURCE_SPECIFIC=MR, TUMOR_SITE=Other, CHEST=0, OTHER=1, HEAD=0, ABDOMEN=0, SOURCE=Radiology Reports (NLP), PELVIS=0
+  CAVEAT (tell the user): The list is one row per patient of each cohort; event counts and days come from the study's timeline, in days from diagnosis as the study records them. 5,368 patients. Sorted by Events (highest first), missing values last, across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. 
+  METHOD:
+    - Count the patients per cohort and study: The timeline:counts query above: every patient of each cohort, with their sample count and, from the study's timeline, the number of events, the first and last event day (a range counted to its stop) and the event types — a patient with no timeline is listed with none.
+        Breast · msk_chord_2024: 5,368
+    - Sort them together and take one page: The timeline:rows query above: the patients of every cohort and study combined in one query, sorted by Events descending with missing values last, then patient id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset.
+    - Read P-0000012's timeline: The timeline:events and timeline:samples queries above: every clinical event recorded for P-0000012 in msk_chord_2024 with all its attributes, and the patient's samples with their SAMPLE_TYPE. Events are drawn as stored, in days from diagnosis; tracks group them by event type (case-folded) and nest them by attribute as cBioPortal's patient view does.
+        events: 172
+        samples: 2
+    A listing and one patient's record: no statistic is computed, so there is no script — the queries are the numbers.
+
+Patient alterations — patients to choose from: 5,368; the first 25, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	patient	source	samples	mutations	cna	sv
+  Breast	P-0000012	this portal (primary)	2	31	3	0
+  Breast	P-0000015	this portal (primary)	1	7	11	0
+  Breast	P-0000041	this portal (primary)	1	10	0	0
+  Breast	P-0000057	this portal (primary)	1	5	0	0
+  Breast	P-0000058	this portal (primary)	1	1	7	1
+  Breast	P-0000066	this portal (primary)	1	4	5	0
+  Breast	P-0000077	this portal (primary)	1	2	8	0
+  Breast	P-0000081	this portal (primary)	1	6	0	0
+  Breast	P-0000093	this portal (primary)	1	2	8	0
+  Breast	P-0000098	this portal (primary)	1	1	0	0
+  Breast	P-0000102	this portal (primary)	1	4	2	0
+  Breast	P-0000104	this portal (primary)	1	5	5	0
+  Breast	P-0000106	this portal (primary)	1	4	7	0
+  Breast	P-0000107	this portal (primary)	1	4	0	0
+  Breast	P-0000120	this portal (primary)	1	6	2	0
+  Breast	P-0000125	this portal (primary)	1	4	0	0
+  Breast	P-0000126	this portal (primary)	1	5	7	0
+  Breast	P-0000129	this portal (primary)	1	1	6	0
+  Breast	P-0000131	this portal (primary)	1	2	5	0
+  Breast	P-0000138	this portal (primary)	1	33	1	0
+  Breast	P-0000152	this portal (primary)	1	1	5	0
+  Breast	P-0000155	this portal (primary)	1	6	0	0
+  Breast	P-0000158	this portal (primary)	1	2	5	0
+  Breast	P-0000167	this portal (primary)	1	4	1	0
+  Breast	P-0000175	this portal (primary)	1	2	0	0
+  … 5,343 more rows
+P-0000012 (msk_chord_2024); samples: P-0000012-T02-IM3 (Primary), P-0000012-T03-IM3 (Metastasis)
+Mutations: 31 rows; the first 25, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	samples	gene	proteinChange	annotation	mutationType	copy	freq	vaf
+  Breast	P-0000012-T03-IM3	ATRX	K983*	Likely Oncogenic	Nonsense_Mutation		3.3	0.198
+  Breast	P-0000012-T03-IM3	BLM	A363fs	Likely Oncogenic	Frame_Shift_Del		1.4	0.128
+  Breast	P-0000012-T03-IM3	FBXW7	G61*	Likely Oncogenic · Level 3A	Nonsense_Mutation		4.5	0.228
+  Breast	P-0000012-T03-IM3	FBXW7	X413_splice	Likely Oncogenic · Level 3A	Splice_Site		4.5	0.255
+  Breast	P-0000012-T03-IM3	MAP3K1	E302*	Likely Oncogenic	Nonsense_Mutation		3.1	0.174
+  Breast	P-0000012-T03-IM3	MAP3K1	X696_splice	Likely Oncogenic	Splice_Site		3.1	0.201
+  Breast	P-0000012-T03-IM3	PTPRD	E915*	Likely Oncogenic	Nonsense_Mutation		4.3	0.176
+  Breast	P-0000012-T03-IM3	PTPRD	P1338T	Unknown	Missense_Mutation		4.3	0.298
+  Breast	P-0000012-T03-IM3	PTPRT	G969V	Unknown	Missense_Mutation		4.8	0.142
+  Breast	P-0000012-T03-IM3	PTPRT	X1053_splice	Likely Oncogenic	Splice_Site		4.8	0.051
+  Breast	P-0000012-T03-IM3	SMARCA4	E1211*	Likely Oncogenic · Level 3A	Nonsense_Mutation		4.3	0.208
+  Breast	P-0000012-T02-IM3	TP53	T256P	Likely Oncogenic	Missense_Mutation		52.4	0.502
+  Breast	P-0000012-T03-IM3	TP53	P67Qfs*56	Likely Oncogenic	Frame_Shift_Del		52.4	0.402
+  Breast	P-0000012-T03-IM3	ATR	R2431M	Unknown	Missense_Mutation		2.7	0.174
+  Breast	P-0000012-T03-IM3	AXIN2	I375M	Unknown	Missense_Mutation		2.1	0.137
+  Breast	P-0000012-T03-IM3	BRIP1	D393V	Unknown	Missense_Mutation		1.4	0.146
+  Breast	P-0000012-T03-IM3	CCNE1	A47V	Unknown	Missense_Mutation		0.5	0.149
+  Breast	P-0000012-T03-IM3	CREBBP	M2161I	Unknown	Missense_Mutation		3.4	0.248
+  Breast	P-0000012-T03-IM3	DOT1L	W305C	Unknown	Missense_Mutation		2.3	0.158
+  Breast	P-0000012-T03-IM3	FLT4	S293R	Unknown	Missense_Mutation		2.8	0.179
+  Breast	P-0000012-T03-IM3	HGF	W547Cfs*35	Unknown	Frame_Shift_Del		2.2	0.141
+  Breast	P-0000012-T03-IM3	KDM5A	N212I	Unknown	Missense_Mutation		1.8	0.216
+  Breast	P-0000012-T03-IM3	KMT2C	K3105N	Unknown	Missense_Mutation		6.5	0.179
+  Breast	P-0000012-T03-IM3	NSD1	V1780F	Unknown	Missense_Mutation		2.1	0.194
+  Breast	P-0000012-T03-IM3	PDGFRA	L465M	Unknown	Missense_Mutation		2.1	0.204
+  … 6 more rows
+Copy Number Alterations: 3 rows; the first 3, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	samples	gene	cna	annotation	cytoband	freq
+  Breast	P-0000012-T03-IM3	CDK4	AMP	Oncogenic · Level 4	12q14.1	1.6
+  Breast	P-0000012-T02-IM3	MCL1	AMP	Oncogenic	1q21.2	1.5
+  Breast	P-0000012-T02-IM3	RIT1	AMP	Unknown	1q22	0.7
+Structural Variants: 0 rows; the first 0, tab-separated (the web app's view pages and downloads every row, with every column):
+  cohort	samples	gene1	gene2	class	eventInfo	breakpointType	connectionType
+  CAVEAT (tell the user): P-0000012 — Mutations: 31 rows; Copy Number Alterations: 3 rows; Structural Variants: 0 rows. Each table lists a variant, a gene's call or an event once however many of the patient's samples carry it (Samples names them); allele frequency and read counts are the highest among those samples. Copy number is the deep calls only (AMP, HOMDEL), as the portal's patient view lists them; Copy # on a mutation is the gene's deep calls anywhere in the patient. Cohort % is the share of the study's samples profiled for the gene by their panel with a mutation in it, or with the same copy-number call. Annotation is OncoKB v7.5 (oncogenicity and its highest sensitivity level); structural variants carry the study's own annotation, not OncoKB's. The patient list: 5,368 patients. Sorted with patients who have any alteration first, then by patient id across the cohort; every page is a slice of that one order, and the download holds every row kept, with every column. 
+  METHOD:
+    - Count the patients per cohort and study: The alterations:counts query above: every patient of each cohort, with their sample count and how many rows each of the three tables lists for them (a mutation, a gene's deep copy-number call, a structural variant — each counted once however many samples carry it).
+        Breast · msk_chord_2024: 5,368
+    - Sort them together and take one page: The alterations:rows query above: the patients of every cohort and study combined in one query, sorted patients with any alteration first, then by patient id, and the 25 rows of the first page taken. Every other page, and the download, is the same query at another offset.
+    - Read P-0000012's three tables: The alterations:mutations, alterations:cna and alterations:sv queries above, each paged, searched, filtered and sorted in the database like any table view. Mutations: every called mutation of the patient's samples (uncalled calls left out), one row per variant with the samples carrying it, the highest allele frequency among them (variant / (variant + reference) reads) with that sample's reads, and the gene's deep copy-number calls in the patient (Copy #). Copy-number alterations: the deep calls (AMP, HOMDEL), one row per gene and call. Structural variants: one row per event (both genes and breakpoints, class, event info), the highest read counts among its samples. Cohort % is the share of the study's samples profiled for the gene (by their gene panel) with any mutation in it, or with the same copy-number call; the annotation is the OncoKB v7.5 call for the protein change, or for AMPLIFICATION / DELETION.
+        Mutations: 31 rows
+        Copy Number Alterations: 3 rows
+        Structural Variants: 0 rows
+    A listing: no statistic is computed, so there is no script — the queries are the numbers.
diff --git a/test/snapshots/two-studies-four-views.json b/test/snapshots/two-studies-four-views.json
new file mode 100644
index 00000000..12e4b918
--- /dev/null
+++ b/test/snapshots/two-studies-four-views.json
@@ -0,0 +1,1474 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "TP53 mut": {
+      "count": 25555,
+      "text": "OQL[TP53: MUT] — across 2 studies",
+      "studies": [
+        "msk_chord_2024",
+        "msk_met_2021"
+      ]
+    },
+    "TP53 wt": {
+      "count": 24627,
+      "text": "NOT OQL[TP53:] AND profiled for TP53 MUTATION — across 2 studies",
+      "studies": [
+        "msk_chord_2024",
+        "msk_met_2021"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "compare",
+      "cohorts": [
+        "TP53 mut",
+        "TP53 wt"
+      ],
+      "annotation": "OncoKB v7.5 (pinned)",
+      "rows": [
+        {
+          "gene": "APC",
+          "cells": [
+            {
+              "cohort": "TP53 mut",
+              "profiled": 25555,
+              "any": 6084,
+              "driver": 5524,
+              "anyPct": 23.8,
+              "driverPct": 21.6,
+              "byType": {
+                "mutation": {
+                  "profiled": 25555,
+                  "altered": 5974
+                },
+                "cna": {
+                  "profiled": 25555,
+                  "altered": 104
+                },
+                "structuralVariant": {
+                  "profiled": 25555,
+                  "altered": 85
+                }
+              }
+            },
+            {
+              "cohort": "TP53 wt",
+              "profiled": 24627,
+              "any": 2478,
+              "driver": 2040,
+              "anyPct": 10.1,
+              "driverPct": 8.3,
+              "byType": {
+                "mutation": {
+                  "profiled": 24627,
+                  "altered": 2383
+                },
+                "cna": {
+                  "profiled": 24627,
+                  "altered": 84
+                },
+                "structuralVariant": {
+                  "profiled": 24627,
+                  "altered": 48
+                }
+              }
+            }
+          ],
+          "pAny": 0,
+          "pDriver": 0,
+          "qAny": 0,
+          "qDriver": 0
+        },
+        {
+          "gene": "KRAS",
+          "cells": [
+            {
+              "cohort": "TP53 mut",
+              "profiled": 25555,
+              "any": 7678,
+              "driver": 7622,
+              "anyPct": 30,
+              "driverPct": 29.8,
+              "byType": {
+                "mutation": {
+                  "profiled": 25555,
+                  "altered": 7315
+                },
+                "cna": {
+                  "profiled": 25555,
+                  "altered": 508
+                },
+                "structuralVariant": {
+                  "profiled": 25555,
+                  "altered": 6
+                }
+              }
+            },
+            {
+              "cohort": "TP53 wt",
+              "profiled": 24627,
+              "any": 5345,
+              "driver": 5299,
+              "anyPct": 21.7,
+              "driverPct": 21.5,
+              "byType": {
+                "mutation": {
+                  "profiled": 24627,
+                  "altered": 5181
+                },
+                "cna": {
+                  "profiled": 24627,
+                  "altered": 231
+                },
+                "structuralVariant": {
+                  "profiled": 24627,
+                  "altered": 2
+                }
+              }
+            }
+          ],
+          "pAny": 3.683367002397097e-101,
+          "pDriver": 6.541257727317714e-101,
+          "qAny": 9.208417505992743e-101,
+          "qDriver": 1.6353144318294285e-100
+        },
+        {
+          "gene": "CDKN2A",
+          "cells": [
+            {
+              "cohort": "TP53 mut",
+              "profiled": 25555,
+              "any": 3978,
+              "driver": 3590,
+              "anyPct": 15.6,
+              "driverPct": 14,
+              "byType": {
+                "mutation": {
+                  "profiled": 25555,
+                  "altered": 2150
+                },
+                "cna": {
+                  "profiled": 25555,
+                  "altered": 1774
+                },
+                "structuralVariant": {
+                  "profiled": 25555,
+                  "altered": 83
+                }
+              }
+            },
+            {
+              "cohort": "TP53 wt",
+              "profiled": 24627,
+              "any": 2401,
+              "driver": 2207,
+              "anyPct": 9.7,
+              "driverPct": 9,
+              "byType": {
+                "mutation": {
+                  "profiled": 24627,
+                  "altered": 578
+                },
+                "cna": {
+                  "profiled": 24627,
+                  "altered": 1759
+                },
+                "structuralVariant": {
+                  "profiled": 24627,
+                  "altered": 87
+                }
+              }
+            }
+          ],
+          "pAny": 6.348835417681368e-86,
+          "pDriver": 1.1581233622946001e-71,
+          "qAny": 1.058139236280228e-85,
+          "qDriver": 1.9302056038243335e-71
+        },
+        {
+          "gene": "PIK3CA",
+          "cells": [
+            {
+              "cohort": "TP53 mut",
+              "profiled": 25555,
+              "any": 3347,
+              "driver": 3009,
+              "anyPct": 13.1,
+              "driverPct": 11.8,
+              "byType": {
+                "mutation": {
+                  "profiled": 25555,
+                  "altered": 3077
+                },
+                "cna": {
+                  "profiled": 25555,
+                  "altered": 354
+                },
+                "structuralVariant": {
+                  "profiled": 25555,
+                  "altered": 12
+                }
+              }
+            },
+            {
+              "cohort": "TP53 wt",
+              "profiled": 24627,
+              "any": 4218,
+              "driver": 3951,
+              "anyPct": 17.1,
+              "driverPct": 16,
+              "byType": {
+                "mutation": {
+                  "profiled": 24627,
+                  "altered": 4168
+                },
+                "cna": {
+                  "profiled": 24627,
+                  "altered": 76
+                },
+                "structuralVariant": {
+                  "profiled": 24627,
+                  "altered": 4
+                }
+              }
+            }
+          ],
+          "pAny": 1.6718354038852575e-36,
+          "pDriver": 1.616742625373624e-43,
+          "qAny": 2.0897942548565717e-36,
+          "qDriver": 2.02092828171703e-43
+        },
+        {
+          "gene": "EGFR",
+          "cells": [
+            {
+              "cohort": "TP53 mut",
+              "profiled": 25555,
+              "any": 2595,
+              "driver": 2151,
+              "anyPct": 10.2,
+              "driverPct": 8.4,
+              "byType": {
+                "mutation": {
+                  "profiled": 25555,
+                  "altered": 2084
+                },
+                "cna": {
+                  "profiled": 25555,
+                  "altered": 897
+                },
+                "structuralVariant": {
+                  "profiled": 25555,
+                  "altered": 66
+                }
+              }
+            },
+            {
+              "cohort": "TP53 wt",
+              "profiled": 24627,
+              "any": 1805,
+              "driver": 1442,
+              "anyPct": 7.3,
+              "driverPct": 5.9,
+              "byType": {
+                "mutation": {
+                  "profiled": 24627,
+                  "altered": 1667
+                },
+                "cna": {
+                  "profiled": 24627,
+                  "altered": 215
+                },
+                "structuralVariant": {
+                  "profiled": 24627,
+                  "altered": 35
+                }
+              }
+            }
+          ],
+          "pAny": 3.4918653939355507e-29,
+          "pDriver": 6.284452802067991e-29,
+          "qAny": 3.4918653939355507e-29,
+          "qDriver": 6.284452802067991e-29
+        }
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "msk_met_2021",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "studies": [
+        "msk_chord_2024",
+        "msk_met_2021"
+      ],
+      "note": "cross-study comparison (msk_chord_2024, msk_met_2021): denominators are per-study panel-aware; patient overlap across studies is NOT deduplicated; counts are POOLED across studies, so the p-value is unstratified — site composition differences can invert a pooled association (Simpson's paradox), and a stratified test (Cochran–Mantel–Haenszel) would be the principled statistic",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count profiled and altered patients per gene",
+            "detail": "The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.",
+            "scope": "TP53 mut · msk_chord_2024",
+            "rowsIn": 13105,
+            "params": [
+              [
+                "genes",
+                "5"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count profiled and altered patients per gene",
+            "detail": "The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.",
+            "scope": "TP53 mut · msk_met_2021",
+            "rowsIn": 12450,
+            "params": [
+              [
+                "genes",
+                "5"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count profiled and altered patients per gene",
+            "detail": "The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.",
+            "scope": "TP53 wt · msk_chord_2024",
+            "rowsIn": 11594,
+            "params": [
+              [
+                "genes",
+                "5"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count profiled and altered patients per gene",
+            "detail": "The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.",
+            "scope": "TP53 wt · msk_met_2021",
+            "rowsIn": 13033,
+            "params": [
+              [
+                "genes",
+                "5"
+              ]
+            ]
+          },
+          {
+            "kind": "transform",
+            "title": "Sum each cohort's counts across its studies",
+            "detail": "Profiled, altered and driver counts are added study by study, so the denominator stays panel-aware. The test that follows is on the pooled counts, not stratified by study."
+          },
+          {
+            "kind": "statistic",
+            "title": "Fisher's exact test per gene",
+            "detail": "Two-sided, on the 2×2 table of altered vs not altered (profiled − altered) in each cohort — once for any alteration and once for drivers only.",
+            "params": [
+              [
+                "genes tested",
+                "5"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across genes",
+            "detail": "q-values over the 5 genes tested here, separately for the any-alteration and driver-only families."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "fisherExact",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg matches statsmodels 0.15.0 multipletests(method=\"fdr_bh\"), to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "compare:APC:p_any": {
+            "v": 0
+          },
+          "compare:APC:p_driver": {
+            "v": 0
+          },
+          "compare:APC:q_any": {
+            "v": 0
+          },
+          "compare:APC:q_driver": {
+            "v": 0
+          },
+          "compare:KRAS:p_any": {
+            "v": 3.683367002397097e-101
+          },
+          "compare:KRAS:p_driver": {
+            "v": 6.541257727317714e-101
+          },
+          "compare:KRAS:q_any": {
+            "v": 9.208417505992743e-101
+          },
+          "compare:KRAS:q_driver": {
+            "v": 1.6353144318294285e-100
+          },
+          "compare:CDKN2A:p_any": {
+            "v": 6.348835417681368e-86
+          },
+          "compare:CDKN2A:p_driver": {
+            "v": 1.1581233622946001e-71
+          },
+          "compare:CDKN2A:q_any": {
+            "v": 1.058139236280228e-85
+          },
+          "compare:CDKN2A:q_driver": {
+            "v": 1.9302056038243335e-71
+          },
+          "compare:PIK3CA:p_any": {
+            "v": 1.6718354038852575e-36
+          },
+          "compare:PIK3CA:p_driver": {
+            "v": 1.616742625373624e-43
+          },
+          "compare:PIK3CA:q_any": {
+            "v": 2.0897942548565717e-36
+          },
+          "compare:PIK3CA:q_driver": {
+            "v": 2.02092828171703e-43
+          },
+          "compare:EGFR:p_any": {
+            "v": 3.4918653939355507e-29
+          },
+          "compare:EGFR:p_driver": {
+            "v": 6.284452802067991e-29
+          },
+          "compare:EGFR:q_any": {
+            "v": 3.4918653939355507e-29
+          },
+          "compare:EGFR:q_driver": {
+            "v": 6.284452802067991e-29
+          },
+          "compare:KRAS:TP53 mut:profiled": {
+            "v": 25555,
+            "abs": 0
+          },
+          "compare:KRAS:TP53 mut:altered": {
+            "v": 7678,
+            "abs": 0
+          },
+          "compare:KRAS:TP53 mut:driver": {
+            "v": 7622,
+            "abs": 0
+          },
+          "compare:PIK3CA:TP53 mut:profiled": {
+            "v": 25555,
+            "abs": 0
+          },
+          "compare:PIK3CA:TP53 mut:altered": {
+            "v": 3347,
+            "abs": 0
+          },
+          "compare:PIK3CA:TP53 mut:driver": {
+            "v": 3009,
+            "abs": 0
+          },
+          "compare:APC:TP53 mut:profiled": {
+            "v": 25555,
+            "abs": 0
+          },
+          "compare:APC:TP53 mut:altered": {
+            "v": 6084,
+            "abs": 0
+          },
+          "compare:APC:TP53 mut:driver": {
+            "v": 5524,
+            "abs": 0
+          },
+          "compare:EGFR:TP53 mut:profiled": {
+            "v": 25555,
+            "abs": 0
+          },
+          "compare:EGFR:TP53 mut:altered": {
+            "v": 2595,
+            "abs": 0
+          },
+          "compare:EGFR:TP53 mut:driver": {
+            "v": 2151,
+            "abs": 0
+          },
+          "compare:CDKN2A:TP53 mut:profiled": {
+            "v": 25555,
+            "abs": 0
+          },
+          "compare:CDKN2A:TP53 mut:altered": {
+            "v": 3978,
+            "abs": 0
+          },
+          "compare:CDKN2A:TP53 mut:driver": {
+            "v": 3590,
+            "abs": 0
+          },
+          "compare:KRAS:TP53 wt:profiled": {
+            "v": 24627,
+            "abs": 0
+          },
+          "compare:KRAS:TP53 wt:altered": {
+            "v": 5345,
+            "abs": 0
+          },
+          "compare:KRAS:TP53 wt:driver": {
+            "v": 5299,
+            "abs": 0
+          },
+          "compare:PIK3CA:TP53 wt:profiled": {
+            "v": 24627,
+            "abs": 0
+          },
+          "compare:PIK3CA:TP53 wt:altered": {
+            "v": 4218,
+            "abs": 0
+          },
+          "compare:PIK3CA:TP53 wt:driver": {
+            "v": 3951,
+            "abs": 0
+          },
+          "compare:APC:TP53 wt:profiled": {
+            "v": 24627,
+            "abs": 0
+          },
+          "compare:APC:TP53 wt:altered": {
+            "v": 2478,
+            "abs": 0
+          },
+          "compare:APC:TP53 wt:driver": {
+            "v": 2040,
+            "abs": 0
+          },
+          "compare:EGFR:TP53 wt:profiled": {
+            "v": 24627,
+            "abs": 0
+          },
+          "compare:EGFR:TP53 wt:altered": {
+            "v": 1805,
+            "abs": 0
+          },
+          "compare:EGFR:TP53 wt:driver": {
+            "v": 1442,
+            "abs": 0
+          },
+          "compare:CDKN2A:TP53 wt:profiled": {
+            "v": 24627,
+            "abs": 0
+          },
+          "compare:CDKN2A:TP53 wt:altered": {
+            "v": 2401,
+            "abs": 0
+          },
+          "compare:CDKN2A:TP53 wt:driver": {
+            "v": 2207,
+            "abs": 0
+          }
+        }
+      }
+    },
+    {
+      "kind": "survival",
+      "cohorts": [
+        "TP53 mut",
+        "TP53 wt"
+      ],
+      "curves": [
+        {
+          "cohort": "TP53 mut",
+          "points": [
+            {
+              "t": 0,
+              "s": 1
+            },
+            {
+              "t": 0,
+              "s": 0.9982
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+            {
+              "t": 0.032876676,
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+            {
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+            {
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+            {
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+              "s": 0.996
+            },
+            {
+              "t": 0.164383381,
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+            },
+            {
+              "t": 0.197260058,
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+            {
+              "t": 0.2,
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+            {
+              "t": 0.23,
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+            },
+            {
+              "t": 0.230136734,
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+            {
+              "t": 0.26,
+              "s": 0.9944
+            },
+            {
+              "t": 0.26301341,
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+            },
+            {
+              "t": 0.295890087,
+              "s": 0.9939
+            },
+            {
+              "t": 0.3,
+              "s": 0.9935
+            },
+            {
+              "t": 0.328766763,
+              "s": 0.993
+            },
+            {
+              "…": "2979 more",
+              "sha256": "ffc2d62afcd76b56"
+            }
+          ],
+          "median": 30.64106231,
+          "n": 25498,
+          "events": 12607,
+          "atRisk": [
+            25498,
+            24109,
+            23357,
+            22638,
+            21884,
+            21187,
+            20497,
+            19853,
+            19250,
+            18633,
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+            16492,
+            15797,
+            15094,
+            14401,
+            13713,
+            13063,
+            12486,
+            11916,
+            {
+              "…": "99 more",
+              "sha256": "65e42ce7c5247805"
+            }
+          ],
+          "missing": 57
+        },
+        {
+          "cohort": "TP53 wt",
+          "points": [
+            {
+              "t": 0,
+              "s": 1
+            },
+            {
+              "t": 0,
+              "s": 0.9989
+            },
+            {
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+            {
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+            {
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+            {
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+              "s": 0.9973
+            },
+            {
+              "t": 0.164383381,
+              "s": 0.9973
+            },
+            {
+              "t": 0.197260058,
+              "s": 0.9972
+            },
+            {
+              "t": 0.2,
+              "s": 0.9971
+            },
+            {
+              "t": 0.23,
+              "s": 0.9969
+            },
+            {
+              "t": 0.230136734,
+              "s": 0.9968
+            },
+            {
+              "t": 0.26,
+              "s": 0.9967
+            },
+            {
+              "t": 0.26301341,
+              "s": 0.9966
+            },
+            {
+              "t": 0.295890087,
+              "s": 0.9964
+            },
+            {
+              "t": 0.3,
+              "s": 0.9961
+            },
+            {
+              "…": "3051 more",
+              "sha256": "28815bef7a1dd109"
+            }
+          ],
+          "median": 68.44924006,
+          "n": 24570,
+          "events": 8585,
+          "atRisk": [
+            24570,
+            23454,
+            22965,
+            22473,
+            22046,
+            21636,
+            21221,
+            20770,
+            20340,
+            19860,
+            19420,
+            18945,
+            18285,
+            17677,
+            17144,
+            16580,
+            16020,
+            15453,
+            14997,
+            14522,
+            {
+              "…": "96 more",
+              "sha256": "c627134ccc1ae86f"
+            }
+          ],
+          "missing": 57
+        }
+      ],
+      "endpoint": "OS",
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "msk_met_2021",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "logrank": {
+        "chi2": 1594.0863,
+        "p": 0,
+        "df": 1,
+        "observed": [
+          12607,
+          8585
+        ],
+        "expected": [
+          9723.3,
+          11468.7
+        ],
+        "variance": 5216.6577,
+        "strata": 2
+      },
+      "logrankPooled": {
+        "chi2": 1554.7258,
+        "p": 0,
+        "df": 1,
+        "observed": [
+          12607,
+          8585
+        ],
+        "expected": [
+          9754.9,
+          11437.1
+        ],
+        "variance": 5232.0787,
+        "strata": 1
+      },
+      "caveat": "OS from each study's own baseline (left truncation applies; baselines are NOT harmonized across studies) Status '0:...' treated as censored. The log-rank test is STRATIFIED by study (2 strata contributed events): each study is compared only against itself, so a difference in case mix between studies cannot masquerade as a difference between cohorts. The pooled p is shown alongside — where the two disagree, the stratified one is the honest answer. Every site contributes only (months, event, n) counts; no patient-level survival time crosses an instance boundary.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.",
+            "scope": "TP53 mut · msk_chord_2024",
+            "rowsIn": 13105,
+            "rowsOut": 13105,
+            "params": [
+              [
+                "count rows",
+                "4,156"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.",
+            "scope": "TP53 mut · msk_met_2021",
+            "rowsIn": 12450,
+            "rowsOut": 12393,
+            "params": [
+              [
+                "count rows",
+                "3,164"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.",
+            "scope": "TP53 wt · msk_chord_2024",
+            "rowsIn": 11594,
+            "rowsOut": 11594,
+            "params": [
+              [
+                "count rows",
+                "4,433"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.",
+            "scope": "TP53 wt · msk_met_2021",
+            "rowsIn": 13033,
+            "rowsOut": 12976,
+            "params": [
+              [
+                "count rows",
+                "3,444"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Estimate survival by Kaplan–Meier",
+            "detail": "The product-limit estimator, one curve per cohort, pooled over its studies. The median is the first time the curve is at or below 0.5.",
+            "params": [
+              [
+                "TP53 mut",
+                "25,498 patients, 12,607 events, median 30.6 months"
+              ],
+              [
+                "TP53 wt",
+                "24,570 patients, 8,585 events, median 68.4 months"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Two-group log-rank test, stratified by study",
+            "detail": "At each event time, every cohort's observed events against its share of the risk set, with the hypergeometric variance; χ² on 1 df. Each study is its own stratum — compared only against itself — and the strata are summed, so a difference in case mix between studies cannot pass for a difference between cohorts.",
+            "params": [
+              [
+                "χ² / df / p",
+                "1594.0863 / 1 / <1e-300"
+              ],
+              [
+                "strata with events",
+                "2"
+              ],
+              [
+                "pooled, for contrast",
+                "χ² 1554.7258, p <1e-300"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "kaplanMeier",
+            "logRank"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Kaplan–Meier matches lifelines 0.30.3 KaplanMeierFitter (curves are reported to 4 decimals); the log-rank matches statsmodels 0.15.0 survdiff to a relative 1e-6, pooled, stratified, with delayed entry and for k groups (test/survival-golden.test.ts).",
+        "check": {
+          "km:TP53 mut:n": {
+            "v": 25498,
+            "abs": 0
+          },
+          "km:TP53 mut:events": {
+            "v": 12607,
+            "abs": 0
+          },
+          "km:TP53 mut:median": {
+            "v": 30.64106231,
+            "abs": 1e-9
+          },
+          "km:TP53 wt:n": {
+            "v": 24570,
+            "abs": 0
+          },
+          "km:TP53 wt:events": {
+            "v": 8585,
+            "abs": 0
+          },
+          "km:TP53 wt:median": {
+            "v": 68.44924006,
+            "abs": 1e-9
+          },
+          "logrank:chi2": {
+            "v": 1594.0863,
+            "abs": 0.00005
+          },
+          "logrank:p": {
+            "v": 0,
+            "rel": 0.00001,
+            "abs": 1e-300
+          },
+          "logrank:pooled:chi2": {
+            "v": 1554.7258,
+            "abs": 0.00005
+          },
+          "logrank:pooled:p": {
+            "v": 0,
+            "rel": 0.00001,
+            "abs": 1e-300
+          }
+        }
+      }
+    },
+    {
+      "kind": "clinical",
+      "cohorts": [
+        "TP53 mut",
+        "TP53 wt"
+      ],
+      "clinical": [
+        {
+          "attribute": "FRACTION_GENOME_ALTERED",
+          "per": "sample",
+          "kind": "num",
+          "q": 0,
+          "cells": [
+            {
+              "cohort": "TP53 mut",
+              "n": 25537,
+              "mean": 0.224,
+              "sd": 0.198,
+              "min": 0,
+              "max": 1,
+              "median": null,
+              "q1": null,
+              "q3": null,
+              "histogramTruncated": false
+            },
+            {
+              "cohort": "TP53 wt",
+              "n": 24565,
+              "mean": 0.147,
+              "sd": 0.168,
+              "min": 0,
+              "max": 1,
+              "median": null,
+              "q1": null,
+              "q3": null,
+              "histogramTruncated": false
+            }
+          ],
+          "test": "Welch t",
+          "statistic": 47.039,
+          "df": 49327.99,
+          "p": 0
+        },
+        {
+          "attribute": "TMB_NONSYNONYMOUS",
+          "per": "sample",
+          "kind": "num",
+          "q": 1.5375667797084092e-40,
+          "cells": [
+            {
+              "cohort": "TP53 mut",
+              "n": 25622,
+              "mean": 8.693,
+              "sd": 20.068,
+              "min": 0,
+              "max": 654.576457903,
+              "median": null,
+              "q1": null,
+              "q3": null,
+              "histogramTruncated": false
+            },
+            {
+              "cohort": "TP53 wt",
+              "n": 24649,
+              "mean": 6.555,
+              "sd": 15.678,
+              "min": 0,
+              "max": 549.94798841,
+              "median": null,
+              "q1": null,
+              "q3": null,
+              "histogramTruncated": false
+            }
+          ],
+          "test": "Welch t",
+          "statistic": 13.3429,
+          "df": 48254.11,
+          "p": 1.5375667797084092e-40
+        },
+        {
+          "attribute": "CURRENT_AGE_DEID",
+          "per": "patient",
+          "kind": "cat",
+          "cells": [],
+          "skipped": "not declared by msk_met_2021"
+        }
+      ],
+      "studies": [
+        "msk_chord_2024",
+        "msk_met_2021"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "msk_met_2021",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "Categorical attributes are compared with a Pearson chi-squared over the full value vocabulary; numeric ones with Welch's unequal-variance t-test (2 cohorts) or Welch's ANOVA (3+) computed from (n, Σx, Σx²) per cohort — those totals are ADDITIVE across sites, so the pooled mean and variance are exact from per-site aggregates and no individual value is ever transmitted. Counting unit follows the attribute: patients for patient-level, samples for sample-level. q-values are Benjamini–Hochberg across the 2 tested attributes. Counts are pooled across 2 studies without harmonizing vocabularies; an attribute the studies type differently is refused rather than merged.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Describe each attribute",
+            "detail": "The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.",
+            "scope": "TP53 mut · msk_chord_2024",
+            "rowsIn": 13105
+          },
+          {
+            "kind": "fetch",
+            "title": "Describe each attribute",
+            "detail": "The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.",
+            "scope": "TP53 mut · msk_met_2021",
+            "rowsIn": 12450
+          },
+          {
+            "kind": "fetch",
+            "title": "Describe each attribute",
+            "detail": "The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.",
+            "scope": "TP53 wt · msk_chord_2024",
+            "rowsIn": 11594
+          },
+          {
+            "kind": "fetch",
+            "title": "Describe each attribute",
+            "detail": "The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.",
+            "scope": "TP53 wt · msk_met_2021",
+            "rowsIn": 13033
+          },
+          {
+            "kind": "transform",
+            "title": "Add each cohort's counts and moments across its studies",
+            "detail": "Value counts, n, Σx and Σx² add exactly. Vocabularies are not harmonized: the same value spelled differently in two studies stays two values. Medians and rank tests need each study's full distribution in one place, so they are not computed for a view that spans studies."
+          },
+          {
+            "kind": "transform",
+            "title": "Set aside attributes that cannot be tested",
+            "detail": "Each is named with its reason rather than dropped.",
+            "params": [
+              [
+                "CURRENT_AGE_DEID",
+                "not declared by msk_met_2021"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Welch's t-test for each numeric attribute",
+            "detail": "Unequal-variance t from each cohort's n, mean and variance, with Welch–Satterthwaite degrees of freedom; two-sided. Mean and variance are from the summed moments: variance = (Σx² − (Σx)²/n) / (n − 1).",
+            "params": [
+              [
+                "TMB_NONSYNONYMOUS",
+                "t 13.3429 on 48254.11 df · p 1.54e-40"
+              ],
+              [
+                "FRACTION_GENOME_ALTERED",
+                "t 47.039 on 49327.99 df · p <1e-300"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across attributes",
+            "detail": "q-values over the 2 tested attributes, from the χ² and Welch p-values together."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "welchT",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "χ² matches scipy 1.18.1 chi2_contingency; Welch's t scipy ttest_ind(equal_var=False) and Welch's ANOVA statsmodels 0.15.0 anova_generic from the same moments; Mann–Whitney, Kruskal–Wallis and quantiles scipy/numpy on the expanded distribution; Benjamini–Hochberg statsmodels multipletests — all to a relative 1e-6 (test/tables-golden.test.ts, test/moments-golden.test.ts).",
+        "check": {
+          "clinical:TMB_NONSYNONYMOUS:p": {
+            "v": 1.5375667797084092e-40
+          },
+          "clinical:TMB_NONSYNONYMOUS:q": {
+            "v": 1.5375667797084092e-40
+          },
+          "clinical:FRACTION_GENOME_ALTERED:p": {
+            "v": 0
+          },
+          "clinical:FRACTION_GENOME_ALTERED:q": {
+            "v": 0
+          }
+        }
+      }
+    },
+    {
+      "kind": "cooccurrence",
+      "cohorts": [
+        "TP53 mut"
+      ],
+      "pairs": [
+        {
+          "a": "KRAS",
+          "b": "APC",
+          "both": 2253,
+          "onlyA": 5425,
+          "onlyB": 3831,
+          "neither": 14046,
+          "profiled": 25555,
+          "log2Odds": 0.607,
+          "p": 3.781531181941494e-41,
+          "q": 4.7769702136632874e-36,
+          "tendency": "co-occurrence",
+          "pStratified": 7.961617022772145e-37,
+          "oddsRatioMH": 1.4795,
+          "strata": 2
+        },
+        {
+          "a": "KRAS",
+          "b": "PIK3CA",
+          "both": 724,
+          "onlyA": 6954,
+          "onlyB": 2623,
+          "neither": 15254,
+          "profiled": 25555,
+          "log2Odds": -0.724,
+          "p": 1.5284208764989049e-31,
+          "q": 1.8986041405298755e-29,
+          "tendency": "mutual exclusivity",
+          "pStratified": 6.328680468432918e-30,
+          "oddsRatioMH": 0.6056,
+          "strata": 2
+        },
+        {
+          "a": "PIK3CA",
+          "b": "APC",
+          "both": 929,
+          "onlyA": 2418,
+          "onlyB": 5155,
+          "neither": 17053,
+          "profiled": 25555,
+          "log2Odds": 0.346,
+          "p": 1.4753311744155665e-8,
+          "q": 1.404008613205968e-8,
+          "tendency": "co-occurrence",
+          "pStratified": 7.02004306602984e-9,
+          "oddsRatioMH": 1.2751,
+          "strata": 2
+        },
+        {
+          "a": "TP53",
+          "b": "KRAS",
+          "both": 7678,
+          "onlyA": 17877,
+          "onlyB": 0,
+          "neither": 0,
+          "profiled": 25555,
+          "log2Odds": -1.219,
+          "p": 0.9999999999708962,
+          "q": 1,
+          "tendency": "mutual exclusivity",
+          "pStratified": 1,
+          "oddsRatioMH": null,
+          "strata": 0
+        },
+        {
+          "a": "TP53",
+          "b": "PIK3CA",
+          "both": 3347,
+          "onlyA": 22208,
+          "onlyB": 0,
+          "neither": 0,
+          "profiled": 25555,
+          "log2Odds": -2.73,
+          "p": 1,
+          "q": 1,
+          "tendency": "mutual exclusivity",
+          "pStratified": 1,
+          "oddsRatioMH": null,
+          "strata": 0
+        },
+        {
+          "a": "TP53",
+          "b": "APC",
+          "both": 6084,
+          "onlyA": 19471,
+          "onlyB": 0,
+          "neither": 0,
+          "profiled": 25555,
+          "log2Odds": -1.678,
+          "p": 1,
+          "q": 1,
+          "tendency": "mutual exclusivity",
+          "pStratified": 1,
+          "oddsRatioMH": null,
+          "strata": 0
+        }
+      ],
+      "studies": [
+        "msk_chord_2024",
+        "msk_met_2021"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "msk_met_2021",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "Pairwise co-occurrence within “TP53 mut” (the first cohort in this view; co-occurrence describes one cohort, not a contrast). Denominators are the patients profiled for BOTH genes, computed per study against that study's own panels — a pair is never scored on patients whose panel could not have detected it. With 2 studies the reported test is STRATIFIED by study (Cochran–Mantel–Haenszel): each study contributes a 2×2 and the coordinator combines them, so a pair cannot be manufactured by differing case mix between sites. The pooled Fisher p is shown alongside. q-values are Benjamini–Hochberg across the 6 assessable pairs. Every site returns four counts per pair and nothing else.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count each gene pair's 2×2 table",
+            "detail": "The pairCounts query above. Of the patients with a sample on a mutation panel covering both genes: how many are altered in both, only the first, only the second, or neither (altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion)). Four counts per pair, nothing else.",
+            "scope": "TP53 mut · msk_chord_2024",
+            "rowsIn": 13105,
+            "params": [
+              [
+                "pairs",
+                "6"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count each gene pair's 2×2 table",
+            "detail": "The pairCounts query above. Of the patients with a sample on a mutation panel covering both genes: how many are altered in both, only the first, only the second, or neither (altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion)). Four counts per pair, nothing else.",
+            "scope": "TP53 mut · msk_met_2021",
+            "rowsIn": 12450,
+            "params": [
+              [
+                "pairs",
+                "6"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Log₂ odds ratio per pair",
+            "detail": "log₂((both · neither) / (only first · only second)) on the counts summed across studies, with 0.5 added to every cell when any cell is 0. The direction reported (co-occurrence vs mutual exclusivity) follows the Mantel–Haenszel odds ratio below."
+          },
+          {
+            "kind": "statistic",
+            "title": "Cochran–Mantel–Haenszel test, stratified by study",
+            "detail": "Each study's 2×2 is a stratum (one with an empty margin says nothing and is dropped); the test of a common odds ratio of 1, with the 0.5 continuity correction applied when |Σa − ΣE[a]| ≥ 0.5 (as R's mantelhaen.test), on 1 df. This p is the one corrected and ranked; the two-sided Fisher p on the pooled counts is reported alongside."
+          },
+          {
+            "kind": "statistic",
+            "title": "Benjamini–Hochberg across pairs",
+            "detail": "q-values over the 6 assessable pairs, from the stratified p-values."
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "fisherExact",
+            "cochranMantelHaenszel",
+            "log2OddsRatio",
+            "benjaminiHochberg"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Fisher's exact test matches scipy 1.18.1 fisher_exact, the CMH test and Mantel–Haenszel odds ratio statsmodels 0.15.0 StratifiedTable and Benjamini–Hochberg statsmodels 0.15.0 multipletests(method=\"fdr_bh\"), to a relative 1e-6 (test/tables-golden.test.ts).",
+        "check": {
+          "cooccurrence:KRAS/APC:p": {
+            "v": 3.781531181941494e-41
+          },
+          "cooccurrence:KRAS/APC:q": {
+            "v": 4.7769702136632874e-36
+          },
+          "cooccurrence:KRAS/APC:p_cmh": {
+            "v": 7.961617022772145e-37
+          },
+          "cooccurrence:KRAS/PIK3CA:p": {
+            "v": 1.5284208764989049e-31
+          },
+          "cooccurrence:KRAS/PIK3CA:q": {
+            "v": 1.8986041405298755e-29
+          },
+          "cooccurrence:KRAS/PIK3CA:p_cmh": {
+            "v": 6.328680468432918e-30
+          },
+          "cooccurrence:PIK3CA/APC:p": {
+            "v": 1.4753311744155665e-8
+          },
+          "cooccurrence:PIK3CA/APC:q": {
+            "v": 1.404008613205968e-8
+          },
+          "cooccurrence:PIK3CA/APC:p_cmh": {
+            "v": 7.02004306602984e-9
+          },
+          "cooccurrence:TP53/KRAS:p": {
+            "v": 0.9999999999708962
+          },
+          "cooccurrence:TP53/KRAS:q": {
+            "v": 1
+          },
+          "cooccurrence:TP53/KRAS:p_cmh": {
+            "v": 1
+          },
+          "cooccurrence:TP53/PIK3CA:p": {
+            "v": 1
+          },
+          "cooccurrence:TP53/PIK3CA:q": {
+            "v": 1
+          },
+          "cooccurrence:TP53/PIK3CA:p_cmh": {
+            "v": 1
+          },
+          "cooccurrence:TP53/APC:p": {
+            "v": 1
+          },
+          "cooccurrence:TP53/APC:q": {
+            "v": 1
+          },
+          "cooccurrence:TP53/APC:p_cmh": {
+            "v": 1
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/two-studies-four-views.sql b/test/snapshots/two-studies-four-views.sql
new file mode 100644
index 00000000..739ceb46
--- /dev/null
+++ b/test/snapshots/two-studies-four-views.sql
@@ -0,0 +1,553 @@
+-- cohort TP53 mut
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- msk_met_2021
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort TP53 wt
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- msk_met_2021
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_met_2021')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))
+
+-- view 0 compare: geneCounts · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid, g.variant_type AS vtype,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid, vtype
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         multiIf(sp.alteration_type = 'MUTATION_EXTENDED', 'mutation',
+                 sp.alteration_type = 'COPY_NUMBER_ALTERATION', 'cna', 'structural_variant') AS vtype
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+raw AS (
+  SELECT gene, pid, vtype, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, vtype, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, vtype, 0 AS is_alt, 0 AS is_drv FROM covered
+  )
+  GROUP BY gene, pid, vtype
+),
+perPatient AS (
+  SELECT gene, pid, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM raw GROUP BY gene, pid
+)
+SELECT gene, vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM raw GROUP BY gene, vtype
+UNION ALL
+SELECT gene, 'any' AS vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM perPatient GROUP BY gene
+
+-- view 0 compare: geneCounts · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid, g.variant_type AS vtype,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid, vtype
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         multiIf(sp.alteration_type = 'MUTATION_EXTENDED', 'mutation',
+                 sp.alteration_type = 'COPY_NUMBER_ALTERATION', 'cna', 'structural_variant') AS vtype
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+raw AS (
+  SELECT gene, pid, vtype, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, vtype, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, vtype, 0 AS is_alt, 0 AS is_drv FROM covered
+  )
+  GROUP BY gene, pid, vtype
+),
+perPatient AS (
+  SELECT gene, pid, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM raw GROUP BY gene, pid
+)
+SELECT gene, vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM raw GROUP BY gene, vtype
+UNION ALL
+SELECT gene, 'any' AS vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM perPatient GROUP BY gene
+
+-- view 0 compare: geneCounts · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid, g.variant_type AS vtype,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid, vtype
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid,
+         multiIf(sp.alteration_type = 'MUTATION_EXTENDED', 'mutation',
+                 sp.alteration_type = 'COPY_NUMBER_ALTERATION', 'cna', 'structural_variant') AS vtype
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+),
+raw AS (
+  SELECT gene, pid, vtype, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, vtype, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, vtype, 0 AS is_alt, 0 AS is_drv FROM covered
+  )
+  GROUP BY gene, pid, vtype
+),
+perPatient AS (
+  SELECT gene, pid, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM raw GROUP BY gene, pid
+)
+SELECT gene, vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM raw GROUP BY gene, vtype
+UNION ALL
+SELECT gene, 'any' AS vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM perPatient GROUP BY gene
+
+-- view 0 compare: geneCounts · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_met_2021')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+altered AS (
+  SELECT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid, g.variant_type AS vtype,
+         max(ann.is_driver) AS is_driver
+  FROM snapshot_db.genomic_event_derived g
+  LEFT JOIN oncokb_annotation.variant_annotation ann
+    ON ann.oncokb_version = 'v7.5' AND ann.is_driver
+   AND ann.hugo_gene_symbol = g.hugo_gene_symbol
+   AND ((g.variant_type = 'mutation' AND ann.alteration_type = 'MUTATION' AND ann.alteration = g.mutation_variant)
+     OR (g.variant_type = 'cna' AND ann.alteration_type = 'CNA' AND ann.alteration = if(g.cna_alteration >= 2, 'AMPLIFICATION', 'DELETION'))
+     OR (g.variant_type = 'structural_variant' AND ann.alteration_type = 'SV' AND ann.alteration = 'Fusion'))
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+  GROUP BY gene, pid, vtype
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid,
+         multiIf(sp.alteration_type = 'MUTATION_EXTENDED', 'mutation',
+                 sp.alteration_type = 'COPY_NUMBER_ALTERATION', 'cna', 'structural_variant') AS vtype
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('KRAS','PIK3CA','APC','EGFR','CDKN2A')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021'
+    AND sp.alteration_type IN ('MUTATION_EXTENDED','COPY_NUMBER_ALTERATION','STRUCTURAL_VARIANT')
+    AND replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+),
+raw AS (
+  SELECT gene, pid, vtype, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM (
+    SELECT gene, pid, vtype, 1 AS is_alt, is_driver > 0 AS is_drv FROM altered
+    UNION ALL
+    SELECT gene, pid, vtype, 0 AS is_alt, 0 AS is_drv FROM covered
+  )
+  GROUP BY gene, pid, vtype
+),
+perPatient AS (
+  SELECT gene, pid, max(is_alt) AS is_alt, max(is_drv) AS is_drv
+  FROM raw GROUP BY gene, pid
+)
+SELECT gene, vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM raw GROUP BY gene, vtype
+UNION ALL
+SELECT gene, 'any' AS vtype,
+       uniqExact(pid) AS profiled, uniqExactIf(pid, is_alt = 1) AS altered, uniqExactIf(pid, is_drv = 1) AS driver
+FROM perPatient GROUP BY gene
+
+-- view 1 survival: survivalCounts:OS · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 1 survival: survivalCounts:OS · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 1 survival: survivalCounts:OS · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_met_2021' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 1 survival: survivalCounts:OS · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_met_2021')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_met_2021' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 2 clinical: clinicalMoments · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))
+SELECT a, per, count() AS n, sum(x) AS sx, sum(x * x) AS sxx, min(x) AS mn, max(x) AS mx
+FROM (
+  SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024'
+    AND attribute_name IN ('CURRENT_AGE_DEID','TMB_NONSYNONYMOUS','FRACTION_GENOME_ALTERED')
+    AND attribute_value != ''
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+)
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per
+
+-- view 2 clinical: clinicalMoments · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53'))))
+SELECT a, per, count() AS n, sum(x) AS sx, sum(x * x) AS sxx, min(x) AS mn, max(x) AS mx
+FROM (
+  SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024'
+    AND attribute_name IN ('CURRENT_AGE_DEID','TMB_NONSYNONYMOUS','FRACTION_GENOME_ALTERED')
+    AND attribute_value != ''
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+)
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per
+
+-- view 2 clinical: clinicalMoments · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))
+SELECT a, per, count() AS n, sum(x) AS sx, sum(x * x) AS sxx, min(x) AS mn, max(x) AS mx
+FROM (
+  SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_met_2021'
+    AND attribute_name IN ('TMB_NONSYNONYMOUS','FRACTION_GENOME_ALTERED')
+    AND attribute_value != ''
+    AND replaceOne(patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+)
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per
+
+-- view 2 clinical: clinicalMoments · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_met_2021')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53'))))
+SELECT a, per, count() AS n, sum(x) AS sx, sum(x * x) AS sxx, min(x) AS mn, max(x) AS mx
+FROM (
+  SELECT attribute_name AS a, type AS per, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS x
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_met_2021'
+    AND attribute_name IN ('TMB_NONSYNONYMOUS','FRACTION_GENOME_ALTERED')
+    AND attribute_value != ''
+    AND replaceOne(patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+)
+WHERE x IS NOT NULL AND isFinite(x)
+GROUP BY a, per
+
+-- view 3 cooccurrence: pairCounts · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+altered AS (
+  SELECT DISTINCT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol IN ('TP53','KRAS','PIK3CA','APC') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('TP53','KRAS','PIK3CA','APC')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+),
+pp AS (
+  SELECT pid,
+         groupUniqArrayIf(gene, k = 1) AS alt,
+         groupUniqArrayIf(gene, k = 0) AS cov
+  FROM (SELECT gene, pid, 1 AS k FROM altered UNION ALL SELECT gene, pid, 0 AS k FROM covered)
+  GROUP BY pid
+)
+SELECT ga, gb,
+       countIf(has(cov, ga) AND has(cov, gb)) AS profiled,
+       countIf(has(cov, ga) AND has(cov, gb) AND has(alt, ga) AND has(alt, gb)) AS both,
+       countIf(has(cov, ga) AND has(cov, gb) AND has(alt, ga) AND NOT has(alt, gb)) AS only_a,
+       countIf(has(cov, ga) AND has(cov, gb) AND NOT has(alt, ga) AND has(alt, gb)) AS only_b
+FROM pp
+ARRAY JOIN ['TP53','TP53','TP53','KRAS','KRAS','PIK3CA'] AS ga, ['KRAS','PIK3CA','APC','PIK3CA','APC','APC'] AS gb
+GROUP BY ga, gb
+
+-- view 3 cooccurrence: pairCounts · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+altered AS (
+  SELECT DISTINCT g.hugo_gene_symbol AS gene, replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol IN ('TP53','KRAS','PIK3CA','APC') AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2)
+    AND replaceOne(g.patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+),
+covered AS (
+  SELECT DISTINCT pg.gene AS gene, replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.gene_panel_to_gene_derived pg ON pg.gene_panel_id = sp.gene_panel_id AND pg.gene IN ('TP53','KRAS','PIK3CA','APC')
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+),
+pp AS (
+  SELECT pid,
+         groupUniqArrayIf(gene, k = 1) AS alt,
+         groupUniqArrayIf(gene, k = 0) AS cov
+  FROM (SELECT gene, pid, 1 AS k FROM altered UNION ALL SELECT gene, pid, 0 AS k FROM covered)
+  GROUP BY pid
+)
+SELECT ga, gb,
+       countIf(has(cov, ga) AND has(cov, gb)) AS profiled,
+       countIf(has(cov, ga) AND has(cov, gb) AND has(alt, ga) AND has(alt, gb)) AS both,
+       countIf(has(cov, ga) AND has(cov, gb) AND has(alt, ga) AND NOT has(alt, gb)) AS only_a,
+       countIf(has(cov, ga) AND has(cov, gb) AND NOT has(alt, ga) AND has(alt, gb)) AS only_b
+FROM pp
+ARRAY JOIN ['TP53','TP53','TP53','KRAS','KRAS','PIK3CA'] AS ga, ['KRAS','PIK3CA','APC','PIK3CA','APC','APC'] AS gb
+GROUP BY ga, gb
diff --git a/test/snapshots/two-studies-four-views.svg b/test/snapshots/two-studies-four-views.svg
new file mode 100644
index 00000000..f99beb25
--- /dev/null
+++ b/test/snapshots/two-studies-four-views.svg
@@ -0,0 +1,8 @@
+<!-- view 0 compare -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 266" width="100%" role="img" aria-label="Altered %, by cohort — TP53 mut, TP53 wt" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Altered %, by cohort — TP53 mut, TP53 wt</title><rect width="720" height="266" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Altered %, by cohort</text><rect x="8" y="29" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="37.9" font-size="11" fill="#16232b">TP53 mut</text><rect x="87.46600000000001" y="29" width="10" height="10" rx="2" fill="#E69F00"/><text x="102.5" y="37.9" font-size="11" fill="#16232b">TP53 wt</text><line x1="78.7" y1="52" x2="78.7" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="116.9" y1="52" x2="116.9" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="155.1" y1="52" x2="155.1" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="193.4" y1="52" x2="193.4" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="231.6" y1="52" x2="231.6" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="269.8" y1="52" x2="269.8" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="308" y1="52" x2="308" y2="163" stroke="#dde4e8" stroke-width="0.75"/><text x="8" y="63" font-size="11" fill="#16232b" font-weight="600">APC</text><text x="8" y="76" font-size="10" fill="#16232b">q &lt; 1e-300</text><rect x="78.7" y="53" width="181.9" height="10" fill="#0072B2"><title>TP53 mut — APC: 23.8% (6,084 of 25,555 profiled)</title></rect><text x="263.6" y="61.5" font-size="10" fill="#5b6b76">23.8%</text><rect x="78.7" y="65" width="77.2" height="10" fill="#E69F00"><title>TP53 wt — APC: 10.1% (2,478 of 24,627 profiled)</title></rect><text x="158.9" y="73.5" font-size="10" fill="#5b6b76">10.1%</text><text x="8" y="100" font-size="11" fill="#16232b" font-weight="600">KRAS</text><text x="8" y="113" font-size="10" fill="#16232b">q = 9.2e-101</text><rect x="78.7" y="90" width="229.3" height="10" fill="#0072B2"><title>TP53 mut — KRAS: 30.0% (7,678 of 25,555 profiled)</title></rect><text x="311" y="98.5" font-size="10" fill="#5b6b76">30.0%</text><rect x="78.7" y="102" width="165.9" height="10" fill="#E69F00"><title>TP53 wt — KRAS: 21.7% (5,345 of 24,627 profiled)</title></rect><text x="247.6" y="110.5" font-size="10" fill="#5b6b76">21.7%</text><text x="8" y="137" font-size="11" fill="#16232b" font-weight="600">CDKN2A</text><text x="8" y="150" font-size="10" fill="#16232b">q = 1.1e-85</text><rect x="78.7" y="127" width="119.2" height="10" fill="#0072B2"><title>TP53 mut — CDKN2A: 15.6% (3,978 of 25,555 profiled)</title></rect><text x="201" y="135.5" font-size="10" fill="#5b6b76">15.6%</text><rect x="78.7" y="139" width="74.1" height="10" fill="#E69F00"><title>TP53 wt — CDKN2A: 9.7% (2,401 of 24,627 profiled)</title></rect><text x="155.9" y="147.5" font-size="10" fill="#5b6b76">9.7%</text><line x1="78.7" y1="52" x2="78.7" y2="163" stroke="#5b6b76" stroke-width="1"/><line x1="78.7" y1="163" x2="78.7" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="71.1" y="179" font-size="10" fill="#5b6b76">0%</text><line x1="116.9" y1="163" x2="116.9" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="109.3" y="179" font-size="10" fill="#5b6b76">5%</text><line x1="155.1" y1="163" x2="155.1" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="144.5" y="179" font-size="10" fill="#5b6b76">10%</text><line x1="193.4" y1="163" x2="193.4" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="182.7" y="179" font-size="10" fill="#5b6b76">15%</text><line x1="231.6" y1="163" x2="231.6" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="220.9" y="179" font-size="10" fill="#5b6b76">20%</text><line x1="269.8" y1="163" x2="269.8" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="259.2" y="179" font-size="10" fill="#5b6b76">25%</text><line x1="308" y1="163" x2="308" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="297.4" y="179" font-size="10" fill="#5b6b76">30%</text><line x1="78.72" y1="163" x2="308.01" y2="163" stroke="#5b6b76" stroke-width="1"/><text x="193.4" y="198" font-size="11" fill="#16232b" text-anchor="middle">altered, % of profiled</text><line x1="442.7" y1="52" x2="442.7" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="480.9" y1="52" x2="480.9" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="519.2" y1="52" x2="519.2" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="557.4" y1="52" x2="557.4" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="595.6" y1="52" x2="595.6" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="633.8" y1="52" x2="633.8" y2="163" stroke="#dde4e8" stroke-width="0.75"/><line x1="672" y1="52" x2="672" y2="163" stroke="#dde4e8" stroke-width="0.75"/><text x="372" y="63" font-size="11" fill="#16232b" font-weight="600">PIK3CA</text><text x="372" y="76" font-size="10" fill="#16232b">q = 2.1e-36</text><rect x="442.7" y="53" width="100.1" height="10" fill="#0072B2"><title>TP53 mut — PIK3CA: 13.1% (3,347 of 25,555 profiled)</title></rect><text x="545.8" y="61.5" font-size="10" fill="#5b6b76">13.1%</text><rect x="442.7" y="65" width="130.7" height="10" fill="#E69F00"><title>TP53 wt — PIK3CA: 17.1% (4,218 of 24,627 profiled)</title></rect><text x="576.4" y="73.5" font-size="10" fill="#5b6b76">17.1%</text><text x="372" y="100" font-size="11" fill="#16232b" font-weight="600">EGFR</text><text x="372" y="113" font-size="10" fill="#16232b">q = 3.5e-29</text><rect x="442.7" y="90" width="78" height="10" fill="#0072B2"><title>TP53 mut — EGFR: 10.2% (2,595 of 25,555 profiled)</title></rect><text x="523.7" y="98.5" font-size="10" fill="#5b6b76">10.2%</text><rect x="442.7" y="102" width="55.8" height="10" fill="#E69F00"><title>TP53 wt — EGFR: 7.3% (1,805 of 24,627 profiled)</title></rect><text x="501.5" y="110.5" font-size="10" fill="#5b6b76">7.3%</text><line x1="442.7" y1="52" x2="442.7" y2="163" stroke="#5b6b76" stroke-width="1"/><line x1="442.7" y1="163" x2="442.7" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="435.1" y="179" font-size="10" fill="#5b6b76">0%</text><line x1="480.9" y1="163" x2="480.9" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="473.3" y="179" font-size="10" fill="#5b6b76">5%</text><line x1="519.2" y1="163" x2="519.2" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="508.5" y="179" font-size="10" fill="#5b6b76">10%</text><line x1="557.4" y1="163" x2="557.4" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="546.7" y="179" font-size="10" fill="#5b6b76">15%</text><line x1="595.6" y1="163" x2="595.6" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="584.9" y="179" font-size="10" fill="#5b6b76">20%</text><line x1="633.8" y1="163" x2="633.8" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="623.2" y="179" font-size="10" fill="#5b6b76">25%</text><line x1="672" y1="163" x2="672" y2="166" stroke="#5b6b76" stroke-width="1"/><text x="661.4" y="179" font-size="10" fill="#5b6b76">30%</text><line x1="442.72" y1="163" x2="672.01" y2="163" stroke="#5b6b76" stroke-width="1"/><text x="557.4" y="198" font-size="11" fill="#16232b" text-anchor="middle">altered, % of profiled</text><text x="8" y="220" font-size="10" fill="#5b6b76">cross-study comparison (msk_chord_2024, msk_met_2021): denominators are per-study panel-aware; patient overlap across studies is NOT deduplicated;</text><text x="8" y="234" font-size="10" fill="#5b6b76">counts are POOLED across studies, so the p-value is unstratified — site composition differences can invert a pooled association (Simpson's paradox), and a</text><text x="8" y="248" font-size="10" fill="#5b6b76">stratified test (Cochran–Mantel–Haenszel) would be the principled statistic. The q under each gene compares its altered share across the cohorts.</text></svg>
+<!-- view 1 survival -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 457" width="100%" role="img" aria-label="Kaplan–Meier — TP53 mut, TP53 wt" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Kaplan–Meier — TP53 mut, TP53 wt</title><rect width="720" height="457" fill="#ffffff"/><line x1="82.06" y1="242" x2="704" y2="242" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="245.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="82.06" y1="184.5" x2="704" y2="184.5" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="188" font-size="10" fill="#5b6b76" text-anchor="end">25%</text><line x1="82.06" y1="127" x2="704" y2="127" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="130.5" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><line x1="82.06" y1="69.5" x2="704" y2="69.5" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="73" font-size="10" fill="#5b6b76" text-anchor="end">75%</text><line x1="82.06" y1="12" x2="704" y2="12" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="15.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><text x="35.9" y="127" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 35.9 127)">Survival probability</text><line x1="82.1" y1="242" x2="82.1" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="79.1" y="258" font-size="10" fill="#5b6b76">0</text><line x1="215.9" y1="242" x2="215.9" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="209.9" y="258" font-size="10" fill="#5b6b76">24</text><line x1="349.8" y1="242" x2="349.8" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="343.8" y="258" font-size="10" fill="#5b6b76">48</text><line x1="483.6" y1="242" x2="483.6" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="477.6" y="258" font-size="10" fill="#5b6b76">72</text><line x1="617.5" y1="242" x2="617.5" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="611.5" y="258" font-size="10" fill="#5b6b76">96</text><line x1="82.06" y1="242" x2="704" y2="242" stroke="#5b6b76" stroke-width="1"/><text x="393" y="277" font-size="11" fill="#16232b" text-anchor="middle">Months</text><path d="M82.1 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fill="none" stroke="#E69F00" stroke-width="2" stroke-linejoin="round"/><text x="8" y="299" font-size="10" fill="#5b6b76" font-weight="600">Number at risk</text><line x1="8" y1="313" x2="24" y2="313" stroke="#0072B2" stroke-width="2"/><text x="28" y="316.5" font-size="10" fill="#16232b">TP53 mut</text><text x="78.1" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 25,498 at risk at 0 months</title>25,498</text><text x="202.5" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 9,604 at risk at 24 months</title>9,604</text><text x="336.4" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 3,615 at risk at 48 months</title>3,615</text><text x="474.6" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 977 at risk at 72 months</title>977</text><text x="608.5" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 129 at risk at 96 months</title>129</text><line x1="8" y1="329" x2="24" y2="329" stroke="#E69F00" stroke-width="2"/><text x="28" y="332.5" font-size="10" fill="#16232b">TP53 wt</text><text x="78.1" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 24,570 at risk at 0 months</title>24,570</text><text x="199.5" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 12,411 at risk at 24 months</title>12,411</text><text x="336.4" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 5,548 at risk at 48 months</title>5,548</text><text x="470.2" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 1,687 at risk at 72 months</title>1,687</text><text x="608.5" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 240 at risk at 96 months</title>240</text><line x1="8" y1="353" x2="26" y2="353" stroke="#0072B2" stroke-width="2"/><text x="31" y="356.9" font-size="11" fill="#16232b">TP53 mut<tspan dx="6" fill="#5b6b76">median 30.6 mo · n = 25,498 · 12,607 events</tspan></text><line x1="368" y1="353" x2="386" y2="353" stroke="#E69F00" stroke-width="2"/><text x="391" y="356.9" font-size="11" fill="#16232b">TP53 wt<tspan dx="6" fill="#5b6b76">median 68.4 mo · n = 24,570 · 8,585 events</tspan></text><text x="8" y="378" font-size="11" fill="#16232b" class="logrank">log-rank χ² = 1,594 (1 df) · p &lt; 1e-300 · stratified by study, 2 strata · pooled p &lt; 1e-300</text><text x="8" y="397" font-size="10" fill="#5b6b76">OS from each study's own baseline (left truncation applies; baselines are NOT harmonized across studies) Status '0:...' treated as censored. The log-rank test</text><text x="8" y="411" font-size="10" fill="#5b6b76">is STRATIFIED by study (2 strata contributed events): each study is compared only against itself, so a difference in case mix between studies cannot</text><text x="8" y="425" font-size="10" fill="#5b6b76">masquerade as a difference between cohorts. The pooled p is shown alongside — where the two disagree, the stratified one is the honest answer. Every site</text><text x="8" y="439" font-size="10" fill="#5b6b76">contributes only (months, event, n) counts; no patient-level survival time crosses an instance boundary.</text></svg>
+<!-- view 2 clinical -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 258" width="100%" role="img" aria-label="Clinical comparison — −log₁₀ q" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Clinical comparison — −log₁₀ q</title><rect width="720" height="258" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Clinical comparison — −log₁₀ q</text><rect x="8" y="28.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="37" font-size="10" fill="#16232b">q &lt; 0.05</text><rect x="76.24000000000001" y="28.5" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="91.2" y="37" font-size="10" fill="#16232b">not significant</text><line x1="176.8" y1="61" x2="176.8" y2="127" stroke="#dde4e8" stroke-width="0.75"/><line x1="242.5" y1="61" x2="242.5" y2="127" stroke="#dde4e8" stroke-width="0.75"/><line x1="308.1" y1="61" x2="308.1" y2="127" stroke="#dde4e8" stroke-width="0.75"/><line x1="373.7" y1="61" x2="373.7" y2="127" stroke="#dde4e8" stroke-width="0.75"/><line x1="439.4" y1="61" x2="439.4" y2="127" stroke="#dde4e8" stroke-width="0.75"/><line x1="505" y1="61" x2="505" y2="127" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="61" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="75.9" font-size="11" fill="#16232b">FRACTION_GENOME_ALTERED</text><rect x="176.8" y="66" width="328.2" height="12" rx="1.5" fill="#0072B2"></rect><path d="M512 72 L505 65 L505 79 Z" fill="#0072B2"/><text x="712" y="75.5" font-size="10" fill="#16232b" text-anchor="end">Welch t · q &lt; 1e-300</text><text x="8" y="97.9" font-size="11" fill="#16232b">TMB_NONSYNONYMOUS</text><rect x="176.8" y="88" width="261.3" height="12" rx="1.5" fill="#0072B2"></rect><text x="712" y="97.5" font-size="10" fill="#16232b" text-anchor="end">Welch t · q = 1.5e-40</text><rect x="4" y="105" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="119.9" font-size="11" fill="#5b6b76">CURRENT_AGE_DEID</text><text x="712" y="119.5" font-size="10" fill="#5b6b76" text-anchor="end">not tested: not declared by msk_met_2021</text><line x1="185.4" y1="61" x2="185.4" y2="127" stroke="#5b6b76" stroke-width="1" stroke-dasharray="3 3"/><text x="188.4" y="58" font-size="10" fill="#5b6b76">q = 0.05</text><line x1="176.8" y1="127" x2="176.8" y2="130" stroke="#5b6b76" stroke-width="1"/><text x="173.8" y="143" font-size="10" fill="#5b6b76">0</text><line x1="242.5" y1="127" x2="242.5" y2="130" stroke="#5b6b76" stroke-width="1"/><text x="236.5" y="143" font-size="10" fill="#5b6b76">10</text><line x1="308.1" y1="127" x2="308.1" y2="130" stroke="#5b6b76" stroke-width="1"/><text x="302.1" y="143" font-size="10" fill="#5b6b76">20</text><line x1="373.7" y1="127" x2="373.7" y2="130" stroke="#5b6b76" stroke-width="1"/><text x="367.7" y="143" font-size="10" fill="#5b6b76">30</text><line x1="439.4" y1="127" x2="439.4" y2="130" stroke="#5b6b76" stroke-width="1"/><text x="433.4" y="143" font-size="10" fill="#5b6b76">40</text><line x1="505" y1="127" x2="505" y2="130" stroke="#5b6b76" stroke-width="1"/><text x="499" y="143" font-size="10" fill="#5b6b76">50</text><line x1="176.827" y1="127" x2="505" y2="127" stroke="#5b6b76" stroke-width="1"/><text x="340.9" y="162" font-size="11" fill="#16232b" text-anchor="middle"><title>−log₁₀ q (Benjamini–Hochberg) — longer is stronger evidence of a difference</title>−log₁₀ q (Benjamini–Hochberg) — longer is stronger evidence of…</text><text x="8" y="184" font-size="10" fill="#5b6b76">Categorical attributes are compared with a Pearson chi-squared over the full value vocabulary; numeric ones with Welch's unequal-variance t-test (2</text><text x="8" y="198" font-size="10" fill="#5b6b76">cohorts) or Welch's ANOVA (3+) computed from (n, Σx, Σx²) per cohort — those totals are ADDITIVE across sites, so the pooled mean and variance are exact</text><text x="8" y="212" font-size="10" fill="#5b6b76">from per-site aggregates and no individual value is ever transmitted. Counting unit follows the attribute: patients for patient-level, samples for sample-level.</text><text x="8" y="226" font-size="10" fill="#5b6b76">q-values are Benjamini–Hochberg across the 2 tested attributes. Counts are pooled across 2 studies without harmonizing vocabularies; an attribute the</text><text x="8" y="240" font-size="10" fill="#5b6b76">studies type differently is refused rather than merged. 1 bar runs past the axis and ends in an arrow; the number beside it is exact.</text></svg>
+<!-- view 3 cooccurrence -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 310" width="100%" role="img" aria-label="Co-occurrence — log₂ odds ratio" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Co-occurrence — log₂ odds ratio</title><rect width="720" height="310" fill="#ffffff"/><text x="8" y="16" font-size="12" fill="#16232b" font-weight="600">Co-occurrence — log₂ odds ratio</text><rect x="8" y="28.5" width="10" height="10" rx="2" fill="#0072B2"/><text x="23" y="37" font-size="10" fill="#16232b">co-occur, q &lt; 0.05</text><rect x="120.95" y="28.5" width="10" height="10" rx="2" fill="#E69F00"/><text x="136" y="37" font-size="10" fill="#16232b">mutually exclusive, q &lt; 0.05</text><rect x="277.77" y="28.5" width="10" height="10" rx="2" fill="#9aa5ad"/><text x="292.8" y="37" font-size="10" fill="#16232b">not significant</text><line x1="95.4" y1="47" x2="95.4" y2="179" stroke="#dde4e8" stroke-width="0.75"/><line x1="180.6" y1="47" x2="180.6" y2="179" stroke="#dde4e8" stroke-width="0.75"/><line x1="265.8" y1="47" x2="265.8" y2="179" stroke="#dde4e8" stroke-width="0.75"/><line x1="351" y1="47" x2="351" y2="179" stroke="#dde4e8" stroke-width="0.75"/><line x1="436.2" y1="47" x2="436.2" y2="179" stroke="#dde4e8" stroke-width="0.75"/><line x1="521.4" y1="47" x2="521.4" y2="179" stroke="#dde4e8" stroke-width="0.75"/><line x1="606.6" y1="47" x2="606.6" y2="179" stroke="#dde4e8" stroke-width="0.75"/><rect x="4" y="47" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="61.9" font-size="11" fill="#16232b">KRAS / APC</text><rect x="351" y="52" width="48.1" height="12" rx="1.5" fill="#0072B2"><title>KRAS / APC: both 2,253, KRAS only 5,425, APC only 3,831, neither 14,046 · stratified p = 8e-37</title></rect><text x="712" y="61.5" font-size="10" fill="#16232b" text-anchor="end">0.565 · q = 4.8e-36</text><text x="8" y="83.9" font-size="11" fill="#16232b">KRAS / PIK3CA</text><rect x="289.3" y="74" width="61.6" height="12" rx="1.5" fill="#E69F00"><title>KRAS / PIK3CA: both 724, KRAS only 6,954, PIK3CA only 2,623, neither 15,254 · stratified p = 6.3e-30</title></rect><text x="712" y="83.5" font-size="10" fill="#16232b" text-anchor="end">−0.724 · q = 1.9e-29</text><rect x="4" y="91" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="105.9" font-size="11" fill="#16232b">PIK3CA / APC</text><rect x="351" y="96" width="29.9" height="12" rx="1.5" fill="#0072B2"><title>PIK3CA / APC: both 929, PIK3CA only 2,418, APC only 5,155, neither 17,053 · stratified p = 7e-9</title></rect><text x="712" y="105.5" font-size="10" fill="#16232b" text-anchor="end">0.351 · q = 1.4e-8</text><text x="8" y="127.9" font-size="11" fill="#16232b">TP53 / KRAS</text><rect x="247.1" y="118" width="103.9" height="12" rx="1.5" fill="#9aa5ad"><title>TP53 / KRAS: both 7,678, TP53 only 17,877, KRAS only 0, neither 0 · stratified p = 1.00</title></rect><text x="712" y="127.5" font-size="10" fill="#16232b" text-anchor="end">−1.22 · q = 1.00</text><rect x="4" y="135" width="712" height="22" fill="#dde4e8" opacity="0.3"/><text x="8" y="149.9" font-size="11" fill="#16232b">TP53 / PIK3CA</text><rect x="118.4" y="140" width="232.6" height="12" rx="1.5" fill="#9aa5ad"><title>TP53 / PIK3CA: both 3,347, TP53 only 22,208, PIK3CA only 0, neither 0 · stratified p = 1.00</title></rect><text x="712" y="149.5" font-size="10" fill="#16232b" text-anchor="end">−2.73 · q = 1.00</text><text x="8" y="171.9" font-size="11" fill="#16232b">TP53 / APC</text><rect x="208" y="162" width="143" height="12" rx="1.5" fill="#9aa5ad"><title>TP53 / APC: both 6,084, TP53 only 19,471, APC only 0, neither 0 · stratified p = 1.00</title></rect><text x="712" y="171.5" font-size="10" fill="#16232b" text-anchor="end">−1.68 · q = 1.00</text><line x1="351" y1="45" x2="351" y2="181" stroke="#16232b" stroke-width="1"/><line x1="95.4" y1="179" x2="95.4" y2="182" stroke="#5b6b76" stroke-width="1"/><text x="89.4" y="195" font-size="10" fill="#5b6b76">−3</text><line x1="180.6" y1="179" x2="180.6" y2="182" stroke="#5b6b76" stroke-width="1"/><text x="174.6" y="195" font-size="10" fill="#5b6b76">−2</text><line x1="265.8" y1="179" x2="265.8" y2="182" stroke="#5b6b76" stroke-width="1"/><text x="259.8" y="195" font-size="10" fill="#5b6b76">−1</text><line x1="351" y1="179" x2="351" y2="182" stroke="#5b6b76" stroke-width="1"/><text x="348" y="195" font-size="10" fill="#5b6b76">0</text><line x1="436.2" y1="179" x2="436.2" y2="182" stroke="#5b6b76" stroke-width="1"/><text x="433.2" y="195" font-size="10" fill="#5b6b76">1</text><line x1="521.4" y1="179" x2="521.4" y2="182" stroke="#5b6b76" stroke-width="1"/><text x="518.4" y="195" font-size="10" fill="#5b6b76">2</text><line x1="606.6" y1="179" x2="606.6" y2="182" stroke="#5b6b76" stroke-width="1"/><text x="603.6" y="195" font-size="10" fill="#5b6b76">3</text><line x1="95.383" y1="179" x2="606.58" y2="179" stroke="#5b6b76" stroke-width="1"/><text x="351" y="214" font-size="11" fill="#16232b" text-anchor="middle">log₂ odds ratio — left: mutually exclusive, right: co-occurring</text><text x="8" y="236" font-size="10" fill="#5b6b76">Pairwise co-occurrence within “TP53 mut” (the first cohort in this view; co-occurrence describes one cohort, not a contrast). Denominators are the patients</text><text x="8" y="250" font-size="10" fill="#5b6b76">profiled for BOTH genes, computed per study against that study's own panels — a pair is never scored on patients whose panel could not have detected it.</text><text x="8" y="264" font-size="10" fill="#5b6b76">With 2 studies the reported test is STRATIFIED by study (Cochran–Mantel–Haenszel): each study contributes a 2×2 and the coordinator combines them, so a</text><text x="8" y="278" font-size="10" fill="#5b6b76">pair cannot be manufactured by differing case mix between sites. The pooled Fisher p is shown alongside. q-values are Benjamini–Hochberg across the 6</text><text x="8" y="292" font-size="10" fill="#5b6b76">assessable pairs. Every site returns four counts per pair and nothing else.</text></svg>
diff --git a/test/snapshots/two-studies-four-views.txt b/test/snapshots/two-studies-four-views.txt
new file mode 100644
index 00000000..3834cb9c
--- /dev/null
+++ b/test/snapshots/two-studies-four-views.txt
@@ -0,0 +1,87 @@
+Analysis over msk_chord_2024 + msk_met_2021 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- TP53 mut: 25,555 patients — OQL[TP53: MUT] — across 2 studies
+- TP53 wt: 24,627 patients — NOT OQL[TP53:] AND profiled for TP53 MUTATION — across 2 studies
+
+Compare (driver%/any%, panel-aware denominators; annotation OncoKB v7.5 (pinned)):
+  APC: TP53 mut 21.6%/23.8% | TP53 wt 8.3%/10.1%  p=0.0e+0
+  KRAS: TP53 mut 29.8%/30% | TP53 wt 21.5%/21.7%  p=3.7e-101
+  CDKN2A: TP53 mut 14%/15.6% | TP53 wt 9%/9.7%  p=6.3e-86
+  PIK3CA: TP53 mut 11.8%/13.1% | TP53 wt 16%/17.1%  p=1.7e-36
+  EGFR: TP53 mut 8.4%/10.2% | TP53 wt 5.9%/7.3%  p=3.5e-29
+  cross-study comparison (msk_chord_2024, msk_met_2021): denominators are per-study panel-aware; patient overlap across studies is NOT deduplicated; counts are POOLED across studies, so the p-value is unstratified — site composition differences can invert a pooled association (Simpson's paradox), and a stratified test (Cochran–Mantel–Haenszel) would be the principled statistic
+  METHOD:
+    - Count profiled and altered patients per gene [TP53 mut · msk_chord_2024]: The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.
+        genes: 5
+    - Count profiled and altered patients per gene [TP53 mut · msk_met_2021]: The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.
+        genes: 5
+    - Count profiled and altered patients per gene [TP53 wt · msk_chord_2024]: The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.
+        genes: 5
+    - Count profiled and altered patients per gene [TP53 wt · msk_met_2021]: The geneCounts query above. Per gene: patients with a sample on a gene panel that covers the gene (profiled — the denominator, so a gene a panel omits is not counted as wild-type), altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion), and driver: an alteration OncoKB calls oncogenic, from the OncoKB release pinned for this run (v7.5). Counts only.
+        genes: 5
+    - Sum each cohort's counts across its studies: Profiled, altered and driver counts are added study by study, so the denominator stays panel-aware. The test that follows is on the pooled counts, not stratified by study.
+    - Fisher's exact test per gene: Two-sided, on the 2×2 table of altered vs not altered (profiled − altered) in each cohort — once for any alteration and once for drivers only.
+        genes tested: 5
+    - Benjamini–Hochberg across genes: q-values over the 5 genes tested here, separately for the any-alteration and driver-only families.
+    Fisher's exact test matches scipy 1.18.1 fisher_exact and Benjamini–Hochberg matches statsmodels 0.15.0 multipletests(method="fdr_bh"), to a relative 1e-6 (test/tables-golden.test.ts).
+
+Survival (KM, OS):
+  TP53 mut: median 30.6mo (n=25498, 12607 events, 57 missing OS)
+  TP53 wt: median 68.4mo (n=24570, 8585 events, 57 missing OS)
+  log-rank p=<1e-300 (stratified by study)
+CAVEAT (tell the user): OS from each study's own baseline (left truncation applies; baselines are NOT harmonized across studies) Status '0:...' treated as censored. The log-rank test is STRATIFIED by study (2 strata contributed events): each study is compared only against itself, so a difference in case mix between studies cannot masquerade as a difference between cohorts. The pooled p is shown alongside — where the two disagree, the stratified one is the honest answer. Every site contributes only (months, event, n) counts; no patient-level survival time crosses an instance boundary.
+  METHOD:
+    - Count patients by time and status [TP53 mut · msk_chord_2024] (13,105 → 13,105 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.
+        count rows: 4,156
+    - Count patients by time and status [TP53 mut · msk_met_2021] (12,450 → 12,393 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.
+        count rows: 3,164
+    - Count patients by time and status [TP53 wt · msk_chord_2024] (11,594 → 11,594 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.
+        count rows: 4,433
+    - Count patients by time and status [TP53 wt · msk_met_2021] (13,033 → 12,976 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.
+        count rows: 3,444
+    - Estimate survival by Kaplan–Meier: The product-limit estimator, one curve per cohort, pooled over its studies. The median is the first time the curve is at or below 0.5.
+        TP53 mut: 25,498 patients, 12,607 events, median 30.6 months
+        TP53 wt: 24,570 patients, 8,585 events, median 68.4 months
+    - Two-group log-rank test, stratified by study: At each event time, every cohort's observed events against its share of the risk set, with the hypergeometric variance; χ² on 1 df. Each study is its own stratum — compared only against itself — and the strata are summed, so a difference in case mix between studies cannot pass for a difference between cohorts.
+        χ² / df / p: 1594.0863 / 1 / <1e-300
+        strata with events: 2
+        pooled, for contrast: χ² 1554.7258, p <1e-300
+    Kaplan–Meier matches lifelines 0.30.3 KaplanMeierFitter (curves are reported to 4 decimals); the log-rank matches statsmodels 0.15.0 survdiff to a relative 1e-6, pooled, stratified, with delayed entry and for k groups (test/survival-golden.test.ts).
+
+Clinical comparison across cohorts:
+  FRACTION_GENOME_ALTERED [sample, num]: TP53 mut mean 0.22 sd 0.20 (n=25,537) | TP53 wt mean 0.15 sd 0.17 (n=24,565)  Welch t p=<1e-300 q=<1e-300
+  TMB_NONSYNONYMOUS [sample, num]: TP53 mut mean 8.69 sd 20.07 (n=25,622) | TP53 wt mean 6.55 sd 15.68 (n=24,649)  Welch t p=1.5e-40 q=1.5e-40
+  CURRENT_AGE_DEID [patient, cat]:   SKIPPED: not declared by msk_met_2021
+  Categorical attributes are compared with a Pearson chi-squared over the full value vocabulary; numeric ones with Welch's unequal-variance t-test (2 cohorts) or Welch's ANOVA (3+) computed from (n, Σx, Σx²) per cohort — those totals are ADDITIVE across sites, so the pooled mean and variance are exact from per-site aggregates and no individual value is ever transmitted. Counting unit follows the attribute: patients for patient-level, samples for sample-level. q-values are Benjamini–Hochberg across the 2 tested attributes. Counts are pooled across 2 studies without harmonizing vocabularies; an attribute the studies type differently is refused rather than merged.
+  METHOD:
+    - Describe each attribute [TP53 mut · msk_chord_2024]: The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.
+    - Describe each attribute [TP53 mut · msk_met_2021]: The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.
+    - Describe each attribute [TP53 wt · msk_chord_2024]: The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.
+    - Describe each attribute [TP53 wt · msk_met_2021]: The attributeStats query above. A categorical attribute comes back as a count per value; a numeric one as n, Σx and Σx² (from which the mean and variance are exact, and which add across studies). Patients for a patient-level attribute, samples for a sample-level one. No individual value leaves the database.
+    - Add each cohort's counts and moments across its studies: Value counts, n, Σx and Σx² add exactly. Vocabularies are not harmonized: the same value spelled differently in two studies stays two values. Medians and rank tests need each study's full distribution in one place, so they are not computed for a view that spans studies.
+    - Set aside attributes that cannot be tested: Each is named with its reason rather than dropped.
+        CURRENT_AGE_DEID: not declared by msk_met_2021
+    - Welch's t-test for each numeric attribute: Unequal-variance t from each cohort's n, mean and variance, with Welch–Satterthwaite degrees of freedom; two-sided. Mean and variance are from the summed moments: variance = (Σx² − (Σx)²/n) / (n − 1).
+        TMB_NONSYNONYMOUS: t 13.3429 on 48254.11 df · p 1.54e-40
+        FRACTION_GENOME_ALTERED: t 47.039 on 49327.99 df · p <1e-300
+    - Benjamini–Hochberg across attributes: q-values over the 2 tested attributes, from the χ² and Welch p-values together.
+    χ² matches scipy 1.18.1 chi2_contingency; Welch's t scipy ttest_ind(equal_var=False) and Welch's ANOVA statsmodels 0.15.0 anova_generic from the same moments; Mann–Whitney, Kruskal–Wallis and quantiles scipy/numpy on the expanded distribution; Benjamini–Hochberg statsmodels multipletests — all to a relative 1e-6 (test/tables-golden.test.ts, test/moments-golden.test.ts).
+
+Co-occurrence (2x2 per pair, among patients profiled for BOTH genes):
+  KRAS/APC: both 2,253, KRAS only 5,425, APC only 3,831, neither 14,046 (of 25,555 profiled)  log2OR=0.61 (co-occur)  p=3.8e-41 q=4.8e-36  stratified p=8.0e-37
+  KRAS/PIK3CA: both 724, KRAS only 6,954, PIK3CA only 2,623, neither 15,254 (of 25,555 profiled)  log2OR=-0.72 (mutually exclusive)  p=1.5e-31 q=1.9e-29  stratified p=6.3e-30
+  PIK3CA/APC: both 929, PIK3CA only 2,418, APC only 5,155, neither 17,053 (of 25,555 profiled)  log2OR=0.35 (co-occur)  p=1.5e-8 q=1.4e-8  stratified p=7.0e-9
+  TP53/KRAS: both 7,678, TP53 only 17,877, KRAS only 0, neither 0 (of 25,555 profiled)  log2OR=-1.22 (mutually exclusive)  p=1.0000 q=1.0000  stratified p=1.0000
+  TP53/PIK3CA: both 3,347, TP53 only 22,208, PIK3CA only 0, neither 0 (of 25,555 profiled)  log2OR=-2.73 (mutually exclusive)  p=1.0000 q=1.0000  stratified p=1.0000
+  TP53/APC: both 6,084, TP53 only 19,471, APC only 0, neither 0 (of 25,555 profiled)  log2OR=-1.68 (mutually exclusive)  p=1.0000 q=1.0000  stratified p=1.0000
+  Pairwise co-occurrence within “TP53 mut” (the first cohort in this view; co-occurrence describes one cohort, not a contrast). Denominators are the patients profiled for BOTH genes, computed per study against that study's own panels — a pair is never scored on patients whose panel could not have detected it. With 2 studies the reported test is STRATIFIED by study (Cochran–Mantel–Haenszel): each study contributes a 2×2 and the coordinator combines them, so a pair cannot be manufactured by differing case mix between sites. The pooled Fisher p is shown alongside. q-values are Benjamini–Hochberg across the 6 assessable pairs. Every site returns four counts per pair and nothing else.
+  METHOD:
+    - Count each gene pair's 2×2 table [TP53 mut · msk_chord_2024]: The pairCounts query above. Of the patients with a sample on a mutation panel covering both genes: how many are altered in both, only the first, only the second, or neither (altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion)). Four counts per pair, nothing else.
+        pairs: 6
+    - Count each gene pair's 2×2 table [TP53 mut · msk_met_2021]: The pairCounts query above. Of the patients with a sample on a mutation panel covering both genes: how many are altered in both, only the first, only the second, or neither (altered: any mutation or structural variant, or a copy-number change of ±2 (amplification or deep deletion)). Four counts per pair, nothing else.
+        pairs: 6
+    - Log₂ odds ratio per pair: log₂((both · neither) / (only first · only second)) on the counts summed across studies, with 0.5 added to every cell when any cell is 0. The direction reported (co-occurrence vs mutual exclusivity) follows the Mantel–Haenszel odds ratio below.
+    - Cochran–Mantel–Haenszel test, stratified by study: Each study's 2×2 is a stratum (one with an empty margin says nothing and is dropped); the test of a common odds ratio of 1, with the 0.5 continuity correction applied when |Σa − ΣE[a]| ≥ 0.5 (as R's mantelhaen.test), on 1 df. This p is the one corrected and ranked; the two-sided Fisher p on the pooled counts is reported alongside.
+    - Benjamini–Hochberg across pairs: q-values over the 6 assessable pairs, from the stratified p-values.
+    Fisher's exact test matches scipy 1.18.1 fisher_exact, the CMH test and Mantel–Haenszel odds ratio statsmodels 0.15.0 StratifiedTable and Benjamini–Hochberg statsmodels 0.15.0 multipletests(method="fdr_bh"), to a relative 1e-6 (test/tables-golden.test.ts).
diff --git a/test/snapshots/two-studies-plot.json b/test/snapshots/two-studies-plot.json
new file mode 100644
index 00000000..61434bb6
--- /dev/null
+++ b/test/snapshots/two-studies-plot.json
@@ -0,0 +1,873 @@
+{
+  "study": "luad_tcga_pan_can_atlas_2018",
+  "cohorts": {
+    "Lung (TCGA)": {
+      "count": 1053,
+      "text": "all patients in the study — across 2 studies",
+      "studies": [
+        "luad_tcga_pan_can_atlas_2018",
+        "lusc_tcga_pan_can_atlas_2018"
+      ]
+    },
+    "KRAS mut": {
+      "count": 175,
+      "text": "OQL[KRAS: MUT] — across 2 studies",
+      "studies": [
+        "luad_tcga_pan_can_atlas_2018",
+        "lusc_tcga_pan_can_atlas_2018"
+      ]
+    },
+    "TP53 mut": {
+      "count": 699,
+      "text": "OQL[TP53: MUT] — across 2 studies",
+      "studies": [
+        "luad_tcga_pan_can_atlas_2018",
+        "lusc_tcga_pan_can_atlas_2018"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "plot",
+      "plot": {
+        "shape": "scatter",
+        "n": 968,
+        "x": {
+          "label": "AGE",
+          "kind": "num"
+        },
+        "y": {
+          "label": "TMB_NONSYNONYMOUS",
+          "kind": "num"
+        },
+        "extent": {
+          "x0": 33,
+          "x1": 90,
+          "y0": 0,
+          "y1": 67.2
+        },
+        "grid": [
+          240,
+          160
+        ],
+        "bins": [
+          {
+            "bx": 37,
+            "by": 11,
+            "n": 1
+          },
+          {
+            "bx": 181,
+            "by": 8,
+            "n": 1
+          },
+          {
+            "bx": 105,
+            "by": 31,
+            "n": 1
+          },
+          {
+            "bx": 134,
+            "by": 14,
+            "n": 2
+          },
+          {
+            "bx": 134,
+            "by": 29,
+            "n": 1
+          },
+          {
+            "bx": 113,
+            "by": 19,
+            "n": 3
+          },
+          {
+            "bx": 227,
+            "by": 15,
+            "n": 1
+          },
+          {
+            "bx": 202,
+            "by": 26,
+            "n": 1
+          },
+          {
+            "bx": 138,
+            "by": 8,
+            "n": 1
+          },
+          {
+            "bx": 164,
+            "by": 27,
+            "n": 2
+          },
+          {
+            "bx": 122,
+            "by": 0,
+            "n": 3
+          },
+          {
+            "bx": 164,
+            "by": 8,
+            "n": 1
+          },
+          {
+            "bx": 168,
+            "by": 14,
+            "n": 4
+          },
+          {
+            "bx": 71,
+            "by": 31,
+            "n": 1
+          },
+          {
+            "bx": 176,
+            "by": 2,
+            "n": 1
+          },
+          {
+            "bx": 155,
+            "by": 8,
+            "n": 2
+          },
+          {
+            "bx": 155,
+            "by": 14,
+            "n": 1
+          },
+          {
+            "bx": 75,
+            "by": 31,
+            "n": 1
+          },
+          {
+            "bx": 151,
+            "by": 8,
+            "n": 2
+          },
+          {
+            "bx": 143,
+            "by": 4,
+            "n": 1
+          },
+          {
+            "…": "707 more",
+            "sha256": "b764315ea2e5f343"
+          }
+        ],
+        "stats": {
+          "pearson": -0.1312,
+          "spearman": null,
+          "slope": -0.12815725764648467,
+          "intercept": 18.373049900699872,
+          "pPearson": 0.000042245416729427556,
+          "pSpearman": null
+        },
+        "note": "2 studies pooled (luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). Each site reports six sums (n, Σx, Σy, Σxy, Σx², Σy²), exact and additive, for Pearson's r and the regression line; the bin grid uses one shared scale (each site's own extent, pooled) so cells sum correctly. Spearman rank correlation is not reported for a federated plot: it needs the pooled GLOBAL ranking, which per-site aggregates cannot reconstruct exactly.",
+        "coverage": {
+          "total": 1053,
+          "nx": 971,
+          "ny": 1050,
+          "multi": 0
+        },
+        "layers": [
+          {
+            "kind": "cohort",
+            "label": "KRAS mut",
+            "color": "#D55E00",
+            "name": "KRAS mut",
+            "patients": 175,
+            "inPlot": 175,
+            "n": 158,
+            "shown": "points",
+            "points": [
+              {
+                "id": "TCGA-MP-A4T7",
+                "x": 75,
+                "y": 4.166666667
+              },
+              {
+                "id": "TCGA-91-6836",
+                "x": 52,
+                "y": 15.33333333
+              },
+              {
+                "id": "TCGA-69-8253",
+                "x": 59,
+                "y": 4.366666667
+              },
+              {
+                "id": "TCGA-64-5778",
+                "x": 60,
+                "y": 14.26666667
+              },
+              {
+                "id": "TCGA-95-A4VP",
+                "x": 66,
+                "y": 3.766666667
+              },
+              {
+                "id": "TCGA-99-8032",
+                "x": 61,
+                "y": 12.26666667
+              },
+              {
+                "id": "TCGA-78-7145",
+                "x": 52,
+                "y": 6.266666667
+              },
+              {
+                "id": "TCGA-05-4417",
+                "x": 51,
+                "y": 9.633333333
+              },
+              {
+                "id": "TCGA-97-8176",
+                "x": 63,
+                "y": 4.6
+              },
+              {
+                "id": "TCGA-78-7166",
+                "x": 84,
+                "y": 6.466666667
+              },
+              {
+                "id": "TCGA-73-4670",
+                "x": 69,
+                "y": 10.96666667
+              },
+              {
+                "id": "TCGA-44-3917",
+                "x": 33,
+                "y": 6.666666667
+              },
+              {
+                "id": "TCGA-95-7039",
+                "x": 54,
+                "y": 40.36666667
+              },
+              {
+                "id": "TCGA-62-A46R",
+                "x": 54,
+                "y": 5.666666667
+              },
+              {
+                "id": "TCGA-49-4506",
+                "x": 68,
+                "y": 3.9
+              },
+              {
+                "id": "TCGA-50-7109",
+                "x": 60,
+                "y": 8
+              },
+              {
+                "id": "TCGA-NJ-A4YI",
+                "x": 87,
+                "y": 13.16666667
+              },
+              {
+                "id": "TCGA-50-5933",
+                "x": 72,
+                "y": 16.5
+              },
+              {
+                "id": "TCGA-55-8090",
+                "x": 80,
+                "y": 2.566666667
+              },
+              {
+                "id": "TCGA-95-7562",
+                "x": 71,
+                "y": 13.1
+              },
+              {
+                "…": "138 more",
+                "sha256": "d53b61b911fb2f10"
+              }
+            ],
+            "drawn": 158
+          },
+          {
+            "kind": "cohort",
+            "label": "TP53 mut",
+            "color": "#0072B2",
+            "name": "TP53 mut",
+            "patients": 699,
+            "inPlot": 699,
+            "n": 652,
+            "shown": "points",
+            "points": [
+              {
+                "id": "TCGA-71-8520",
+                "x": 60,
+                "y": 2.666666667
+              },
+              {
+                "id": "TCGA-49-4490",
+                "x": 45,
+                "y": 1.6
+              },
+              {
+                "id": "TCGA-78-7150",
+                "x": 59,
+                "y": 12.26666667
+              },
+              {
+                "id": "TCGA-05-4402",
+                "x": 57,
+                "y": 4.033333333
+              },
+              {
+                "id": "TCGA-62-A471",
+                "x": 64,
+                "y": 3.033333333
+              },
+              {
+                "id": "TCGA-44-7662",
+                "x": 61,
+                "y": 36.23333333
+              },
+              {
+                "id": "TCGA-53-A4EZ",
+                "x": 63,
+                "y": 15.13333333
+              },
+              {
+                "id": "TCGA-55-8085",
+                "x": 64,
+                "y": 7.5
+              },
+              {
+                "id": "TCGA-55-6980",
+                "x": 56,
+                "y": 1
+              },
+              {
+                "id": "TCGA-95-7043",
+                "x": 63,
+                "y": 34.16666667
+              },
+              {
+                "id": "TCGA-55-A4DG",
+                "x": 71,
+                "y": 20.9
+              },
+              {
+                "id": "TCGA-49-AAQV",
+                "x": 63,
+                "y": 2.233333333
+              },
+              {
+                "id": "TCGA-05-4410",
+                "x": 62,
+                "y": 33.8
+              },
+              {
+                "id": "TCGA-44-7670",
+                "x": 47,
+                "y": 67.2
+              },
+              {
+                "id": "TCGA-55-1594",
+                "x": 68,
+                "y": 5.6
+              },
+              {
+                "id": "TCGA-MP-A4T9",
+                "x": 54,
+                "y": 1.033333333
+              },
+              {
+                "id": "TCGA-55-6968",
+                "x": 61,
+                "y": 15.4
+              },
+              {
+                "id": "TCGA-86-8672",
+                "x": 59,
+                "y": 8.766666666999999
+              },
+              {
+                "id": "TCGA-38-6178",
+                "x": 70,
+                "y": 2.233333333
+              },
+              {
+                "id": "TCGA-55-8511",
+                "x": 73,
+                "y": 17.16666667
+              },
+              {
+                "…": "574 more",
+                "sha256": "95e1b03d778f70ae"
+              }
+            ],
+            "drawn": 594,
+            "shared": [
+              {
+                "id": "TCGA-91-6836",
+                "x": 52,
+                "y": 15.33333333
+              },
+              {
+                "id": "TCGA-64-5778",
+                "x": 60,
+                "y": 14.26666667
+              },
+              {
+                "id": "TCGA-95-7039",
+                "x": 54,
+                "y": 40.36666667
+              },
+              {
+                "id": "TCGA-62-A46R",
+                "x": 54,
+                "y": 5.666666667
+              },
+              {
+                "id": "TCGA-05-4433",
+                "x": 82,
+                "y": 1.1
+              },
+              {
+                "id": "TCGA-83-5908",
+                "x": 59,
+                "y": 7.2
+              },
+              {
+                "id": "TCGA-55-8094",
+                "x": 51,
+                "y": 13.23333333
+              },
+              {
+                "id": "TCGA-MN-A4N5",
+                "x": 63,
+                "y": 13.5
+              },
+              {
+                "id": "TCGA-44-6144",
+                "x": 58,
+                "y": 13.1
+              },
+              {
+                "id": "TCGA-55-7576",
+                "x": 54,
+                "y": 24.26666667
+              },
+              {
+                "id": "TCGA-44-8117",
+                "x": 54,
+                "y": 22.7
+              },
+              {
+                "id": "TCGA-55-7914",
+                "x": 71,
+                "y": 5.366666667
+              },
+              {
+                "id": "TCGA-44-7661",
+                "x": 69,
+                "y": 18.16666667
+              },
+              {
+                "id": "TCGA-MP-A4TK",
+                "x": 56,
+                "y": 10.5
+              },
+              {
+                "id": "TCGA-55-7726",
+                "x": 72,
+                "y": 3.7
+              },
+              {
+                "id": "TCGA-99-8025",
+                "x": 72,
+                "y": 14.63333333
+              },
+              {
+                "id": "TCGA-69-7980",
+                "x": 70,
+                "y": 24.56666667
+              },
+              {
+                "id": "TCGA-05-4415",
+                "x": 57,
+                "y": 6.233333333
+              },
+              {
+                "id": "TCGA-55-8514",
+                "x": 70,
+                "y": 8.333333333
+              },
+              {
+                "id": "TCGA-55-7283",
+                "x": 76,
+                "y": 14.06666667
+              },
+              {
+                "…": "38 more",
+                "sha256": "a8eb0df5226d320c"
+              }
+            ],
+            "sharedOwners": [
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              0,
+              {
+                "…": "38 more",
+                "sha256": "e25d6ef7100931ea"
+              }
+            ]
+          }
+        ]
+      },
+      "cohorts": [
+        "Lung (TCGA)"
+      ],
+      "studies": [
+        "luad_tcga_pan_can_atlas_2018",
+        "lusc_tcga_pan_can_atlas_2018"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "luad_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "lusc_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "968 of 1,053 patients have a value on both axes (82 have no AGE; 3 have no TMB_NONSYNONYMOUS); only those are drawn. 2 studies pooled (luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). Each site reports six sums (n, Σx, Σy, Σxy, Σx², Σy²), exact and additive, for Pearson's r and the regression line; the bin grid uses one shared scale (each site's own extent, pooled) so cells sum correctly. Spearman rank correlation is not reported for a federated plot: it needs the pooled GLOBAL ranking, which per-site aggregates cannot reconstruct exactly. Point layers — “KRAS mut”: 175 patients, 158 patients drawn as points; “TP53 mut”: 699 patients, 652 patients drawn as points (58 drawn by an earlier layer). The statistics are over the whole plotted cohort."
+    },
+    {
+      "kind": "plot",
+      "plot": {
+        "shape": "box",
+        "n": 996,
+        "x": {
+          "label": "SEX",
+          "kind": "cat"
+        },
+        "y": {
+          "label": "TMB_NONSYNONYMOUS",
+          "kind": "num"
+        },
+        "groups": [
+          {
+            "g": "Female",
+            "n": 402,
+            "lo": 0,
+            "hi": 67.2,
+            "q1": 3.4337479268034823,
+            "median": 6.917744610159204,
+            "q3": 12.18067993243781,
+            "loWhisker": 0,
+            "hiWhisker": 25.3010779408893
+          },
+          {
+            "g": "Male",
+            "n": 594,
+            "lo": 0,
+            "hi": 53.36666667,
+            "q1": 4.387598204329966,
+            "median": 7.345566778835017,
+            "q3": 12.602132434124579,
+            "loWhisker": 0,
+            "hiWhisker": 24.923933778816497
+          }
+        ],
+        "overlay": "points",
+        "note": "2 studies pooled (luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). Counts and extremes per group sum exactly; the quartiles are a size-weighted approximation across sites (it can shift where an outlier bar falls, never a count). A federated box does not draw a density behind it — that needs the same shared-scale binning a scatter does, not yet built for this shape — so points are shown when an entitled site has few enough to draw.",
+        "points": [
+          {
+            "id": "TCGA-NJ-A7XG",
+            "g": "Male",
+            "v": 0.9
+          },
+          {
+            "id": "TCGA-91-6836",
+            "g": "Female",
+            "v": 15.33333333
+          },
+          {
+            "id": "TCGA-62-8397",
+            "g": "Female",
+            "v": 0.8
+          },
+          {
+            "id": "TCGA-64-5778",
+            "g": "Male",
+            "v": 14.26666667
+          },
+          {
+            "id": "TCGA-95-A4VP",
+            "g": "Female",
+            "v": 3.766666667
+          },
+          {
+            "id": "TCGA-49-4490",
+            "g": "Female",
+            "v": 1.6
+          },
+          {
+            "id": "TCGA-99-AA5R",
+            "g": "Female",
+            "v": 1.8
+          },
+          {
+            "id": "TCGA-05-4402",
+            "g": "Female",
+            "v": 4.033333333
+          },
+          {
+            "id": "TCGA-62-A471",
+            "g": "Male",
+            "v": 3.033333333
+          },
+          {
+            "id": "TCGA-78-7159",
+            "g": "Female",
+            "v": 10.2
+          },
+          {
+            "id": "TCGA-44-7662",
+            "g": "Male",
+            "v": 36.23333333
+          },
+          {
+            "id": "TCGA-95-7039",
+            "g": "Female",
+            "v": 40.36666667
+          },
+          {
+            "id": "TCGA-78-7537",
+            "g": "Male",
+            "v": 4.466666667
+          },
+          {
+            "id": "TCGA-55-8085",
+            "g": "Male",
+            "v": 7.5
+          },
+          {
+            "id": "TCGA-69-7761",
+            "g": "Male",
+            "v": 0.966666667
+          },
+          {
+            "id": "TCGA-95-7043",
+            "g": "Female",
+            "v": 34.16666667
+          },
+          {
+            "id": "TCGA-NJ-A55R",
+            "g": "Male",
+            "v": 7.7
+          },
+          {
+            "id": "TCGA-69-8453",
+            "g": "Male",
+            "v": 0
+          },
+          {
+            "id": "TCGA-55-A4DG",
+            "g": "Male",
+            "v": 20.9
+          },
+          {
+            "id": "TCGA-75-7025",
+            "g": "Male",
+            "v": 1.033333333
+          },
+          {
+            "…": "976 more",
+            "sha256": "178485cca72f6fe3"
+          }
+        ],
+        "pointSet": {
+          "rule": "all",
+          "drawn": 996
+        },
+        "coverage": {
+          "total": 1053,
+          "nx": 999,
+          "ny": 1050,
+          "multi": 0
+        },
+        "layers": [
+          {
+            "kind": "cohort",
+            "label": "KRAS mut",
+            "color": "#D55E00",
+            "name": "KRAS mut",
+            "patients": 175,
+            "inPlot": 175,
+            "n": 164,
+            "shown": "points",
+            "points": [
+              {
+                "id": "TCGA-MP-A4T7",
+                "g": "Female",
+                "v": 4.166666667
+              },
+              {
+                "id": "TCGA-91-6836",
+                "g": "Female",
+                "v": 15.33333333
+              },
+              {
+                "id": "TCGA-69-8253",
+                "g": "Female",
+                "v": 4.366666667
+              },
+              {
+                "id": "TCGA-64-5778",
+                "g": "Male",
+                "v": 14.26666667
+              },
+              {
+                "id": "TCGA-95-A4VP",
+                "g": "Female",
+                "v": 3.766666667
+              },
+              {
+                "id": "TCGA-99-8032",
+                "g": "Male",
+                "v": 12.26666667
+              },
+              {
+                "id": "TCGA-78-7145",
+                "g": "Female",
+                "v": 6.266666667
+              },
+              {
+                "id": "TCGA-05-4417",
+                "g": "Female",
+                "v": 9.633333333
+              },
+              {
+                "id": "TCGA-97-8176",
+                "g": "Male",
+                "v": 4.6
+              },
+              {
+                "id": "TCGA-78-7166",
+                "g": "Male",
+                "v": 6.466666667
+              },
+              {
+                "id": "TCGA-73-4670",
+                "g": "Female",
+                "v": 10.96666667
+              },
+              {
+                "id": "TCGA-44-3917",
+                "g": "Female",
+                "v": 6.666666667
+              },
+              {
+                "id": "TCGA-95-7039",
+                "g": "Female",
+                "v": 40.36666667
+              },
+              {
+                "id": "TCGA-62-A46R",
+                "g": "Female",
+                "v": 5.666666667
+              },
+              {
+                "id": "TCGA-49-4506",
+                "g": "Female",
+                "v": 3.9
+              },
+              {
+                "id": "TCGA-50-7109",
+                "g": "Male",
+                "v": 8
+              },
+              {
+                "id": "TCGA-NJ-A4YI",
+                "g": "Female",
+                "v": 13.16666667
+              },
+              {
+                "id": "TCGA-50-5933",
+                "g": "Male",
+                "v": 16.5
+              },
+              {
+                "id": "TCGA-55-8090",
+                "g": "Male",
+                "v": 2.566666667
+              },
+              {
+                "id": "TCGA-95-7562",
+                "g": "Male",
+                "v": 13.1
+              },
+              {
+                "…": "144 more",
+                "sha256": "1cf2834cffb46c2d"
+              }
+            ],
+            "drawn": 164
+          }
+        ]
+      },
+      "cohorts": [
+        "Lung (TCGA)"
+      ],
+      "studies": [
+        "luad_tcga_pan_can_atlas_2018",
+        "lusc_tcga_pan_can_atlas_2018"
+      ],
+      "participation": {
+        "studies": [
+          {
+            "study": "luad_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "lusc_tcga_pan_can_atlas_2018",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "note": "996 of 1,053 patients have a value on both axes (54 have no SEX; 3 have no TMB_NONSYNONYMOUS); only those are drawn. 2 studies pooled (luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). Counts and extremes per group sum exactly; the quartiles are a size-weighted approximation across sites (it can shift where an outlier bar falls, never a count). A federated box does not draw a density behind it — that needs the same shared-scale binning a scatter does, not yet built for this shape — so points are shown when an entitled site has few enough to draw. Point layers — “KRAS mut”: 175 patients, 164 patients drawn as points. The statistics are over the whole plotted cohort."
+    }
+  ]
+}
diff --git a/test/snapshots/two-studies-plot.sql b/test/snapshots/two-studies-plot.sql
new file mode 100644
index 00000000..71ca4cbd
--- /dev/null
+++ b/test/snapshots/two-studies-plot.sql
@@ -0,0 +1,713 @@
+-- cohort Lung (TCGA)
+-- luad_tcga_pan_can_atlas_2018
+SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- lusc_tcga_pan_can_atlas_2018
+SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+
+-- cohort KRAS mut
+-- luad_tcga_pan_can_atlas_2018
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- lusc_tcga_pan_can_atlas_2018
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort TP53 mut
+-- luad_tcga_pan_can_atlas_2018
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- lusc_tcga_pan_can_atlas_2018
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- view 0 plot: plot:bins · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     pair AS (SELECT xs.v AS x, ys.v AS y FROM xs INNER JOIN ys ON ys.id = xs.id)
+,    e AS (SELECT 33 AS x0, 90 AS x1, 0 AS y0, 67.2 AS y1)
+SELECT least(239, toUInt32(floor((x - x0) / nullIf(x1 - x0, 0) * 240))) AS bx,
+       least(159, toUInt32(floor((y - y0) / nullIf(y1 - y0, 0) * 160))) AS by,
+       count() AS n
+FROM pair, e GROUP BY bx, by
+
+-- view 0 plot: plot:bins · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     pair AS (SELECT xs.v AS x, ys.v AS y FROM xs INNER JOIN ys ON ys.id = xs.id)
+,    e AS (SELECT 33 AS x0, 90 AS x1, 0 AS y0, 67.2 AS y1)
+SELECT least(239, toUInt32(floor((x - x0) / nullIf(x1 - x0, 0) * 240))) AS bx,
+       least(159, toUInt32(floor((y - y0) / nullIf(y1 - y0, 0) * 160))) AS by,
+       count() AS n
+FROM pair, e GROUP BY bx, by
+
+-- view 0 plot: plot:coverage · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'),
+     cases AS (SELECT pid AS id FROM cohort),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT count() AS total, countIf(id IN (SELECT id FROM xs)) AS nx, countIf(id IN (SELECT id FROM ys)) AS ny,
+  (SELECT count() FROM (SELECT patient_unique_id FROM snapshot_db.sample_derived
+    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+    GROUP BY patient_unique_id HAVING count() > 1)) AS multi FROM cases
+
+-- view 0 plot: plot:coverage · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'),
+     cases AS (SELECT pid AS id FROM cohort),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT count() AS total, countIf(id IN (SELECT id FROM xs)) AS nx, countIf(id IN (SELECT id FROM ys)) AS ny,
+  (SELECT count() FROM (SELECT patient_unique_id FROM snapshot_db.sample_derived
+    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+    GROUP BY patient_unique_id HAVING count() > 1)) AS multi FROM cases
+
+-- view 0 plot: plot:extent · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     pair AS (SELECT xs.v AS x, ys.v AS y FROM xs INNER JOIN ys ON ys.id = xs.id)
+SELECT count() AS n, min(x) AS x0, max(x) AS x1, min(y) AS y0, max(y) AS y1 FROM pair
+
+-- view 0 plot: plot:extent · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     pair AS (SELECT xs.v AS x, ys.v AS y FROM xs INNER JOIN ys ON ys.id = xs.id)
+SELECT count() AS n, min(x) AS x0, max(x) AS x1, min(y) AS y0, max(y) AS y1 FROM pair
+
+-- view 0 plot: plot:highlight:count · luad_tcga_pan_can_atlas_2018
+SELECT count() AS n FROM (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))))
+
+-- view 0 plot: plot:highlight:count · luad_tcga_pan_can_atlas_2018
+SELECT count() AS n FROM (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))))
+
+-- view 0 plot: plot:highlight:count · lusc_tcga_pan_can_atlas_2018
+SELECT count() AS n FROM (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))))
+
+-- view 0 plot: plot:highlight:count · lusc_tcga_pan_can_atlas_2018
+SELECT count() AS n FROM (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))))
+
+-- view 0 plot: plot:highlight:points · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))))),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT xs.id AS id, xs.v AS x, ys.v AS y
+FROM xs INNER JOIN ys ON ys.id = xs.id
+LIMIT 10001
+
+-- view 0 plot: plot:highlight:points · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))))),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT xs.id AS id, xs.v AS x, ys.v AS y
+FROM xs INNER JOIN ys ON ys.id = xs.id
+LIMIT 9843
+
+-- view 0 plot: plot:highlight:points · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))))),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT xs.id AS id, xs.v AS x, ys.v AS y
+FROM xs INNER JOIN ys ON ys.id = xs.id
+LIMIT 10001
+
+-- view 0 plot: plot:highlight:points · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))))),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT xs.id AS id, xs.v AS x, ys.v AS y
+FROM xs INNER JOIN ys ON ys.id = xs.id
+LIMIT 9843
+
+-- view 0 plot: plot:moments · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     pair AS (SELECT xs.v AS x, ys.v AS y FROM xs INNER JOIN ys ON ys.id = xs.id)
+SELECT count() AS n, sum(x) AS sx, sum(y) AS sy, sum(x * y) AS sxy, sum(x * x) AS sxx, sum(y * y) AS syy
+FROM pair
+
+-- view 0 plot: plot:moments · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'AGE' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     pair AS (SELECT xs.v AS x, ys.v AS y FROM xs INNER JOIN ys ON ys.id = xs.id)
+SELECT count() AS n, sum(x) AS sx, sum(y) AS sy, sum(x * y) AS sxy, sum(x * x) AS sxx, sum(y * y) AS syy
+FROM pair
+
+-- view 1 plot: plot:box · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT gs.v AS g, count() AS n, min(vs.v) AS lo, max(vs.v) AS hi,
+       quantilesExact(0.25, 0.5, 0.75)(vs.v) AS q
+FROM vs INNER JOIN gs ON gs.id = vs.id
+GROUP BY g ORDER BY n DESC LIMIT 151
+
+-- view 1 plot: plot:box · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT gs.v AS g, count() AS n, min(vs.v) AS lo, max(vs.v) AS hi,
+       quantilesExact(0.25, 0.5, 0.75)(vs.v) AS q
+FROM vs INNER JOIN gs ON gs.id = vs.id
+GROUP BY g ORDER BY n DESC LIMIT 151
+
+-- view 1 plot: plot:coverage · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'),
+     cases AS (SELECT pid AS id FROM cohort),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT count() AS total, countIf(id IN (SELECT id FROM xs)) AS nx, countIf(id IN (SELECT id FROM ys)) AS ny,
+  (SELECT count() FROM (SELECT patient_unique_id FROM snapshot_db.sample_derived
+    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+    GROUP BY patient_unique_id HAVING count() > 1)) AS multi FROM cases
+
+-- view 1 plot: plot:coverage · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'),
+     cases AS (SELECT pid AS id FROM cohort),
+     xs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     ys AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT count() AS total, countIf(id IN (SELECT id FROM xs)) AS nx, countIf(id IN (SELECT id FROM ys)) AS ny,
+  (SELECT count() FROM (SELECT patient_unique_id FROM snapshot_db.sample_derived
+    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+    GROUP BY patient_unique_id HAVING count() > 1)) AS multi FROM cases
+
+-- view 1 plot: plot:highlight:count · luad_tcga_pan_can_atlas_2018
+SELECT count() AS n FROM (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))))
+
+-- view 1 plot: plot:highlight:count · lusc_tcga_pan_can_atlas_2018
+SELECT count() AS n FROM (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation')))))
+
+-- view 1 plot: plot:highlight:points · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))))),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT vs.id AS id, gs.v AS g, vs.v AS v
+FROM vs INNER JOIN gs ON gs.id = vs.id
+LIMIT 10001
+
+-- view 1 plot: plot:highlight:points · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT pid FROM (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') WHERE pid IN (SELECT pid FROM (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
+    AND g.hugo_gene_symbol = 'KRAS'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))))),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT vs.id AS id, gs.v AS g, vs.v AS v
+FROM vs INNER JOIN gs ON gs.id = vs.id
+LIMIT 10001
+
+-- view 1 plot: plot:points · luad_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'luad_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT vs.id AS id, gs.v AS g, vs.v AS v
+FROM vs INNER JOIN gs ON gs.id = vs.id
+LIMIT 10001
+
+-- view 1 plot: plot:points · lusc_tcga_pan_can_atlas_2018
+WITH cohort AS (SELECT DISTINCT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'),
+     vs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), avgIf(v, pref = 0)) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'TMB_NONSYNONYMOUS' AND attribute_value != '' AND if(isFinite(ifNull(toFloat64OrNull(attribute_value), nan)), toFloat64OrNull(attribute_value), NULL) IS NOT NULL
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id),
+     gs AS (SELECT id, if(countIf(pref = 1) > 0, anyIf(v, pref = 1), if(uniqExactIf(v, pref = 0) > 1, 'Multiple', anyIf(v, pref = 0))) AS v FROM (
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 1 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'patient' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+  UNION ALL
+SELECT replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') AS id, attribute_value AS v, 0 AS pref
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND attribute_name = 'SEX' AND attribute_value != ''
+    AND type = 'sample' AND replaceOne(patient_unique_id, 'lusc_tcga_pan_can_atlas_2018_', '') IN (SELECT pid FROM cohort)
+) GROUP BY id)
+SELECT vs.id AS id, gs.v AS g, vs.v AS v
+FROM vs INNER JOIN gs ON gs.id = vs.id
+LIMIT 10001
diff --git a/test/snapshots/two-studies-plot.svg b/test/snapshots/two-studies-plot.svg
new file mode 100644
index 00000000..f219e9c7
--- /dev/null
+++ b/test/snapshots/two-studies-plot.svg
@@ -0,0 +1,4 @@
+<!-- view 0 plot -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 541" width="100%" role="img" aria-label="Plot — TMB_NONSYNONYMOUS against AGE" style="max-width:960px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Plot — TMB_NONSYNONYMOUS against AGE</title><rect width="960" height="541" fill="#ffffff"/><text x="47" y="16" font-size="11" fill="#16232b">Pearson r = −0.13 (p = 4.2e-5)</text><line x1="47" y1="374" x2="944" y2="374" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="377.5" font-size="10" fill="#5b6b76" text-anchor="end">0</text><line x1="47" y1="322.5" x2="944" y2="322.5" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="326" font-size="10" fill="#5b6b76" text-anchor="end">10</text><line x1="47" y1="271" x2="944" y2="271" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="274.5" font-size="10" fill="#5b6b76" text-anchor="end">20</text><line x1="47" y1="219.5" x2="944" y2="219.5" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="223" font-size="10" fill="#5b6b76" text-anchor="end">30</text><line x1="47" y1="168" x2="944" y2="168" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="171.5" font-size="10" fill="#5b6b76" text-anchor="end">40</text><line x1="47" y1="116.6" x2="944" y2="116.6" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="120.1" font-size="10" fill="#5b6b76" text-anchor="end">50</text><line x1="47" y1="65.1" x2="944" y2="65.1" stroke="#dde4e8" stroke-width="0.75"/><text x="41" y="68.6" font-size="10" fill="#5b6b76" text-anchor="end">60</text><text x="16.8" y="201" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 16.8 201)">TMB_NONSYNONYMOUS</text><line x1="157.2" y1="28" x2="157.2" y2="374" stroke="#dde4e8" stroke-width="0.75"/><line x1="157.2" y1="374" x2="157.2" y2="377" stroke="#5b6b76" stroke-width="1"/><text x="151.2" y="390" font-size="10" fill="#5b6b76">40</text><line x1="314.5" y1="28" x2="314.5" y2="374" stroke="#dde4e8" stroke-width="0.75"/><line 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patients have a value on both axes (82 have no AGE; 3 have no TMB_NONSYNONYMOUS); only those are drawn. 2 studies pooled</text><text x="8" y="467" font-size="10" fill="#5b6b76">(luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). 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+<!-- view 1 plot -->
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cy="288.9" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="286.8" cy="263.3" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="280.1" cy="264.8" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="261" cy="291.4" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="278.9" cy="311.9" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="253.9" cy="275.7" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="291.8" cy="270.4" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="257.2" cy="296" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="265.6" cy="313.6" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="263.4" cy="321.1" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="269.8" cy="304.1" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="271.7" cy="223.9" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="275.9" cy="260" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="259.5" cy="300.9" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="274.1" cy="225" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="255.1" cy="302.4" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="275.3" cy="306.1" r="2.5" fill="#adb6bc" fill-opacity="0.85"/><circle cx="264" cy="329.1" r="2.5" fill="#adb6bc" fill-opacity="0.85"/></g><line x1="271.3" y1="348.2" x2="271.3" y2="185" stroke="#5b6b76" stroke-width="1"/><rect x="247.3" y="269.6" width="48" height="56.4" fill="#adb6bc" fill-opacity="0.1" stroke="#adb6bc"><title>Female: median 6.92, quartiles 3.43–12.2, n = 402</title></rect><line x1="247.3" y1="303.6" x2="295.3" y2="303.6" stroke="#adb6bc" stroke-width="2"/><g data-layer="0"><circle cx="255.5" cy="321.3" r="2.5" fill="#D55E00" fill-opacity="0.9" stroke="#ffffff" stroke-width="0.6"/><circle cx="254.9" cy="249.3" r="2.5" fill="#D55E00" fill-opacity="0.9" stroke="#ffffff" stroke-width="0.6"/><circle cx="266.5" cy="320" r="2.5" fill="#D55E00" fill-opacity="0.9" stroke="#ffffff" 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fill="#5b6b76">(luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). Counts and extremes per group sum exactly; the quartiles are a size-weighted approximation across</text><text x="8" y="463" font-size="10" fill="#5b6b76">sites (it can shift where an outlier bar falls, never a count). A federated box does not draw a density behind it — that needs the same shared-scale binning a scatter does,</text><text x="8" y="477" font-size="10" fill="#5b6b76">not yet built for this shape — so points are shown when an entitled site has few enough to draw. Point layers — “KRAS mut”: 175 patients, 164 patients drawn as points.</text><text x="8" y="491" font-size="10" fill="#5b6b76">The statistics are over the whole plotted cohort. Box: quartiles with Tukey whiskers, computed over every case in the group; each name carries its count. Every value is</text><text x="8" y="505" font-size="10" fill="#5b6b76">drawn over its box; the jitter is fixed per case. 5 values lie more than a whisker span past the whiskers and are drawn as a triangle at the frame's edge.</text></svg>
diff --git a/test/snapshots/two-studies-plot.txt b/test/snapshots/two-studies-plot.txt
new file mode 100644
index 00000000..0b3299a5
--- /dev/null
+++ b/test/snapshots/two-studies-plot.txt
@@ -0,0 +1,16 @@
+Analysis over luad_tcga_pan_can_atlas_2018 + lusc_tcga_pan_can_atlas_2018 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- Lung (TCGA): 1,053 patients — all patients in the study — across 2 studies
+- KRAS mut: 175 patients — OQL[KRAS: MUT] — across 2 studies
+- TP53 mut: 699 patients — OQL[TP53: MUT] — across 2 studies
+
+Plot (scatter): AGE vs TMB_NONSYNONYMOUS — 968 patients, binned onto a 240x160 grid (the payload is bins, never points).
+  Pearson r=-0.131 p=4.2e-5; Spearman rho=—; least-squares slope -0.128
+  968 of 1,053 patients have a value on both axes (82 have no AGE; 3 have no TMB_NONSYNONYMOUS); only those are drawn. 2 studies pooled (luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). Each site reports six sums (n, Σx, Σy, Σxy, Σx², Σy²), exact and additive, for Pearson's r and the regression line; the bin grid uses one shared scale (each site's own extent, pooled) so cells sum correctly. Spearman rank correlation is not reported for a federated plot: it needs the pooled GLOBAL ranking, which per-site aggregates cannot reconstruct exactly. Point layers — “KRAS mut”: 175 patients, 158 patients drawn as points; “TP53 mut”: 699 patients, 652 patients drawn as points (58 drawn by an earlier layer). The statistics are over the whole plotted cohort.
+
+Plot (box): TMB_NONSYNONYMOUS by SEX — 996 patients.
+  Female: median 6.92 (IQR 3.43–12.18, whiskers 0.00–25.30, n=402)
+  Male: median 7.35 (IQR 4.39–12.60, whiskers 0.00–24.92, n=594)
+  Values are drawn as individual points.
+  996 of 1,053 patients have a value on both axes (54 have no SEX; 3 have no TMB_NONSYNONYMOUS); only those are drawn. 2 studies pooled (luad_tcga_pan_can_atlas_2018, lusc_tcga_pan_can_atlas_2018). Counts and extremes per group sum exactly; the quartiles are a size-weighted approximation across sites (it can shift where an outlier bar falls, never a count). A federated box does not draw a density behind it — that needs the same shared-scale binning a scatter does, not yet built for this shape — so points are shown when an entitled site has few enough to draw. Point layers — “KRAS mut”: 175 patients, 164 patients drawn as points. The statistics are over the whole plotted cohort.
diff --git a/test/snapshots/two-studies-survival.json b/test/snapshots/two-studies-survival.json
new file mode 100644
index 00000000..34226020
--- /dev/null
+++ b/test/snapshots/two-studies-survival.json
@@ -0,0 +1,467 @@
+{
+  "study": "msk_chord_2024",
+  "cohorts": {
+    "TP53 mut": {
+      "count": 25555,
+      "text": "OQL[TP53: MUT] — across 2 studies",
+      "studies": [
+        "msk_chord_2024",
+        "msk_met_2021"
+      ]
+    },
+    "TP53 wt": {
+      "count": 24627,
+      "text": "NOT OQL[TP53:] AND profiled for TP53 MUTATION — across 2 studies",
+      "studies": [
+        "msk_chord_2024",
+        "msk_met_2021"
+      ]
+    }
+  },
+  "views": [
+    {
+      "kind": "survival",
+      "cohorts": [
+        "TP53 mut",
+        "TP53 wt"
+      ],
+      "curves": [
+        {
+          "cohort": "TP53 mut",
+          "points": [
+            {
+              "t": 0,
+              "s": 1
+            },
+            {
+              "t": 0,
+              "s": 0.9982
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+            {
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+            {
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+            {
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+            {
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+            {
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+            {
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+              "s": 0.9962
+            },
+            {
+              "t": 0.16,
+              "s": 0.996
+            },
+            {
+              "t": 0.164383381,
+              "s": 0.9957
+            },
+            {
+              "t": 0.197260058,
+              "s": 0.9956
+            },
+            {
+              "t": 0.2,
+              "s": 0.9953
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+            {
+              "t": 0.23,
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+            {
+              "t": 0.230136734,
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+            {
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+            {
+              "t": 0.26301341,
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+            },
+            {
+              "t": 0.295890087,
+              "s": 0.9939
+            },
+            {
+              "t": 0.3,
+              "s": 0.9935
+            },
+            {
+              "t": 0.328766763,
+              "s": 0.993
+            },
+            {
+              "…": "2979 more",
+              "sha256": "ffc2d62afcd76b56"
+            }
+          ],
+          "median": 30.64106231,
+          "n": 25498,
+          "events": 12607,
+          "atRisk": [
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+            24109,
+            23357,
+            22638,
+            21884,
+            21187,
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+            16492,
+            15797,
+            15094,
+            14401,
+            13713,
+            13063,
+            12486,
+            11916,
+            {
+              "…": "99 more",
+              "sha256": "65e42ce7c5247805"
+            }
+          ],
+          "missing": 57
+        },
+        {
+          "cohort": "TP53 wt",
+          "points": [
+            {
+              "t": 0,
+              "s": 1
+            },
+            {
+              "t": 0,
+              "s": 0.9989
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+            {
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+            {
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+            {
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+            {
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+            {
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+            {
+              "t": 0.2,
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+            {
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+            },
+            {
+              "t": 0.230136734,
+              "s": 0.9968
+            },
+            {
+              "t": 0.26,
+              "s": 0.9967
+            },
+            {
+              "t": 0.26301341,
+              "s": 0.9966
+            },
+            {
+              "t": 0.295890087,
+              "s": 0.9964
+            },
+            {
+              "t": 0.3,
+              "s": 0.9961
+            },
+            {
+              "…": "3051 more",
+              "sha256": "28815bef7a1dd109"
+            }
+          ],
+          "median": 68.44924006,
+          "n": 24570,
+          "events": 8585,
+          "atRisk": [
+            24570,
+            23454,
+            22965,
+            22473,
+            22046,
+            21636,
+            21221,
+            20770,
+            20340,
+            19860,
+            19420,
+            18945,
+            18285,
+            17677,
+            17144,
+            16580,
+            16020,
+            15453,
+            14997,
+            14522,
+            {
+              "…": "96 more",
+              "sha256": "c627134ccc1ae86f"
+            }
+          ],
+          "missing": 57
+        }
+      ],
+      "endpoint": "OS",
+      "participation": {
+        "studies": [
+          {
+            "study": "msk_chord_2024",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          },
+          {
+            "study": "msk_met_2021",
+            "mode": "patient-level",
+            "instance": "primary",
+            "source": 0
+          }
+        ],
+        "requiresPatientLevel": false,
+        "aggregateOnly": []
+      },
+      "logrank": {
+        "chi2": 1594.0863,
+        "p": 0,
+        "df": 1,
+        "observed": [
+          12607,
+          8585
+        ],
+        "expected": [
+          9723.3,
+          11468.7
+        ],
+        "variance": 5216.6577,
+        "strata": 2
+      },
+      "logrankPooled": {
+        "chi2": 1554.7258,
+        "p": 0,
+        "df": 1,
+        "observed": [
+          12607,
+          8585
+        ],
+        "expected": [
+          9754.9,
+          11437.1
+        ],
+        "variance": 5232.0787,
+        "strata": 1
+      },
+      "caveat": "OS from each study's own baseline (left truncation applies; baselines are NOT harmonized across studies) Status '0:...' treated as censored. The log-rank test is STRATIFIED by study (2 strata contributed events): each study is compared only against itself, so a difference in case mix between studies cannot masquerade as a difference between cohorts. The pooled p is shown alongside — where the two disagree, the stratified one is the honest answer. Every site contributes only (months, event, n) counts; no patient-level survival time crosses an instance boundary.",
+      "method": {
+        "steps": [
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.",
+            "scope": "TP53 mut · msk_chord_2024",
+            "rowsIn": 13105,
+            "rowsOut": 13105,
+            "params": [
+              [
+                "count rows",
+                "4,156"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.",
+            "scope": "TP53 mut · msk_met_2021",
+            "rowsIn": 12450,
+            "rowsOut": 12393,
+            "params": [
+              [
+                "count rows",
+                "3,164"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.",
+            "scope": "TP53 wt · msk_chord_2024",
+            "rowsIn": 11594,
+            "rowsOut": 11594,
+            "params": [
+              [
+                "count rows",
+                "4,433"
+              ]
+            ]
+          },
+          {
+            "kind": "fetch",
+            "title": "Count patients by time and status",
+            "detail": "The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.",
+            "scope": "TP53 wt · msk_met_2021",
+            "rowsIn": 13033,
+            "rowsOut": 12976,
+            "params": [
+              [
+                "count rows",
+                "3,444"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Estimate survival by Kaplan–Meier",
+            "detail": "The product-limit estimator, one curve per cohort, pooled over its studies. The median is the first time the curve is at or below 0.5.",
+            "params": [
+              [
+                "TP53 mut",
+                "25,498 patients, 12,607 events, median 30.6 months"
+              ],
+              [
+                "TP53 wt",
+                "24,570 patients, 8,585 events, median 68.4 months"
+              ]
+            ]
+          },
+          {
+            "kind": "statistic",
+            "title": "Two-group log-rank test, stratified by study",
+            "detail": "At each event time, every cohort's observed events against its share of the risk set, with the hypergeometric variance; χ² on 1 df. Each study is its own stratum — compared only against itself — and the strata are summed, so a difference in case mix between studies cannot pass for a difference between cohorts.",
+            "params": [
+              [
+                "χ² / df / p",
+                "1594.0863 / 1 / <1e-300"
+              ],
+              [
+                "strata with events",
+                "2"
+              ],
+              [
+                "pooled, for contrast",
+                "χ² 1554.7258, p <1e-300"
+              ]
+            ]
+          }
+        ],
+        "implementation": {
+          "functions": [
+            "kaplanMeier",
+            "logRank"
+          ],
+          "file": "lib/stats.ts",
+          "commit": null
+        },
+        "verification": "Kaplan–Meier matches lifelines 0.30.3 KaplanMeierFitter (curves are reported to 4 decimals); the log-rank matches statsmodels 0.15.0 survdiff to a relative 1e-6, pooled, stratified, with delayed entry and for k groups (test/survival-golden.test.ts).",
+        "check": {
+          "km:TP53 mut:n": {
+            "v": 25498,
+            "abs": 0
+          },
+          "km:TP53 mut:events": {
+            "v": 12607,
+            "abs": 0
+          },
+          "km:TP53 mut:median": {
+            "v": 30.64106231,
+            "abs": 1e-9
+          },
+          "km:TP53 wt:n": {
+            "v": 24570,
+            "abs": 0
+          },
+          "km:TP53 wt:events": {
+            "v": 8585,
+            "abs": 0
+          },
+          "km:TP53 wt:median": {
+            "v": 68.44924006,
+            "abs": 1e-9
+          },
+          "logrank:chi2": {
+            "v": 1594.0863,
+            "abs": 0.00005
+          },
+          "logrank:p": {
+            "v": 0,
+            "rel": 0.00001,
+            "abs": 1e-300
+          },
+          "logrank:pooled:chi2": {
+            "v": 1554.7258,
+            "abs": 0.00005
+          },
+          "logrank:pooled:p": {
+            "v": 0,
+            "rel": 0.00001,
+            "abs": 1e-300
+          }
+        }
+      }
+    }
+  ]
+}
diff --git a/test/snapshots/two-studies-survival.sql b/test/snapshots/two-studies-survival.sql
new file mode 100644
index 00000000..5412169f
--- /dev/null
+++ b/test/snapshots/two-studies-survival.sql
@@ -0,0 +1,155 @@
+-- cohort TP53 mut
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- msk_met_2021
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))
+
+-- cohort TP53 wt
+-- msk_chord_2024
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))
+
+-- combined at the analysis layer: each study is evaluated separately against its own
+-- instance and the counts are ADDED; patient overlap across studies is not deduplicated.
+
+-- msk_met_2021
+((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_met_2021')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))
+
+-- view 0 survival: survivalCounts:OS · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 0 survival: survivalCounts:OS · msk_chord_2024
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_chord_2024'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_chord_2024_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_chord_2024' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_chord_2024_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_chord_2024' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_chord_2024_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 0 survival: survivalCounts:OS · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND g.variant_type = 'mutation'))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_met_2021' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
+
+-- view 0 survival: survivalCounts:OS · msk_met_2021
+WITH cohort AS (((SELECT DISTINCT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid FROM snapshot_db.sample_derived
+  WHERE cancer_study_identifier = 'msk_met_2021')
+EXCEPT DISTINCT
+((SELECT DISTINCT replaceOne(g.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.genomic_event_derived g
+  WHERE g.cancer_study_identifier = 'msk_met_2021'
+    AND g.hugo_gene_symbol = 'TP53'
+    AND g.mutation_status != 'UNCALLED' AND (g.variant_type != 'cna' OR abs(g.cna_alteration) >= 2))))
+INTERSECT DISTINCT
+(SELECT DISTINCT replaceOne(sd.patient_unique_id, 'msk_met_2021_', '') AS pid
+  FROM snapshot_db.sample_to_gene_panel_derived sp
+  JOIN snapshot_db.sample_derived sd ON sd.sample_unique_id = sp.sample_unique_id
+  WHERE sp.cancer_study_identifier = 'msk_met_2021' AND sp.alteration_type = 'MUTATION_EXTENDED'
+    AND (sp.gene_panel_id = 'WES' OR sp.gene_panel_id IN (
+      SELECT gene_panel_id FROM snapshot_db.gene_panel_to_gene_derived WHERE gene = 'TP53')))),
+os AS (
+  SELECT replaceOne(patient_unique_id, 'msk_met_2021_', '') AS pid,
+         anyIf(attribute_value, attribute_name='OS_MONTHS') AS months,
+         anyIf(attribute_value, attribute_name='OS_STATUS') AS status
+  FROM snapshot_db.clinical_data_derived
+  WHERE cancer_study_identifier = 'msk_met_2021' AND type='patient'
+    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
+    AND replaceOne(patient_unique_id, 'msk_met_2021_', '') IN (SELECT pid FROM cohort)
+  GROUP BY pid
+)
+SELECT if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) AS m, if(status LIKE '1%', 1, 0) AS ev, count() AS n
+FROM os WHERE if(isFinite(ifNull(toFloat64OrNull(months), nan)), toFloat64OrNull(months), NULL) IS NOT NULL
+GROUP BY m, ev ORDER BY m
diff --git a/test/snapshots/two-studies-survival.svg b/test/snapshots/two-studies-survival.svg
new file mode 100644
index 00000000..807cd0ba
--- /dev/null
+++ b/test/snapshots/two-studies-survival.svg
@@ -0,0 +1,2 @@
+<!-- view 0 survival -->
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 720 457" width="100%" role="img" aria-label="Kaplan–Meier — TP53 mut, TP53 wt" style="max-width:720px;height:auto;display:block;font-family:'IBM Plex Sans',system-ui,sans-serif"><title>Kaplan–Meier — TP53 mut, TP53 wt</title><rect width="720" height="457" fill="#ffffff"/><line x1="82.06" y1="242" x2="704" y2="242" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="245.5" font-size="10" fill="#5b6b76" text-anchor="end">0%</text><line x1="82.06" y1="184.5" x2="704" y2="184.5" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="188" font-size="10" fill="#5b6b76" text-anchor="end">25%</text><line x1="82.06" y1="127" x2="704" y2="127" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="130.5" font-size="10" fill="#5b6b76" text-anchor="end">50%</text><line x1="82.06" y1="69.5" x2="704" y2="69.5" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="73" font-size="10" fill="#5b6b76" text-anchor="end">75%</text><line x1="82.06" y1="12" x2="704" y2="12" stroke="#dde4e8" stroke-width="0.75"/><text x="76.1" y="15.5" font-size="10" fill="#5b6b76" text-anchor="end">100%</text><text x="35.9" y="127" font-size="11" fill="#16232b" text-anchor="middle" transform="rotate(-90 35.9 127)">Survival probability</text><line x1="82.1" y1="242" x2="82.1" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="79.1" y="258" font-size="10" fill="#5b6b76">0</text><line x1="215.9" y1="242" x2="215.9" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="209.9" y="258" font-size="10" fill="#5b6b76">24</text><line x1="349.8" y1="242" x2="349.8" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="343.8" y="258" font-size="10" fill="#5b6b76">48</text><line x1="483.6" y1="242" x2="483.6" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="477.6" y="258" font-size="10" fill="#5b6b76">72</text><line x1="617.5" y1="242" x2="617.5" y2="245" stroke="#5b6b76" stroke-width="1"/><text x="611.5" y="258" font-size="10" fill="#5b6b76">96</text><line x1="82.06" y1="242" x2="704" y2="242" stroke="#5b6b76" stroke-width="1"/><text x="393" y="277" font-size="11" fill="#16232b" text-anchor="middle">Months</text><path d="M82.1 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fill="none" stroke="#E69F00" stroke-width="2" stroke-linejoin="round"/><text x="8" y="299" font-size="10" fill="#5b6b76" font-weight="600">Number at risk</text><line x1="8" y1="313" x2="24" y2="313" stroke="#0072B2" stroke-width="2"/><text x="28" y="316.5" font-size="10" fill="#16232b">TP53 mut</text><text x="78.1" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 25,498 at risk at 0 months</title>25,498</text><text x="202.5" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 9,604 at risk at 24 months</title>9,604</text><text x="336.4" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 3,615 at risk at 48 months</title>3,615</text><text x="474.6" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 977 at risk at 72 months</title>977</text><text x="608.5" y="316.5" font-size="10" fill="#5b6b76"><title>TP53 mut: 129 at risk at 96 months</title>129</text><line x1="8" y1="329" x2="24" y2="329" stroke="#E69F00" stroke-width="2"/><text x="28" y="332.5" font-size="10" fill="#16232b">TP53 wt</text><text x="78.1" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 24,570 at risk at 0 months</title>24,570</text><text x="199.5" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 12,411 at risk at 24 months</title>12,411</text><text x="336.4" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 5,548 at risk at 48 months</title>5,548</text><text x="470.2" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 1,687 at risk at 72 months</title>1,687</text><text x="608.5" y="332.5" font-size="10" fill="#5b6b76"><title>TP53 wt: 240 at risk at 96 months</title>240</text><line x1="8" y1="353" x2="26" y2="353" stroke="#0072B2" stroke-width="2"/><text x="31" y="356.9" font-size="11" fill="#16232b">TP53 mut<tspan dx="6" fill="#5b6b76">median 30.6 mo · n = 25,498 · 12,607 events</tspan></text><line x1="368" y1="353" x2="386" y2="353" stroke="#E69F00" stroke-width="2"/><text x="391" y="356.9" font-size="11" fill="#16232b">TP53 wt<tspan dx="6" fill="#5b6b76">median 68.4 mo · n = 24,570 · 8,585 events</tspan></text><text x="8" y="378" font-size="11" fill="#16232b" class="logrank">log-rank χ² = 1,594 (1 df) · p &lt; 1e-300 · stratified by study, 2 strata · pooled p &lt; 1e-300</text><text x="8" y="397" font-size="10" fill="#5b6b76">OS from each study's own baseline (left truncation applies; baselines are NOT harmonized across studies) Status '0:...' treated as censored. The log-rank test</text><text x="8" y="411" font-size="10" fill="#5b6b76">is STRATIFIED by study (2 strata contributed events): each study is compared only against itself, so a difference in case mix between studies cannot</text><text x="8" y="425" font-size="10" fill="#5b6b76">masquerade as a difference between cohorts. The pooled p is shown alongside — where the two disagree, the stratified one is the honest answer. Every site</text><text x="8" y="439" font-size="10" fill="#5b6b76">contributes only (months, event, n) counts; no patient-level survival time crosses an instance boundary.</text></svg>
diff --git a/test/snapshots/two-studies-survival.txt b/test/snapshots/two-studies-survival.txt
new file mode 100644
index 00000000..2693df71
--- /dev/null
+++ b/test/snapshots/two-studies-survival.txt
@@ -0,0 +1,28 @@
+Analysis over msk_chord_2024 + msk_met_2021 — OncoKB pin v7.5 — (time)ms.
+
+Cohorts:
+- TP53 mut: 25,555 patients — OQL[TP53: MUT] — across 2 studies
+- TP53 wt: 24,627 patients — NOT OQL[TP53:] AND profiled for TP53 MUTATION — across 2 studies
+
+Survival (KM, OS):
+  TP53 mut: median 30.6mo (n=25498, 12607 events, 57 missing OS)
+  TP53 wt: median 68.4mo (n=24570, 8585 events, 57 missing OS)
+  log-rank p=<1e-300 (stratified by study)
+CAVEAT (tell the user): OS from each study's own baseline (left truncation applies; baselines are NOT harmonized across studies) Status '0:...' treated as censored. The log-rank test is STRATIFIED by study (2 strata contributed events): each study is compared only against itself, so a difference in case mix between studies cannot masquerade as a difference between cohorts. The pooled p is shown alongside — where the two disagree, the stratified one is the honest answer. Every site contributes only (months, event, n) counts; no patient-level survival time crosses an instance boundary.
+  METHOD:
+    - Count patients by time and status [TP53 mut · msk_chord_2024] (13,105 → 13,105 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.
+        count rows: 4,156
+    - Count patients by time and status [TP53 mut · msk_met_2021] (12,450 → 12,393 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.
+        count rows: 3,164
+    - Count patients by time and status [TP53 wt · msk_chord_2024] (11,594 → 11,594 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. Every cohort patient had a value.
+        count rows: 4,433
+    - Count patients by time and status [TP53 wt · msk_met_2021] (13,033 → 12,976 rows): The survivalCounts:OS query above: patients grouped by identical OS_MONTHS, event, returned as counts — no patient identifier leaves the database. Event: OS_STATUS starting with “1”; any other value is censored. 57 cohort patients had no OS_MONTHS value.
+        count rows: 3,444
+    - Estimate survival by Kaplan–Meier: The product-limit estimator, one curve per cohort, pooled over its studies. The median is the first time the curve is at or below 0.5.
+        TP53 mut: 25,498 patients, 12,607 events, median 30.6 months
+        TP53 wt: 24,570 patients, 8,585 events, median 68.4 months
+    - Two-group log-rank test, stratified by study: At each event time, every cohort's observed events against its share of the risk set, with the hypergeometric variance; χ² on 1 df. Each study is its own stratum — compared only against itself — and the strata are summed, so a difference in case mix between studies cannot pass for a difference between cohorts.
+        χ² / df / p: 1594.0863 / 1 / <1e-300
+        strata with events: 2
+        pooled, for contrast: χ² 1554.7258, p <1e-300
+    Kaplan–Meier matches lifelines 0.30.3 KaplanMeierFitter (curves are reported to 4 decimals); the log-rank matches statsmodels 0.15.0 survdiff to a relative 1e-6, pooled, stratified, with delayed entry and for k groups (test/survival-golden.test.ts).
diff --git a/test/view-snapshots.test.ts b/test/view-snapshots.test.ts
new file mode 100644
index 00000000..77e3809f
--- /dev/null
+++ b/test/view-snapshots.test.ts
@@ -0,0 +1,229 @@
+// Every view kind's output, pinned: one gallery example per kind (and a few that combine studies)
+// runs through the whole engine (cohort compiler, runner, statistics, figure, results text) with
+// the warehouse replaced by answers recorded from it. Four files per case in test/snapshots/ are
+// compared byte for byte:
+//   <case>.sql   each cohort's compiled SQL and each view's queries, by label and study
+//   <case>.json  what each view computed (its result, without timings or the queries above; a long
+//                array, or an object of more than 50 keys, as its first 20 items, then the rest's
+//                count and digest)
+//   <case>.svg   each view's figure;  <case>.txt  the results text MCP clients and the AI tools read
+// A change to shared code that moves another view's numbers, figure, text or queries fails here,
+// named by the case. A changed query has no recorded answer, so it fails as "not recorded".
+// Each case runs twice, its answers delivered in the order its queries were sent and then in the
+// reverse, and the two runs must agree exactly: output that depends on which query finished first
+// fails as such. (A view's
+// queries are listed sorted in <case>.sql: independent queries form a set, and the snapshot pins
+// which are sent, not the order they happen to be sent in.)
+//
+//   UPDATE_SNAPSHOTS=1 npm test          output changed on purpose, queries did not: rewrite the four files
+//   RECORD_SNAPSHOTS=1 node --env-file=.env.local --test test/view-snapshots.test.ts
+//                                        queries changed: re-ask the warehouse, then rewrite everything
+// Recording reads the public warehouse only (the second instance is unset first); the answers are
+// stored gzipped in test/snapshots/answers/, keyed by statement, under the database name below.
+import { test } from "node:test";
+import assert from "node:assert/strict";
+import { createHash } from "node:crypto";
+import { existsSync, mkdirSync, readFileSync, writeFileSync } from "node:fs";
+import { gunzipSync, gzipSync } from "node:zlib";
+import { join } from "node:path";
+
+const RECORD = !!process.env.RECORD_SNAPSHOTS;
+const UPDATE = RECORD || !!process.env.UPDATE_SNAPSHOTS;
+/** the database name every snapshot is written under, whatever the recording ran against */
+const SNAPSHOT_DB = "snapshot_db";
+const realDb = (process.env.CLICKHOUSE_DB || "").replace(/[^A-Za-z0-9_]/g, "");
+if (RECORD) {
+  for (const k of Object.keys(process.env)) if (k.startsWith("CLICKHOUSE2_") || k === "LOOPBACK_PEER") delete process.env[k];
+  if (!realDb) throw new Error("RECORD_SNAPSHOTS needs the warehouse: run with node --env-file=.env.local");
+} else {
+  process.env.CLICKHOUSE_DB = SNAPSHOT_DB;
+  for (const k of Object.keys(process.env)) if (k.startsWith("CLICKHOUSE2_")) delete process.env[k];
+}
+
+const { setChTransport } = await import("../lib/ch.ts");
+const { EXAMPLES } = await import("../lib/dsl-reference.ts");
+const { runAnalysis } = await import("../lib/analysis.ts");
+const { withRequestCustomData } = await import("../lib/custom-data-server.ts");
+const { viewToSvg } = await import("../lib/view-svg.ts");
+const { analysisText } = await import("../lib/result-text.ts");
+import type { AnalysisDoc, AnalysisResult } from "../lib/types.ts";
+
+/**
+ * One case per view kind: the smallest gallery example that draws it (the oncoprint is pinned by
+ * test/oncoprint-svg.test.ts); then examples that combine studies, where a view adds up answers
+ * from several databases.
+ */
+const CASES: Record<string, string> = {
+  compare: "A missing alteration type is disclosed, never a false negative",
+  survival: "A non-standard survival endpoint, named directly",
+  cox: "Cox with a gene-SET covariate: any of three genes, one term",
+  logistic: "Logistic regression: does APC status predict MSI-high status",
+  course: "The clinical course: who is still on treatment, and when",
+  sequence: "What comes after what: lines of therapy, and what ending a path means",
+  hotspots: "Lollipop plot: mutations by protein position",
+  plot: "Plot axes from the study's own profiles",
+  coexpression: "Co-expression from sums alone",
+  clinical: "Median/IQR and a rank-sum test, single-study only",
+  cooccurrence: "Co-occurrence and mutual exclusivity in NSCLC",
+  attrition: "Cohort attrition: which criterion removed whom",
+  genome: "Genome-wide copy number: colorectal against breast",
+  pathways: "Oncogenic signalling pathways: colorectal against pancreatic",
+  diffexpr: "Differential expression and enrichment",
+  paired: "Paired samples: what differs between a patient's primary and",
+  clonality: "Clonality, LOH and genome doubling from FACETS",
+  incidence: "Brain metastasis, with death as a competing risk",
+  labs: "PSA response to hormone therapy, by TP53 status",
+  riskmodel: "A survival risk model from mutated genes in lung cancer",
+  cancertypes: "Cancer types summary: RAS genes across 50,000 tumours",
+  overlap: "Cohort overlap: how four groups share their patients",
+  enrichment: "Alteration enrichment: what separates colorectal from pancreatic",
+  assays: "Mutational signatures: what UV light and tobacco leave behind",
+  composition: "Composition: T and NK cell states by mutational signature",
+  correlates: "Co-expression: the genes that rise and fall with ESR1",
+  table: "Clinical data: every pancreatic cancer sample, oldest patients first",
+  timeline: "A patient's timeline: treatments, diagnoses, samples and lab",
+  mutations: "KRAS codon 12/13 mutations in colorectal cancer",
+  alterations: "A patient's mutations, copy-number alterations and structural",
+  "two-studies-survival": "Study-stratified survival across two studies",
+  "two-studies-four-views": "Federated end to end: two studies, four views",
+  "two-studies-plot": "Point layers across two studies",
+  "pooled-paired": "Before and after treatment, pooled across studies",
+};
+
+const DIR = join(import.meta.dirname, "snapshots");
+const ANSWERS = join(DIR, "answers");
+const toSnapshotDb = (s: string) => realDb ? s.replaceAll(realDb + ".", SNAPSHOT_DB + ".").replaceAll("'" + realDb + "'", "'" + SNAPSHOT_DB + "'") : s;
+
+/** a statement's key: the instance, the format, the text and every table sent with it */
+async function keyOf(st: { conn: { id: string }; format: string; query: string; externals: { name: string; structure: string; blob: Blob }[] }): Promise<string> {
+  const h = createHash("sha256").update(st.conn.id + "\n" + st.format + "\n" + toSnapshotDb(st.query));
+  for (const e of st.externals) h.update("\n" + e.name + "\n" + e.structure + "\n" + await e.blob.text());
+  return h.digest("hex").slice(0, 32);
+}
+
+/** a ClickHouse answer without its timing statistics, which differ on every run */
+function steady(text: string): string {
+  try { const j = JSON.parse(text); delete j.statistics; return JSON.stringify(j); } catch { return text; }
+}
+
+/**
+ * A long array (or an object keyed by gene, by value, …) keeps its first items in full and stands in
+ * for the rest by their count and digest, so a change anywhere still fails the comparison while the
+ * file stays small enough to review.
+ */
+const KEEP = 20, KEEP_KEYS = 50;
+function compact(x: unknown): unknown {
+  if (Array.isArray(x)) {
+    const head = x.slice(0, KEEP).map(compact);
+    if (x.length <= KEEP) return head;
+    return [...head, { "…": x.length - KEEP + " more", sha256: createHash("sha256").update(JSON.stringify(x.slice(KEEP))).digest("hex").slice(0, 16) }];
+  }
+  if (x && typeof x === "object") {
+    const entries = Object.entries(x);
+    const head = entries.slice(0, entries.length > KEEP_KEYS ? KEEP : entries.length).map(([k, v]) => [k, compact(v)]);
+    if (entries.length > KEEP_KEYS) head.push(["…", { more: entries.length - KEEP, sha256: createHash("sha256").update(JSON.stringify(entries.slice(KEEP))).digest("hex").slice(0, 16) }]);
+    return Object.fromEntries(head);
+  }
+  return x;
+}
+const json = (x: unknown) => JSON.stringify(compact(x), null, 2) + "\n";
+
+/** the four files of one case */
+function render(doc: AnalysisDoc, r: AnalysisResult): Record<string, string> {
+  const sql: string[] = [];
+  for (const [name, c] of Object.entries(r.cohorts)) sql.push("-- cohort " + name + "\n" + toSnapshotDb(String((c as { sql?: string }).sql ?? "")));
+  // a view's independent queries form a set (see this file's header)
+  r.views.forEach((v, i) => sql.push(...(v.queries || []).map((q) =>
+    "-- view " + i + " " + v.kind + ": " + q.label + " · " + q.study + (q.remote ? " · remote" : "") + "\n" + toSnapshotDb(q.sql)).sort()));
+  const views = r.views.map(({ ms: _ms, renderMs: _r, queries: _q, method, ...v }) =>
+    ({ ...v, ...(method ? { method: { ...method, python: undefined } } : {}) }));
+  const cohorts = Object.fromEntries(Object.entries(r.cohorts).map(([n, c]) => {
+    const { sql: _sql, ...rest } = c as { sql?: string };
+    return [n, rest];
+  }));
+  const svg = r.views.map((v, i) => "<!-- view " + i + " " + v.kind + " -->\n" + (viewToSvg(v, { theme: "light" }) ?? "(no figure)")).join("\n") + "\n";
+  return {
+    sql: sql.join("\n\n") + "\n",
+    json: json({ study: r.study, cohorts, splits: r.splits, views }),
+    svg,
+    txt: analysisText(doc, r).join("\n\n").replace(/ — \d+ms\./, " — (time)ms.") + "\n",
+  };
+}
+
+if (UPDATE) mkdirSync(ANSWERS, { recursive: true });
+
+/**
+ * Every case's recorded answers, pooled: what the engine asks once per process (which database
+ * holds a study, a study's profiles) is recorded by whichever case asked it first, so a case run
+ * alone finds it in another case's file.
+ */
+const POOL: Record<string, string> = {};
+if (!RECORD) for (const name of Object.keys(CASES)) {
+  const f = join(ANSWERS, name + ".json.gz");
+  if (existsSync(f)) Object.assign(POOL, JSON.parse(gunzipSync(readFileSync(f)).toString("utf8")));
+}
+
+/**
+ * One thread per statement while recording: ClickHouse otherwise adds floats across threads in
+ * whatever order they finish, and returns rows of a query without ORDER BY in no fixed order, so
+ * a re-recorded case would differ from the last recording where nothing changed.
+ */
+const RECORD_SETTINGS = "max_threads=1";
+
+/**
+ * Delivery in a chosen order: answers that are waiting together are held for a moment, then
+ * released in the order their statements were sent ("sent") or the reverse ("reversed"). Two runs
+ * in opposite orders give any two concurrent answers opposite arrival orders, so output that depends
+ * on which arrived first differs between them, every time.
+ */
+type Delivery = "sent" | "reversed";
+let waiting: (() => void)[] = [];
+function arrive(order: Delivery): Promise<void> {
+  return new Promise((release) => {
+    waiting.push(release);
+    if (waiting.length === 1) setTimeout(() => {
+      const batch = order === "reversed" ? waiting.reverse() : waiting;
+      waiting = [];
+      for (const r of batch) r();
+    }, 1);
+  });
+}
+
+/** run the example once: live while recording, else from the pool, delivered in `order` */
+async function runCase(ex: (typeof EXAMPLES)[number], recorded: Record<string, string>, order: Delivery | null): Promise<AnalysisResult> {
+  setChTransport(async (st, send) => {
+    const key = await keyOf(st);
+    if (RECORD) return (recorded[key] = toSnapshotDb(steady(await send(RECORD_SETTINGS))));
+    if (!(key in POOL)) throw new Error("not recorded (a query changed; RECORD_SNAPSHOTS=1, see this file's header):\n" + toSnapshotDb(st.query).slice(0, 2000));
+    if (order) await arrive(order);
+    return POOL[key];
+  });
+  try { return await withRequestCustomData(ex.customData, () => runAnalysis(ex.doc as unknown as AnalysisDoc, { oncoprint: "spec" })); }
+  finally { setChTransport(null); }
+}
+
+for (const [kind, title] of Object.entries(CASES)) {
+  test("snapshot: " + kind, async () => {
+    const ex = EXAMPLES.find((e) => e.title.startsWith(title));
+    assert.ok(ex, "no gallery example titled “" + title + "…”");
+    const answersFile = join(ANSWERS, kind + ".json.gz");
+    const recorded: Record<string, string> = {};
+    const result = await runCase(ex, recorded, RECORD ? null : "sent");
+    const failed = result.views.filter((v) => v.error && /not recorded/.test(v.error));
+    assert.deepEqual(failed.map((v) => v.error), [], "a view asked something the recording never answered");
+    if (RECORD) writeFileSync(answersFile, gzipSync(JSON.stringify(Object.fromEntries(Object.entries(recorded).sort())), { level: 9 }));
+    const out = render(ex.doc as unknown as AnalysisDoc, result);
+    if (!RECORD) {
+      const again = render(ex.doc as unknown as AnalysisDoc, await runCase(ex, recorded, "reversed"));
+      for (const ext of Object.keys(out))
+        assert.equal(again[ext], out[ext], kind + "." + ext + " depends on the order database answers arrive: combine a view's answers " +
+          "after they are all in, in a fixed order (askEachSite in lib/views/shared.ts), not as each one lands");
+    }
+    for (const [ext, text] of Object.entries(out)) {
+      const file = join(DIR, kind + "." + ext);
+      if (UPDATE) { writeFileSync(file, text); continue; }
+      assert.ok(existsSync(file), file + " is missing — RECORD_SNAPSHOTS=1, see this file's header");
+      assert.equal(text, readFileSync(file, "utf8"), kind + "." + ext + " changed — review the diff; if intended, UPDATE_SNAPSHOTS=1 npm test");
+    }
+  });
+}