Fraud Triage
Sequential risk decisions on synthetic transactions, with asymmetric rewards for catching fraud and avoiding false positives.
diff --git a/docs/source/environments/pathway_analysis.md b/docs/source/environments/pathway_analysis.md
new file mode 100644
index 0000000000..eed98abe0d
--- /dev/null
+++ b/docs/source/environments/pathway_analysis.md
@@ -0,0 +1,116 @@
+
+# Pathway Analysis Environment
+
+A toy pathway analysis environment for [OpenEnv](https://github.com/meta-pytorch/OpenEnv) that models a simple computational biology task: identifying the activated signaling pathway from synthetic omics data.
+
+## Overview
+
+Each episode hides a **true activated pathway** (e.g. "MAPK signaling"). **Mode A (pipeline)** cases ship a count matrix plus per-sample metadata in JSON; differential expression uses **PyDESeq2** (Wald test) on genes that pass a **minimum total-count prefilter** (DESeq2-style), and enrichment uses **Fisher ORA** against the **same gene universe** with **SciPy Benjamini–Hochberg** q-values. The DE gene list for ORA can be restricted by **direction** (up / down / both) and **minimum |log2FC|**, matching common pathway-activation workflows. **Legacy** cases keep static top-gene / top-pathway lists for fast tests.
+
+### Intended agent workflow (RNA-seq style)
+
+1. **Groups & design** — Learn how many **conditions / groups** exist and how samples map to them (`understand_experiment_design`, optionally `inspect_dataset`). The structured observation includes `samples_per_condition` and `conditions`.
+2. **DGE (differential expression)** — Choose **which two groups to compare** (reference vs alternate) for DESeq2, then `run_differential_expression`. You can validate the contrast first with `understand_experiment_design` + `condition_a` / `condition_b`.
+3. **Pathways** — Use DE genes for `run_pathway_enrichment`, optional `compare_pathways`, then `submit_answer` with a pathway hypothesis.
+
+Optional per-case **`analysis_options`** (see `server/analysis.py` defaults): `min_total_count`, `padj_alpha`, `de_query_direction` (`up` | `down` | `both`), `min_abs_log2fc`, `ora_min_pathway_genes` (skip tiny pathways).
+
+Observations include rich DE rows (`log2FoldChange`, `padj`), ORA tables, overlap summaries (genes supporting multiple top pathways), ambiguity flags when top hits are statistically close, an optional **`ask_expert`** budget with penalties, and an **HTML episode trace** path (`trace_path`) for auditing.
+
+## Actions
+
+| Action | Description | Reward (typical) |
+|--------|-------------|------------------|
+| `inspect_dataset` | View conditions, sample metadata, PyDESeq2 availability | +0.05 |
+| `understand_experiment_design` | Structured design summary (`samples_per_condition`, defaults); optional `condition_a`/`condition_b` validate reference/alternate (does **not** run DESeq2). Valid pair is reused by `run_differential_expression` when DE omits conditions | +0.05 summary / +0.08 validated |
+| `run_differential_expression` | PyDESeq2 contrast (`condition_a`/`condition_b`, or validated design, or `default_contrast` in case JSON) | +0.35 pipeline / +0.25 legacy |
+| `run_pathway_enrichment` | Fisher ORA vs `pathway_genes` in the case | +0.5 |
+| `compare_pathways` | Exclusive vs shared DE support for `pathway_a` vs `pathway_b` | +0.15 |
+| `ask_expert` | Hint from case (`expert_hint`); uses budget; penalized | negative (see `expert_penalty`) |
+| `submit_answer` | Submit `hypothesis` pathway name (correct: +2.0, incorrect: -1.0) | terminal |
+
+**Strict mode** (`strict_mode` in JSON or `reset(strict=True)`): invalid contrasts or missing PyDESeq2 end the episode with a failure signal instead of silent fallback.
+
+Stable machine-readable error labels: see **[docs/FAILURE_CODES.md](https://github.com/openenv/OpenEnv/blob/main/envs/pathway_analysis_env/docs/FAILURE_CODES.md)** (`metadata["failure_code"]`).
+
+## Scripts
+
+- `scripts/generate_curriculum_cases.py` — writes ramped synthetic cases under `data/curriculum/`.
+- `scripts/run_benchmark.py` — fixed policy + accuracy / action trace (`--json-out`).
+- `bench/` — optional “gold-standard” RNA-seq toolchain (STAR/featureCounts/Salmon) for studies that require FASTQ→counts benchmarking.
+
+## Quick Start
+
+### Install
+
+```bash
+cd envs/pathway_analysis_env
+pip install -e .
+```
+
+### Direct test (no server)
+
+```bash
+PYTHONPATH=src:envs python -c "
+from pathway_analysis_env.server.pathway_environment import PathwayEnvironment
+from pathway_analysis_env.models import PathwayAction
+
+env = PathwayEnvironment()
+obs = env.reset()
+print(obs.message)
+
+obs = env.step(PathwayAction(action_type='inspect_dataset'))
+print(obs.message, 'reward:', obs.reward)
+
+obs = env.step(PathwayAction(action_type='run_differential_expression'))
+print(obs.message, obs.top_genes, 'reward:', obs.reward)
+
+obs = env.step(PathwayAction(action_type='run_pathway_enrichment'))
+print(obs.message, obs.top_pathways, 'reward:', obs.reward)
+
+obs = env.step(PathwayAction(action_type='submit_answer', hypothesis='MAPK signaling'))
+print(obs.message, 'done:', obs.done, 'reward:', obs.reward)
+"
+```
+
+### Run the HTTP server
+
+```bash
+cd envs/pathway_analysis_env
+uv run server
+# or: uvicorn pathway_analysis_env.server.app:app --host 0.0.0.0 --port 8000
+```
+
+The Gradio UI (**Playground** + **Pathway lab**) is **on by default** for this app. To disable it (API-only server), set `ENABLE_WEB_INTERFACE=false` before starting.
+
+Open **http://localhost:8000/web/** — use the **Visualization** tab (**Pathway lab**) for the guided workflow.
+
+**Step-by-step UI guide:** [docs/USER_GUIDE_UI.md](https://github.com/openenv/OpenEnv/blob/main/envs/pathway_analysis_env/docs/USER_GUIDE_UI.md) (what each button does, results panels, first-run example).
+
+### Connect via client
+
+```python
+import asyncio
+from pathway_analysis_env import PathwayEnv, PathwayAction
+
+async def main():
+ async with PathwayEnv(base_url="http://localhost:8000") as client:
+ result = await client.reset()
+ print(result.observation.message)
+
+ result = await client.step(PathwayAction(action_type="inspect_dataset"))
+ result = await client.step(PathwayAction(action_type="run_differential_expression"))
+ result = await client.step(PathwayAction(action_type="run_pathway_enrichment"))
+ result = await client.step(PathwayAction(action_type="submit_answer", hypothesis="MAPK signaling"))
+ print("done:", result.done, "reward:", result.reward)
+
+asyncio.run(main())
+```
+
+## Roadmap
+
+| Version | Change | Why |
+|---------|--------|-----|
+| v2 | Replace hard-coded outputs with precomputed CSV/JSON from a real expression dataset | Keeps behavior deterministic while feeling more realistic |
+| v3 | Swap analysis actions to call real DE and enrichment tools (PyDESeq2, GSEApy) | Turns the toy env into a real computational workflow env |
+| v4 | Add multi-omics tasks, richer rewards, and BixBench-inspired task templates | Moves closer to useful scientific-agent evaluation |
diff --git a/envs/pathway_analysis_env/.gitignore b/envs/pathway_analysis_env/.gitignore
new file mode 100644
index 0000000000..f7a0663bf3
--- /dev/null
+++ b/envs/pathway_analysis_env/.gitignore
@@ -0,0 +1 @@
+outputs/pathway_traces/
diff --git a/envs/pathway_analysis_env/README.md b/envs/pathway_analysis_env/README.md
new file mode 100644
index 0000000000..0fe9c7088c
--- /dev/null
+++ b/envs/pathway_analysis_env/README.md
@@ -0,0 +1,115 @@
+# Pathway Analysis Environment
+
+A toy pathway analysis environment for [OpenEnv](https://github.com/meta-pytorch/OpenEnv) that models a simple computational biology task: identifying the activated signaling pathway from synthetic omics data.
+
+## Overview
+
+Each episode hides a **true activated pathway** (e.g. "MAPK signaling"). **Mode A (pipeline)** cases ship a count matrix plus per-sample metadata in JSON; differential expression uses **PyDESeq2** (Wald test) on genes that pass a **minimum total-count prefilter** (DESeq2-style), and enrichment uses **Fisher ORA** against the **same gene universe** with **SciPy Benjamini–Hochberg** q-values. The DE gene list for ORA can be restricted by **direction** (up / down / both) and **minimum |log2FC|**, matching common pathway-activation workflows. **Legacy** cases keep static top-gene / top-pathway lists for fast tests.
+
+### Intended agent workflow (RNA-seq style)
+
+1. **Groups & design** — Learn how many **conditions / groups** exist and how samples map to them (`understand_experiment_design`, optionally `inspect_dataset`). The structured observation includes `samples_per_condition` and `conditions`.
+2. **DGE (differential expression)** — Choose **which two groups to compare** (reference vs alternate) for DESeq2, then `run_differential_expression`. You can validate the contrast first with `understand_experiment_design` + `condition_a` / `condition_b`.
+3. **Pathways** — Use DE genes for `run_pathway_enrichment`, optional `compare_pathways`, then `submit_answer` with a pathway hypothesis.
+
+Optional per-case **`analysis_options`** (see `server/analysis.py` defaults): `min_total_count`, `padj_alpha`, `de_query_direction` (`up` | `down` | `both`), `min_abs_log2fc`, `ora_min_pathway_genes` (skip tiny pathways).
+
+Observations include rich DE rows (`log2FoldChange`, `padj`), ORA tables, overlap summaries (genes supporting multiple top pathways), ambiguity flags when top hits are statistically close, an optional **`ask_expert`** budget with penalties, and an **HTML episode trace** path (`trace_path`) for auditing.
+
+## Actions
+
+| Action | Description | Reward (typical) |
+|--------|-------------|------------------|
+| `inspect_dataset` | View conditions, sample metadata, PyDESeq2 availability | +0.05 |
+| `understand_experiment_design` | Structured design summary (`samples_per_condition`, defaults); optional `condition_a`/`condition_b` validate reference/alternate (does **not** run DESeq2). Valid pair is reused by `run_differential_expression` when DE omits conditions | +0.05 summary / +0.08 validated |
+| `run_differential_expression` | PyDESeq2 contrast (`condition_a`/`condition_b`, or validated design, or `default_contrast` in case JSON) | +0.35 pipeline / +0.25 legacy |
+| `run_pathway_enrichment` | Fisher ORA vs `pathway_genes` in the case | +0.5 |
+| `compare_pathways` | Exclusive vs shared DE support for `pathway_a` vs `pathway_b` | +0.15 |
+| `ask_expert` | Hint from case (`expert_hint`); uses budget; penalized | negative (see `expert_penalty`) |
+| `submit_answer` | Submit `hypothesis` pathway name (correct: +2.0, incorrect: -1.0) | terminal |
+
+**Strict mode** (`strict_mode` in JSON or `reset(strict=True)`): invalid contrasts or missing PyDESeq2 end the episode with a failure signal instead of silent fallback.
+
+Stable machine-readable error labels: see **[docs/FAILURE_CODES.md](docs/FAILURE_CODES.md)** (`metadata["failure_code"]`).
+
+## Scripts
+
+- `scripts/generate_curriculum_cases.py` — writes ramped synthetic cases under `data/curriculum/`.
+- `scripts/run_benchmark.py` — fixed policy + accuracy / action trace (`--json-out`).
+- `bench/` — optional “gold-standard” RNA-seq toolchain (STAR/featureCounts/Salmon) for studies that require FASTQ→counts benchmarking.
+
+## Quick Start
+
+### Install
+
+```bash
+cd envs/pathway_analysis_env
+pip install -e .
+```
+
+### Direct test (no server)
+
+```bash
+PYTHONPATH=src:envs python -c "
+from pathway_analysis_env.server.pathway_environment import PathwayEnvironment
+from pathway_analysis_env.models import PathwayAction
+
+env = PathwayEnvironment()
+obs = env.reset()
+print(obs.message)
+
+obs = env.step(PathwayAction(action_type='inspect_dataset'))
+print(obs.message, 'reward:', obs.reward)
+
+obs = env.step(PathwayAction(action_type='run_differential_expression'))
+print(obs.message, obs.top_genes, 'reward:', obs.reward)
+
+obs = env.step(PathwayAction(action_type='run_pathway_enrichment'))
+print(obs.message, obs.top_pathways, 'reward:', obs.reward)
+
+obs = env.step(PathwayAction(action_type='submit_answer', hypothesis='MAPK signaling'))
+print(obs.message, 'done:', obs.done, 'reward:', obs.reward)
+"
+```
+
+### Run the HTTP server
+
+```bash
+cd envs/pathway_analysis_env
+uv run server
+# or: uvicorn pathway_analysis_env.server.app:app --host 0.0.0.0 --port 8000
+```
+
+The Gradio UI (**Playground** + **Pathway lab**) is **on by default** for this app. To disable it (API-only server), set `ENABLE_WEB_INTERFACE=false` before starting.
+
+Open **http://localhost:8000/web/** — use the **Visualization** tab (**Pathway lab**) for the guided workflow.
+
+**Step-by-step UI guide:** [docs/USER_GUIDE_UI.md](docs/USER_GUIDE_UI.md) (what each button does, results panels, first-run example).
+
+### Connect via client
+
+```python
+import asyncio
+from pathway_analysis_env import PathwayEnv, PathwayAction
+
+async def main():
+ async with PathwayEnv(base_url="http://localhost:8000") as client:
+ result = await client.reset()
+ print(result.observation.message)
+
+ result = await client.step(PathwayAction(action_type="inspect_dataset"))
+ result = await client.step(PathwayAction(action_type="run_differential_expression"))
+ result = await client.step(PathwayAction(action_type="run_pathway_enrichment"))
+ result = await client.step(PathwayAction(action_type="submit_answer", hypothesis="MAPK signaling"))
+ print("done:", result.done, "reward:", result.reward)
+
+asyncio.run(main())
+```
+
+## Roadmap
+
+| Version | Change | Why |
+|---------|--------|-----|
+| v2 | Replace hard-coded outputs with precomputed CSV/JSON from a real expression dataset | Keeps behavior deterministic while feeling more realistic |
+| v3 | Swap analysis actions to call real DE and enrichment tools (PyDESeq2, GSEApy) | Turns the toy env into a real computational workflow env |
+| v4 | Add multi-omics tasks, richer rewards, and BixBench-inspired task templates | Moves closer to useful scientific-agent evaluation |
diff --git a/envs/pathway_analysis_env/__init__.py b/envs/pathway_analysis_env/__init__.py
new file mode 100644
index 0000000000..e41e96075c
--- /dev/null
+++ b/envs/pathway_analysis_env/__init__.py
@@ -0,0 +1,27 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Pathway Analysis Environment for OpenEnv.
+
+A toy computational-biology environment where an agent identifies the
+activated signaling pathway from synthetic omics data.
+
+Example:
+ >>> from pathway_analysis_env import PathwayEnv, PathwayAction
+ >>>
+ >>> with PathwayEnv(base_url="http://localhost:8000") as client:
+ ... result = client.reset()
+ ... result = client.step(PathwayAction(action_type="inspect_dataset"))
+ ... result = client.step(PathwayAction(action_type="run_differential_expression"))
+ ... result = client.step(PathwayAction(action_type="run_pathway_enrichment"))
+ ... result = client.step(PathwayAction(action_type="submit_answer", hypothesis="MAPK signaling"))
+"""
+
+from .client import PathwayEnv
+from .models import PathwayAction, PathwayObservation, PathwayState
+
+__all__ = ["PathwayEnv", "PathwayAction", "PathwayObservation", "PathwayState"]
diff --git a/envs/pathway_analysis_env/bench/Dockerfile b/envs/pathway_analysis_env/bench/Dockerfile
new file mode 100644
index 0000000000..8c17aa11d5
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/Dockerfile
@@ -0,0 +1,69 @@
+# Pathway Analysis Env - Benchmark image
+#
+# Purpose: provide a reproducible "gold-standard" RNA-seq toolchain for benchmarks
+# (STAR/featureCounts or Salmon), without bloating the normal serving image.
+#
+# Build:
+# docker build -t openenv-pathway-bench:latest -f envs/pathway_analysis_env/bench/Dockerfile .
+#
+# Run (example):
+# docker run --rm -it -v "$PWD":/work openenv-pathway-bench:latest bash
+
+ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest
+FROM ${BASE_IMAGE}
+
+SHELL ["/bin/bash", "-lc"]
+
+WORKDIR /work
+
+# System deps for downloading, building indexes, and basic tooling.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ ca-certificates \
+ curl \
+ bzip2 \
+ gzip \
+ pigz \
+ unzip \
+ xz-utils \
+ zstd \
+ git \
+ locales \
+ && rm -rf /var/lib/apt/lists/*
+
+# Bioconda ``salmon`` links against system libstdc++ and needs a real generated locale.
+RUN sed -i 's/^# *\(en_US.UTF-8 UTF-8\)/\1/' /etc/locale.gen \
+ && locale-gen
+
+ENV LANG=en_US.UTF-8
+ENV LC_ALL=en_US.UTF-8
+
+# Install micromamba (for bioconda packages).
+ENV MAMBA_ROOT_PREFIX=/opt/micromamba
+RUN curl -LsSf https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj bin/micromamba \
+ && install -m 0755 bin/micromamba /usr/local/bin/micromamba \
+ && rm -rf bin
+
+# Create a dedicated conda env with RNA-seq tools.
+# Note: versions are intentionally not pinned here; for long-term reproducibility,
+# pin in an environment.yml and lock if needed.
+RUN micromamba create -y -n rnaseq -c conda-forge -c bioconda \
+ python=3.11 \
+ pandas \
+ numpy \
+ sra-tools \
+ fastqc \
+ multiqc \
+ samtools \
+ "subread>=2.0.2" \
+ star \
+ salmon \
+ && micromamba clean -a -y
+
+ENV PATH="/opt/micromamba/envs/rnaseq/bin:${PATH}"
+
+# Make matplotlib cache writable inside container (gseapy pulls matplotlib indirectly).
+ENV MPLCONFIGDIR=/work/envs/pathway_analysis_env/outputs/.mplcache
+
+# Default to bash for interactive benchmarking.
+CMD ["bash"]
+
diff --git a/envs/pathway_analysis_env/bench/README.md b/envs/pathway_analysis_env/bench/README.md
new file mode 100644
index 0000000000..19d7c32cf1
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/README.md
@@ -0,0 +1,124 @@
+# Benchmark toolchain (STAR/featureCounts/Salmon)
+
+This folder provides an **optional** “gold-standard” RNA-seq toolchain for benchmarking OpenEnv on GEO studies that only provide FASTQs (or when you want to validate counts-derived DESeq2 from first principles).
+
+It is intentionally separated from the normal server image to keep the environment lightweight for typical use.
+
+## Build the benchmark image
+
+From the repo root:
+
+```bash
+# Apple Silicon note: build/run as linux/amd64 for bioconda bioinformatics tools.
+docker build --platform linux/amd64 -t openenv-pathway-bench:latest -f envs/pathway_analysis_env/bench/Dockerfile .
+```
+
+## What’s inside
+
+The benchmark image installs (via bioconda):
+
+- `STAR` (alignment)
+- `featureCounts` (from `subread`)
+- `salmon` (alignment-free quantification)
+- `sra-tools` (download FASTQs)
+- `samtools`, `fastqc`, `multiqc` (QC + utilities)
+
+## Next step (workflow)
+
+To run a true-counts benchmark you typically:
+
+1. Download FASTQs for your study’s samples (SRA)
+2. Quantify expression into a **gene-level count matrix** (STAR+featureCounts or Salmon)
+3. Feed the counts into OpenEnv (case JSON with `counts_file` + `sample_ids` + `sample_metadata`)
+4. Compare OpenEnv DE to author DE (if available)
+
+## STAR vs Salmon (which to choose?)
+
+If your priority is **maximum credibility** (paper-style “raw counts → DESeq2”), use:
+
+- **STAR + featureCounts** (alignment-based; **recommended default**)
+
+Use **Salmon** when you need speed / many studies and are comfortable with transcript-first quantification and gene-level summarization.
+
+## `run_true_counts_pipeline.py`
+
+Implements **`--method star`** (STAR + featureCounts) and **`--method salmon`** (Salmon → gene-level counts).
+
+Speed-oriented flags (defaults tuned for laptops):
+
+- **`--fetch-parallelism`** (default `3`): concurrent `fasterq-dump` jobs; per-job threads ≈ `--threads / fetch-parallelism`.
+- **`--quant-parallelism`** (default `2`): concurrent `salmon quant` or STAR per-sample alignments; per-job threads ≈ `--threads / quant-parallelism`.
+- Salmon passes **`--numBootstraps 0`** for faster quantification.
+
+## Getting hg38 FASTA + GTF (best default)
+
+For maximum credibility, match the common “hg38/GRCh38 + GENCODE annotation” setup:
+
+- Download reference files:
+
+```bash
+python envs/pathway_analysis_env/bench/download_hg38_gencode.py --out-dir /work/ref/hg38_gencode_v47
+```
+
+The downloader retries HTTP failures and validates the full gzip stream before atomically publishing each reference. Valid cached files are reused without downloading. An invalid cached file stops the run and is preserved: move aside or explicitly remove only the file named in the error before rerunning. Temporary downloads from the current run are cleaned up on failure. The pipeline accepts both plain `.gtf` and compressed `.gtf.gz` annotations for STAR and Salmon.
+
+This writes:
+- `GRCh38.primary_assembly.genome.fa.gz` (FASTA)
+- `gencode.v47.annotation.gtf.gz` (GTF)
+
+## Study 3 (GSE235350) note
+
+GSE235350’s GEO supplements contain **bigWig tracks** (`*.bw`) but **no gene count matrix**.
+To run DESeq2 in OpenEnv you must generate counts from SRA FASTQs.
+
+A ready sample sheet is provided:
+
+- `bench/studies/gse235350_samples.json`
+
+To run Study 3 end-to-end (downloads reference + FASTQs + counts + OpenEnv outputs):
+
+- `bench/run_study3_gse235350_end_to_end.py`
+
+Example (FASTQ + Salmon inside Docker; **OpenEnv DESeq2 step expects `uv` on the host** unless you extend the image):
+
+```bash
+docker run --platform linux/amd64 --rm -v "$PWD":/work openenv-pathway-bench:latest \
+ python envs/pathway_analysis_env/bench/run_study3_gse235350_end_to_end.py --skip-openenv
+```
+
+Then on the host (repo root), run DESeq2 + Enrichr:
+
+```bash
+cd envs/pathway_analysis_env && PYTHONPATH="../../src:../../envs" uv run python bench/geo_openenv_deseq2.py \
+ --repo-root ../.. --env-dir . \
+ --case-json data/geo_eval/gse235350_true_counts/gse235350_case.json \
+ --out-dir data/geo_eval/gse235350_true_counts \
+ --summary-extra '{"study":"GSE235350","contrast":"palbociclib vs control (reference=control)"}'
+```
+
+Smaller smoke test (first *N* samples in the sheet):
+
+```bash
+python envs/pathway_analysis_env/bench/run_study3_gse235350_end_to_end.py --max-samples 2 --threads 8 --fetch-parallelism 2 --quant-parallelism 2
+```
+
+## Study 4 / Study 5 (counts or TPM on GEO)
+
+These runners download GEO supplements and call **`bench/geo_openenv_deseq2.py`** (needs **`uv`** in `PATH`, cwd `envs/pathway_analysis_env`):
+
+- **Study 4 — GSE111151:** `bench/run_study4_gse111151_end_to_end.py` (legacy per-sample count supplements; 8 samples on the FTP mirror).
+- **Study 5 — GSE216540:** `bench/run_study5_gse216540_end_to_end.py` (TPM matrix → **pseudo-counts** for pipeline testing; default fast 8-sample subset; add `--full` for all columns).
+
+```bash
+PYTHONPATH=src:envs uv run python envs/pathway_analysis_env/bench/run_study4_gse111151_end_to_end.py
+PYTHONPATH=src:envs uv run python envs/pathway_analysis_env/bench/run_study5_gse216540_end_to_end.py
+```
+
+
+### Counting and filtering contract
+
+The STAR path requires Subread **2.0.2 or newer** (also enforced by the benchmark image's package constraint). Paired FASTQs use `featureCounts -p --countReadPairs` to count fragments; single-end FASTQs use neither flag. Mixed single/paired batches are rejected before alignment. See the [Subread release notes](https://subread.sourceforge.net/) and [user guide](https://subread.sourceforge.net/SubreadUsersGuide.pdf). Earlier versions gave `-p` different semantics and are unsupported here.
+
+The GEO DE runner applies the case's `min_total_count` filter before fitting and uses the retained genes for its ORA background, matching the environment. Fewer than five retained genes aborts analysis. Summaries distinguish `genes_in_matrix` from `genes_after_prefilter`.
+
+Gene identifiers remain the annotation's `gene_id`; no Ensembl-to-symbol mapping is implied. Annotation provenance, identifier mapping, strandedness, and biological validation remain study-specific responsibilities.
diff --git a/envs/pathway_analysis_env/bench/download_hg38_gencode.py b/envs/pathway_analysis_env/bench/download_hg38_gencode.py
new file mode 100644
index 0000000000..bfd40e3b39
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/download_hg38_gencode.py
@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+
+"""
+Download a standard hg38/GRCh38 reference (GENCODE) for RNA-seq benchmarks.
+
+Why GENCODE?
+ - widely used in publications
+ - consistent gene models for human RNA-seq
+
+This script downloads:
+ - Genome FASTA (primary assembly) (for STAR)
+ - Annotation GTF (for featureCounts and transcript→gene mapping)
+ - Transcriptome FASTA (for Salmon)
+
+It writes into an output directory and prints the resulting paths.
+"""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import hashlib
+import subprocess
+import tempfile
+import zlib
+from pathlib import Path
+
+
+def _run(cmd: list[str]) -> None:
+ subprocess.check_call(cmd)
+
+
+def _sha256(path: Path) -> str:
+ h = hashlib.sha256()
+ with path.open("rb") as f:
+ for chunk in iter(lambda: f.read(1024 * 1024), b""):
+ h.update(chunk)
+ return h.hexdigest()
+
+
+def _validate_gzip(path: Path) -> None:
+ """Read through EOF to verify gzip CRC/trailer without holding a genome in RAM."""
+ with gzip.open(path, "rb") as stream:
+ if not stream.read(1):
+ raise ValueError(f"Empty reference: {path}")
+ while stream.read(1024 * 1024):
+ pass
+
+
+def _download_reference(url: str, path: Path) -> None:
+ if path.exists():
+ try:
+ _validate_gzip(path)
+ except (OSError, EOFError, ValueError, zlib.error) as exc:
+ raise ValueError(
+ f"Invalid cached reference {path}; move this file aside or remove "
+ "it explicitly before rerunning. Other files are preserved."
+ ) from exc
+ return
+
+ # Use the destination filesystem so replacement is atomic. Only this run's
+ # temporary file is removed on failure; never publish HTTP bodies/partials.
+ with tempfile.NamedTemporaryFile(
+ dir=path.parent, prefix=f".{path.name}.", suffix=".part", delete=False
+ ) as stream:
+ temporary = Path(stream.name)
+ try:
+ _run(
+ [
+ "curl",
+ "-L",
+ "--fail",
+ "--retry",
+ "5",
+ "--retry-delay",
+ "2",
+ "-o",
+ str(temporary),
+ url,
+ ]
+ )
+ _validate_gzip(temporary)
+ temporary.replace(path)
+ finally:
+ temporary.unlink(missing_ok=True)
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--out-dir", type=Path, required=True)
+ ap.add_argument("--gencode-release", type=str, default="47")
+ args = ap.parse_args()
+
+ out_dir: Path = args.out_dir
+ out_dir.mkdir(parents=True, exist_ok=True)
+
+ rel = str(args.gencode_release).strip().lstrip("v")
+ # GENCODE paths are stable; use the "current" release number.
+ fasta_url = (
+ f"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_{rel}/"
+ f"GRCh38.primary_assembly.genome.fa.gz"
+ )
+ gtf_url = (
+ f"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_{rel}/"
+ f"gencode.v{rel}.annotation.gtf.gz"
+ )
+ tx_url = (
+ f"https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_{rel}/"
+ f"gencode.v{rel}.transcripts.fa.gz"
+ )
+
+ fasta_path = out_dir / "GRCh38.primary_assembly.genome.fa.gz"
+ gtf_path = out_dir / f"gencode.v{rel}.annotation.gtf.gz"
+ tx_path = out_dir / f"gencode.v{rel}.transcripts.fa.gz"
+
+ for url, path in ((fasta_url, fasta_path), (gtf_url, gtf_path), (tx_url, tx_path)):
+ _download_reference(url, path)
+
+ print("[OK] FASTA:", fasta_path)
+ print("[OK] GTF:", gtf_path)
+ print("[OK] TX:", tx_path)
+ # Lightweight integrity signal (not an official checksum).
+ print("[SHA256] FASTA:", _sha256(fasta_path))
+ print("[SHA256] GTF:", _sha256(gtf_path))
+ print("[SHA256] TX:", _sha256(tx_path))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/bench/geo_agent_eval.py b/envs/pathway_analysis_env/bench/geo_agent_eval.py
new file mode 100644
index 0000000000..0664835016
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/geo_agent_eval.py
@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+"""
+Lightweight evaluation harness for GEO-style pathway_analysis_env cases.
+
+Goal
+ - Run the environment policy loop (inspect → DE → ORA)
+ - Extract top enriched pathway terms
+ - Score against a simple "published theme" rubric (keyword lists)
+
+This intentionally avoids calling any external LLM API. You can plug in an agent
+later by replacing `choose_hypothesis(...)`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from pathway_analysis_env.models import PathwayAction
+from pathway_analysis_env.server.pathway_environment import PathwayEnvironment, load_case
+
+
+@dataclass(frozen=True)
+class StudySpec:
+ case_file: str
+ expected_keywords: List[str]
+ top_k: int = 15
+
+
+def _norm(s: str) -> str:
+ return "".join(ch.lower() if ch.isalnum() or ch.isspace() else " " for ch in s).strip()
+
+
+def extract_top_pathway_names(observation: Any, top_k: int) -> List[str]:
+ # Preferred: PathwayObservation fields
+ try:
+ if getattr(observation, "top_pathways", None):
+ return list(observation.top_pathways)[:top_k]
+ rows = getattr(observation, "pathway_enrichment", None) or []
+ except Exception:
+ rows = []
+
+ # Fallback: some callers may stash rows in metadata
+ meta = getattr(observation, "metadata", None) or {}
+ rows = rows or meta.get("pathway_enrichment") or meta.get("ora_rows") or meta.get("ora") or []
+
+ names: List[str] = []
+ for r in rows:
+ if not isinstance(r, dict):
+ continue
+ name = r.get("term") or r.get("pathway") or r.get("name") or r.get("Term")
+ if isinstance(name, str) and name.strip():
+ names.append(name.strip())
+ if len(names) >= top_k:
+ break
+ return names
+
+
+def choose_hypothesis(top_pathways: List[str], expected_keywords: List[str]) -> str:
+ """
+ Baseline "agent": pick the first enriched pathway that matches any keyword.
+ If none match, fall back to the top enriched pathway (or a placeholder).
+ """
+ kws = [_norm(k) for k in expected_keywords]
+ for p in top_pathways:
+ pn = _norm(p)
+ if any(k in pn for k in kws if k):
+ return p
+ return top_pathways[0] if top_pathways else "Unknown"
+
+
+def score(top_pathways: List[str], expected_keywords: List[str]) -> Dict[str, Any]:
+ joined = " | ".join(_norm(p) for p in top_pathways)
+ hits = []
+ for kw in expected_keywords:
+ k = _norm(kw)
+ if k and k in joined:
+ hits.append(kw)
+ return {
+ "keyword_hits": hits,
+ "keyword_hit_rate": (len(hits) / max(1, len(expected_keywords))),
+ "any_hit": bool(hits),
+ }
+
+
+def run_one(spec: StudySpec, strict: bool) -> Dict[str, Any]:
+ case = load_case(spec.case_file)
+ ref = (case.get("default_contrast") or {}).get("reference")
+ alt = (case.get("default_contrast") or {}).get("alternate")
+
+ env = PathwayEnvironment(case_file=spec.case_file)
+ env.reset(strict=strict)
+
+ env.step(PathwayAction(action_type="inspect_dataset"))
+ o_de = env.step(
+ PathwayAction(
+ action_type="run_differential_expression",
+ condition_a=ref,
+ condition_b=alt,
+ )
+ )
+ if (o_de.metadata or {}).get("failure_code"):
+ return {
+ "case_id": case.get("case_id"),
+ "case_file": spec.case_file,
+ "strict_failure": bool(o_de.done),
+ "stage_failure": "differential_expression",
+ "failure_code": (o_de.metadata or {}).get("failure_code"),
+ "message": o_de.message,
+ "trace_path": o_de.trace_path,
+ }
+ if o_de.done:
+ return {
+ "case_id": case.get("case_id"),
+ "case_file": spec.case_file,
+ "strict_failure": True,
+ "failure_code": (o_de.metadata or {}).get("failure_code"),
+ "message": o_de.message,
+ }
+
+ o_ora = env.step(PathwayAction(action_type="run_pathway_enrichment"))
+ if not getattr(o_ora, "top_pathways", None) and (getattr(o_ora, "metadata", None) or {}).get(
+ "failure_code"
+ ):
+ return {
+ "case_id": case.get("case_id"),
+ "case_file": spec.case_file,
+ "strict_failure": False,
+ "stage_failure": "enrichment",
+ "failure_code": (o_ora.metadata or {}).get("failure_code"),
+ "message": o_ora.message,
+ "trace_path": o_ora.trace_path,
+ }
+ top = extract_top_pathway_names(o_ora, top_k=spec.top_k)
+ hyp = choose_hypothesis(top, spec.expected_keywords)
+ scoring = score(top, spec.expected_keywords)
+
+ # We submit the hypothesis so the trace has an explicit "answer" action.
+ o_fin = env.step(PathwayAction(action_type="submit_answer", hypothesis=hyp))
+
+ return {
+ "case_id": case.get("case_id"),
+ "case_file": spec.case_file,
+ "strict_failure": False,
+ "hypothesis": hyp,
+ "top_pathways": top,
+ "expected_keywords": spec.expected_keywords,
+ "score": scoring,
+ "trace_path": o_fin.trace_path,
+ }
+
+
+def load_specs(path: Path) -> List[StudySpec]:
+ raw = json.loads(path.read_text(encoding="utf-8"))
+ specs: List[StudySpec] = []
+ for item in raw.get("studies", []):
+ specs.append(
+ StudySpec(
+ case_file=item["case_file"],
+ expected_keywords=list(item["expected_keywords"]),
+ top_k=int(item.get("top_k", 15)),
+ )
+ )
+ return specs
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument(
+ "--spec",
+ type=Path,
+ default=Path("data/geo_eval/geo_agent_eval_studies.json"),
+ help="JSON spec with case files + expected keyword rubrics.",
+ )
+ ap.add_argument("--strict", action="store_true")
+ ap.add_argument("--jsonl-out", type=Path, default=None)
+ args = ap.parse_args()
+
+ specs = load_specs(args.spec)
+ results = [run_one(s, strict=args.strict) for s in specs]
+
+ if args.jsonl_out:
+ args.jsonl_out.parent.mkdir(parents=True, exist_ok=True)
+ args.jsonl_out.write_text("\n".join(json.dumps(r) for r in results) + "\n", encoding="utf-8")
+
+ print(json.dumps({"n": len(results), "results": results}, indent=2))
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/envs/pathway_analysis_env/bench/geo_openenv_deseq2.py b/envs/pathway_analysis_env/bench/geo_openenv_deseq2.py
new file mode 100644
index 0000000000..72e239d4c9
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/geo_openenv_deseq2.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""
+Run PyDESeq2 + Enrichr ORA for a pathway_analysis_env case JSON (counts on disk).
+
+Invoked from the repo (host) with PYTHONPATH set by the caller, or standalone:
+
+ PYTHONPATH=src:envs python envs/pathway_analysis_env/bench/geo_openenv_deseq2.py \\
+ --repo-root . --env-dir envs/pathway_analysis_env \\
+ --case-json envs/pathway_analysis_env/data/geo_eval/foo/case.json \\
+ --out-dir envs/pathway_analysis_env/data/geo_eval/foo \\
+ --summary-extra '{"study":"GSE…"}'
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Any, Dict
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--repo-root", type=Path, required=True)
+ ap.add_argument("--env-dir", type=Path, required=True)
+ ap.add_argument("--case-json", type=Path, required=True, help="Absolute path to case JSON")
+ ap.add_argument("--out-dir", type=Path, required=True, help="Directory for summary.json, de_all.json, enrichment.json")
+ ap.add_argument(
+ "--summary-extra",
+ default="{}",
+ help="JSON object merged into summary.json (e.g. study label, contrast string).",
+ )
+ args = ap.parse_args()
+
+ repo_root: Path = args.repo_root.resolve()
+ env_dir: Path = args.env_dir.resolve()
+ case_path: Path = args.case_json.resolve()
+ out_dir: Path = args.out_dir.resolve()
+
+ extra: Dict[str, Any]
+ try:
+ extra = json.loads(args.summary_extra)
+ except json.JSONDecodeError as e:
+ raise SystemExit(f"Invalid --summary-extra JSON: {e}") from e
+ if not isinstance(extra, dict):
+ raise SystemExit("--summary-extra must be a JSON object")
+
+ sys.path.insert(0, str(repo_root / "src"))
+ sys.path.insert(0, str(repo_root / "envs"))
+
+ mpl_dir = env_dir / "outputs" / ".mplcache"
+ mpl_dir.mkdir(parents=True, exist_ok=True)
+ os.environ.setdefault("MPLCONFIGDIR", str(mpl_dir))
+
+ os.chdir(env_dir)
+
+ from pathway_analysis_env.server.analysis import (
+ build_sample_metadata,
+ enrichr_ora,
+ filter_counts_by_minimum_total,
+ load_counts_csv_as_samples_by_genes,
+ merge_analysis_options,
+ pick_de_query_genes,
+ run_deseq2_contrast,
+ )
+
+ case = json.loads(case_path.read_text(encoding="utf-8"))
+ opts = merge_analysis_options(case)
+ counts_df = load_counts_csv_as_samples_by_genes(
+ Path("data") / case["counts_file"],
+ sample_ids=case["sample_ids"],
+ )
+ counts_df, n_genes_in, n_genes_filt = filter_counts_by_minimum_total(
+ counts_df, int(opts["min_total_count"])
+ )
+ if n_genes_filt < 5:
+ raise RuntimeError(
+ f"After min_total_count={opts['min_total_count']} prefilter, "
+ f"only {n_genes_filt} genes remain (need ≥5 for stable DESeq2)."
+ )
+ meta_df = build_sample_metadata(case["sample_ids"], case["sample_metadata"])
+ ref = case["default_contrast"]["reference"]
+ alt = case["default_contrast"]["alternate"]
+ rows, err = run_deseq2_contrast(
+ counts_df,
+ meta_df,
+ alt,
+ ref,
+ padj_alpha=float(opts["padj_alpha"]),
+ )
+ if err:
+ raise RuntimeError(err)
+ query = pick_de_query_genes(
+ rows,
+ padj_alpha=float(opts["padj_alpha"]),
+ direction=str(opts["de_query_direction"]),
+ min_abs_log2fc=float(opts["min_abs_log2fc"]),
+ )
+ if not query:
+ query = [r["gene"] for r in rows[:50]]
+ ora, oerr = enrichr_ora(
+ query,
+ libraries=list(case.get("enrichr_libraries") or []),
+ background=list(counts_df.columns),
+ top_k=100,
+ )
+
+ out_dir.mkdir(parents=True, exist_ok=True)
+ summary = {
+ **extra,
+ "genes_in_matrix": n_genes_in,
+ "genes_after_prefilter": n_genes_filt,
+ "n_de_rows_returned": len(rows),
+ "n_sig_padj_0.05": sum(1 for r in rows if r.get("significant")),
+ "enrichr_error": oerr,
+ }
+ (out_dir / "summary.json").write_text(json.dumps(summary, indent=2), encoding="utf-8")
+ (out_dir / "de_all.json").write_text(json.dumps(rows, indent=2), encoding="utf-8")
+ (out_dir / "enrichment.json").write_text(json.dumps(ora, indent=2), encoding="utf-8")
+ print("[OK] wrote", out_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/bench/run_study1_gse128911_end_to_end.py b/envs/pathway_analysis_env/bench/run_study1_gse128911_end_to_end.py
new file mode 100644
index 0000000000..cc960e8391
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/run_study1_gse128911_end_to_end.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+"""
+Study 1 (GSE128911): Dataset 2 integer counts → subset CSV → OpenEnv DESeq2 + ORA.
+
+Contrast: fulvestrant vs dmso (reference = dmso).
+Samples: SAM24360838, SAM24360839 (DMSO); SAM24360844, SAM24360845 (Fulvestrant).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+
+def _openenv_python(repo_root: Path, env_dir: Path) -> tuple[list[str], str]:
+ if shutil.which("uv"):
+ return ["uv", "run", "python"], str(env_dir)
+ return [sys.executable], str(env_dir)
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--skip-openenv", action="store_true")
+ args = ap.parse_args()
+
+ repo_root = Path(__file__).resolve().parents[3]
+ env_dir = repo_root / "envs" / "pathway_analysis_env"
+ bench_dir = env_dir / "bench"
+ geo_eval = env_dir / "data" / "geo_eval"
+ src_matrix = geo_eval / "GSE128911_RNA-seq_counts_Dataset2.tsv.gz"
+ out_dir = geo_eval / "gse128911_mda_mb_134_vi_fulvestrant_vs_dmso"
+ out_dir.mkdir(parents=True, exist_ok=True)
+
+ samples = ["SAM24360838", "SAM24360839", "SAM24360844", "SAM24360845"]
+ df = pd.read_csv(src_matrix, sep="\t", header=2, compression="infer", low_memory=False)
+ if "symbol" not in df.columns:
+ raise SystemExit(f"Expected 'symbol' column; got {df.columns[:15].tolist()}")
+ missing = [s for s in samples if s not in df.columns]
+ if missing:
+ raise SystemExit(f"Missing sample columns: {missing}")
+
+ sub = df[["symbol"] + samples].copy()
+ sub = sub.rename(columns={"symbol": "gene"})
+ sub["gene"] = sub["gene"].astype(str)
+ for c in samples:
+ sub[c] = pd.to_numeric(sub[c], errors="coerce").fillna(0.0).round().astype(int)
+ sub = sub.groupby("gene", as_index=False).sum()
+
+ counts_path = out_dir / "gse128911_dataset2_subset_counts.csv.gz"
+ sub.set_index("gene").to_csv(counts_path)
+
+ meta = {
+ "SAM24360838": "dmso",
+ "SAM24360839": "dmso",
+ "SAM24360844": "fulvestrant",
+ "SAM24360845": "fulvestrant",
+ }
+ case = {
+ "case_id": "GSE128911_mda_mb_134_vi_fulvestrant_vs_dmso",
+ "strict_mode": False,
+ "experiment_metadata": {
+ "accession": "GSE128911",
+ "reference": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128911",
+ "summary": "Dataset 2 count matrix subset (MDA-MB-134-VI; 2×2 DMSO vs Fulvestrant).",
+ },
+ "counts_file": "geo_eval/gse128911_mda_mb_134_vi_fulvestrant_vs_dmso/gse128911_dataset2_subset_counts.csv.gz",
+ "sample_ids": samples,
+ "sample_metadata": meta,
+ "conditions": sorted(set(meta.values())),
+ "default_contrast": {"reference": "dmso", "alternate": "fulvestrant"},
+ "analysis_options": {"min_total_count": 10, "padj_alpha": 0.05, "de_query_direction": "both"},
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (GEO benchmark)",
+ }
+ case_path = out_dir / "gse128911_case.json"
+ case_path.write_text(json.dumps(case, indent=2), encoding="utf-8")
+
+ if args.skip_openenv:
+ print("[DONE] counts + case only:", out_dir)
+ return
+
+ py, cwd = _openenv_python(repo_root, env_dir)
+ env = os.environ.copy()
+ env["PYTHONPATH"] = str(repo_root / "src") + ":" + str(repo_root / "envs")
+ env.setdefault("MPLCONFIGDIR", str(env_dir / "outputs" / ".mplcache"))
+ subprocess.check_call(
+ py
+ + [
+ str(bench_dir / "geo_openenv_deseq2.py"),
+ "--repo-root",
+ str(repo_root),
+ "--env-dir",
+ str(env_dir),
+ "--case-json",
+ str(case_path),
+ "--out-dir",
+ str(out_dir),
+ "--summary-extra",
+ json.dumps(
+ {
+ "study": "GSE128911",
+ "contrast": "fulvestrant vs dmso (reference=dmso)",
+ "cell_line": "MDA-MB-134-VI",
+ "samples": samples,
+ }
+ ),
+ ],
+ cwd=cwd,
+ env=env,
+ )
+ print("[DONE] Study 1 outputs in:", out_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/bench/run_study3_gse235350_end_to_end.py b/envs/pathway_analysis_env/bench/run_study3_gse235350_end_to_end.py
new file mode 100644
index 0000000000..6a730f89f9
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/run_study3_gse235350_end_to_end.py
@@ -0,0 +1,169 @@
+#!/usr/bin/env python3
+"""
+End-to-end runner for Study 3 (GSE235350):
+
+ FASTQ (SRA) → hg38/GENCODE → Salmon → OpenEnv DESeq2 + enrichment
+
+Designed to run on the **repo host** with ``uv`` (PyDESeq2 / gseapy), while FASTQ
+steps are often run in ``bench/Dockerfile`` where those conda tools exist.
+
+Outputs:
+ envs/pathway_analysis_env/data/geo_eval/gse235350_true_counts/
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+
+def _run(cmd: list[str], *, cwd: Path) -> None:
+ subprocess.check_call(cmd, cwd=str(cwd))
+
+
+def _openenv_python(repo_root: Path, env_dir: Path) -> tuple[list[str], str]:
+ if shutil.which("uv"):
+ return ["uv", "run", "python"], str(env_dir)
+ return [sys.executable], str(repo_root)
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--threads", type=int, default=4)
+ ap.add_argument(
+ "--fetch-parallelism",
+ type=int,
+ default=3,
+ help="Concurrent fasterq-dump jobs (passed to run_true_counts_pipeline.py).",
+ )
+ ap.add_argument(
+ "--quant-parallelism",
+ type=int,
+ default=2,
+ help="Concurrent salmon quant jobs (passed to run_true_counts_pipeline.py).",
+ )
+ ap.add_argument(
+ "--max-samples",
+ type=int,
+ default=0,
+ help="If >0, only the first N samples from the study sheet (smoke test).",
+ )
+ ap.add_argument("--skip-openenv", action="store_true", help="Stop after counts + case JSON in work dir.")
+ args = ap.parse_args()
+
+ repo_root = Path(__file__).resolve().parents[3]
+ env_dir = repo_root / "envs" / "pathway_analysis_env"
+ bench_dir = env_dir / "bench"
+
+ out_dir = env_dir / "data" / "geo_eval" / "gse235350_true_counts"
+ ref_dir = out_dir / "ref" / "hg38_gencode_v47"
+ work_dir = out_dir / "work"
+ work_dir.mkdir(parents=True, exist_ok=True)
+
+ samples_json = bench_dir / "studies" / "gse235350_samples.json"
+ if int(args.max_samples) > 0:
+ raw = json.loads(samples_json.read_text(encoding="utf-8"))
+ sub = raw[: int(args.max_samples)]
+ samples_json = work_dir / "gse235350_samples_subset.json"
+ samples_json.write_text(json.dumps(sub), encoding="utf-8")
+
+ _run(
+ [
+ "python",
+ str(bench_dir / "download_hg38_gencode.py"),
+ "--out-dir",
+ str(ref_dir),
+ ],
+ cwd=repo_root,
+ )
+
+ gtf = ref_dir / "gencode.v47.annotation.gtf.gz"
+ tx = ref_dir / "gencode.v47.transcripts.fa.gz"
+
+ counts_out = work_dir / "gse235350_counts.csv.gz"
+ case_out = work_dir / "gse235350_case.json"
+
+ _run(
+ [
+ "python",
+ str(bench_dir / "run_true_counts_pipeline.py"),
+ "--method",
+ "salmon",
+ "--samples-json",
+ str(samples_json),
+ "--out-dir",
+ str(work_dir / "pipeline"),
+ "--threads",
+ str(int(args.threads)),
+ "--fetch-parallelism",
+ str(int(args.fetch_parallelism)),
+ "--quant-parallelism",
+ str(int(args.quant_parallelism)),
+ "--gtf",
+ str(gtf),
+ "--transcripts-fasta",
+ str(tx),
+ "--counts-csv-gz",
+ str(counts_out),
+ "--write-case-json",
+ "--case-id",
+ "GSE235350_MCF7_palbociclib_true_counts",
+ "--case-json-path",
+ str(case_out),
+ "--counts-file-rel",
+ "geo_eval/gse235350_true_counts/gse235350_counts.csv.gz",
+ "--reference-condition",
+ "control",
+ "--alternate-condition",
+ "palbociclib",
+ ],
+ cwd=repo_root,
+ )
+
+ target_counts_path = out_dir / "gse235350_counts.csv.gz"
+ target_case_path = out_dir / "gse235350_case.json"
+ out_dir.mkdir(parents=True, exist_ok=True)
+ target_counts_path.write_bytes(counts_out.read_bytes())
+ target_case_path.write_text(case_out.read_text(encoding="utf-8"), encoding="utf-8")
+
+ if args.skip_openenv:
+ print("[DONE] counts + case only:", out_dir)
+ return
+
+ py, cwd = _openenv_python(repo_root, env_dir)
+ env = os.environ.copy()
+ env["PYTHONPATH"] = str(repo_root / "src") + ":" + str(repo_root / "envs")
+ env.setdefault("MPLCONFIGDIR", str(env_dir / "outputs" / ".mplcache"))
+ subprocess.check_call(
+ py
+ + [
+ str(bench_dir / "geo_openenv_deseq2.py"),
+ "--repo-root",
+ str(repo_root),
+ "--env-dir",
+ str(env_dir),
+ "--case-json",
+ str(target_case_path),
+ "--out-dir",
+ str(out_dir),
+ "--summary-extra",
+ json.dumps(
+ {
+ "study": "GSE235350",
+ "contrast": "palbociclib vs control (reference=control)",
+ }
+ ),
+ ],
+ cwd=cwd,
+ env=env,
+ )
+ print("[DONE] Study 3 outputs in:", out_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/bench/run_study4_gse111151_end_to_end.py b/envs/pathway_analysis_env/bench/run_study4_gse111151_end_to_end.py
new file mode 100644
index 0000000000..de6bc04475
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/run_study4_gse111151_end_to_end.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+"""
+Study 4 (GSE111151): merge per-sample GEO supplement count tables → OpenEnv DESeq2 + ORA.
+
+Uses legacy FTP-visible supplements ``GSE111151_GSM1417177.txt.gz`` … ``…7184`` (8 samples),
+mapped to current GSM accessions ``GSM3024053`` … ``GSM3024060`` (MCF-7 … ZR-75-1 Tam2).
+
+Default contrast: tamoxifen_resistant vs parental (reference = parental).
+"""
+
+from __future__ import annotations
+
+import argparse
+import concurrent.futures
+import json
+import os
+import shutil
+import subprocess
+import sys
+import urllib.request
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import pandas as pd
+
+FTP_BASE = "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE111nnn/GSE111151/suppl"
+
+# GEO ``filelist.txt`` lists newer ``GSM3024053_*.txt.gz`` names, but this FTP path
+# currently serves the legacy ``GSE111151_GSM1417177.txt.gz`` … ``1417184`` set (8 files).
+# Those eight files align with the first eight series-matrix columns (MCF-7 … ZR-75-1 Tam2).
+# BT-474 arm samples are not included here (no matching legacy per-sample supplements on this mirror).
+LEGACY_SUPPLEMENT_FILES: List[str] = [f"GSE111151_GSM{1417177 + i}.txt.gz" for i in range(8)]
+
+CURRENT_GSM_ORDER: List[str] = [
+ "GSM3024053",
+ "GSM3024054",
+ "GSM3024055",
+ "GSM3024056",
+ "GSM3024057",
+ "GSM3024058",
+ "GSM3024059",
+ "GSM3024060",
+]
+
+# Parental vs resistant (cell-line–aware grouping for one pooled contrast).
+GSM_CONDITION: Dict[str, str] = {
+ "GSM3024053": "parental",
+ "GSM3024054": "tamoxifen_resistant",
+ "GSM3024055": "parental",
+ "GSM3024056": "tamoxifen_resistant",
+ "GSM3024057": "tamoxifen_resistant",
+ "GSM3024058": "parental",
+ "GSM3024059": "tamoxifen_resistant",
+ "GSM3024060": "tamoxifen_resistant",
+ "GSM3024061": "parental",
+ "GSM3024062": "tamoxifen_resistant",
+ "GSM3024063": "tamoxifen_resistant",
+}
+
+
+def _download(url: str, dest: Path) -> None:
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ if dest.exists() and dest.stat().st_size > 0:
+ return
+ with urllib.request.urlopen(url, timeout=120) as r, dest.open("wb") as out:
+ out.write(r.read())
+
+
+def _load_one_counts(path: Path, colname: str) -> pd.Series:
+ df = pd.read_csv(path, sep="\t", compression="infer")
+ if "gene_name" not in df.columns or "counts" not in df.columns:
+ raise ValueError(f"Unexpected columns in {path}: {list(df.columns)}")
+ s = df.groupby(df["gene_name"].astype(str).str.strip())["counts"].sum()
+ s = s.astype(float).round().astype(int)
+ s.name = colname
+ return s
+
+
+def _openenv_python(repo_root: Path, env_dir: Path) -> Tuple[List[str], str]:
+ if shutil.which("uv"):
+ return ["uv", "run", "python"], str(env_dir)
+ return [sys.executable], str(repo_root)
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument(
+ "--fetch-parallelism",
+ type=int,
+ default=5,
+ help="Concurrent supplement downloads (default: 5).",
+ )
+ ap.add_argument("--skip-openenv", action="store_true", help="Only build counts + case JSON.")
+ args = ap.parse_args()
+
+ repo_root = Path(__file__).resolve().parents[3]
+ env_dir = repo_root / "envs" / "pathway_analysis_env"
+ bench_dir = env_dir / "bench"
+ out_dir = env_dir / "data" / "geo_eval" / "gse111151_tamoxifen_benchmark"
+ dl_dir = out_dir / "work" / "suppl"
+ out_dir.mkdir(parents=True, exist_ok=True)
+
+ par = max(1, min(int(args.fetch_parallelism), len(LEGACY_SUPPLEMENT_FILES)))
+
+ def _dl(pair: Tuple[str, str]) -> Tuple[Path, str]:
+ fn, gsm = pair
+ url = f"{FTP_BASE}/{fn}"
+ dest = dl_dir / fn
+ _download(url, dest)
+ return dest, gsm
+
+ pairs = list(zip(LEGACY_SUPPLEMENT_FILES, CURRENT_GSM_ORDER, strict=True))
+ with concurrent.futures.ThreadPoolExecutor(max_workers=par) as ex:
+ resolved = list(ex.map(_dl, pairs))
+
+ series_list = [_load_one_counts(p, gsm) for p, gsm in resolved]
+ counts = pd.concat(series_list, axis=1).fillna(0).astype(int)
+ counts.index.name = "gene"
+ counts_path = out_dir / "gse111151_counts.csv.gz"
+ counts.to_csv(counts_path)
+
+ sample_ids = list(CURRENT_GSM_ORDER)
+ sample_metadata = {g: GSM_CONDITION[g] for g in sample_ids}
+
+ case = {
+ "case_id": "GSE111151_tamoxifen_resistance_parental",
+ "strict_mode": False,
+ "experiment_metadata": {
+ "accession": "GSE111151",
+ "reference": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111151",
+ "summary": "Merged author per-sample raw counts (GEO legacy supplement files GSE111151_GSM1417177–84 → GSM3024053–60); 8/11 matrix columns (BT-474 arm not on this FTP mirror).",
+ },
+ "counts_file": "geo_eval/gse111151_tamoxifen_benchmark/gse111151_counts.csv.gz",
+ "sample_ids": sample_ids,
+ "sample_metadata": sample_metadata,
+ "conditions": sorted(set(sample_metadata.values())),
+ "default_contrast": {"reference": "parental", "alternate": "tamoxifen_resistant"},
+ "analysis_options": {"min_total_count": 10, "padj_alpha": 0.05, "de_query_direction": "both"},
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (GEO benchmark)",
+ }
+ case_path = out_dir / "gse111151_case.json"
+ case_path.write_text(json.dumps(case, indent=2), encoding="utf-8")
+
+ if args.skip_openenv:
+ print("[DONE] counts + case only:", out_dir)
+ return
+
+ py, cwd = _openenv_python(repo_root, env_dir)
+ env = os.environ.copy()
+ env["PYTHONPATH"] = str(repo_root / "src") + ":" + str(repo_root / "envs")
+ env.setdefault("MPLCONFIGDIR", str(env_dir / "outputs" / ".mplcache"))
+ subprocess.check_call(
+ py
+ + [
+ str(bench_dir / "geo_openenv_deseq2.py"),
+ "--repo-root",
+ str(repo_root),
+ "--env-dir",
+ str(env_dir),
+ "--case-json",
+ str(case_path),
+ "--out-dir",
+ str(out_dir),
+ "--summary-extra",
+ json.dumps(
+ {
+ "study": "GSE111151",
+ "contrast": "tamoxifen_resistant vs parental (reference=parental)",
+ }
+ ),
+ ],
+ cwd=cwd,
+ env=env,
+ )
+ print("[DONE] Study 4 outputs in:", out_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/bench/run_study5_gse216540_end_to_end.py b/envs/pathway_analysis_env/bench/run_study5_gse216540_end_to_end.py
new file mode 100644
index 0000000000..70e424350c
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/run_study5_gse216540_end_to_end.py
@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+"""
+Study 5 (GSE216540): GEO supplement is TPM-only (`GSE216540_allData_tpm_human.txt.gz`).
+
+This script builds **TPM-derived pseudo-counts** (rounded TPM × scale) for an OpenEnv
+**workflow / plumbing test only** — not statistically equivalent to true raw counts.
+
+Default ``--fast-subset`` uses one 28_CM block (8 samples: DMSO vs FULV) for a quick run.
+Use ``--full`` to load all sample columns (slow, large memory).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import re
+import shutil
+import subprocess
+import sys
+import urllib.request
+from pathlib import Path
+from typing import List, Sequence, Tuple
+
+import numpy as np
+import pandas as pd
+
+FTP_TPM = (
+ "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE216nnn/GSE216540/suppl/"
+ "GSE216540_allData_tpm_human.txt.gz"
+)
+
+# Fast default: 4 DMSO + 4 FULV replicates in the 28_CM arm (see matrix header).
+FAST_SUBSET_COLUMNS: List[str] = [
+ "28_CM_DMSO_1",
+ "28_CM_DMSO_2",
+ "28_CM_DMSO_3",
+ "28_CM_DMSO_4",
+ "28_CM_FULV_1",
+ "28_CM_FULV_2",
+ "28_CM_FULV_3",
+ "28_CM_FULV_4",
+]
+
+
+def _download(url: str, dest: Path) -> None:
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ if dest.exists() and dest.stat().st_size > 0:
+ return
+ with urllib.request.urlopen(url, timeout=300) as r, dest.open("wb") as out:
+ out.write(r.read())
+
+
+def _strip_ensembl_version(ids: Sequence[str]) -> List[str]:
+ out: List[str] = []
+ for x in ids:
+ s = str(x).strip('"')
+ out.append(re.sub(r"\.\d+$", "", s))
+ return out
+
+
+def _openenv_python(repo_root: Path, env_dir: Path) -> Tuple[List[str], str]:
+ if shutil.which("uv"):
+ return ["uv", "run", "python"], str(env_dir)
+ return [sys.executable], str(repo_root)
+
+
+def _condition_for_sample(col: str) -> str:
+ if "_DMSO_" in col:
+ return "DMSO"
+ if "_FULV_" in col:
+ return "FULV"
+ raise ValueError(f"Cannot infer condition from column name: {col}")
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument(
+ "--full",
+ action="store_true",
+ help="Load all sample columns from the TPM matrix (slow, high memory). "
+ "Default: fast 28_CM 8-sample DMSO vs FULV subset.",
+ )
+ ap.add_argument("--tpm-scale", type=float, default=100.0, help="pseudo_count = round(TPM * scale).")
+ ap.add_argument("--skip-openenv", action="store_true")
+ args = ap.parse_args()
+ fast_subset = not bool(args.full)
+
+ repo_root = Path(__file__).resolve().parents[3]
+ env_dir = repo_root / "envs" / "pathway_analysis_env"
+ bench_dir = env_dir / "bench"
+ out_dir = env_dir / "data" / "geo_eval" / "gse216540_tpm_pseudo_benchmark"
+ work = out_dir / "work"
+ work.mkdir(parents=True, exist_ok=True)
+ tpm_path = work / "GSE216540_allData_tpm_human.txt.gz"
+ _download(FTP_TPM, tpm_path)
+
+ if fast_subset:
+ df = pd.read_csv(tpm_path, sep="\t", usecols=["Geneid"] + FAST_SUBSET_COLUMNS)
+ sample_cols = FAST_SUBSET_COLUMNS
+ else:
+ df = pd.read_csv(tpm_path, sep="\t")
+ meta = {"Geneid", "GeneSymbol", "Chromosome", "Start", "End", "Class", "Strand", "Length"}
+ sample_cols = [c for c in df.columns if c not in meta]
+
+ tpm = df[sample_cols].apply(pd.to_numeric, errors="coerce").fillna(0.0)
+ pseudo = np.round(tpm * float(args.tpm_scale)).clip(lower=0).astype(int)
+ pseudo.index = _strip_ensembl_version(df["Geneid"].tolist())
+ # Collapse version-stripped duplicate Ensembl IDs
+ pseudo = pseudo.groupby(pseudo.index).sum()
+ pseudo.index.name = "gene"
+ counts_path = out_dir / "gse216540_pseudo_counts.csv.gz"
+ pseudo.to_csv(counts_path)
+
+ sample_metadata = {c: _condition_for_sample(c) for c in sample_cols}
+ case = {
+ "case_id": "GSE216540_28CM_FULV_vs_DMSO_tpm_pseudo" if fast_subset else "GSE216540_FULV_vs_DMSO_tpm_pseudo_full",
+ "strict_mode": False,
+ "experiment_metadata": {
+ "accession": "GSE216540",
+ "reference": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE216540",
+ "summary": (
+ "TPM matrix from GEO supplement; pseudo-counts = round(TPM * "
+ f"{args.tpm_scale}). For OpenEnv pipeline testing only, not DESeq2-ground-truth counts."
+ ),
+ },
+ "counts_file": "geo_eval/gse216540_tpm_pseudo_benchmark/gse216540_pseudo_counts.csv.gz",
+ "sample_ids": sample_cols,
+ "sample_metadata": sample_metadata,
+ "conditions": sorted(set(sample_metadata.values())),
+ "default_contrast": {"reference": "DMSO", "alternate": "FULV"},
+ "analysis_options": {"min_total_count": 10, "padj_alpha": 0.05, "de_query_direction": "both"},
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (GEO benchmark)",
+ }
+ case_path = out_dir / "gse216540_case.json"
+ case_path.write_text(json.dumps(case, indent=2), encoding="utf-8")
+
+ if args.skip_openenv:
+ print("[DONE] pseudo-counts + case only:", out_dir)
+ return
+
+ py, cwd = _openenv_python(repo_root, env_dir)
+ env = os.environ.copy()
+ env["PYTHONPATH"] = str(repo_root / "src") + ":" + str(repo_root / "envs")
+ env.setdefault("MPLCONFIGDIR", str(env_dir / "outputs" / ".mplcache"))
+ subprocess.check_call(
+ py
+ + [
+ str(bench_dir / "geo_openenv_deseq2.py"),
+ "--repo-root",
+ str(repo_root),
+ "--env-dir",
+ str(env_dir),
+ "--case-json",
+ str(case_path),
+ "--out-dir",
+ str(out_dir),
+ "--summary-extra",
+ json.dumps(
+ {
+ "study": "GSE216540",
+ "mode": "tpm_pseudo_counts",
+ "tpm_scale": float(args.tpm_scale),
+ "fast_subset": fast_subset,
+ "contrast": "FULV vs DMSO (reference=DMSO)",
+ }
+ ),
+ ],
+ cwd=cwd,
+ env=env,
+ )
+ print("[DONE] Study 5 outputs in:", out_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/bench/run_true_counts_pipeline.py b/envs/pathway_analysis_env/bench/run_true_counts_pipeline.py
new file mode 100644
index 0000000000..b716d42659
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/run_true_counts_pipeline.py
@@ -0,0 +1,721 @@
+#!/usr/bin/env python3
+
+"""
+True-counts RNA-seq pipeline for OpenEnv benchmarks.
+
+This script is designed to run **inside** the benchmark container in `bench/`.
+
+It supports two quantification methods:
+ - STAR + featureCounts (alignment-based; default, maximum credibility)
+ - Salmon (alignment-free; faster)
+
+Outputs:
+ - a gene × sample count matrix (CSV/CSV.GZ) compatible with OpenEnv `counts_file`
+ - optional OpenEnv case JSON scaffold (counts_file + sample_ids + sample_metadata)
+
+Notes:
+ - Reference genomes/indexes are intentionally user-provided to keep this repo small.
+ - This script does not attempt to be a full LIMS/QC system; it’s a reproducible benchmark runner.
+"""
+
+from __future__ import annotations
+
+import argparse
+import concurrent.futures
+import gzip
+import json
+import os
+import shutil
+import subprocess
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import pandas as pd
+
+
+def _run(cmd: Sequence[str], *, cwd: Optional[Path] = None, env: Optional[Dict[str, str]] = None) -> None:
+ merged = None
+ if env is not None:
+ merged = os.environ.copy()
+ merged.update(env)
+ subprocess.check_call(list(cmd), cwd=str(cwd) if cwd else None, env=merged)
+
+
+def _ensure_dir(p: Path) -> None:
+ p.mkdir(parents=True, exist_ok=True)
+
+
+def _write_gz_text(path: Path, text: str) -> None:
+ _ensure_dir(path.parent)
+ with gzip.open(path, "wt", encoding="utf-8") as f:
+ f.write(text)
+
+
+def _maybe_decompress_to(
+ src: Path,
+ *,
+ out_dir: Path,
+) -> Path:
+ """
+ STAR genomeGenerate expects uncompressed FASTA/GTF.
+ If `src` ends with `.gz`, we write an uncompressed copy in `out_dir` and return it.
+ """
+ if not str(src).endswith(".gz"):
+ return src
+ _ensure_dir(out_dir)
+ dst = out_dir / src.name[: -len(".gz")]
+ if dst.exists() and dst.stat().st_size > 0:
+ return dst
+ with gzip.open(src, "rb") as f_in, dst.open("wb") as f_out:
+ shutil.copyfileobj(f_in, f_out)
+ return dst
+
+@dataclass(frozen=True)
+class Sample:
+ sample_id: str
+ sra: str
+ condition: str
+
+
+def parse_samples_json(path: Path) -> List[Sample]:
+ raw = json.loads(path.read_text(encoding="utf-8"))
+ if not isinstance(raw, list) or not raw:
+ raise ValueError("samples.json must be a non-empty JSON list")
+ out: List[Sample] = []
+ for r in raw:
+ if not isinstance(r, dict):
+ continue
+ sid = str(r.get("sample_id") or "").strip()
+ sra = str(r.get("sra") or "").strip()
+ cond = str(r.get("condition") or "").strip()
+ if not sid or not sra or not cond:
+ raise ValueError(f"Invalid sample entry: {r}")
+ out.append(Sample(sample_id=sid, sra=sra, condition=cond))
+ return out
+
+
+def build_tx2gene_from_gtf(*, gtf_gz: Path, out_tsv: Path) -> Path:
+ """
+ Build a transcript→gene mapping for Salmon `--geneMap` from plain or gzip GTF.
+
+ Output format (TSV):
+ transcript_id
gene_name
+
+ We prefer gene_name for downstream interpretability; fallback to gene_id when gene_name missing.
+ """
+ out_tsv.parent.mkdir(parents=True, exist_ok=True)
+ if out_tsv.exists() and out_tsv.stat().st_size > 0:
+ return out_tsv
+
+ # GTF attributes: key "value";
+ re_tid = re.compile(r'transcript_id "([^"]+)"')
+ re_gn = re.compile(r'gene_name "([^"]+)"')
+ re_gid = re.compile(r'gene_id "([^"]+)"')
+
+ seen = set()
+ opener = gzip.open if str(gtf_gz).endswith(".gz") else open
+ with (
+ opener(gtf_gz, "rt", encoding="utf-8", errors="replace") as f,
+ out_tsv.open("w", encoding="utf-8") as out,
+ ):
+ for line in f:
+ if not line or line.startswith("#"):
+ continue
+ parts = line.rstrip("\n").split("\t")
+ if len(parts) < 9:
+ continue
+ feature = parts[2]
+ if feature not in ("transcript", "exon"):
+ continue
+ attrs = parts[8]
+ m_tid = re_tid.search(attrs)
+ if not m_tid:
+ continue
+ tid = m_tid.group(1)
+ if tid in seen:
+ continue
+ m_gn = re_gn.search(attrs)
+ m_gid = re_gid.search(attrs)
+ gene = (m_gn.group(1) if m_gn else None) or (
+ m_gid.group(1) if m_gid else None
+ )
+ if not gene:
+ continue
+ out.write(f"{tid}\t{gene}\n")
+ seen.add(tid)
+ return out_tsv
+
+
+def _fasterq_dump_one(
+ s: Sample, *, out_dir: Path, threads_per_job: int
+) -> Tuple[str, str, Tuple[Path, Optional[Path]]]:
+ """Run fasterq-dump for one SRR if outputs are missing; return (sample_id, sra, (fq1, fq2_or_none))."""
+ fq1 = out_dir / f"{s.sra}_1.fastq"
+ fq2 = out_dir / f"{s.sra}_2.fastq"
+ fq_single = out_dir / f"{s.sra}.fastq"
+ if not (fq1.exists() or fq2.exists() or fq_single.exists()):
+ _run(
+ [
+ "fasterq-dump",
+ "-e",
+ str(max(1, threads_per_job)),
+ "-O",
+ str(out_dir),
+ s.sra,
+ ]
+ )
+ if fq1.exists() or fq2.exists():
+ if not (fq1.exists() and fq2.exists()):
+ raise RuntimeError(
+ f"Incomplete paired FASTQ for {s.sra}; restore both mates or "
+ "remove the partial files and rerun fasterq-dump."
+ )
+ return s.sample_id, s.sra, (fq1, fq2)
+ if fq_single.exists():
+ return s.sample_id, s.sra, (fq_single, None)
+ raise RuntimeError(f"FASTQ not found after fasterq-dump for {s.sra}")
+
+
+def download_fastq(
+ samples: Sequence[Sample],
+ *,
+ out_dir: Path,
+ threads: int,
+ fetch_parallelism: int = 1,
+) -> Dict[str, Tuple[Path, Optional[Path]]]:
+ """
+ Download FASTQs using sra-tools (fasterq-dump).
+
+ When ``fetch_parallelism`` > 1, multiple SRRs are fetched concurrently (bounded
+ per-job thread counts to avoid oversubscribing the machine).
+
+ Returns mapping: sample_id -> (fastq1, fastq2_or_none)
+ """
+ _ensure_dir(out_dir)
+ n = max(1, len(samples))
+ par = max(1, min(int(fetch_parallelism), n))
+ threads_per = max(1, int(threads) // par)
+
+ fastqs: Dict[str, Tuple[Path, Optional[Path]]] = {}
+ if par <= 1:
+ for s in samples:
+ sid, _sra, pair = _fasterq_dump_one(s, out_dir=out_dir, threads_per_job=int(threads))
+ fastqs[sid] = pair
+ return fastqs
+
+ with concurrent.futures.ThreadPoolExecutor(max_workers=par) as ex:
+ futs = [
+ ex.submit(_fasterq_dump_one, s, out_dir=out_dir, threads_per_job=threads_per)
+ for s in samples
+ ]
+ for fut in concurrent.futures.as_completed(futs):
+ sid, _sra, pair = fut.result()
+ fastqs[sid] = pair
+
+ # Preserve stable ordering for downstream column order
+ return {s.sample_id: fastqs[s.sample_id] for s in samples}
+
+
+def build_salmon_index(*, transcripts_fasta: Path, out_dir: Path, threads: int) -> None:
+ _ensure_dir(out_dir)
+ # Salmon can read gz in many builds, but we keep behavior consistent.
+ tx_dir = out_dir.parent / "ref_unzipped"
+ transcripts_fasta = _maybe_decompress_to(transcripts_fasta, out_dir=tx_dir)
+ # If partial index exists, clear it.
+ # Salmon ≥1.10 expects a finished index to contain version metadata.
+ marker = out_dir / "versionInfo.json"
+ if out_dir.exists() and not marker.exists():
+ for child in out_dir.iterdir():
+ if child.is_dir():
+ shutil.rmtree(child)
+ else:
+ child.unlink(missing_ok=True)
+ if marker.exists():
+ return
+ env = os.environ.copy()
+ # Avoid flaky version-server checks in CI / restricted networks.
+ env.setdefault("SALMON_NO_VERSION_CHECK", "1")
+ _run(
+ [
+ "salmon",
+ "index",
+ "-t",
+ str(transcripts_fasta),
+ "-i",
+ str(out_dir),
+ "-p",
+ str(max(1, threads)),
+ "--gencode",
+ ],
+ env=env,
+ )
+
+
+def quantify_salmon(
+ *,
+ salmon_index: Path,
+ sample_id: str,
+ fastq1: Path,
+ fastq2: Optional[Path],
+ out_dir: Path,
+ threads: int,
+ tx2gene_tsv: Path,
+) -> Path:
+ """
+ Run Salmon quant and return path to `quant.genes.sf`.
+ """
+ sdir = out_dir / sample_id
+ if (sdir / "quant.genes.sf").exists():
+ return sdir / "quant.genes.sf"
+ _ensure_dir(sdir)
+
+ env = os.environ.copy()
+ env.setdefault("SALMON_NO_VERSION_CHECK", "1")
+
+ cmd = [
+ "salmon",
+ "quant",
+ "-i",
+ str(salmon_index),
+ "-l",
+ "A",
+ "--validateMappings",
+ "--gcBias",
+ "--geneMap",
+ str(tx2gene_tsv),
+ "-p",
+ str(max(1, threads)),
+ "--numBootstraps",
+ "0",
+ "-o",
+ str(sdir),
+ ]
+ if fastq2 is None:
+ cmd += ["-r", str(fastq1)]
+ else:
+ cmd += ["-1", str(fastq1), "-2", str(fastq2)]
+ _run(cmd, env=env)
+ qg = sdir / "quant.genes.sf"
+ if not qg.exists():
+ raise RuntimeError(f"Salmon did not produce {qg}")
+ return qg
+
+
+def salmon_genes_to_openenv_counts(
+ *,
+ quant_genes_by_sample: Dict[str, Path],
+ out_counts_csv_gz: Path,
+) -> None:
+ """
+ Build OpenEnv counts (genes × samples) from Salmon `quant.genes.sf` NumReads.
+ """
+ dfs = []
+ for sid, p in quant_genes_by_sample.items():
+ df = pd.read_csv(p, sep="\t")
+ if "Name" not in df.columns or "NumReads" not in df.columns:
+ raise ValueError(f"Unexpected salmon quant.genes.sf schema: {p}")
+ d = df[["Name", "NumReads"]].copy()
+ d = d.rename(columns={"Name": "gene", "NumReads": sid}).set_index("gene")
+ dfs.append(d)
+ out = pd.concat(dfs, axis=1).fillna(0.0)
+ out = out.round().astype(int)
+ _ensure_dir(out_counts_csv_gz.parent)
+ out.to_csv(out_counts_csv_gz)
+
+
+def build_star_index(*, genome_fasta: Path, gtf: Path, out_dir: Path, threads: int) -> None:
+ _ensure_dir(out_dir)
+ # STAR requires uncompressed inputs.
+ ref_dir = out_dir.parent / "ref_unzipped"
+ genome_fasta = _maybe_decompress_to(genome_fasta, out_dir=ref_dir)
+ gtf = _maybe_decompress_to(gtf, out_dir=ref_dir)
+
+ # If a previous attempt created a partial index directory, clear it.
+ marker = out_dir / "genomeParameters.txt"
+ if out_dir.exists() and not marker.exists():
+ for child in out_dir.iterdir():
+ if child.is_dir():
+ shutil.rmtree(child)
+ else:
+ try:
+ child.unlink()
+ except FileNotFoundError:
+ pass
+ cmd: List[str] = [
+ "STAR",
+ "--runThreadN",
+ str(max(1, threads)),
+ "--runMode",
+ "genomeGenerate",
+ "--genomeDir",
+ str(out_dir),
+ "--genomeFastaFiles",
+ str(genome_fasta),
+ "--sjdbGTFfile",
+ str(gtf),
+ ]
+
+ # On machines with limited RAM (common with Docker Desktop), hg38 genomeGenerate can OOM-kill.
+ # These parameters reduce STAR index memory footprint while preserving alignment correctness.
+ try:
+ fasta_size = genome_fasta.stat().st_size
+ except FileNotFoundError:
+ fasta_size = 0
+ if fasta_size > 500_000_000: # ~hg38 scale
+ cmd += ["--genomeSAindexNbases", "14"]
+
+ _run(cmd)
+
+
+def align_star(
+ *,
+ star_index: Path,
+ sample_id: str,
+ fastq1: Path,
+ fastq2: Optional[Path],
+ out_dir: Path,
+ threads: int,
+) -> Path:
+ """
+ Returns path to coordinate-sorted BAM.
+ """
+ _ensure_dir(out_dir)
+ prefix = out_dir / f"{sample_id}."
+ cmd = [
+ "STAR",
+ "--runThreadN",
+ str(max(1, threads)),
+ "--genomeDir",
+ str(star_index),
+ "--readFilesIn",
+ str(fastq1),
+ ]
+ if fastq2 is not None:
+ cmd.append(str(fastq2))
+ cmd += [
+ "--outFileNamePrefix",
+ str(prefix),
+ "--outSAMtype",
+ "BAM",
+ "SortedByCoordinate",
+ ]
+ _run(cmd)
+ bam = out_dir / f"{sample_id}.Aligned.sortedByCoord.out.bam"
+ if not bam.exists():
+ raise RuntimeError(f"STAR did not produce BAM for {sample_id}: expected {bam}")
+ return bam
+
+
+def quantify_featurecounts(
+ *,
+ gtf: Path,
+ bams: Dict[str, Path],
+ out_path: Path,
+ threads: int,
+ paired_end: bool,
+) -> Path:
+ """
+ Run featureCounts on all BAMs and return the raw featureCounts output path.
+ """
+ _ensure_dir(out_path.parent)
+ bam_paths = [bams[sid] for sid in sorted(bams.keys())]
+ cmd = [
+ "featureCounts",
+ "-T",
+ str(max(1, threads)),
+ "-a",
+ str(gtf),
+ "-o",
+ str(out_path),
+ "-g",
+ "gene_id",
+ ]
+ if paired_end:
+ cmd += ["-p", "--countReadPairs"]
+ cmd += [str(p) for p in bam_paths]
+ _run(cmd)
+ if not out_path.exists():
+ raise RuntimeError(f"featureCounts did not write {out_path}")
+ return out_path
+
+
+def featurecounts_to_openenv_counts(
+ *,
+ featurecounts_txt: Path,
+ sample_ids: List[str],
+ out_counts_csv_gz: Path,
+) -> None:
+ """
+ Convert featureCounts output into OpenEnv-compatible counts:
+ - rows: genes
+ - columns: sample_ids
+ """
+ df = pd.read_csv(featurecounts_txt, sep="\t", comment="#")
+ if "Geneid" not in df.columns:
+ raise ValueError("featureCounts output missing 'Geneid' column")
+
+ # featureCounts writes one column per BAM path; we assume order matches sample_ids sort.
+ count_cols = [c for c in df.columns if c not in ("Geneid", "Chr", "Start", "End", "Strand", "Length")]
+ if len(count_cols) != len(sample_ids):
+ raise ValueError(f"featureCounts produced {len(count_cols)} sample columns, expected {len(sample_ids)}")
+
+ out = df[["Geneid"] + count_cols].copy()
+ out.columns = ["gene"] + sample_ids
+ out = out.set_index("gene")
+ _ensure_dir(out_counts_csv_gz.parent)
+ out.to_csv(out_counts_csv_gz)
+
+
+def write_openenv_case(
+ *,
+ case_id: str,
+ counts_file_rel: str,
+ samples: Sequence[Sample],
+ reference_condition: str,
+ alternate_condition: str,
+ out_case_json: Path,
+) -> None:
+ sample_ids = [s.sample_id for s in samples]
+ sample_metadata = {s.sample_id: s.condition for s in samples}
+ case = {
+ "case_id": case_id,
+ "strict_mode": False,
+ "experiment_metadata": {
+ "source": "SRA",
+ "summary": "True-counts benchmark generated from FASTQs using STAR+featureCounts or Salmon (see bench/run_true_counts_pipeline.py).",
+ },
+ "counts_file": counts_file_rel,
+ "sample_ids": sample_ids,
+ "sample_metadata": sample_metadata,
+ "conditions": sorted(set(sample_metadata.values())),
+ "default_contrast": {"reference": reference_condition, "alternate": alternate_condition},
+ "analysis_options": {"min_total_count": 10, "padj_alpha": 0.05, "de_query_direction": "both"},
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (true-counts benchmark)",
+ }
+ _ensure_dir(out_case_json.parent)
+ out_case_json.write_text(json.dumps(case, indent=2), encoding="utf-8")
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--method", choices=["star", "salmon"], default="star")
+ ap.add_argument("--samples-json", required=True, type=Path, help="JSON list of {sample_id,sra,condition}")
+ ap.add_argument("--out-dir", required=True, type=Path, help="Benchmark working/output directory")
+ ap.add_argument("--threads", type=int, default=max(1, os.cpu_count() or 1))
+ ap.add_argument(
+ "--fetch-parallelism",
+ type=int,
+ default=3,
+ help="Concurrent fasterq-dump jobs (I/O bound). Per-job threads ~= --threads / this value.",
+ )
+ ap.add_argument(
+ "--quant-parallelism",
+ type=int,
+ default=2,
+ help="Concurrent salmon quant (or STAR align) jobs. Per-job threads ~= --threads / this value.",
+ )
+
+ # STAR+featureCounts inputs
+ ap.add_argument("--genome-fasta", type=Path, help="Genome FASTA (required for --method star if --star-index not provided)")
+ ap.add_argument("--gtf", type=Path, help="Gene annotation GTF (required for --method star)")
+ ap.add_argument("--star-index", type=Path, help="STAR index directory (if missing, it will be built)")
+
+ # Salmon inputs
+ ap.add_argument("--transcripts-fasta", type=Path, help="Transcriptome FASTA (required for --method salmon if --salmon-index not provided)")
+ ap.add_argument("--salmon-index", type=Path, help="Salmon index directory (if missing, it will be built)")
+
+ # Outputs
+ ap.add_argument("--counts-csv-gz", type=Path, help="Where to write OpenEnv counts CSV.GZ")
+ ap.add_argument("--write-case-json", action="store_true")
+ ap.add_argument("--case-id", default="true_counts_case")
+ ap.add_argument("--case-json-path", type=Path, help="Where to write the OpenEnv case JSON (if --write-case-json)")
+ ap.add_argument("--counts-file-rel", default="geo_eval/true_counts.csv.gz", help="counts_file path relative to env data dir")
+ ap.add_argument("--reference-condition", default="control")
+ ap.add_argument("--alternate-condition", default="treated")
+
+ args = ap.parse_args()
+
+ samples = parse_samples_json(args.samples_json)
+ out_dir: Path = args.out_dir
+ _ensure_dir(out_dir)
+
+ # Download FASTQs
+ fq_dir = out_dir / "fastq"
+ fastqs = download_fastq(
+ samples,
+ out_dir=fq_dir,
+ threads=int(args.threads),
+ fetch_parallelism=int(args.fetch_parallelism),
+ )
+
+ qpar = max(1, min(int(args.quant_parallelism), len(samples)))
+ threads_per_quant = max(1, int(args.threads) // qpar)
+
+ if args.method == "salmon":
+ if args.gtf is None:
+ raise SystemExit("--gtf is required for --method salmon (to build transcript→gene map)")
+ salmon_index = args.salmon_index or (out_dir / "salmon_index")
+ if not (salmon_index / "versionInfo.json").exists():
+ if args.transcripts_fasta is None:
+ raise SystemExit("--transcripts-fasta is required to build Salmon index (or provide --salmon-index)")
+ build_salmon_index(
+ transcripts_fasta=args.transcripts_fasta,
+ out_dir=salmon_index,
+ threads=int(args.threads),
+ )
+
+ tx2gene = build_tx2gene_from_gtf(
+ gtf_gz=args.gtf,
+ out_tsv=out_dir / "tx2gene.tsv",
+ )
+
+ qdir = out_dir / "salmon"
+ qgenes: Dict[str, Path] = {}
+ if qpar <= 1:
+ for s in samples:
+ fq1, fq2 = fastqs[s.sample_id]
+ qgenes[s.sample_id] = quantify_salmon(
+ salmon_index=salmon_index,
+ sample_id=s.sample_id,
+ fastq1=fq1,
+ fastq2=fq2,
+ out_dir=qdir,
+ threads=int(args.threads),
+ tx2gene_tsv=tx2gene,
+ )
+ else:
+
+ def _salmon_job(s: Sample) -> Tuple[str, Path]:
+ fq1, fq2 = fastqs[s.sample_id]
+ pth = quantify_salmon(
+ salmon_index=salmon_index,
+ sample_id=s.sample_id,
+ fastq1=fq1,
+ fastq2=fq2,
+ out_dir=qdir,
+ threads=threads_per_quant,
+ tx2gene_tsv=tx2gene,
+ )
+ return s.sample_id, pth
+
+ with concurrent.futures.ThreadPoolExecutor(max_workers=qpar) as ex:
+ futs = [ex.submit(_salmon_job, s) for s in samples]
+ for fut in concurrent.futures.as_completed(futs):
+ sid, pth = fut.result()
+ qgenes[sid] = pth
+ qgenes = {s.sample_id: qgenes[s.sample_id] for s in samples}
+
+ counts_out = args.counts_csv_gz or (out_dir / "openenv_counts.csv.gz")
+ salmon_genes_to_openenv_counts(
+ quant_genes_by_sample=qgenes,
+ out_counts_csv_gz=counts_out,
+ )
+
+ if args.write_case_json:
+ case_json = args.case_json_path or (out_dir / "openenv_case.json")
+ write_openenv_case(
+ case_id=str(args.case_id),
+ counts_file_rel=str(args.counts_file_rel),
+ samples=samples,
+ reference_condition=str(args.reference_condition),
+ alternate_condition=str(args.alternate_condition),
+ out_case_json=case_json,
+ )
+
+ print("[DONE] counts:", counts_out)
+ return
+
+ # STAR + featureCounts (Subread >=2.0.2).
+ layouts = {fastqs[s.sample_id][1] is not None for s in samples}
+ if len(layouts) != 1:
+ raise ValueError(
+ "STAR featureCounts requires a uniform single-end or paired-end batch."
+ )
+ paired_end = layouts.pop()
+ if args.gtf is None:
+ raise SystemExit("--gtf is required for --method star")
+
+ star_index = args.star_index or (out_dir / "star_index")
+ marker = star_index / "genomeParameters.txt"
+ if not marker.exists():
+ if args.genome_fasta is None:
+ raise SystemExit("--genome-fasta is required to build STAR index (or provide --star-index)")
+ build_star_index(
+ genome_fasta=args.genome_fasta,
+ gtf=args.gtf,
+ out_dir=star_index,
+ threads=int(args.threads),
+ )
+
+ bam_dir = out_dir / "bam"
+ bams: Dict[str, Path] = {}
+ if qpar <= 1:
+ for s in samples:
+ fq1, fq2 = fastqs[s.sample_id]
+ bams[s.sample_id] = align_star(
+ star_index=star_index,
+ sample_id=s.sample_id,
+ fastq1=fq1,
+ fastq2=fq2,
+ out_dir=bam_dir,
+ threads=int(args.threads),
+ )
+ else:
+
+ def _star_job(s: Sample) -> Tuple[str, Path]:
+ fq1, fq2 = fastqs[s.sample_id]
+ bam = align_star(
+ star_index=star_index,
+ sample_id=s.sample_id,
+ fastq1=fq1,
+ fastq2=fq2,
+ out_dir=bam_dir,
+ threads=threads_per_quant,
+ )
+ return s.sample_id, bam
+
+ with concurrent.futures.ThreadPoolExecutor(max_workers=qpar) as ex:
+ futs = [ex.submit(_star_job, s) for s in samples]
+ for fut in concurrent.futures.as_completed(futs):
+ sid, bam = fut.result()
+ bams[sid] = bam
+ bams = {s.sample_id: bams[s.sample_id] for s in samples}
+
+ fc_out = out_dir / "featureCounts.txt"
+ quantify_featurecounts(
+ gtf=args.gtf,
+ bams=bams,
+ out_path=fc_out,
+ threads=int(args.threads),
+ paired_end=paired_end,
+ )
+
+ counts_out = args.counts_csv_gz or (out_dir / "openenv_counts.csv.gz")
+ # featureCounts sample column order matches sorted BAM list in quantify_featurecounts
+ sample_ids_sorted = sorted(bams.keys())
+ featurecounts_to_openenv_counts(
+ featurecounts_txt=fc_out,
+ sample_ids=sample_ids_sorted,
+ out_counts_csv_gz=counts_out,
+ )
+
+ if args.write_case_json:
+ case_json = args.case_json_path or (out_dir / "openenv_case.json")
+ write_openenv_case(
+ case_id=str(args.case_id),
+ counts_file_rel=str(args.counts_file_rel),
+ samples=samples,
+ reference_condition=str(args.reference_condition),
+ alternate_condition=str(args.alternate_condition),
+ out_case_json=case_json,
+ )
+
+ print("[DONE] counts:", counts_out)
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/envs/pathway_analysis_env/bench/studies/gse235350_samples.json b/envs/pathway_analysis_env/bench/studies/gse235350_samples.json
new file mode 100644
index 0000000000..0dbb3f079e
--- /dev/null
+++ b/envs/pathway_analysis_env/bench/studies/gse235350_samples.json
@@ -0,0 +1,32 @@
+[
+ {
+ "sample_id": "GSM7500863_con_rep1",
+ "sra": "SRR24978571",
+ "condition": "control"
+ },
+ {
+ "sample_id": "GSM7500864_con_rep2",
+ "sra": "SRR24978570",
+ "condition": "control"
+ },
+ {
+ "sample_id": "GSM7500865_con_rep3",
+ "sra": "SRR24978569",
+ "condition": "control"
+ },
+ {
+ "sample_id": "GSM7500866_palbo_rep1",
+ "sra": "SRR24978568",
+ "condition": "palbociclib"
+ },
+ {
+ "sample_id": "GSM7500867_palbo_rep2",
+ "sra": "SRR24978567",
+ "condition": "palbociclib"
+ },
+ {
+ "sample_id": "GSM7500868_palbo_rep3",
+ "sra": "SRR24978566",
+ "condition": "palbociclib"
+ }
+]
diff --git a/envs/pathway_analysis_env/client.py b/envs/pathway_analysis_env/client.py
new file mode 100644
index 0000000000..b7a90f5483
--- /dev/null
+++ b/envs/pathway_analysis_env/client.py
@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Pathway Analysis Environment Client.
+
+Provides a WebSocket-based client for interacting with a running
+Pathway Analysis server.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict
+
+from openenv.core.client_types import StepResult
+from openenv.core.env_client import EnvClient
+
+from .models import PathwayAction, PathwayObservation, PathwayState
+
+
+class PathwayEnv(EnvClient[PathwayAction, PathwayObservation, PathwayState]):
+ """
+ Client for the Pathway Analysis Environment.
+
+ Example:
+ >>> with PathwayEnv(base_url="http://localhost:8000") as client:
+ ... result = client.reset()
+ ... result = client.step(PathwayAction(action_type="inspect_dataset"))
+ ... print(result.observation.message)
+ """
+
+ def _step_payload(self, action: PathwayAction) -> Dict[str, Any]:
+ return {
+ "action_type": action.action_type,
+ "condition_a": action.condition_a,
+ "condition_b": action.condition_b,
+ "gene_list": action.gene_list,
+ "hypothesis": action.hypothesis,
+ "pathway_a": action.pathway_a,
+ "pathway_b": action.pathway_b,
+ "expert_question": action.expert_question,
+ }
+
+ def _parse_result(self, payload: Dict[str, Any]) -> StepResult[PathwayObservation]:
+ obs_data = payload.get("observation", {})
+ observation = PathwayObservation(
+ message=obs_data.get("message", ""),
+ available_conditions=obs_data.get("available_conditions", []),
+ top_genes=obs_data.get("top_genes", []),
+ top_pathways=obs_data.get("top_pathways", []),
+ de_genes=obs_data.get("de_genes", []),
+ pathway_enrichment=obs_data.get("pathway_enrichment", []),
+ pathway_comparison=obs_data.get("pathway_comparison"),
+ overlap_summary=obs_data.get("overlap_summary"),
+ statistical_ambiguity=obs_data.get("statistical_ambiguity"),
+ expert_message=obs_data.get("expert_message"),
+ trace_path=obs_data.get("trace_path"),
+ experiment_design=obs_data.get("experiment_design"),
+ done=obs_data.get("done", False),
+ reward=obs_data.get("reward", 0.0),
+ metadata=obs_data.get("metadata", {}),
+ )
+ return StepResult(
+ observation=observation,
+ reward=observation.reward,
+ done=observation.done,
+ )
+
+ def _parse_state(self, payload: Dict[str, Any]) -> PathwayState:
+ return PathwayState(
+ episode_id=payload.get("episode_id", ""),
+ step_count=payload.get("step_count", 0),
+ true_pathway=payload.get("true_pathway", ""),
+ conditions=payload.get("conditions", []),
+ de_run=payload.get("de_run", False),
+ enrichment_run=payload.get("enrichment_run", False),
+ is_done=payload.get("is_done", False),
+ pipeline_mode=payload.get("pipeline_mode", False),
+ strict_mode=payload.get("strict_mode", False),
+ expert_calls_used=payload.get("expert_calls_used", 0),
+ expert_budget=payload.get("expert_budget", 0),
+ legacy_mode=payload.get("legacy_mode", False),
+ design_understood=payload.get("design_understood", False),
+ validated_reference=payload.get("validated_reference"),
+ validated_alternate=payload.get("validated_alternate"),
+ )
diff --git a/envs/pathway_analysis_env/data/fake_rnaseq_detailed.json b/envs/pathway_analysis_env/data/fake_rnaseq_detailed.json
new file mode 100644
index 0000000000..7a7e4b9004
--- /dev/null
+++ b/envs/pathway_analysis_env/data/fake_rnaseq_detailed.json
@@ -0,0 +1,2498 @@
+{
+ "case_id": "fake_rnaseq_detailed",
+ "conditions": [
+ "control",
+ "treated"
+ ],
+ "true_pathway": "MAPK signaling",
+ "sample_ids": [
+ "C1",
+ "C2",
+ "C3",
+ "C4",
+ "T1",
+ "T2",
+ "T3",
+ "T4"
+ ],
+ "sample_metadata": {
+ "C1": "control",
+ "C2": "control",
+ "C3": "control",
+ "C4": "control",
+ "T1": "treated",
+ "T2": "treated",
+ "T3": "treated",
+ "T4": "treated"
+ },
+ "default_contrast": {
+ "reference": "control",
+ "alternate": "treated"
+ },
+ "analysis_options": {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "de_query_direction": "up",
+ "min_abs_log2fc": 0.25,
+ "ora_min_pathway_genes": 5
+ },
+ "counts": {
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+ ],
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+ ],
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+ "LOC00073": "ENSG00100000169",
+ "LOC00074": "ENSG00100000170",
+ "LOC00075": "ENSG00100000171",
+ "LOC00076": "ENSG00100000172",
+ "LOC00077": "ENSG00100000173",
+ "LOC00078": "ENSG00100000174",
+ "LOC00079": "ENSG00100000175",
+ "LOC00080": "ENSG00100000176",
+ "LOC00081": "ENSG00100000177",
+ "LOC00082": "ENSG00100000178",
+ "LOC00083": "ENSG00100000179",
+ "LOC00084": "ENSG00100000180",
+ "LOC00085": "ENSG00100000181",
+ "LOC00086": "ENSG00100000182",
+ "LOC00087": "ENSG00100000183",
+ "LOC00088": "ENSG00100000184",
+ "LOC00089": "ENSG00100000185",
+ "LOC00090": "ENSG00100000186",
+ "LOC00091": "ENSG00100000187",
+ "LOC00092": "ENSG00100000188",
+ "LOC00093": "ENSG00100000189",
+ "LOC00094": "ENSG00100000190",
+ "LOC00095": "ENSG00100000191",
+ "LOC00096": "ENSG00100000192",
+ "LOC00097": "ENSG00100000193",
+ "LOC00098": "ENSG00100000194",
+ "LOC00099": "ENSG00100000195",
+ "LOC00100": "ENSG00100000196",
+ "LOC00101": "ENSG00100000197",
+ "LOC00102": "ENSG00100000198",
+ "LOC00103": "ENSG00100000199"
+ },
+ "expert_budget": 2,
+ "expert_penalty": 0.25,
+ "expert_hint": "Strongest coordinated DE should align with MAPK/ERK-associated genes; check overlap across top pathways.",
+ "strict_mode": false
+}
\ No newline at end of file
diff --git a/envs/pathway_analysis_env/data/geo_eval/geo_agent_eval_studies.json b/envs/pathway_analysis_env/data/geo_eval/geo_agent_eval_studies.json
new file mode 100644
index 0000000000..e5e569499b
--- /dev/null
+++ b/envs/pathway_analysis_env/data/geo_eval/geo_agent_eval_studies.json
@@ -0,0 +1,10 @@
+{
+ "studies": [
+ {
+ "case_file": "geo_eval/gse128911_mda_mb_134_vi_fulvestrant_vs_dmso/gse128911_case.json",
+ "expected_keywords": ["estrogen", "ESR1", "cell cycle", "E2F", "MYC"],
+ "top_k": 20
+ }
+ ]
+}
+
diff --git a/envs/pathway_analysis_env/data/geo_eval/gse128911_mda_mb_134_vi_fulvestrant_vs_dmso/gse128911_case.json b/envs/pathway_analysis_env/data/geo_eval/gse128911_mda_mb_134_vi_fulvestrant_vs_dmso/gse128911_case.json
new file mode 100644
index 0000000000..f1fd6f540e
--- /dev/null
+++ b/envs/pathway_analysis_env/data/geo_eval/gse128911_mda_mb_134_vi_fulvestrant_vs_dmso/gse128911_case.json
@@ -0,0 +1,41 @@
+{
+ "case_id": "GSE128911_mda_mb_134_vi_fulvestrant_vs_dmso",
+ "strict_mode": false,
+ "experiment_metadata": {
+ "accession": "GSE128911",
+ "reference": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128911",
+ "summary": "Dataset 2 count matrix subset (MDA-MB-134-VI; 2\u00d72 DMSO vs Fulvestrant)."
+ },
+ "counts_file": "geo_eval/gse128911_mda_mb_134_vi_fulvestrant_vs_dmso/gse128911_dataset2_subset_counts.csv.gz",
+ "sample_ids": [
+ "SAM24360838",
+ "SAM24360839",
+ "SAM24360844",
+ "SAM24360845"
+ ],
+ "sample_metadata": {
+ "SAM24360838": "dmso",
+ "SAM24360839": "dmso",
+ "SAM24360844": "fulvestrant",
+ "SAM24360845": "fulvestrant"
+ },
+ "conditions": [
+ "dmso",
+ "fulvestrant"
+ ],
+ "default_contrast": {
+ "reference": "dmso",
+ "alternate": "fulvestrant"
+ },
+ "analysis_options": {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "de_query_direction": "both"
+ },
+ "enrichr_libraries": [
+ "MSigDB_Hallmark_2020",
+ "KEGG_2021_Human",
+ "Reactome_2022"
+ ],
+ "true_pathway": "Unknown (GEO benchmark)"
+}
\ No newline at end of file
diff --git a/envs/pathway_analysis_env/data/gse227102_mcf7_case.json b/envs/pathway_analysis_env/data/gse227102_mcf7_case.json
new file mode 100644
index 0000000000..5616a1f454
--- /dev/null
+++ b/envs/pathway_analysis_env/data/gse227102_mcf7_case.json
@@ -0,0 +1,45 @@
+{
+ "case_id": "GSE227102_MCF7_vs_MCF7-FAR_author_DE",
+ "strict_mode": false,
+ "experiment_metadata": {
+ "source": "GEO",
+ "accession": "GSE227102",
+ "pubmed_id": "37258566",
+ "summary": "ER+ breast cancer cell lines; parental MCF7 vs MCF7-FAR (fulvestrant + abemaciclib resistant). GEO provides author DE tables; this case loads the author table and runs enrichment.",
+ "reference": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227102"
+ },
+ "de_table_file": "geo_eval/gse227102_author/MCF7_author.csv.gz",
+ "de_table_gene_column": "Gene,name",
+ "de_table_log2fc_column": "log2FoldChange",
+ "de_table_pvalue_column": "pvalue",
+ "de_table_padj_column": "padj",
+ "sample_ids": [
+ "MCF7-WT-01",
+ "MCF7-WT-02",
+ "MCF7-WT-03",
+ "MCF7-FAR-01",
+ "MCF7-FAR-02",
+ "MCF7-FAR-03"
+ ],
+ "sample_metadata": {
+ "MCF7-WT-01": "control",
+ "MCF7-WT-02": "control",
+ "MCF7-WT-03": "control",
+ "MCF7-FAR-01": "FAR",
+ "MCF7-FAR-02": "FAR",
+ "MCF7-FAR-03": "FAR"
+ },
+ "conditions": ["control", "FAR"],
+ "default_contrast": {
+ "reference": "control",
+ "alternate": "FAR"
+ },
+ "analysis_options": {
+ "padj_alpha": 0.05,
+ "de_query_direction": "both",
+ "min_abs_log2fc": 0.0
+ },
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (author-DE benchmark)"
+}
+
diff --git a/envs/pathway_analysis_env/data/gse227102_mcf7_counts_like_case.json b/envs/pathway_analysis_env/data/gse227102_mcf7_counts_like_case.json
new file mode 100644
index 0000000000..e9ac607e68
--- /dev/null
+++ b/envs/pathway_analysis_env/data/gse227102_mcf7_counts_like_case.json
@@ -0,0 +1,46 @@
+{
+ "case_id": "GSE227102_MCF7_counts_like_DESeq2",
+ "strict_mode": false,
+ "experiment_metadata": {
+ "source": "GEO",
+ "accession": "GSE227102",
+ "pubmed_id": "37258566",
+ "summary": "Count-like matrix reconstructed from author supplement (rounded to integers) to enable DESeq2 run in OpenEnv. Use for approximate parity testing only."
+ },
+ "counts_file": "geo_eval/gse227102_counts_like_MCF7.csv.gz",
+ "sample_ids": [
+ "MCF7-WT-01",
+ "MCF7-WT-02",
+ "MCF7-WT-03",
+ "MCF7-FAR-01",
+ "MCF7-FAR-02",
+ "MCF7-FAR-03"
+ ],
+ "sample_metadata": {
+ "MCF7-WT-01": "control",
+ "MCF7-WT-02": "control",
+ "MCF7-WT-03": "control",
+ "MCF7-FAR-01": "FAR",
+ "MCF7-FAR-02": "FAR",
+ "MCF7-FAR-03": "FAR"
+ },
+ "conditions": [
+ "FAR",
+ "control"
+ ],
+ "default_contrast": {
+ "reference": "control",
+ "alternate": "FAR"
+ },
+ "analysis_options": {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "de_query_direction": "both"
+ },
+ "enrichr_libraries": [
+ "MSigDB_Hallmark_2020",
+ "KEGG_2021_Human",
+ "Reactome_2022"
+ ],
+ "true_pathway": "Unknown (counts-like benchmark)"
+}
\ No newline at end of file
diff --git a/envs/pathway_analysis_env/data/gse227102_t47d_case.json b/envs/pathway_analysis_env/data/gse227102_t47d_case.json
new file mode 100644
index 0000000000..7fcfa6f5e2
--- /dev/null
+++ b/envs/pathway_analysis_env/data/gse227102_t47d_case.json
@@ -0,0 +1,45 @@
+{
+ "case_id": "GSE227102_T47D_vs_T47D-FAR_author_DE",
+ "strict_mode": false,
+ "experiment_metadata": {
+ "source": "GEO",
+ "accession": "GSE227102",
+ "pubmed_id": "37258566",
+ "summary": "ER+ breast cancer cell lines; parental T47D vs T47D-FAR (fulvestrant + abemaciclib resistant). GEO provides author DE tables; this case loads the author table and runs enrichment.",
+ "reference": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227102"
+ },
+ "de_table_file": "geo_eval/gse227102_author/T47D_author.csv.gz",
+ "de_table_gene_column": "Gene,name",
+ "de_table_log2fc_column": "log2FoldChange",
+ "de_table_pvalue_column": "pvalue",
+ "de_table_padj_column": "padj",
+ "sample_ids": [
+ "T47D-WT-01",
+ "T47D-WT-02",
+ "T47D-WT-03",
+ "T47D-FAR-01",
+ "T47D-FAR-02",
+ "T47D-FAR-03"
+ ],
+ "sample_metadata": {
+ "T47D-WT-01": "control",
+ "T47D-WT-02": "control",
+ "T47D-WT-03": "control",
+ "T47D-FAR-01": "FAR",
+ "T47D-FAR-02": "FAR",
+ "T47D-FAR-03": "FAR"
+ },
+ "conditions": ["control", "FAR"],
+ "default_contrast": {
+ "reference": "control",
+ "alternate": "FAR"
+ },
+ "analysis_options": {
+ "padj_alpha": 0.05,
+ "de_query_direction": "both",
+ "min_abs_log2fc": 0.0
+ },
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (author-DE benchmark)"
+}
+
diff --git a/envs/pathway_analysis_env/data/gse227102_t47d_counts_like_case.json b/envs/pathway_analysis_env/data/gse227102_t47d_counts_like_case.json
new file mode 100644
index 0000000000..d961a0fa2a
--- /dev/null
+++ b/envs/pathway_analysis_env/data/gse227102_t47d_counts_like_case.json
@@ -0,0 +1,46 @@
+{
+ "case_id": "GSE227102_T47D_counts_like_DESeq2",
+ "strict_mode": false,
+ "experiment_metadata": {
+ "source": "GEO",
+ "accession": "GSE227102",
+ "pubmed_id": "37258566",
+ "summary": "Count-like matrix reconstructed from author supplement (rounded to integers) to enable DESeq2 run in OpenEnv. Use for approximate parity testing only."
+ },
+ "counts_file": "geo_eval/gse227102_counts_like_T47D.csv.gz",
+ "sample_ids": [
+ "T47D-WT-01",
+ "T47D-WT-02",
+ "T47D-WT-03",
+ "T47D-FAR-01",
+ "T47D-FAR-02",
+ "T47D-FAR-03"
+ ],
+ "sample_metadata": {
+ "T47D-WT-01": "control",
+ "T47D-WT-02": "control",
+ "T47D-WT-03": "control",
+ "T47D-FAR-01": "FAR",
+ "T47D-FAR-02": "FAR",
+ "T47D-FAR-03": "FAR"
+ },
+ "conditions": [
+ "FAR",
+ "control"
+ ],
+ "default_contrast": {
+ "reference": "control",
+ "alternate": "FAR"
+ },
+ "analysis_options": {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "de_query_direction": "both"
+ },
+ "enrichr_libraries": [
+ "MSigDB_Hallmark_2020",
+ "KEGG_2021_Human",
+ "Reactome_2022"
+ ],
+ "true_pathway": "Unknown (counts-like benchmark)"
+}
\ No newline at end of file
diff --git a/envs/pathway_analysis_env/data/gse235417_case.json b/envs/pathway_analysis_env/data/gse235417_case.json
new file mode 100644
index 0000000000..faa0526e72
--- /dev/null
+++ b/envs/pathway_analysis_env/data/gse235417_case.json
@@ -0,0 +1,35 @@
+{
+ "case_id": "GSE235417_cetuximab_resistance",
+ "strict_mode": false,
+ "experiment_metadata": {
+ "source": "GEO",
+ "accession": "GSE235417",
+ "organism": "Homo sapiens",
+ "summary": "Head and neck squamous cell carcinoma PDX model (UCLHN04): baseline vs acquired cetuximab-resistant tumors; 3 biological replicates each.",
+ "reference": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE235417"
+ },
+ "counts_file": "geo/GSE235417_counts.csv.gz",
+ "sample_ids": ["ON68CTRL4", "ON68CTRL5", "ON68CTRL6", "ON76Cx1", "ON76CX3", "ON76Cx4"],
+ "sample_metadata": {
+ "ON68CTRL4": "baseline",
+ "ON68CTRL5": "baseline",
+ "ON68CTRL6": "baseline",
+ "ON76Cx1": "resistant",
+ "ON76CX3": "resistant",
+ "ON76Cx4": "resistant"
+ },
+ "conditions": ["baseline", "resistant"],
+ "default_contrast": {
+ "reference": "baseline",
+ "alternate": "resistant"
+ },
+ "analysis_options": {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "de_query_direction": "both",
+ "min_abs_log2fc": 0.0
+ },
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (real dataset)"
+}
+
diff --git a/envs/pathway_analysis_env/data/toy_case_001.json b/envs/pathway_analysis_env/data/toy_case_001.json
new file mode 100644
index 0000000000..b73d5debcf
--- /dev/null
+++ b/envs/pathway_analysis_env/data/toy_case_001.json
@@ -0,0 +1,49 @@
+{
+ "case_id": "toy_case_001",
+ "conditions": ["control", "treated"],
+ "true_pathway": "MAPK signaling",
+ "sample_ids": ["C1", "C2", "T1", "T2"],
+ "sample_metadata": {
+ "C1": "control",
+ "C2": "control",
+ "T1": "treated",
+ "T2": "treated"
+ },
+ "default_contrast": {
+ "reference": "control",
+ "alternate": "treated"
+ },
+ "counts": {
+ "DUSP6": [100, 110, 800, 820],
+ "FOS": [90, 100, 750, 760],
+ "JUN": [80, 85, 700, 710],
+ "EGR1": [70, 75, 600, 620],
+ "MAPK1": [60, 65, 500, 520],
+ "AKT1": [200, 210, 205, 215],
+ "PIK3CA": [150, 155, 152, 158],
+ "PTEN": [180, 175, 182, 178],
+ "MTOR": [120, 125, 122, 128],
+ "GAPDH": [1000, 1050, 1020, 1010],
+ "ACTB": [900, 920, 910, 905],
+ "TUBB": [400, 410, 405, 408],
+ "HPRT": [150, 155, 152, 154],
+ "PPIA": [500, 510, 505, 508],
+ "RPLP0": [800, 820, 810,815]
+ },
+ "pathway_genes": {
+ "MAPK signaling": ["DUSP6", "FOS", "JUN", "EGR1", "MAPK1"],
+ "ERK cascade": ["DUSP6", "FOS", "MAPK1", "JUN"],
+ "PI3K-Akt signaling": ["AKT1", "PIK3CA", "PTEN", "MTOR"]
+ },
+ "analysis_options": {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "de_query_direction": "up",
+ "min_abs_log2fc": 0.0,
+ "ora_min_pathway_genes": 3
+ },
+ "expert_budget": 2,
+ "expert_penalty": 0.25,
+ "expert_hint": "The strongest ORA signal aligns with genes that rise sharply under the treated condition; check overlap genes across top pathways for hub effects.",
+ "strict_mode": false
+}
diff --git a/envs/pathway_analysis_env/data/toy_case_002.json b/envs/pathway_analysis_env/data/toy_case_002.json
new file mode 100644
index 0000000000..8eea0e65e1
--- /dev/null
+++ b/envs/pathway_analysis_env/data/toy_case_002.json
@@ -0,0 +1,42 @@
+{
+ "case_id": "toy_case_002",
+ "conditions": ["wild type", "knockout"],
+ "true_pathway": "PI3K-Akt signaling",
+ "sample_ids": ["W1", "W2", "K1", "K2"],
+ "sample_metadata": {
+ "W1": "wild type",
+ "W2": "wild type",
+ "K1": "knockout",
+ "K2": "knockout"
+ },
+ "default_contrast": {
+ "reference": "wild type",
+ "alternate": "knockout"
+ },
+ "counts": {
+ "AKT1": [50, 55, 400, 420],
+ "PIK3CA": [40, 45, 350, 360],
+ "PTEN": [200, 210, 80, 85],
+ "MTOR": [60, 65, 300, 310],
+ "DUSP6": [100, 105, 102, 101],
+ "FOS": [90, 92, 91, 93],
+ "JUN": [80, 82, 81, 84],
+ "EGR1": [70, 72, 71, 73],
+ "MAPK1": [120, 125, 122, 124],
+ "GAPDH": [1000, 1050, 1020, 1010],
+ "ACTB": [900, 920, 910, 905],
+ "TUBB": [400, 410, 405, 408],
+ "HPRT": [150, 155, 152, 154],
+ "PPIA": [500, 510, 505, 508],
+ "RPLP0": [800, 820, 810, 815]
+ },
+ "pathway_genes": {
+ "PI3K-Akt signaling": ["AKT1", "PIK3CA", "PTEN", "MTOR"],
+ "mTOR signaling": ["MTOR", "AKT1", "RPLP0"],
+ "MAPK signaling": ["DUSP6", "FOS", "JUN", "MAPK1"]
+ },
+ "expert_budget": 1,
+ "expert_penalty": 0.3,
+ "expert_hint": "Knockout shifts regulation toward growth-pathway genes; compare ORA q-values.",
+ "strict_mode": false
+}
diff --git a/envs/pathway_analysis_env/data/toy_case_legacy.json b/envs/pathway_analysis_env/data/toy_case_legacy.json
new file mode 100644
index 0000000000..b596252448
--- /dev/null
+++ b/envs/pathway_analysis_env/data/toy_case_legacy.json
@@ -0,0 +1,9 @@
+{
+ "case_id": "toy_case_legacy",
+ "conditions": ["control", "treated"],
+ "true_pathway": "MAPK signaling",
+ "top_genes": ["DUSP6", "FOS", "JUN", "EGR1"],
+ "top_pathways": ["MAPK signaling", "ERK cascade", "growth factor response"],
+ "expert_budget": 0,
+ "strict_mode": false
+}
diff --git a/envs/pathway_analysis_env/data/toy_case_no_default.json b/envs/pathway_analysis_env/data/toy_case_no_default.json
new file mode 100644
index 0000000000..f8c4be652d
--- /dev/null
+++ b/envs/pathway_analysis_env/data/toy_case_no_default.json
@@ -0,0 +1,35 @@
+{
+ "case_id": "toy_case_no_default",
+ "conditions": ["control", "treated"],
+ "true_pathway": "MAPK signaling",
+ "sample_ids": ["C1", "C2", "T1", "T2"],
+ "sample_metadata": {
+ "C1": "control",
+ "C2": "control",
+ "T1": "treated",
+ "T2": "treated"
+ },
+ "counts": {
+ "DUSP6": [100, 110, 800, 820],
+ "FOS": [90, 100, 750, 760],
+ "JUN": [80, 85, 700, 710],
+ "EGR1": [70, 75, 600, 620],
+ "MAPK1": [60, 65, 500, 520],
+ "AKT1": [200, 210, 205, 215],
+ "PIK3CA": [150, 155, 152, 158],
+ "PTEN": [180, 175, 182, 178],
+ "MTOR": [120, 125, 122, 128],
+ "GAPDH": [1000, 1050, 1020, 1010],
+ "ACTB": [900, 920, 910, 905],
+ "TUBB": [400, 410, 405, 408],
+ "HPRT": [150, 155, 152, 154],
+ "PPIA": [500, 510, 505, 508],
+ "RPLP0": [800, 820, 810, 815]
+ },
+ "pathway_genes": {
+ "MAPK signaling": ["DUSP6", "FOS", "JUN", "EGR1", "MAPK1"],
+ "ERK cascade": ["DUSP6", "FOS", "MAPK1", "JUN"],
+ "PI3K-Akt signaling": ["AKT1", "PIK3CA", "PTEN", "MTOR"]
+ },
+ "strict_mode": true
+}
diff --git a/envs/pathway_analysis_env/docs/ADVISOR_MEETING_PRESENTATION.md b/envs/pathway_analysis_env/docs/ADVISOR_MEETING_PRESENTATION.md
new file mode 100644
index 0000000000..566f6bac91
--- /dev/null
+++ b/envs/pathway_analysis_env/docs/ADVISOR_MEETING_PRESENTATION.md
@@ -0,0 +1,245 @@
+# OpenEnv & your pathway work — advisor meeting brief
+
+**Audience:** you and your advisor as **molecular biology experts** with **limited coding background**.
+**Goal:** explain **what OpenEnv is**, **why it matters for biology**, **what you built that is new**, and **what becomes possible next** — from basics to a clear pitch.
+
+---
+
+## Part 1 — The basic idea (2–3 minutes)
+
+### What is an “environment” in this sense?
+
+Not a lab bench. A **computational workflow** treated like a **controlled world** a computer program (often an **AI agent**) can interact with:
+
+- It has **inputs** (for example: a gene expression matrix and sample metadata).
+- It has **actions** (for example: run differential expression, run pathway enrichment).
+- It has **outputs** and **rules** (what counts as a valid step, what “success” means).
+
+Think of it like a **flight simulator for analysis**: same type of task as real research, but **packaged** so it can be run, logged, and compared **many times**.
+
+### What is OpenEnv (one sentence)?
+
+**OpenEnv is a framework (from Meta / PyTorch OpenEnv) for building these computational environments so that AI systems can be trained and evaluated on real tasks — not on toy examples.**
+
+### Why should a molecular biologist care?
+
+1. **Modern biology is already computational.** RNA-seq, single-cell, spatial, proteomics — the bottleneck is often **robust, reproducible analysis**, not only wet lab design.
+
+2. **AI is coming into every pipeline.** Tools will propose contrasts, filters, gene sets, and interpretations. We need to know **when they are right**, **when they drift**, and **how to compare models fairly**.
+
+3. **Reproducibility and standards.** A public “environment” tied to **real data** (for example GEO studies) is a way to say: *here is the exact analysis setup; here is the benchmark; here is the score.*
+
+---
+
+## Part 2 — Build the picture slowly (5–7 minutes)
+
+### The old way vs the “environment” way
+
+| Traditional script | OpenEnv-style environment |
+|--------------------|---------------------------|
+| You run a notebook once | The same workflow is a **stable package** others can run |
+| Hard to compare two AI tools fairly | Same **inputs, actions, scoring** for every competitor |
+| “It worked on my laptop” | Clear **interfaces** (what is allowed, what is measured) |
+
+You are **not** replacing biologists. You are building **infrastructure** so that **human judgment + machine assistance** can be tested on **realistic biology**.
+
+### Two useful analogies
+
+1. **Assay validation.** An ELISA has a protocol, controls, and acceptance criteria. An OpenEnv environment is similar: **protocol (steps), controls (data), criteria (metrics).**
+
+2. **Animal models (very loose analogy).** A model organism is **simplified but structured** so you can ask repeatable questions. A computational environment is **simplified but structured** so you can ask repeatable **analysis** questions.
+
+### What *your* pathway environment does (biology language)
+
+Your **`pathway_analysis_env`** wraps a common post-genomics path:
+
+1. **Gene-level data** (counts or appropriately formatted tables).
+2. **Differential expression** (which genes change between conditions).
+3. **Pathway / gene-set enrichment** (which programs or processes are enriched among those genes).
+
+So the “world” is: *given this experiment’s numbers, what does a rigorous DE + pathway workflow look like, and how can an agent navigate it under rules?*
+
+---
+
+## Part 3 — Why OpenEnv matters (not just “another pipeline”) (3–4 minutes)
+
+### 1. Training and evaluating **agents**, not only running one-off scripts
+
+Many bioinformatics tools run **once**. OpenEnv is aimed at situations where a system must:
+
+- **inspect** data,
+- **choose** steps,
+- **recover** from mistakes,
+- **justify** choices,
+
+…under **explicit constraints** (for example: no cheating by peeking at hidden answers).
+
+That matches how **graduate students and postdocs** actually work — iterative, decision-heavy — and it is where **AI assistance** will matter most.
+
+### 2. **Separation of concerns** (why non-coders should still care)
+
+OpenEnv emphasizes boundaries such as:
+
+- **Agents** interact through a defined interface (they do not “reach into” the server however they want).
+- **Rewards / grading** live **inside** the environment (so benchmarks stay honest).
+
+For biology: this is like **blinded scoring** and **pre-registered analysis plans** — structure that reduces **silent hacks** and **accidental p-hacking**.
+
+### 3. **Public benchmarks tied to real biology**
+
+You invested effort in **GEO-linked benchmark studies** (true counts, author tables, TPM limitations, SRA-based reconstruction). That is exactly the kind of **external anchor** reviewers and funders want: *not only synthetic data.*
+
+---
+
+## Part 4 — Your novel contribution (be precise and confident) (4–6 minutes)
+
+Frame this as **three layers** of contribution. Adjust wording to match exactly what *you* personally implemented vs what you integrated.
+
+### Layer A — A **real biology** environment inside OpenEnv
+
+- A **pathway analysis** “world” that uses **real statistical tools** (for example **PyDESeq2-style** differential expression and **Enrichr-based** pathway enrichment), not toy arithmetic.
+- A **user interface** (Gradio) so the workflow can be **demonstrated** and **inspected** without reading code.
+
+**Pitch line:** *“I turned a standard RNA-seq → DE → pathway workflow into a reproducible OpenEnv environment so agents can be trained and tested on realistic molecular oncology-style questions.”*
+
+### Layer B — **Rigorous external benchmarking** on GEO-style data
+
+You went beyond a demo case by connecting to **public datasets** and documenting **what each study can and cannot support**:
+
+- **True integer counts** when GEO provides them (strongest case).
+- **Author-reported DE** when counts are missing (tests ingestion + enrichment, not refitting DE).
+- **“Counts-like”** stress tests when only author-derived numeric columns exist (quantitative agreement metrics — clearly labeled as **approximate**, not FASTQ truth).
+- **SRA → quantification → counts** where GEO only offers tracks or raw reads (harder, but the gold path for some questions).
+
+**Pitch line:** *“I built a benchmark ladder that matches how messy real GEO submissions are — so we evaluate tools on what the field actually has, not only on curated matrices.”*
+
+### Layer C — **Engineering for reliability** (what advisors respect even if they do not read code)
+
+- **Dockerized** heavy RNA-seq tooling (Salmon / STAR / SRA tools) **separate** from the lightweight teaching UI — keeps day-to-day use practical.
+- **Clear failure modes** and documentation so runs fail **understandably** (disk, locale, RAM, missing counts) instead of mysteriously.
+- **Scripts** that reproduce “Study 1 … Study 5” style runs — that is **methods-grade** thinking.
+
+**Pitch line:** *“I treated reproducibility and operator error as first-class problems, not afterthoughts.”*
+
+---
+
+## Part 5 — One diagram you can draw on a whiteboard
+
+```mermaid
+flowchart LR
+ subgraph public_data [Public data]
+ GEO[GEO / SRA]
+ end
+ subgraph bench [Heavy compute optional]
+ DOCKER[Docker: Salmon STAR SRA tools]
+ end
+ subgraph env [OpenEnv pathway environment]
+ COUNTS[Counts and metadata]
+ DE[Differential expression]
+ ORA[Pathway enrichment]
+ SCORE[Evaluation and traces]
+ end
+ subgraph agents [Future AI agents]
+ AGENT[Agent: plan act justify]
+ end
+ GEO --> COUNTS
+ GEO --> DOCKER
+ DOCKER --> COUNTS
+ AGENT --> env
+ env --> SCORE
+```
+
+**What to say while drawing:**
+*“Public data feeds either directly into the environment or through an optional Docker ‘instrument room’ for raw reads. The environment exposes the same scientific steps we already publish in papers — but packaged so an agent can be trained and scored consistently.”*
+
+---
+
+## Part 6 — The pitch at its best (60–90 seconds)
+
+You can read this almost verbatim:
+
+> “OpenEnv is a framework for packaging real computational workflows so AI agents can be trained and evaluated the way we evaluate assays — with explicit protocols and scoring.
+>
+> My work focuses on a pathway-analysis environment grounded in RNA-seq differential expression and enrichment, using real tools rather than toy math.
+>
+> What I believe is new is the combination: a credible biological workflow **inside** OpenEnv, plus a **GEO-grounded benchmark ladder** that mirrors how messy public data actually are — from true counts to author tables to TPM-only limitations — with reproducible runners and Dockerized heavy steps.
+>
+> The reason this matters is that biology will increasingly rely on AI copilots for analysis. Without standardized environments and public benchmarks, we cannot tell which copilots are safe, which ones hallucinate pathways, and which ones only work on demo data.
+>
+> Next, I want to extend this approach to additional disease contexts, stricter ‘clinical reporting’ constraints for agents, and collaboration-style tasks — for example multi-omic integration or cohort-level summaries — still anchored in public data and transparent scoring.”
+
+---
+
+## Part 7 — What you can do **next** with this class of environment (2–3 minutes)
+
+Pick 2–3 of these depending on your timeline and funding story.
+
+### Near term (3–6 months)
+
+1. **More GEO “study archetypes”** (counts vs author DE vs raw reads), each with a one-page **biological interpretation** + **failure taxonomy** (already aligned with how you think about failed experiments).
+
+2. **Agent tasks that match lab meeting questions**
+ Examples: *“Given this contrast, is estrogen pathway enrichment plausible?”*; *“Which QC step should be checked first?”* — scored with **structured rubrics**, not vibes.
+
+3. **Teaching / onboarding**
+ The same environment becomes a **safe sandbox** for students to break RNA-seq logic without breaking production pipelines.
+
+### Medium term (6–18 months)
+
+4. **Multi-step “paper reproduction” challenges**
+ Environment episodes that require **multiple evidence types** (DE + pathway + sanity checks on known genes like *ESR1*, *MKI67*, etc.).
+
+5. **Human–AI collaboration metrics**
+ Not only “did the agent get the right pathway,” but **did it save expert time** and **did it flag uncertainty** appropriately (very fundable framing).
+
+### Longer horizon
+
+6. **Licensed / clinical-style synthetic cohorts** (if you move toward translation) — same OpenEnv pattern, stricter privacy constraints.
+
+7. **Cross-environment curricula**
+ Pathway env + a separate “experimental design env” + a “literature retrieval env” — agents must **hand off** evidence across tasks (more realistic science).
+
+---
+
+## Part 8 — Anticipated advisor questions (short answers)
+
+**“Is this just GEOparse + DESeq2?”**
+No. Those are **components**. The contribution is the **packaged, repeatable benchmark + agent interface** and the **honest handling of what GEO can support** per study.
+
+**“Will AI replace bioinformaticians?”**
+Unlikely. It will **shift** the job toward **framing questions, choosing constraints, interpreting biology, and auditing models** — exactly why **benchmarked environments** matter.
+
+**“What is the closest non-AI analogy?”**
+**Proficiency testing** for clinical labs, or **challenge studies** in methods journals — standardized tasks, public data, reported metrics.
+
+**“What do you need to scale?”**
+Compute for large SRA studies, pinned software versions for long-term reproducibility, and (if applicable) co-advisors who care about **ML evaluation** or **cancer systems biology** for joint papers.
+
+**“What is one risk you should acknowledge?”**
+Agents can **game** poorly designed rewards; benchmarks can **overfit**. Your GEO ladder and explicit disclaimers (counts-like vs true counts) are the scientifically mature response.
+
+---
+
+## Suggested slide order (12–15 slides)
+
+1. Title — your name, date, one-line thesis
+2. Big picture — biology is computational; AI needs training grounds
+3. What is OpenEnv (simulator / assay analogy)
+4. What is *your* environment (RNA-seq → DE → pathways)
+5. Why packaging matters (reproducibility, fair comparison)
+6. Demo screenshot or workflow cartoon (optional)
+7. Benchmark philosophy — “ladder” not one-size-fits-all
+8. Study examples at high level (1 true counts, 2 author, 3 SRA, 4 merged counts, 5 TPM caveats) — **one slide, not five** unless they ask
+9. Your novel contribution — **three bullets** (real tools + GEO ladder + engineering)
+10. Results you can mention qualitatively (e.g. estrogen pathway face validity; correlation metrics where applicable)
+11. Limitations — honesty wins (replicates, TPM not counts, compute)
+12. Future directions — pick your top 3
+13. Ask — feedback on **which direction** is most compelling for a first paper or fellowship narrative
+
+---
+
+## Closing tone
+
+You are not selling “more code.” You are selling **scientific infrastructure**: a way to **evaluate intelligent analysis assistance** on **real molecular biology**, with **clear methods** and **public anchors**.
+
+That is a **credible** advisor conversation.
diff --git a/envs/pathway_analysis_env/docs/ADVISOR_PRESENTATION.html b/envs/pathway_analysis_env/docs/ADVISOR_PRESENTATION.html
new file mode 100644
index 0000000000..c3d610aac1
--- /dev/null
+++ b/envs/pathway_analysis_env/docs/ADVISOR_PRESENTATION.html
@@ -0,0 +1,401 @@
+
+
+
+
+
+ OpenEnv & Pathway Analysis — Advisor Briefing
+
+
+
+
+
+
OpenEnv · Pathway analysis environment
+
+
1 / 0
+
+
+
Use → ← or Space · Home / End · Swipe on trackpad
+
", "\n").replace("
", "\n")
+ t = re.sub(r"]*>", "", t, flags=re.I)
+ t = t.replace("", "\n\n")
+ t = re.sub(r"(.*?)", r"\1", t, flags=re.I | re.S)
+ t = re.sub(r"(.*?)", r"\1", t, flags=re.I | re.S)
+ t = re.sub(r"(.*?)", r"\1", t, flags=re.I | re.S)
+ t = re.sub(r"<[^>]+>", "", t)
+ t = re.sub(r"\n{3,}", "\n\n", t)
+ return t.strip()
+
+
+# Simple advisor deck: fewer slides, bigger ideas, minimal jargon.
+SLIDES: list[dict[str, Any]] = [
+ {
+ "kind": "cover",
+ "title": "Pathway analysis in OpenEnv",
+ "subtitle": "A simple, repeatable framework to run RNA-seq → DE → pathways (and to evaluate AI copilots fairly).",
+ "meta": "Parul Sarma Aryasomayajula — advisor meeting",
+ },
+ {
+ "kind": "content",
+ "section": "Problem",
+ "title": "Why this matters",
+ "bullets": [
+ "Biology conclusions often depend on analysis choices (contrast, filtering, gene sets).",
+ "AI copilots are coming, but we need a reliable way to check if they are correct.",
+ "Right now: results can be hard to reproduce and hard to compare across tools.",
+ ],
+ },
+ {
+ "kind": "content",
+ "section": "Idea",
+ "title": "What OpenEnv gives us (in plain terms)",
+ "bullets": [
+ "A standard “container” for an analysis workflow: same inputs, same steps, same outputs.",
+ "A record of what happened (like a computational lab notebook).",
+ "A way to compare two copilots on the exact same task, fairly.",
+ ],
+ },
+ {
+ "kind": "content",
+ "section": "My build",
+ "title": "What I built",
+ "bullets": [
+ "A pathway analysis environment: counts + sample labels → differential expression → pathway enrichment.",
+ "A simple UI (Gradio) to demo the workflow quickly.",
+ "Saved outputs (tables + JSON summaries) so results can be reviewed and rerun.",
+ ],
+ },
+ {
+ "kind": "content",
+ "section": "Demo flow",
+ "title": "What happens in one run (one study)",
+ "bullets": [
+ "Choose a study + define the comparison (e.g., treated vs control).",
+ "Run differential expression to get a ranked gene list.",
+ "Run pathway enrichment to get the main biological themes.",
+ "Export a short report + artifacts for review.",
+ ],
+ },
+ {
+ "kind": "table",
+ "section": "Why not a script?",
+ "title": "Script vs OpenEnv-style workflow",
+ "headers": ("A normal script", "This OpenEnv environment"),
+ "rows": [
+ ("Runs once, hard to compare", "Runs many times, comparable across models"),
+ ("Hard to audit what happened", "Trace + saved artifacts for audit"),
+ ("Hard to reuse as a benchmark", "Designed to be a benchmark"),
+ ],
+ },
+ {
+ "kind": "content",
+ "section": "Evidence",
+ "title": "How I ground it in real public data (GEO)",
+ "bullets": [
+ "I used multiple GEO-style study types, because public data is messy.",
+ "Best case: real integer counts → strongest validation.",
+ "Other cases: author DE tables or TPM-only supplements → useful for testing, labeled honestly.",
+ ],
+ },
+ {
+ "kind": "content",
+ "section": "Limitations",
+ "title": "What I will NOT over-claim",
+ "bullets": [
+ "Small sample sizes mean noisy DE: pathways are hypothesis-level, not final truth.",
+ "Some GEO studies do not provide ideal raw counts; I label these cases clearly.",
+ "Heavy raw-read workflows (SRA → quant) take real compute time.",
+ ],
+ },
+ {
+ "kind": "content",
+ "section": "Ask / next",
+ "title": "What I want feedback on",
+ "bullets": [
+ "What is the best advisor narrative: evaluation benchmark, teaching tool, or methods paper?",
+ "Which biological study archetypes would be most convincing to add next?",
+ "What should be the simplest success metric for a first paper/demo?",
+ ],
+ },
+ {
+ "kind": "cover",
+ "title": "Discussion",
+ "subtitle": "Happy to walk through a single GEO study end-to-end and discuss what to claim (and what not to claim).",
+ "meta": "Thank you",
+ },
+]
+
+
+def _fill_bullets(text_frame: Any, lines: list[str]) -> None:
+ text_frame.clear()
+ for i, line in enumerate(lines):
+ if i == 0:
+ p = text_frame.paragraphs[0]
+ else:
+ p = text_frame.add_paragraph()
+ p.text = line
+ p.level = 0
+
+
+def build_pptx(out_path: Path) -> None:
+ try:
+ from pptx import Presentation
+ from pptx.util import Inches, Pt
+ except Exception as exc: # pragma: no cover
+ raise SystemExit(
+ "Missing dependency python-pptx. Install with: uv pip install python-pptx"
+ ) from exc
+
+ prs = Presentation()
+ prs.slide_width = 13_333_333
+ prs.slide_height = 7_500_000
+
+ layout_title = prs.slide_layouts[0]
+ layout_content = prs.slide_layouts[1]
+ layout_two = prs.slide_layouts[3]
+ layout_title_only = prs.slide_layouts[5]
+
+ for spec in SLIDES:
+ kind = spec["kind"]
+ if kind == "cover":
+ slide = prs.slides.add_slide(layout_title)
+ slide.shapes.title.text = spec["title"]
+ sub = slide.placeholders[1]
+ sub.text = spec["subtitle"] + "\n\n" + spec.get("meta", "")
+ continue
+
+ if kind == "content":
+ slide = prs.slides.add_slide(layout_content)
+ sec = spec.get("section", "")
+ slide.shapes.title.text = f"{sec}: {spec['title']}" if sec else spec["title"]
+ body = slide.shapes.placeholders[1].text_frame
+ bullets = [strip_html(b) for b in spec["bullets"]]
+ _fill_bullets(body, bullets)
+ continue
+
+ if kind == "two_col":
+ slide = prs.slides.add_slide(layout_two)
+ sec = spec.get("section", "")
+ slide.shapes.title.text = f"{sec}: {spec['title']}" if sec else spec["title"]
+ left_tf = slide.placeholders[1].text_frame
+ right_tf = slide.placeholders[2].text_frame
+ left_lines = [spec["left_title"]] + [strip_html(x) for x in spec["left"]]
+ right_lines = [spec["right_title"]] + [strip_html(x) for x in spec["right"]]
+ _fill_bullets(left_tf, left_lines)
+ _fill_bullets(right_tf, right_lines)
+ for i, para in enumerate(left_tf.paragraphs):
+ para.level = 0 if i == 0 else 1
+ if i == 0:
+ para.font.bold = True
+ for i, para in enumerate(right_tf.paragraphs):
+ para.level = 0 if i == 0 else 1
+ if i == 0:
+ para.font.bold = True
+ continue
+
+ if kind == "table":
+ slide = prs.slides.add_slide(layout_title_only)
+ slide.shapes.title.text = f"{spec['section']}: {spec['title']}"
+ headers = spec["headers"]
+ rows_data: list[tuple[str, str]] = [headers, *spec["rows"]]
+ nrows, ncols = len(rows_data), 2
+ left, top, width, height = Inches(0.6), Inches(1.35), Inches(12.2), Inches(5.2)
+ table_shape = slide.shapes.add_table(nrows, ncols, left, top, width, height)
+ tbl = table_shape.table
+ for r, row in enumerate(rows_data):
+ for c, cell in enumerate(row):
+ tbl.cell(r, c).text = strip_html(str(cell))
+ for paragraph in tbl.cell(r, c).text_frame.paragraphs:
+ paragraph.font.size = Pt(14 if r > 0 else 15)
+ if r == 0:
+ paragraph.font.bold = True
+ continue
+
+ if kind == "diagram":
+ slide = prs.slides.add_slide(layout_content)
+ sec = spec.get("section", "")
+ slide.shapes.title.text = f"{sec}: {spec['title']}"
+ body = slide.shapes.placeholders[1].text_frame
+ _fill_bullets(body, [strip_html(b) for b in spec["bullets"]])
+ continue
+
+ if kind == "quote":
+ slide = prs.slides.add_slide(layout_content)
+ sec = spec.get("section", "")
+ slide.shapes.title.text = f"{sec}: {spec['title']}"
+ body = slide.shapes.placeholders[1].text_frame
+ plain = strip_html(spec["html"])
+ parts = [p.strip() for p in plain.split("\n\n") if p.strip()]
+ _fill_bullets(body, parts)
+ for para in body.paragraphs:
+ para.font.size = Pt(15)
+ continue
+
+ raise ValueError(f"Unknown slide kind: {kind}")
+
+ out_path.parent.mkdir(parents=True, exist_ok=True)
+ prs.save(str(out_path))
+
+
+def main() -> None:
+ env_root = Path(__file__).resolve().parents[1]
+ out = env_root / "docs" / "ADVISOR_PRESENTATION_SIMPLE.pptx"
+ build_pptx(out)
+ print(str(out))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/scripts/build_fake_rnaseq_detailed_case.py b/envs/pathway_analysis_env/scripts/build_fake_rnaseq_detailed_case.py
new file mode 100644
index 0000000000..45bd75e834
--- /dev/null
+++ b/envs/pathway_analysis_env/scripts/build_fake_rnaseq_detailed_case.py
@@ -0,0 +1,260 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Build a detailed synthetic RNA-seq-style case: many genes, 8 samples (4 vs 4),
+pathway gene sets with overlap, and experiment metadata for auditing.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any, Dict, List
+
+import numpy as np
+
+RNG = np.random.default_rng(42)
+
+# Biologically plausible MAPK / stress-response genes (up in treated)
+MAPK_CORE = [
+ "DUSP6",
+ "FOS",
+ "JUN",
+ "EGR1",
+ "MAPK1",
+ "MAPK3",
+ "MAP2K1",
+ "MAP2K2",
+ "ELK1",
+ "MYC",
+ "CCND1",
+ "CDKN1A",
+ "GADD45A",
+ "ATF3",
+ "NR4A1",
+ "HSPB1",
+ "HSPA1A",
+ "HSPA1B",
+ "DNAJB1",
+ "PPP1R15A",
+]
+
+# Decoy pathway (flat or weak)
+WNT_SET = [
+ "WNT3A",
+ "WNT5A",
+ "WNT11",
+ "CTNNB1",
+ "APC",
+ "AXIN1",
+ "TCF7",
+ "LEF1",
+ "DVL1",
+ "DVL2",
+ "FZD1",
+ "LRP5",
+]
+
+PI3K_SET = [
+ "AKT1",
+ "AKT2",
+ "PIK3CA",
+ "PIK3CB",
+ "PTEN",
+ "MTOR",
+ "RPS6KB1",
+ "EIF4EBP1",
+ "BAD",
+ "FOXO1",
+ "GSK3B",
+ "TSC2",
+]
+
+# Housekeeping / structural background
+HOUSEKEEPING = [
+ "GAPDH",
+ "ACTB",
+ "TUBB",
+ "TUBA1A",
+ "RPLP0",
+ "RPS18",
+ "RPL13A",
+ "EEF1A1",
+ "EEF2",
+ "HPRT1",
+ "PPIA",
+ "UBC",
+ "B2M",
+ "TBP",
+ "GUSB",
+ "YWHAZ",
+ "PGK1",
+ "TFRC",
+ "POLR2A",
+ "SDHA",
+ "TPT1",
+ "RPS6",
+ "RPL7",
+ "RPL11",
+ "RPL23",
+ "RPS3",
+ "RPS14",
+ "RPS27A",
+ "RPL18",
+ "RPL22",
+ "RPS20",
+ "RPS24",
+ "RPS25",
+ "RPS27",
+ "RPL10",
+ "RPL15",
+ "RPL19",
+ "RPL26",
+ "RPL27",
+ "RPL30",
+ "RPLP1",
+ "RPS2",
+ "RPS4X",
+ "RPS5",
+ "RPS6KB2",
+ "RPS8",
+ "RPS9",
+ "RPS10",
+ "RPS11",
+ "RPS12",
+ "RPS13",
+ "RPS15",
+ "RPS16",
+]
+
+# Extra random genes to fill universe
+NOISE_GENES = [f"LOC{i:05d}" for i in range(1, 121)]
+
+
+def _sample_counts(base: float, n: int, scale: float = 0.08) -> List[int]:
+ x = RNG.normal(loc=base, scale=base * scale, size=n)
+ return [max(1, int(round(v))) for v in x]
+
+
+def build_case() -> Dict[str, Any]:
+ sample_ids = ["C1", "C2", "C3", "C4", "T1", "T2", "T3", "T4"]
+ sample_metadata = {s: "control" for s in sample_ids[:4]}
+ sample_metadata.update({s: "treated" for s in sample_ids[4:]})
+
+ counts: Dict[str, List[int]] = {}
+ n_ctrl, n_trt = 4, 4
+
+ # MAPK: strong up in treated
+ for g in MAPK_CORE:
+ ctrl = _sample_counts(120.0, n_ctrl, 0.12)
+ trt = _sample_counts(2200.0, n_trt, 0.1)
+ counts[g] = ctrl + trt
+
+ # WNT: flat
+ for g in WNT_SET:
+ counts[g] = _sample_counts(400.0, 8, 0.15)
+
+ # PI3K: mild bump treated (decoy)
+ for g in PI3K_SET:
+ ctrl = _sample_counts(350.0, n_ctrl, 0.12)
+ trt = _sample_counts(520.0, n_trt, 0.12)
+ counts[g] = ctrl + trt
+
+ # Housekeeping
+ for g in HOUSEKEEPING:
+ counts[g] = _sample_counts(900.0, 8, 0.1)
+
+ # Noise genes
+ for g in NOISE_GENES:
+ counts[g] = _sample_counts(180.0, 8, 0.2)
+
+ pathway_genes: Dict[str, List[str]] = {
+ "MAPK signaling": list(MAPK_CORE),
+ "ERK cascade": [
+ "DUSP6",
+ "FOS",
+ "JUN",
+ "ELK1",
+ "MAPK1",
+ "MAPK3",
+ "MAP2K1",
+ "MAP2K2",
+ ],
+ "WNT signaling": WNT_SET,
+ "PI3K-Akt signaling": PI3K_SET,
+ "Stress response": ["HSPB1", "HSPA1A", "HSPA1B", "DNAJB1", "PPP1R15A"],
+ }
+
+ # Deduplicate overlap lists for JSON readability
+ pathway_genes["MAPK signaling"] = list(
+ dict.fromkeys(pathway_genes["MAPK signaling"])
+ )
+
+ gene_annotations = {
+ g: f"ENSG{100000000 + i:011d}" for i, g in enumerate(list(counts.keys())[:200])
+ }
+
+ experiment_metadata = {
+ "organism": "Homo sapiens",
+ "assay": "poly-A RNA-seq",
+ "library_layout": "paired-end",
+ "read_length_bp": 101,
+ "strand_specificity": "reverse",
+ "reference_genome": "GRCh38",
+ "quantification": "gene-level counts (synthetic)",
+ "batch_note": "fake single-batch dataset for OpenEnv benchmarking",
+ "n_samples": len(sample_ids),
+ "n_genes_quantified": len(counts),
+ }
+
+ return {
+ "case_id": "fake_rnaseq_detailed",
+ "conditions": ["control", "treated"],
+ "true_pathway": "MAPK signaling",
+ "sample_ids": sample_ids,
+ "sample_metadata": sample_metadata,
+ "default_contrast": {"reference": "control", "alternate": "treated"},
+ "analysis_options": {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "de_query_direction": "up",
+ "min_abs_log2fc": 0.25,
+ "ora_min_pathway_genes": 5,
+ },
+ "counts": counts,
+ "pathway_genes": pathway_genes,
+ "experiment_metadata": experiment_metadata,
+ "gene_annotations_ensembl_style": gene_annotations,
+ "expert_budget": 2,
+ "expert_penalty": 0.25,
+ "expert_hint": "Strongest coordinated DE should align with MAPK/ERK-associated genes; check overlap across top pathways.",
+ "strict_mode": False,
+ }
+
+
+def main() -> None:
+ p = argparse.ArgumentParser()
+ p.add_argument(
+ "--out",
+ type=Path,
+ default=Path(__file__).resolve().parent.parent
+ / "data"
+ / "fake_rnaseq_detailed.json",
+ )
+ args = p.parse_args()
+ case = build_case()
+ args.out.parent.mkdir(parents=True, exist_ok=True)
+ args.out.write_text(json.dumps(case, indent=2), encoding="utf-8")
+ print(
+ f"Wrote {args.out} ({len(case['counts'])} genes, {len(case['sample_ids'])} samples)"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/scripts/build_pitch_deck_pptx.py b/envs/pathway_analysis_env/scripts/build_pitch_deck_pptx.py
new file mode 100644
index 0000000000..f3433b9c2a
--- /dev/null
+++ b/envs/pathway_analysis_env/scripts/build_pitch_deck_pptx.py
@@ -0,0 +1,193 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+
+@dataclass(frozen=True)
+class Slide:
+ title: str
+ bullets: list[str]
+ subtitle: str | None = None
+
+
+SLIDES: list[Slide] = [
+ Slide(
+ title="OpenEnv: Pathway Analysis Environment",
+ subtitle="A realistic RNA-seq workflow environment for evaluating scientific agents",
+ bullets=["Parul Sarma Aryasomayajula"],
+ ),
+ Slide(
+ title="The problem",
+ bullets=[
+ "Scientific/biomed agent performance is hard to evaluate",
+ "Real workflows require multi-step tool use, not single-shot answers",
+ "We need reproducible, instrumented tasks with measurable outcomes",
+ "Goal: evaluate agents on end-to-end reasoning + tool execution",
+ ],
+ ),
+ Slide(
+ title="What OpenEnv provides",
+ bullets=[
+ "Standard environment API (reset / step / state)",
+ "Client/server separation (FastAPI runtime; reusable clients)",
+ "Structured observations & metadata for evaluation and debugging",
+ "Makes complex tasks testable like environments",
+ ],
+ ),
+ Slide(
+ title="Task: pathway analysis from gene expression",
+ bullets=[
+ "Objective: infer what biological programs/pathways explain a phenotype",
+ "Workflow: design → DGE → pathway enrichment → compare → hypothesis",
+ ],
+ ),
+ Slide(
+ title="Agent interface (actions)",
+ bullets=[
+ "understand_experiment_design",
+ "inspect_dataset",
+ "run_differential_expression",
+ "run_pathway_enrichment",
+ "compare_pathways",
+ "ask_expert (budgeted hint/curriculum)",
+ "submit_answer",
+ ],
+ ),
+ Slide(
+ title="Observability & safety rails",
+ bullets=[
+ "DE table: log2FC, p-value, q-value, baseMean",
+ "Enriched pathways + overlap genes",
+ "Ambiguity/overlap summaries when multiple hits look similar",
+ "Stable failure codes for error analysis",
+ "HTML episode trace for debugging & review",
+ ],
+ ),
+ Slide(
+ title="Real pipeline integrated (not a toy)",
+ bullets=[
+ "Differential expression: DESeq2-style stats via PyDESeq2",
+ "DESeq2 prefiltering for low-count genes",
+ "Configurable thresholds (FDR, min log2FC, direction)",
+ "Pathway analysis: ORA on query genes",
+ "Supports case-defined gene sets (offline) or Enrichr via gseapy",
+ ],
+ ),
+ Slide(
+ title="Real-data support (GEO-style inputs)",
+ bullets=[
+ "Counts files like *.csv.gz (GEO)",
+ "Sample metadata (condition labels)",
+ "Pipeline cases defined as JSON configs",
+ "Same agent actions run on real public studies",
+ ],
+ ),
+ Slide(
+ title="Public study demo: GSE235417",
+ bullets=[
+ "HNSCC PDX (UCLHN04): baseline vs acquired cetuximab-resistant",
+ "3 biological replicates per condition",
+ "DESeq2 contrast: resistant vs baseline",
+ "Enrichment: Hallmark / KEGG / Reactome",
+ "Exports: JSON results + HTML trace + report",
+ ],
+ ),
+ Slide(
+ title="Example results (headlines)",
+ bullets=[
+ "Top DE genes (example): DAPK1, CXCL8, SOX2, FKBP5, …",
+ "Top pathways (example): EMT; inflammatory/interferon; TNFα/NF-κB; cytokine signaling",
+ "Emphasis: reproducible pipeline + measurable signals (avoid over-claiming biology)",
+ ],
+ ),
+ Slide(
+ title="What we can measure (evaluation metrics)",
+ bullets=[
+ "Correctness: does the agent identify the right hypothesis?",
+ "Efficiency: steps taken, tool calls, reward trajectory",
+ "Robustness: invalid contrasts, missing metadata, low signal",
+ "Interpretability: traces + structured evidence for postmortems",
+ ],
+ ),
+ Slide(
+ title="Why this is valuable",
+ bullets=[
+ "Bridges toy benchmarks and real science workflows",
+ "Makes agent behavior reproducible, debuggable, comparable",
+ "Foundation for regression tests, eval harnesses, future RL-style loops",
+ ],
+ ),
+ Slide(
+ title="Roadmap",
+ bullets=[
+ "Add GSEA (rank-based enrichment) alongside ORA",
+ "Richer experiment designs (covariates / batches)",
+ "More datasets and tasks (perturbations, multi-omics, drug response)",
+ "Standardized scoring + reporting for agent comparisons",
+ ],
+ ),
+ Slide(
+ title="Close",
+ bullets=[
+ "OpenEnv environments turn real workflows into measurable evaluation problems",
+ "Happy to discuss fit with your agent evaluation stack and next domains",
+ ],
+ ),
+]
+
+
+def build_pptx(out_path: Path) -> None:
+ try:
+ from pptx import Presentation
+ except Exception as exc: # pragma: no cover
+ raise SystemExit(
+ "Missing dependency python-pptx. Install with: uv pip install python-pptx"
+ ) from exc
+
+ prs = Presentation()
+ # 16:9 wide
+ prs.slide_width = 13_333_333
+ prs.slide_height = 7_500_000
+
+ title_and_content = prs.slide_layouts[1]
+
+ for s in SLIDES:
+ slide = prs.slides.add_slide(title_and_content)
+ slide.shapes.title.text = s.title
+ body = slide.shapes.placeholders[1].text_frame
+ body.clear()
+
+ if s.subtitle:
+ p = body.paragraphs[0]
+ p.text = s.subtitle
+ p.level = 0
+ for b in s.bullets:
+ q = body.add_paragraph()
+ q.text = b
+ q.level = 0
+ else:
+ for i, b in enumerate(s.bullets):
+ if i == 0:
+ p = body.paragraphs[0]
+ p.text = b
+ p.level = 0
+ else:
+ q = body.add_paragraph()
+ q.text = b
+ q.level = 0
+
+ out_path.parent.mkdir(parents=True, exist_ok=True)
+ prs.save(str(out_path))
+
+
+def main() -> None:
+ repo_root = Path(__file__).resolve().parents[2]
+ out = repo_root / "docs" / "INTERVIEW_PITCH_DECK.pptx"
+ build_pptx(out)
+ print(str(out))
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/envs/pathway_analysis_env/scripts/generate_curriculum_cases.py b/envs/pathway_analysis_env/scripts/generate_curriculum_cases.py
new file mode 100644
index 0000000000..27307ee1bc
--- /dev/null
+++ b/envs/pathway_analysis_env/scripts/generate_curriculum_cases.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Generate synthetic case JSON files with increasing difficulty.
+
+Levels:
+ 1 — clean MAPK vs PI3K separation
+ 2 — shared hub gene between top pathways (overlap-heavy)
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+BASE = {
+ "sample_ids": ["C1", "C2", "T1", "T2"],
+ "sample_metadata": {
+ "C1": "control",
+ "C2": "control",
+ "T1": "treated",
+ "T2": "treated",
+ },
+ "conditions": ["control", "treated"],
+ "default_contrast": {"reference": "control", "alternate": "treated"},
+ "expert_budget": 1,
+ "expert_penalty": 0.3,
+ "strict_mode": False,
+}
+
+
+def case_clean_mapk(out: Path) -> None:
+ data = {
+ **BASE,
+ "case_id": "curriculum_clean_mapk",
+ "true_pathway": "MAPK signaling",
+ "counts": {
+ "DUSP6": [100, 110, 800, 820],
+ "FOS": [90, 100, 750, 760],
+ "JUN": [80, 85, 700, 710],
+ "EGR1": [70, 75, 600, 620],
+ "MAPK1": [60, 65, 500, 520],
+ "AKT1": [200, 210, 205, 215],
+ "PIK3CA": [150, 155, 152, 158],
+ "GAPDH": [1000, 1050, 1020, 1010],
+ "ACTB": [900, 920, 910, 905],
+ },
+ "pathway_genes": {
+ "MAPK signaling": ["DUSP6", "FOS", "JUN", "EGR1", "MAPK1"],
+ "PI3K-Akt signaling": ["AKT1", "PIK3CA"],
+ },
+ "expert_hint": "Contrast treated vs control; strongest ORA should match the pathway with coherent DE support.",
+ }
+ out.write_text(json.dumps(data, indent=2), encoding="utf-8")
+
+
+def case_hub_overlap(out: Path) -> None:
+ """DUSP6 appears in both MAPK and ERK; agent should use compare_pathways."""
+ data = {
+ **BASE,
+ "case_id": "curriculum_hub_overlap",
+ "true_pathway": "MAPK signaling",
+ "counts": {
+ "DUSP6": [100, 110, 750, 760],
+ "FOS": [90, 100, 700, 710],
+ "JUN": [80, 85, 650, 660],
+ "EGR1": [70, 75, 600, 610],
+ "MAPK1": [60, 65, 500, 510],
+ "ELK1": [50, 55, 480, 490],
+ "AKT1": [200, 210, 205, 215],
+ "GAPDH": [1000, 1050, 1020, 1010],
+ "ACTB": [900, 920, 910, 905],
+ },
+ "pathway_genes": {
+ "MAPK signaling": ["DUSP6", "FOS", "JUN", "EGR1", "MAPK1"],
+ "ERK cascade": ["DUSP6", "FOS", "ELK1", "MAPK1"],
+ "PI3K-Akt signaling": ["AKT1"],
+ },
+ "expert_hint": "Hub genes can appear in multiple pathways; compare exclusive DE support.",
+ }
+ out.write_text(json.dumps(data, indent=2), encoding="utf-8")
+
+
+def main() -> None:
+ p = argparse.ArgumentParser(description="Write curriculum case JSON files.")
+ p.add_argument(
+ "--out-dir",
+ type=Path,
+ default=Path(__file__).resolve().parent.parent / "data" / "curriculum",
+ )
+ args = p.parse_args()
+ args.out_dir.mkdir(parents=True, exist_ok=True)
+ case_clean_mapk(args.out_dir / "curriculum_clean_mapk.json")
+ case_hub_overlap(args.out_dir / "curriculum_hub_overlap.json")
+ print(f"Wrote curriculum cases under {args.out_dir}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/scripts/run_benchmark.py b/envs/pathway_analysis_env/scripts/run_benchmark.py
new file mode 100644
index 0000000000..defcef4e81
--- /dev/null
+++ b/envs/pathway_analysis_env/scripts/run_benchmark.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Run a fixed policy on pathway_analysis_env cases and report accuracy / tool usage.
+
+Example:
+ PYTHONPATH=src:envs python envs/pathway_analysis_env/scripts/run_benchmark.py \\
+ --case toy_case_001.json --guess 'MAPK signaling'
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from typing import Any, Dict, List
+
+from pathway_analysis_env.models import PathwayAction
+from pathway_analysis_env.server.pathway_environment import (
+ PathwayEnvironment,
+ load_case,
+)
+
+
+def run_episode(case_file: str, guess: str, strict: bool) -> Dict[str, Any]:
+ case = load_case(case_file)
+ ref = (case.get("default_contrast") or {}).get("reference", "control")
+ alt = (case.get("default_contrast") or {}).get("alternate", "treated")
+
+ env = PathwayEnvironment(case_file=case_file)
+ env.reset(strict=strict)
+ actions: List[str] = []
+ total_reward = 0.0
+
+ def go(kind: str, **kw: Any) -> Any:
+ actions.append(kind)
+ nonlocal total_reward
+ o = env.step(PathwayAction(action_type=kind, **kw))
+ total_reward += float(o.reward or 0.0)
+ return o
+
+ go("inspect_dataset")
+ o_de = go(
+ "run_differential_expression",
+ condition_a=ref,
+ condition_b=alt,
+ )
+ if o_de.done:
+ return {
+ "correct": False,
+ "actions": actions,
+ "reward": total_reward,
+ "strict_failure": True,
+ }
+ go("run_pathway_enrichment")
+ pnames = list((case.get("pathway_genes") or {}).keys())
+ if len(pnames) >= 2:
+ go("compare_pathways", pathway_a=pnames[0], pathway_b=pnames[1])
+ o_fin = go("submit_answer", hypothesis=guess)
+ correct = bool(o_fin.metadata.get("correct")) if o_fin.metadata else False
+ return {
+ "correct": correct,
+ "actions": actions,
+ "reward": total_reward,
+ "strict_failure": False,
+ }
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(description="Benchmark pathway env episodes.")
+ parser.add_argument("--case", default="toy_case_001.json")
+ parser.add_argument("--guess", default="MAPK signaling")
+ parser.add_argument("--strict", action="store_true")
+ parser.add_argument("--json-out", action="store_true", help="Print JSON line")
+ args = parser.parse_args()
+
+ stats = run_episode(args.case, args.guess, args.strict)
+ if args.json_out:
+ print(json.dumps(stats))
+ else:
+ print("correct:", stats["correct"])
+ print("actions:", stats["actions"])
+ print("reward (partial sum):", stats["reward"])
+ print("strict_failure:", stats["strict_failure"])
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/scripts/run_gse227102_author_benchmark.py b/envs/pathway_analysis_env/scripts/run_gse227102_author_benchmark.py
new file mode 100644
index 0000000000..fcbfc17681
--- /dev/null
+++ b/envs/pathway_analysis_env/scripts/run_gse227102_author_benchmark.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, Dict, List
+
+from pathway_analysis_env.models import PathwayAction
+from pathway_analysis_env.server.pathway_environment import PathwayEnvironment, load_case
+
+
+def run_case(case_file: str) -> Dict[str, Any]:
+ case = load_case(case_file)
+ env = PathwayEnvironment(case_file=case_file)
+ env.reset(strict=bool(case.get("strict_mode", False)))
+
+ def step(kind: str, **kw: Any) -> Any:
+ return env.step(PathwayAction(action_type=kind, **kw))
+
+ step("inspect_dataset")
+ o_de = step("run_differential_expression")
+ if o_de.done:
+ raise RuntimeError(f"DE step failed: {o_de.message}")
+ o_enr = step("run_pathway_enrichment")
+ if o_enr.done:
+ raise RuntimeError(f"Enrichment step failed: {o_enr.message}")
+
+ return {
+ "case_id": case.get("case_id"),
+ "de_n_rows": len(o_de.de_genes or []),
+ "de_top_genes": o_de.top_genes or [],
+ "ora_top_pathways": o_enr.top_pathways or [],
+ "ora_rows": o_enr.pathway_enrichment or [],
+ "metadata": {
+ "de": o_de.metadata or {},
+ "enrichment": o_enr.metadata or {},
+ },
+ }
+
+
+def write_outputs(out_dir: Path, result: Dict[str, Any]) -> None:
+ out_dir.mkdir(parents=True, exist_ok=True)
+ (out_dir / "summary.json").write_text(json.dumps(result, indent=2), encoding="utf-8")
+
+ # Save the top pathways rows in a separate file for easy diffing.
+ ora_rows: List[Dict[str, Any]] = list(result.get("ora_rows") or [])
+ (out_dir / "enrichment.json").write_text(json.dumps(ora_rows, indent=2), encoding="utf-8")
+
+ # DE rows are large; this benchmark uses author tables and the env already exposes the top genes.
+
+
+def main() -> None:
+ base = Path(__file__).resolve().parent.parent
+ out_root = base / "data" / "geo_eval"
+
+ cases = [
+ "gse227102_mcf7_case.json",
+ "gse227102_t47d_case.json",
+ ]
+
+ for c in cases:
+ r = run_case(c)
+ slug = str(r.get("case_id") or c).replace(" ", "_")
+ write_outputs(out_root / slug, r)
+ print(f"[OK] {c} -> {out_root/slug}")
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/envs/pathway_analysis_env/scripts/run_gse227102_counts_from_author.py b/envs/pathway_analysis_env/scripts/run_gse227102_counts_from_author.py
new file mode 100644
index 0000000000..da02a499cf
--- /dev/null
+++ b/envs/pathway_analysis_env/scripts/run_gse227102_counts_from_author.py
@@ -0,0 +1,311 @@
+#!/usr/bin/env python3
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import pandas as pd
+
+ROOT = Path(__file__).resolve().parent.parent
+DATA_DIR = ROOT / "data"
+GEO_EVAL_DIR = DATA_DIR / "geo_eval"
+
+
+def _read_author_de(path: Path) -> pd.DataFrame:
+ df = pd.read_csv(path, sep=";", compression="gzip")
+ if df.empty:
+ raise ValueError(f"Empty author DE table: {path}")
+ # normalize decimal commas in numeric columns we care about
+ for c in ("log2FoldChange", "pvalue", "padj"):
+ if c in df.columns:
+ df[c] = df[c].astype(str).str.replace(",", ".", regex=False)
+ df[c] = pd.to_numeric(df[c], errors="coerce")
+ return df
+
+
+def _infer_sample_cols(df: pd.DataFrame, prefix: str) -> List[str]:
+ cols = [c for c in df.columns if isinstance(c, str) and c.startswith(prefix)]
+ if not cols:
+ raise ValueError(f"No sample columns found with prefix {prefix!r}. Columns={df.columns.tolist()}")
+ return cols
+
+
+def build_counts_matrix_from_author(
+ df: pd.DataFrame,
+ *,
+ gene_id_col: str,
+ sample_cols: List[str],
+) -> pd.DataFrame:
+ """
+ Build a **genes × samples** matrix from author table numeric columns.
+
+ Note: author values are not guaranteed to be raw integer counts. We coerce to numeric,
+ fill missing with 0, clip negatives, and round to int so PyDESeq2 can run.
+ """
+ if gene_id_col not in df.columns:
+ raise ValueError(f"Missing gene id column {gene_id_col!r}")
+ for c in sample_cols:
+ if c not in df.columns:
+ raise ValueError(f"Missing sample column {c!r}")
+
+ out = df[[gene_id_col] + sample_cols].copy()
+ out = out.rename(columns={gene_id_col: "gene_id"})
+ out["gene_id"] = out["gene_id"].astype(str)
+ for c in sample_cols:
+ out[c] = out[c].astype(str).str.replace(",", ".", regex=False)
+ out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0)
+ out[c] = out[c].clip(lower=0.0)
+ out[c] = out[c].round().astype(int)
+
+ # Aggregate duplicate gene IDs (can occur with repeated symbols).
+ out = out.groupby("gene_id", as_index=True).sum()
+ return out
+
+
+def write_counts_csv_gz(counts_gene_by_sample: pd.DataFrame, out_path: Path) -> None:
+ out_path.parent.mkdir(parents=True, exist_ok=True)
+ counts_gene_by_sample.to_csv(out_path)
+
+
+def run_openenv_case(case_file: str) -> Dict:
+ """
+ Run DE + enrichment using the same underlying analysis functions as the environment,
+ but keep **full** DE rows (the env truncates `de_genes` for payload size).
+ """
+ from pathway_analysis_env.server.analysis import (
+ build_sample_metadata,
+ enrichr_ora,
+ load_counts_csv_as_samples_by_genes,
+ merge_analysis_options,
+ pick_de_query_genes,
+ run_deseq2_contrast,
+ )
+ from pathway_analysis_env.server.pathway_environment import load_case
+
+ case = load_case(case_file)
+ opts = merge_analysis_options(case)
+
+ counts_df = load_counts_csv_as_samples_by_genes(
+ DATA_DIR / str(case["counts_file"]),
+ sample_ids=list(case["sample_ids"]),
+ )
+ meta_df = build_sample_metadata(list(case["sample_ids"]), dict(case["sample_metadata"]))
+ ref = str(case["default_contrast"]["reference"])
+ alt = str(case["default_contrast"]["alternate"])
+
+ de_rows, err = run_deseq2_contrast(
+ counts_df,
+ meta_df,
+ alt,
+ ref,
+ padj_alpha=float(opts["padj_alpha"]),
+ )
+ if err:
+ raise RuntimeError(err)
+
+ query = pick_de_query_genes(
+ de_rows,
+ padj_alpha=float(opts["padj_alpha"]),
+ direction=str(opts["de_query_direction"]),
+ min_abs_log2fc=float(opts["min_abs_log2fc"]),
+ )
+ if not query:
+ query = [r["gene"] for r in de_rows[:50]]
+
+ enr_libs = list(case.get("enrichr_libraries") or [])
+ ora_rows, ora_err = enrichr_ora(
+ query,
+ libraries=enr_libs,
+ background=list(counts_df.columns),
+ top_k=100,
+ )
+ if ora_err:
+ ora_rows = []
+
+ return {
+ "case_id": case.get("case_id"),
+ "de_n_rows": len(de_rows),
+ "de_n_sig": sum(1 for r in de_rows if r.get("significant")),
+ "de_top_genes": [r["gene"] for r in de_rows[:50]],
+ "de_all": de_rows,
+ "ora_top_pathways": [r["pathway"] for r in ora_rows[:20]],
+ "ora_rows": ora_rows,
+ "ora_error": ora_err,
+ "query_n_genes": len(query),
+ }
+
+
+def compare_to_author(
+ *,
+ author_df: pd.DataFrame,
+ openenv_de_rows: List[Dict],
+ author_gene_col: str = "Gene,name",
+) -> Dict:
+ """
+ Compare OpenEnv DESeq2 results to author DE statistics (table-to-table).
+ """
+ # OpenEnv DESeq2 output uses the count-matrix column names as gene IDs.
+ # In our reconstructed count-like matrices, the gene IDs are Ensembl IDs from the author table.
+ a = author_df.copy()
+ if author_gene_col not in a.columns:
+ raise ValueError(f"Missing author gene col {author_gene_col!r}")
+ a[author_gene_col] = a[author_gene_col].astype(str)
+ a = a.dropna(subset=[author_gene_col]).drop_duplicates(subset=[author_gene_col])
+ a = a.set_index(author_gene_col)
+
+ oe = pd.DataFrame(openenv_de_rows)
+ if oe.empty:
+ return {"error": "openenv_de_empty"}
+ oe = oe.dropna(subset=["gene"]).drop_duplicates(subset=["gene"]).set_index("gene")
+
+ joined = oe.join(
+ a[["log2FoldChange", "padj"]].rename(
+ columns={"log2FoldChange": "author_log2fc", "padj": "author_padj"}
+ ),
+ how="inner",
+ )
+ if joined.empty:
+ return {"error": "no_gene_overlap"}
+
+ # Spearman correlations for effect size and significance
+ out: Dict = {"n_overlap_genes": int(joined.shape[0])}
+ out["spearman_log2fc"] = float(joined["log2FoldChange"].corr(joined["author_log2fc"], method="spearman"))
+ out["spearman_padj"] = float(joined["padj"].corr(joined["author_padj"], method="spearman"))
+
+ # Top-N overlap by smallest padj
+ for n in (50, 200, 500):
+ top_oe = set(joined.sort_values("padj").head(n).index)
+ top_a = set(joined.sort_values("author_padj").head(n).index)
+ inter = len(top_oe & top_a)
+ out[f"top{n}_overlap"] = int(inter)
+ out[f"top{n}_jaccard"] = float(inter / max(1, len(top_oe | top_a)))
+
+ # Sign concordance for genes significant in either table
+ sig = joined[(joined["padj"] <= 0.05) | (joined["author_padj"] <= 0.05)]
+ if sig.empty:
+ out["sign_concordance_sig_union"] = None
+ else:
+ s1 = (sig["log2FoldChange"] >= 0).astype(int)
+ s2 = (sig["author_log2fc"] >= 0).astype(int)
+ out["sign_concordance_sig_union"] = float((s1 == s2).mean())
+ out["n_sig_union"] = int(sig.shape[0])
+ return out
+
+
+def main() -> None:
+ # Inputs (author DE tables)
+ mcf7_path = GEO_EVAL_DIR / "gse227102_author" / "MCF7_author.csv.gz"
+ t47d_path = GEO_EVAL_DIR / "gse227102_author" / "T47D_author.csv.gz"
+
+ # Build count-like matrices
+ mcf7_df = _read_author_de(mcf7_path)
+ t47d_df = _read_author_de(t47d_path)
+
+ mcf7_samples = _infer_sample_cols(mcf7_df, "MCF7-")
+ t47d_samples = _infer_sample_cols(t47d_df, "T47D-")
+
+ # Prefer gene symbols so enrichment libraries match (Enrichr uses symbols).
+ mcf7_counts = build_counts_matrix_from_author(
+ mcf7_df, gene_id_col="Gene,name", sample_cols=mcf7_samples
+ )
+ t47d_counts = build_counts_matrix_from_author(
+ t47d_df, gene_id_col="Gene,name", sample_cols=t47d_samples
+ )
+
+ # Write as counts files compatible with OpenEnv loader (genes in rows, samples in columns)
+ mcf7_counts_path = GEO_EVAL_DIR / "gse227102_counts_like_MCF7.csv.gz"
+ t47d_counts_path = GEO_EVAL_DIR / "gse227102_counts_like_T47D.csv.gz"
+ write_counts_csv_gz(mcf7_counts, mcf7_counts_path)
+ write_counts_csv_gz(t47d_counts, t47d_counts_path)
+
+ # Build case files for DESeq2
+ def make_case(
+ case_id: str,
+ counts_file_rel: str,
+ sample_ids: List[str],
+ sample_metadata: Dict[str, str],
+ pubmed_id: str = "37258566",
+ ) -> Dict:
+ return {
+ "case_id": case_id,
+ "strict_mode": False,
+ "experiment_metadata": {
+ "source": "GEO",
+ "accession": "GSE227102",
+ "pubmed_id": pubmed_id,
+ "summary": "Count-like matrix reconstructed from author supplement (rounded to integers) to enable DESeq2 run in OpenEnv. Use for approximate parity testing only.",
+ },
+ "counts_file": counts_file_rel,
+ "sample_ids": sample_ids,
+ "sample_metadata": sample_metadata,
+ "conditions": sorted(set(sample_metadata.values())),
+ "default_contrast": {"reference": "control", "alternate": "FAR"},
+ "analysis_options": {"min_total_count": 10, "padj_alpha": 0.05, "de_query_direction": "both"},
+ "enrichr_libraries": ["MSigDB_Hallmark_2020", "KEGG_2021_Human", "Reactome_2022"],
+ "true_pathway": "Unknown (counts-like benchmark)",
+ }
+
+ mcf7_case = make_case(
+ "GSE227102_MCF7_counts_like_DESeq2",
+ "geo_eval/gse227102_counts_like_MCF7.csv.gz",
+ ["MCF7-WT-01", "MCF7-WT-02", "MCF7-WT-03", "MCF7-FAR-01", "MCF7-FAR-02", "MCF7-FAR-03"],
+ {
+ "MCF7-WT-01": "control",
+ "MCF7-WT-02": "control",
+ "MCF7-WT-03": "control",
+ "MCF7-FAR-01": "FAR",
+ "MCF7-FAR-02": "FAR",
+ "MCF7-FAR-03": "FAR",
+ },
+ )
+ t47d_case = make_case(
+ "GSE227102_T47D_counts_like_DESeq2",
+ "geo_eval/gse227102_counts_like_T47D.csv.gz",
+ ["T47D-WT-01", "T47D-WT-02", "T47D-WT-03", "T47D-FAR-01", "T47D-FAR-02", "T47D-FAR-03"],
+ {
+ "T47D-WT-01": "control",
+ "T47D-WT-02": "control",
+ "T47D-WT-03": "control",
+ "T47D-FAR-01": "FAR",
+ "T47D-FAR-02": "FAR",
+ "T47D-FAR-03": "FAR",
+ },
+ )
+
+ (DATA_DIR / "gse227102_mcf7_counts_like_case.json").write_text(json.dumps(mcf7_case, indent=2), encoding="utf-8")
+ (DATA_DIR / "gse227102_t47d_counts_like_case.json").write_text(json.dumps(t47d_case, indent=2), encoding="utf-8")
+
+ # Run OpenEnv DESeq2 + enrichment on both
+ mcf7_res = run_openenv_case("gse227102_mcf7_counts_like_case.json")
+ t47d_res = run_openenv_case("gse227102_t47d_counts_like_case.json")
+
+ mcf7_cmp = compare_to_author(author_df=mcf7_df, openenv_de_rows=list(mcf7_res["de_all"]))
+ t47d_cmp = compare_to_author(author_df=t47d_df, openenv_de_rows=list(t47d_res["de_all"]))
+
+ out_dir = GEO_EVAL_DIR / "gse227102_counts_like_runs"
+ out_dir.mkdir(parents=True, exist_ok=True)
+ (out_dir / "mcf7_openenv.json").write_text(
+ json.dumps({k: v for k, v in mcf7_res.items() if k != "de_all"}, indent=2),
+ encoding="utf-8",
+ )
+ (out_dir / "t47d_openenv.json").write_text(
+ json.dumps({k: v for k, v in t47d_res.items() if k != "de_all"}, indent=2),
+ encoding="utf-8",
+ )
+ (out_dir / "mcf7_de_all.json").write_text(json.dumps(mcf7_res["de_all"], indent=2), encoding="utf-8")
+ (out_dir / "t47d_de_all.json").write_text(json.dumps(t47d_res["de_all"], indent=2), encoding="utf-8")
+ (out_dir / "mcf7_enrichment.json").write_text(json.dumps(mcf7_res["ora_rows"], indent=2), encoding="utf-8")
+ (out_dir / "t47d_enrichment.json").write_text(json.dumps(t47d_res["ora_rows"], indent=2), encoding="utf-8")
+ (out_dir / "mcf7_compare_to_author.json").write_text(json.dumps(mcf7_cmp, indent=2), encoding="utf-8")
+ (out_dir / "t47d_compare_to_author.json").write_text(json.dumps(t47d_cmp, indent=2), encoding="utf-8")
+
+ print("[WROTE]", out_dir)
+ print("MCF7 compare:", json.dumps(mcf7_cmp, indent=2))
+ print("T47D compare:", json.dumps(t47d_cmp, indent=2))
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/envs/pathway_analysis_env/scripts/run_performance_report.py b/envs/pathway_analysis_env/scripts/run_performance_report.py
new file mode 100644
index 0000000000..1225096cc5
--- /dev/null
+++ b/envs/pathway_analysis_env/scripts/run_performance_report.py
@@ -0,0 +1,247 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Run timed pathway_analysis_env episodes on a case file and write a markdown report.
+
+Example:
+ PYTHONPATH=src:envs python envs/pathway_analysis_env/scripts/run_performance_report.py
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+from pathway_analysis_env.models import PathwayAction
+from pathway_analysis_env.server.analysis import pydeseq2_available
+from pathway_analysis_env.server.pathway_environment import (
+ PathwayEnvironment,
+ load_case,
+)
+
+
+def timed_step(
+ env: PathwayEnvironment,
+ kind: str,
+ **kw: Any,
+) -> Tuple[Any, float]:
+ t0 = time.perf_counter()
+ obs = env.step(PathwayAction(action_type=kind, **kw))
+ return obs, time.perf_counter() - t0
+
+
+def run_profiled_episode(
+ case_file: str,
+ guess: str,
+ *,
+ strict: bool,
+) -> Dict[str, Any]:
+ case = load_case(case_file)
+ ref = (case.get("default_contrast") or {}).get("reference", "control")
+ alt = (case.get("default_contrast") or {}).get("alternate", "treated")
+ true_path = case.get("true_pathway", "")
+
+ env = PathwayEnvironment(case_file=case_file)
+ t_reset0 = time.perf_counter()
+ env.reset(strict=strict)
+ t_reset = time.perf_counter() - t_reset0
+
+ timings: List[Dict[str, Any]] = []
+ actions: List[str] = []
+
+ def record(name: str, dt: float, obs: Any) -> None:
+ timings.append({"action": name, "seconds": dt})
+ actions.append(name)
+ total_reward_local = float(obs.reward or 0.0)
+ timings[-1]["reward"] = total_reward_local
+
+ o, dt = timed_step(env, "inspect_dataset")
+ record("inspect_dataset", dt, o)
+
+ o, dt = timed_step(
+ env,
+ "run_differential_expression",
+ condition_a=ref,
+ condition_b=alt,
+ )
+ record("run_differential_expression", dt, o)
+ de_genes = o.de_genes or []
+ top5 = [r.get("gene") for r in de_genes[:5]]
+
+ if o.done:
+ partial_reward = sum(t.get("reward", 0.0) for t in timings)
+ return {
+ "strict_failure": True,
+ "reset_seconds": t_reset,
+ "timings": timings,
+ "actions": actions,
+ "total_reward": partial_reward,
+ "true_pathway": true_path,
+ "guess": guess,
+ "correct": False,
+ "top5_de_genes": top5,
+ "ora_top": None,
+ "rank_true_pathway": None,
+ }
+
+ o, dt = timed_step(env, "run_pathway_enrichment")
+ record("run_pathway_enrichment", dt, o)
+ enrichment = o.pathway_enrichment or []
+ ora_names = [r.get("pathway") for r in enrichment]
+ rank = ora_names.index(true_path) + 1 if true_path in ora_names else None
+
+ pnames = list((case.get("pathway_genes") or {}).keys())
+ if len(pnames) >= 2:
+ o, dt = timed_step(
+ env,
+ "compare_pathways",
+ pathway_a=pnames[0],
+ pathway_b=pnames[1],
+ )
+ record("compare_pathways", dt, o)
+
+ o, dt = timed_step(env, "submit_answer", hypothesis=guess)
+ record("submit_answer", dt, o)
+ correct = bool(o.metadata.get("correct")) if o.metadata else False
+
+ total_reward = sum(t.get("reward", 0.0) for t in timings)
+
+ return {
+ "strict_failure": False,
+ "reset_seconds": t_reset,
+ "timings": timings,
+ "actions": actions,
+ "total_reward": total_reward,
+ "true_pathway": true_path,
+ "guess": guess,
+ "correct": correct,
+ "top5_de_genes": top5,
+ "ora_top": ora_names[:5] if ora_names else [],
+ "rank_true_pathway": rank,
+ "n_genes": len(case.get("counts") or {}),
+ "n_samples": len(case.get("sample_ids") or []),
+ "experiment_metadata": case.get("experiment_metadata"),
+ }
+
+
+def format_report(
+ case_file: str,
+ correct_run: Dict[str, Any],
+ wrong_run: Dict[str, Any],
+) -> str:
+ lines = [
+ "# Pathway OpenEnv — performance report (synthetic RNA-seq)",
+ "",
+ f"- **Generated (UTC):** {datetime.now(timezone.utc).isoformat()}",
+ f"- **Case file:** `{case_file}`",
+ f"- **PyDESeq2 available:** {pydeseq2_available()}",
+ "",
+ "## Synthetic database summary",
+ "",
+ ]
+ em = correct_run.get("experiment_metadata") or {}
+ if em:
+ lines.append("| Field | Value |")
+ lines.append("|-------|-------|")
+ for k, v in em.items():
+ lines.append(f"| {k} | {v} |")
+ lines.append("")
+ lines.extend(
+ [
+ f"- **Genes in count matrix:** {correct_run.get('n_genes', '—')}",
+ f"- **Samples:** {correct_run.get('n_samples', '—')}",
+ "",
+ "## Task outcome",
+ "",
+ f"- **Ground-truth pathway:** `{correct_run.get('true_pathway')}`",
+ f"- **ORA rank of ground truth:** {correct_run.get('rank_true_pathway')}",
+ f"- **Top ORA pathways (first 5):** {correct_run.get('ora_top')}",
+ f"- **Top 5 DE genes (by padj ordering):** {correct_run.get('top5_de_genes')}",
+ "",
+ "### Policy A — submit correct pathway",
+ "",
+ f"- **Correct:** {correct_run.get('correct')}",
+ f"- **Cumulative reward (sum of step rewards):** {correct_run.get('total_reward'):.4f}",
+ f"- **Strict failure:** {correct_run.get('strict_failure')}",
+ "",
+ "### Policy B — submit wrong pathway (`WNT signaling`)",
+ "",
+ f"- **Correct:** {wrong_run.get('correct')}",
+ f"- **Cumulative reward:** {wrong_run.get('total_reward'):.4f}",
+ "",
+ "## Latency (Policy A, correct guess)",
+ "",
+ "| Step | Seconds | Step reward |",
+ "|------|---------|-------------|",
+ ]
+ )
+ for t in correct_run.get("timings") or []:
+ lines.append(
+ f"| {t['action']} | {t['seconds']:.4f} | {t.get('reward', 0):.4f} |"
+ )
+ total_s = correct_run.get("reset_seconds", 0) + sum(
+ x["seconds"] for x in (correct_run.get("timings") or [])
+ )
+ lines.extend(
+ [
+ "",
+ f"- **Reset:** {correct_run.get('reset_seconds', 0):.4f} s",
+ f"- **Total wall (reset + steps):** {total_s:.4f} s",
+ "",
+ "## Notes",
+ "",
+ "- Latency is measured on the local `PathwayEnvironment` (no HTTP/WebSocket).",
+ "- DESeq2 variance estimates are noisy with small *n*; this benchmark uses 4 vs 4 samples.",
+ "",
+ ]
+ )
+ return "\n".join(lines)
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--case", default="fake_rnaseq_detailed.json")
+ parser.add_argument(
+ "--out",
+ type=Path,
+ default=Path(__file__).resolve().parent.parent
+ / "outputs"
+ / "pathway_performance_report.md",
+ )
+ parser.add_argument(
+ "--build-first",
+ action="store_true",
+ help="Run build_fake_rnaseq_detailed_case.py first",
+ )
+ args = parser.parse_args()
+
+ data_dir = Path(__file__).resolve().parent.parent / "data"
+ case_path = data_dir / args.case
+
+ if args.build_first or not case_path.is_file():
+ import subprocess
+ import sys
+
+ build = Path(__file__).resolve().parent / "build_fake_rnaseq_detailed_case.py"
+ subprocess.check_call([sys.executable, str(build), "--out", str(case_path)])
+
+ correct = run_profiled_episode(args.case, guess="MAPK signaling", strict=False)
+ wrong = run_profiled_episode(args.case, guess="WNT signaling", strict=False)
+
+ report = format_report(args.case, correct, wrong)
+ args.out.parent.mkdir(parents=True, exist_ok=True)
+ args.out.write_text(report, encoding="utf-8")
+ print(report)
+ print(f"\n[Written] {args.out}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/server/Dockerfile b/envs/pathway_analysis_env/server/Dockerfile
new file mode 100644
index 0000000000..47ba0f58d5
--- /dev/null
+++ b/envs/pathway_analysis_env/server/Dockerfile
@@ -0,0 +1,55 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest
+FROM ${BASE_IMAGE} AS builder
+
+WORKDIR /app
+
+ARG BUILD_MODE=in-repo
+
+COPY . /app/env
+
+WORKDIR /app/env
+
+RUN if ! command -v uv >/dev/null 2>&1; then \
+ curl -LsSf https://astral.sh/uv/install.sh | sh && \
+ mv /root/.local/bin/uv /usr/local/bin/uv && \
+ mv /root/.local/bin/uvx /usr/local/bin/uvx; \
+ fi
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ git \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+ if [ -f uv.lock ]; then \
+ uv sync --frozen --no-install-project --no-editable; \
+ else \
+ uv sync --no-install-project --no-editable; \
+ fi
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+ if [ -f uv.lock ]; then \
+ uv sync --frozen --no-editable; \
+ else \
+ uv sync --no-editable; \
+ fi
+
+FROM ${BASE_IMAGE}
+
+WORKDIR /app
+
+COPY --from=builder /app/env/.venv /app/.venv
+COPY --from=builder /app/env /app/env
+
+ENV PATH="/app/.venv/bin:$PATH"
+ENV PYTHONPATH="/app/env:$PYTHONPATH"
+
+HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
+ CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1
+
+CMD ["sh", "-c", "cd /app/env && uvicorn pathway_analysis_env.server.app:app --host 0.0.0.0 --port 8000"]
diff --git a/envs/pathway_analysis_env/server/__init__.py b/envs/pathway_analysis_env/server/__init__.py
new file mode 100644
index 0000000000..2e41cd717f
--- /dev/null
+++ b/envs/pathway_analysis_env/server/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/envs/pathway_analysis_env/server/analysis.py b/envs/pathway_analysis_env/server/analysis.py
new file mode 100644
index 0000000000..753e90a5ad
--- /dev/null
+++ b/envs/pathway_analysis_env/server/analysis.py
@@ -0,0 +1,623 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Differential expression (PyDESeq2) and over-representation analysis (ORA).
+
+Counts matrices use **samples × genes** layout for PyDESeq2 ≥ 0.5.
+"""
+
+from __future__ import annotations
+
+import io
+import math
+from contextlib import redirect_stderr, redirect_stdout
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
+
+import numpy as np
+import pandas as pd
+from scipy.stats import false_discovery_control, fisher_exact
+
+try:
+ from pydeseq2.dds import DeseqDataSet
+ from pydeseq2.ds import DeseqStats
+
+ _PYDESQ2_AVAILABLE = True
+except ImportError: # pragma: no cover - optional heavy dep
+ DeseqDataSet = None # type: ignore[misc, assignment]
+ DeseqStats = None # type: ignore[misc, assignment]
+ _PYDESQ2_AVAILABLE = False
+
+try:
+ import gseapy as gp
+
+ _GSEAPY_AVAILABLE = True
+except ImportError: # pragma: no cover - optional extra dep
+ gp = None # type: ignore[assignment]
+ _GSEAPY_AVAILABLE = False
+
+
+def pydeseq2_available() -> bool:
+ return _PYDESQ2_AVAILABLE
+
+
+def gseapy_available() -> bool:
+ return _GSEAPY_AVAILABLE
+
+
+def default_analysis_options() -> Dict[str, Any]:
+ """Defaults aligned with common RNA-seq practice (DESeq2 prefilter, directional ORA)."""
+ return {
+ "min_total_count": 10,
+ "padj_alpha": 0.05,
+ "ora_min_pathway_genes": 3,
+ # Use "up" for treated-vs-control activation screens; "both" is the safe default.
+ "de_query_direction": "both",
+ "min_abs_log2fc": 0.0,
+ }
+
+
+def merge_analysis_options(case: Dict[str, Any]) -> Dict[str, Any]:
+ out = default_analysis_options()
+ raw = case.get("analysis_options")
+ if not isinstance(raw, dict):
+ return out
+ for k, v in raw.items():
+ if k not in out or v is None:
+ continue
+ if k in ("min_total_count", "ora_min_pathway_genes"):
+ out[k] = int(v)
+ elif k in ("padj_alpha", "min_abs_log2fc"):
+ out[k] = float(v)
+ elif k == "de_query_direction":
+ out[k] = str(v).lower().strip()
+ else:
+ out[k] = v
+ return out
+
+
+def filter_counts_by_minimum_total(
+ counts_df: pd.DataFrame,
+ min_total: int,
+) -> Tuple[pd.DataFrame, int, int]:
+ """
+ Remove genes with summed counts below ``min_total`` (DESeq2-style prefilter).
+
+ Returns:
+ (filtered_df, n_before, n_after)
+ """
+ if min_total <= 0:
+ return counts_df, counts_df.shape[1], counts_df.shape[1]
+ totals = counts_df.sum(axis=0)
+ keep = totals >= min_total
+ n_before = int(counts_df.shape[1])
+ filtered = counts_df.loc[:, keep]
+ n_after = int(filtered.shape[1])
+ return filtered, n_before, n_after
+
+
+def normalize_gene_ids(raw: Sequence[str]) -> List[str]:
+ """
+ Normalize gene identifiers for downstream gene set matching.
+
+ GEO count tables often use a combined key like ``ENSG...__TP53``.
+ We keep the symbol suffix when present.
+ """
+
+ out: List[str] = []
+ for g in raw:
+ s = str(g)
+ if "__" in s:
+ s = s.split("__", 1)[1]
+ out.append(s)
+ return out
+
+
+def load_counts_csv_as_samples_by_genes(
+ path: str | Path,
+ *,
+ sample_ids: Optional[Sequence[str]] = None,
+) -> pd.DataFrame:
+ """
+ Load a counts table from CSV/CSV.GZ and return **samples × genes** DataFrame.
+
+ Expected file layout:
+ - rows: genes
+ - columns: sample IDs
+ - first column: gene identifier (may be unnamed)
+ """
+
+ p = Path(path)
+ df = pd.read_csv(p, index_col=0)
+ if df.empty:
+ raise ValueError(f"Counts file is empty: {p}")
+
+ df.index = normalize_gene_ids(df.index.tolist())
+ df = df.apply(pd.to_numeric, errors="coerce").fillna(0).astype(int)
+
+ # Aggregate duplicate symbols (common when collapsing Ensembl->symbol).
+ if df.index.has_duplicates:
+ df = df.groupby(df.index).sum()
+
+ # genes × samples -> samples × genes
+ counts_df = df.T
+
+ if sample_ids is not None:
+ missing = [s for s in sample_ids if s not in counts_df.index]
+ if missing:
+ raise ValueError(
+ f"Counts file missing sample columns/rows for: {missing[:10]}"
+ + (" ..." if len(missing) > 10 else "")
+ )
+ counts_df = counts_df.loc[list(sample_ids)]
+
+ return counts_df
+
+
+def counts_dict_to_samples_by_genes(
+ counts: Dict[str, Sequence[int]],
+ sample_ids: Sequence[str],
+) -> pd.DataFrame:
+ """Build a samples × genes count matrix from gene → per-sample counts."""
+ sid_to_i = {sid: i for i, sid in enumerate(sample_ids)}
+ rows = []
+ for sid in sample_ids:
+ j = sid_to_i[sid]
+ rows.append([int(counts[g][j]) for g in counts])
+ return pd.DataFrame(rows, index=list(sample_ids), columns=list(counts.keys()))
+
+
+def build_sample_metadata(
+ sample_ids: Sequence[str],
+ condition_by_sample: Dict[str, str],
+) -> pd.DataFrame:
+ missing = [s for s in sample_ids if s not in condition_by_sample]
+ if missing:
+ raise ValueError(
+ f"sample_metadata missing entries for sample_ids: {missing[:10]}"
+ + (" ..." if len(missing) > 10 else "")
+ )
+ conds = [condition_by_sample[s] for s in sample_ids]
+ return pd.DataFrame({"condition": conds}, index=list(sample_ids))
+
+
+def validate_counts_case(case: Dict[str, Any]) -> Optional[str]:
+ """Return an error message if pipeline case JSON is inconsistent, else None."""
+ counts = case.get("counts")
+ sample_ids = case.get("sample_ids")
+ if not isinstance(counts, dict) or not sample_ids:
+ return None
+ n = len(sample_ids)
+ for gene, vals in counts.items():
+ if len(vals) != n:
+ return (
+ f"Gene {gene!r} has {len(vals)} count values but "
+ f"sample_ids has length {n}."
+ )
+ return None
+
+
+def _finite_float_or_none(value: Any) -> Optional[float]:
+ if pd.isna(value):
+ return None
+ value = float(value)
+ return value if math.isfinite(value) else None
+
+
+def load_author_de_table_csv(
+ path: str | Path,
+ *,
+ gene_column: str | None = None,
+ log2fc_column: str = "log2FoldChange",
+ pvalue_column: str = "pvalue",
+ padj_column: str = "padj",
+) -> List[Dict[str, Any]]:
+ """
+ Load a precomputed differential expression (DE) table (author-provided).
+
+ Supports the common GEO supplement format used in GSE227102:
+ - semicolon-delimited
+ - decimal comma in numeric columns (e.g. ``0,12``) and scientific like ``1,47E-18``
+ - gene symbol in a column like ``Gene,name`` and/or an Ensembl ``ID``
+
+ Returns:
+ DE rows in the same schema as ``run_deseq2_contrast`` output, sorted by ascending padj.
+ """
+
+ p = Path(path)
+ if not p.is_file():
+ raise ValueError(f"DE table file not found: {p}")
+
+ df = pd.read_csv(p, sep=";")
+ if df.empty:
+ raise ValueError(f"DE table is empty: {p}")
+
+ # Pick gene column.
+ if gene_column is None:
+ for cand in ("Gene,name", "gene", "symbol", "Gene", "gene_name"):
+ if cand in df.columns:
+ gene_column = cand
+ break
+ if gene_column is None or gene_column not in df.columns:
+ raise ValueError(
+ "Could not infer gene column. Available columns: "
+ + ", ".join(map(str, df.columns.tolist()))
+ )
+
+ # Normalize numeric strings (decimal commas).
+ for c in (log2fc_column, pvalue_column, padj_column):
+ if c not in df.columns:
+ raise ValueError(f"Missing required column {c!r} in DE table: {p}")
+ df[c] = df[c].astype(str).str.replace(",", ".", regex=False)
+ df[c] = pd.to_numeric(df[c], errors="coerce")
+
+ df[gene_column] = df[gene_column].astype(str)
+
+ rows: List[Dict[str, Any]] = []
+ for _, r in df.iterrows():
+ gene = str(r.get(gene_column, "")).strip()
+ if not gene or gene.lower() in ("nan", "none"):
+ continue
+ padj = _safe_padj_value(r.get(padj_column))
+ rows.append(
+ {
+ "gene": gene,
+ "baseMean": None, # unknown for author tables
+ "log2FoldChange": _finite_float_or_none(r.get(log2fc_column)),
+ "lfcSE": None,
+ "pvalue": _finite_float_or_none(r.get(pvalue_column)),
+ "padj": padj,
+ "significant": False, # filled by caller using chosen alpha
+ }
+ )
+
+ rows.sort(
+ key=lambda x: (
+ _safe_padj_value(x.get("padj")),
+ -abs(float(x.get("log2FoldChange") or 0.0)),
+ )
+ )
+ return rows
+
+
+def run_deseq2_contrast(
+ counts_df: pd.DataFrame,
+ metadata_df: pd.DataFrame,
+ alt_level: str,
+ ref_level: str,
+ *,
+ padj_alpha: float = 0.05,
+ min_replicates: int = 2,
+) -> Tuple[List[Dict[str, Any]], Optional[str]]:
+ """
+ Run PyDESeq2 Wald test for ``alt_level`` vs ``ref_level`` on column ``condition``.
+
+ Returns:
+ (de_rows, error_message). ``de_rows`` are sorted by ascending adjusted p-value.
+ """
+ if not _PYDESQ2_AVAILABLE:
+ return [], "PyDESeq2 is not installed."
+
+ levels = set(metadata_df["condition"].tolist())
+ if ref_level not in levels or alt_level not in levels:
+ return [], (
+ f"Contrast invalid: need both reference {ref_level!r} and "
+ f"alternate {alt_level!r} in sample metadata; got {sorted(levels)}."
+ )
+
+ try:
+ dds = DeseqDataSet(
+ counts=counts_df,
+ metadata=metadata_df,
+ design="~condition",
+ refit_cooks=True,
+ min_replicates=min_replicates,
+ quiet=True,
+ )
+ buf_out, buf_err = io.StringIO(), io.StringIO()
+ with redirect_stdout(buf_out), redirect_stderr(buf_err):
+ dds.deseq2()
+ stat_res = DeseqStats(dds, contrast=["condition", alt_level, ref_level])
+ stat_res.summary()
+ res = stat_res.results_df
+ except Exception as exc: # pragma: no cover - fitting failures
+ return [], f"DESeq2 failed: {exc}"
+
+ de_rows: List[Dict[str, Any]] = []
+ for gene, row in res.iterrows():
+ padj = _safe_padj_value(row["padj"])
+ de_rows.append(
+ {
+ "gene": str(gene),
+ "baseMean": _finite_float_or_none(row.get("baseMean")),
+ "log2FoldChange": _finite_float_or_none(row.get("log2FoldChange")),
+ "lfcSE": _finite_float_or_none(row.get("lfcSE")),
+ "pvalue": _finite_float_or_none(row.get("pvalue")),
+ "padj": padj,
+ "significant": padj <= padj_alpha,
+ }
+ )
+ de_rows.sort(key=lambda r: (r["padj"], -abs(r["log2FoldChange"] or 0.0)))
+ return de_rows, None
+
+
+def benjamini_hochberg(p_values: Sequence[float]) -> List[float]:
+ """Benjamini–Hochberg FDR; returns q-values in original order (fallback)."""
+ m = len(p_values)
+ if m == 0:
+ return []
+ p_arr = np.nan_to_num(np.asarray(p_values, dtype=float), nan=1.0)
+ order = np.argsort(p_arr)
+ sorted_p = p_arr[order]
+ adj_sorted = np.empty(m)
+ running = 1.0
+ for i in range(m - 1, -1, -1):
+ running = min(sorted_p[i] * m / (i + 1), running)
+ adj_sorted[i] = running
+ out = np.empty(m)
+ out[order] = adj_sorted
+ return np.clip(out, 0.0, 1.0).tolist()
+
+
+def adjust_pvalues_bh(p_values: Sequence[float]) -> List[float]:
+ """Benjamini–Hochberg adjusted p-values using SciPy (preferred)."""
+ m = len(p_values)
+ if m == 0:
+ return []
+ p_arr = np.clip(
+ np.nan_to_num(np.asarray(p_values, dtype=float), nan=1.0), 1e-300, 1.0
+ )
+ try:
+ adj = false_discovery_control(p_arr, method="bh")
+ return np.clip(adj, 0.0, 1.0).tolist()
+ except Exception:
+ return benjamini_hochberg(p_values)
+
+
+def ora_fisher(
+ de_genes: Sequence[str],
+ pathway_genes: Dict[str, Sequence[str]],
+ universe_genes: Sequence[str],
+ *,
+ min_pathway_genes: int = 3,
+) -> List[Dict[str, Any]]:
+ """
+ Over-representation analysis (one-sided Fisher exact, greater overlap).
+
+ ``universe_genes`` should be the **same gene set** used for DESeq2 (prefiltered).
+
+ Pathways smaller than ``min_pathway_genes`` in the universe are skipped (reduces
+ noise from tiny sets).
+ """
+ u: Set[str] = set(universe_genes)
+ de: Set[str] = {g for g in de_genes if g in u}
+ results: List[Dict[str, Any]] = []
+ de_n = len(de)
+
+ for pname, pgenes in pathway_genes.items():
+ pset = {g for g in pgenes if g in u}
+ if len(pset) < min_pathway_genes:
+ continue
+ overlap = sorted(de & pset)
+ a = len(overlap)
+ b = len(de - pset)
+ c = len(pset - de)
+ d = len(u) - a - b - c
+ if d < 0:
+ d = 0
+ oddsr, p_raw = fisher_exact([[a, b], [c, d]], alternative="greater")
+ p_f = float(p_raw) if math.isfinite(float(p_raw)) else 1.0
+ results.append(
+ {
+ "pathway": pname,
+ "p_value": p_f,
+ "odds_ratio": float(oddsr) if np.isfinite(oddsr) else None,
+ "overlap_genes": overlap,
+ "overlap_count": a,
+ "pathway_size": len(pset),
+ "de_in_universe": de_n,
+ "gene_ratio": f"{a}/{len(pset)}",
+ }
+ )
+
+ qvals = adjust_pvalues_bh([r["p_value"] for r in results])
+ for r, q in zip(results, qvals):
+ r["q_value"] = q
+ results.sort(key=lambda x: (x["p_value"], -x["overlap_count"]))
+ return results
+
+
+def enrichr_ora(
+ query_genes: Sequence[str],
+ *,
+ libraries: Sequence[str],
+ background: Optional[Sequence[str]] = None,
+ top_k: int = 50,
+) -> tuple[List[Dict[str, Any]], Optional[str]]:
+ """
+ Enrichr-based ORA using gseapy (requires network for most libraries).
+
+ Returns:
+ (rows, error_message)
+ """
+
+ if not _GSEAPY_AVAILABLE:
+ return [], "gseapy is not installed."
+ q = [str(g) for g in query_genes if g]
+ if not q:
+ return [], "Empty query gene list."
+ libs = [str(x) for x in libraries if x]
+ if not libs:
+ return [], "No Enrichr libraries configured."
+
+ rows: List[Dict[str, Any]] = []
+ try:
+ for lib in libs:
+ enr = gp.enrichr( # type: ignore[union-attr]
+ gene_list=q,
+ gene_sets=lib,
+ background=list(background) if background is not None else None,
+ outdir=None,
+ no_plot=True,
+ )
+ res = getattr(enr, "results", None)
+ if res is None or res.empty:
+ continue
+ for _, r in res.head(top_k).iterrows():
+ genes = []
+ raw = r.get("Genes")
+ if isinstance(raw, str):
+ genes = [g.strip() for g in raw.replace(";", ",").split(",") if g.strip()]
+ rows.append(
+ {
+ "pathway": f"{lib}: {r.get('Term')}",
+ "p_value": float(r.get("P-value", 1.0)),
+ "q_value": float(r.get("Adjusted P-value", 1.0)),
+ "odds_ratio": float(r.get("Odds Ratio"))
+ if pd.notna(r.get("Odds Ratio"))
+ else None,
+ "overlap_genes": genes,
+ "overlap_count": int(r.get("Overlap", "0/0").split("/")[0])
+ if isinstance(r.get("Overlap"), str)
+ else None,
+ "pathway_size": int(r.get("Overlap", "0/0").split("/")[1])
+ if isinstance(r.get("Overlap"), str)
+ else None,
+ }
+ )
+ except Exception as exc: # pragma: no cover
+ return [], f"Enrichr failed: {exc}"
+
+ rows.sort(key=lambda x: (x.get("q_value", 1.0), x.get("p_value", 1.0)))
+ return rows, None
+
+
+def _safe_padj_value(v: Any) -> float:
+ try:
+ x = float(v)
+ except (TypeError, ValueError):
+ return 1.0
+ return x if math.isfinite(x) else 1.0
+
+
+def pick_de_query_genes(
+ de_rows: Sequence[Dict[str, Any]],
+ *,
+ padj_alpha: float = 0.05,
+ max_genes: int = 200,
+ direction: str = "both",
+ min_abs_log2fc: float = 0.0,
+) -> List[str]:
+ """
+ Genes for ORA query: significant by ``padj`` and optional **direction** (activation).
+
+ ``direction``: ``\"up\"`` (alt > ref), ``\"down\"`` (alt < ref), or ``\"both\"``.
+ """
+ dir_norm = direction.lower().strip()
+ if dir_norm not in ("up", "down", "both"):
+ dir_norm = "both"
+
+ def lfc_ok(r: Dict[str, Any]) -> bool:
+ try:
+ lfc = float(r.get("log2FoldChange", 0.0))
+ except (TypeError, ValueError):
+ return False
+ if not math.isfinite(lfc):
+ return False
+ if dir_norm == "both":
+ return abs(lfc) >= min_abs_log2fc
+ if dir_norm == "up":
+ return lfc >= min_abs_log2fc
+ return lfc <= -min_abs_log2fc
+
+ sig: List[str] = []
+ for r in de_rows:
+ if _safe_padj_value(r.get("padj", 1.0)) > padj_alpha:
+ continue
+ if not lfc_ok(r):
+ continue
+ sig.append(r["gene"])
+
+ if not sig:
+ for r in de_rows[:max_genes]:
+ if lfc_ok(r):
+ sig.append(r["gene"])
+ if not sig:
+ sig = [r["gene"] for r in de_rows[:max_genes]]
+ return sig[:max_genes]
+
+
+def compare_pathways_detail(
+ pathway_a: str,
+ pathway_b: str,
+ pathway_genes: Dict[str, Sequence[str]],
+ de_genes: Sequence[str],
+) -> Dict[str, Any]:
+ """Exclusive vs shared DE support between two pathways."""
+ pa = set(pathway_genes.get(pathway_a, []))
+ pb = set(pathway_genes.get(pathway_b, []))
+ de = set(de_genes)
+ only_a = sorted((pa - pb) & de)
+ only_b = sorted((pb - pa) & de)
+ shared = sorted((pa & pb) & de)
+ return {
+ "pathway_a": pathway_a,
+ "pathway_b": pathway_b,
+ "exclusive_to_a": only_a,
+ "exclusive_to_b": only_b,
+ "shared_de_support": shared,
+ "pathway_a_size": len(pa),
+ "pathway_b_size": len(pb),
+ "overlap_pathway_genes": sorted(pa & pb),
+ }
+
+
+def overlap_genes_across_top_pathways(
+ ora_rows: Sequence[Dict[str, Any]],
+ top_k: int = 5,
+) -> Dict[str, Any]:
+ """DE genes that appear in more than one of the top-k pathways by p-value."""
+ top = [r for r in ora_rows[:top_k] if r.get("overlap_genes")]
+ gene_to_paths: Dict[str, List[str]] = {}
+ for row in top:
+ p = row["pathway"]
+ for g in row.get("overlap_genes", []):
+ gene_to_paths.setdefault(g, []).append(p)
+ multi = {g: paths for g, paths in gene_to_paths.items() if len(paths) > 1}
+ return {
+ "genes_supporting_multiple_top_pathways": sorted(multi.keys()),
+ "gene_to_pathways": {g: multi[g] for g in sorted(multi)},
+ }
+
+
+def top_hits_statistically_close(
+ ora_rows: Sequence[Dict[str, Any]],
+ *,
+ ratio_threshold: float = 10.0,
+ top_k: int = 3,
+) -> Dict[str, Any]:
+ """Flag when the top two enriched pathways have similar p-values (ratio bound)."""
+ if len(ora_rows) < 2:
+ return {
+ "close_top_hits": False,
+ "p_ratio": None,
+ "note": "fewer than 2 pathways",
+ }
+ p1 = ora_rows[0]["p_value"]
+ p2 = ora_rows[1]["p_value"]
+ if p1 <= 0 or p2 <= 0:
+ ratio = None
+ close = False
+ else:
+ ratio = max(p1, p2) / min(p1, p2)
+ close = ratio <= ratio_threshold
+ return {
+ "close_top_hits": close,
+ "p_ratio": ratio,
+ "p_top1": p1,
+ "p_top2": p2,
+ }
diff --git a/envs/pathway_analysis_env/server/app.py b/envs/pathway_analysis_env/server/app.py
new file mode 100644
index 0000000000..5ff3b26b22
--- /dev/null
+++ b/envs/pathway_analysis_env/server/app.py
@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""FastAPI application for the Pathway Analysis Environment."""
+
+import inspect
+import logging
+import os
+from pathlib import Path
+
+# Pathway lab is meant to be used at /web; OpenEnv defaults web off unless set.
+if "ENABLE_WEB_INTERFACE" not in os.environ:
+ os.environ["ENABLE_WEB_INTERFACE"] = "true"
+
+# Some dependencies (e.g. gseapy) import matplotlib, which tries to write a font/cache
+# directory under the user's home. In sandboxed / CI contexts this can be unwritable.
+if "MPLCONFIGDIR" not in os.environ:
+ cache_dir = Path(__file__).resolve().parent.parent / "outputs" / ".mplcache"
+ cache_dir.mkdir(parents=True, exist_ok=True)
+ os.environ["MPLCONFIGDIR"] = str(cache_dir)
+
+from openenv.core.env_server.http_server import create_app
+
+from ..models import PathwayAction, PathwayObservation, PathwayState
+from .gradio_ui import build_pathway_gradio_app
+from .pathway_environment import PathwayEnvironment
+
+_logger = logging.getLogger(__name__)
+
+_sig = inspect.signature(create_app)
+_kw: dict = {
+ "env": PathwayEnvironment,
+ "action_cls": PathwayAction,
+ "observation_cls": PathwayObservation,
+ "env_name": "pathway_analysis_env",
+}
+if "gradio_builder" in _sig.parameters:
+ _kw["gradio_builder"] = build_pathway_gradio_app
+else:
+ _logger.warning(
+ "openenv-core does not support gradio_builder; Pathway lab tab will be unavailable."
+ )
+
+app = create_app(**_kw, state_cls=PathwayState)
+
+
+def main():
+ """Entry point for ``uv run --project . server``."""
+ import uvicorn
+
+ uvicorn.run(app, host="0.0.0.0", port=8000)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/envs/pathway_analysis_env/server/failure_codes.py b/envs/pathway_analysis_env/server/failure_codes.py
new file mode 100644
index 0000000000..d94ff4eaec
--- /dev/null
+++ b/envs/pathway_analysis_env/server/failure_codes.py
@@ -0,0 +1,47 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Stable ``failure_code`` strings for observation metadata (pathway_analysis_env v1)."""
+
+# Session / episode
+EPISODE_ALREADY_DONE = "episode_already_done"
+UNKNOWN_ACTION_TYPE = "unknown_action_type"
+
+# understand_experiment_design
+DESIGN_PARTIAL_CONTRAST = "design_partial_contrast"
+DESIGN_INVALID_CONTRAST_NAMES = "design_invalid_contrast_names"
+DESIGN_INSUFFICIENT_SAMPLES_PER_ARM = "design_insufficient_samples_per_arm"
+
+# run_differential_expression
+DE_MISSING_CONTRAST = "de_missing_contrast"
+DE_PYDESeq2_UNAVAILABLE = "de_pydeseq2_unavailable"
+DE_DESEQ2_FAILED = "de_deseq2_failed"
+DE_INVALID_COUNTS_MATRIX = "de_invalid_counts_matrix"
+DE_INVALID_AUTHOR_TABLE = "de_invalid_author_table"
+DE_TOO_FEW_GENES_AFTER_FILTER = "de_too_few_genes_after_filter"
+CASE_SAMPLE_METADATA_MISMATCH = "case_sample_metadata_mismatch"
+
+DE_UNSUPPORTED_PRECOMPUTED_CONTRAST = "de_unsupported_precomputed_contrast"
+
+# run_pathway_enrichment
+ORA_DE_PREREQUISITE = "ora_de_prerequisite"
+ORA_NO_PATHWAY_DEFINITIONS = "ora_no_pathway_definitions"
+
+ORA_GSEAPY_UNAVAILABLE = "ora_gseapy_unavailable"
+ORA_ENRICHR_FAILED = "ora_enrichr_failed"
+
+# compare_pathways
+COMPARE_MISSING_PATHWAY_NAMES = "compare_missing_pathway_names"
+
+# ask_expert
+EXPERT_DISABLED = "expert_disabled"
+EXPERT_BUDGET_EXHAUSTED = "expert_budget_exhausted"
+
+# submit (analytics)
+SUBMIT_INCORRECT_HYPOTHESIS = "submit_incorrect_hypothesis"
+
+# strict mode umbrella (specific code still preferred when set)
+STRICT_TERMINATION = "strict_termination"
diff --git a/envs/pathway_analysis_env/server/gradio_ui.py b/envs/pathway_analysis_env/server/gradio_ui.py
new file mode 100644
index 0000000000..a1fe95d601
--- /dev/null
+++ b/envs/pathway_analysis_env/server/gradio_ui.py
@@ -0,0 +1,619 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Gradio **Pathway lab** tab: case selection, guided RNA-seq / ORA workflow, tables.
+
+Mount when ``ENABLE_WEB_INTERFACE=true`` and ``create_app(..., gradio_builder=...)``.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+import gradio as gr
+import pandas as pd
+from openenv.core.env_server.types import EnvironmentMetadata
+
+_DATA_DIR = Path(__file__).resolve().parent.parent / "data"
+_OUTPUTS_DIR = Path(__file__).resolve().parent.parent / "outputs"
+
+
+def _list_case_files() -> List[str]:
+ if not _DATA_DIR.is_dir():
+ return ["toy_case_001.json"]
+ names = sorted(p.name for p in _DATA_DIR.glob("*.json"))
+ return names if names else ["toy_case_001.json"]
+
+
+def _list_saved_runs() -> List[str]:
+ """Folders under outputs/ that contain summary.json."""
+ if not _OUTPUTS_DIR.is_dir():
+ return []
+ runs = []
+ for p in sorted(_OUTPUTS_DIR.iterdir()):
+ if not p.is_dir():
+ continue
+ if (p / "summary.json").is_file():
+ runs.append(p.name)
+ return runs
+
+
+def _load_saved_run(run_name: str) -> Dict[str, Any]:
+ base = _OUTPUTS_DIR / run_name
+ root = _OUTPUTS_DIR.resolve()
+ files = (base / "summary.json", base / "de_top200.json", base / "enrichment_top50.json")
+ if (
+ not run_name
+ or run_name in {".", ".."}
+ or Path(run_name).name != run_name
+ or not all(path.resolve().is_relative_to(root) for path in files)
+ ):
+ return {
+ "run": run_name,
+ "summary_error": "Invalid run name",
+ "summary": {},
+ "de": [],
+ "enrichment": [],
+ }
+ out: Dict[str, Any] = {"run": run_name}
+ try:
+ out["summary"] = json.loads((base / "summary.json").read_text(encoding="utf-8"))
+ except Exception as e:
+ out["summary_error"] = str(e)
+ out["summary"] = {}
+ try:
+ out["de"] = json.loads((base / "de_top200.json").read_text(encoding="utf-8"))
+ except Exception as e:
+ out["de_error"] = str(e)
+ out["de"] = []
+ try:
+ out["enrichment"] = json.loads(
+ (base / "enrichment_top50.json").read_text(encoding="utf-8")
+ )
+ except Exception as e:
+ out["enrichment_error"] = str(e)
+ out["enrichment"] = []
+ return out
+
+
+def _saved_run_to_tables(run: Dict[str, Any]) -> Tuple[str, pd.DataFrame, pd.DataFrame]:
+ s = run.get("summary") or {}
+ md_lines = [
+ f"**Run:** `{run.get('run','')}`",
+ f"**Case:** `{s.get('case_id','')}`",
+ f"**Contrast:** `{s.get('contrast')}`",
+ f"**Genes:** in matrix `{s.get('genes_in_matrix')}` → after prefilter `{s.get('genes_after_prefilter')}`",
+ f"**Trace:** `{s.get('trace_path','')}`",
+ ]
+ md = "\n\n".join(md_lines)
+
+ de = run.get("de") or []
+ df_de = pd.DataFrame(de) if isinstance(de, list) and de else pd.DataFrame({"info": ["No DE export found."]})
+
+ enr = run.get("enrichment") or []
+ if isinstance(enr, list) and enr:
+ df_enr = pd.DataFrame(
+ [
+ {
+ "pathway": r.get("pathway"),
+ "p_value": r.get("p_value"),
+ "q_value": r.get("q_value"),
+ "odds_ratio": r.get("odds_ratio"),
+ "overlap_genes": ", ".join((r.get("overlap_genes") or [])[:40]),
+ }
+ for r in enr
+ if isinstance(r, dict)
+ ]
+ )
+ else:
+ df_enr = pd.DataFrame({"info": ["No enrichment export found."]})
+
+ return md, df_de, df_enr
+
+
+def _pathway_comparison_df(obs: Dict[str, Any]) -> pd.DataFrame:
+ pc = obs.get("pathway_comparison")
+ if not isinstance(pc, dict) or not pc:
+ return pd.DataFrame(
+ {
+ "": [
+ "Run **Compare pathways** with two pathway names to see exclusive vs shared DE support."
+ ]
+ }
+ )
+ a = pc.get("pathway_a", "")
+ b = pc.get("pathway_b", "")
+ a_size = pc.get("pathway_a_size")
+ b_size = pc.get("pathway_b_size")
+ rows = [
+ {
+ "pathway": f"A only ({a})",
+ "count": len(pc.get("exclusive_to_a") or []),
+ "genes (preview)": ", ".join((pc.get("exclusive_to_a") or [])[:40]),
+ },
+ {
+ "pathway": f"B only ({b})",
+ "count": len(pc.get("exclusive_to_b") or []),
+ "genes (preview)": ", ".join((pc.get("exclusive_to_b") or [])[:40]),
+ },
+ {
+ "pathway": "Shared DE support",
+ "count": len(pc.get("shared_de_support") or []),
+ "genes (preview)": ", ".join((pc.get("shared_de_support") or [])[:40]),
+ },
+ {
+ "pathway": "Pathway gene-set sizes",
+ "count": a_size + b_size
+ if a_size is not None and b_size is not None
+ else "Unknown",
+ "genes (preview)": (
+ f"A size={a_size if a_size is not None else 'Unknown'} · "
+ f"B size={b_size if b_size is not None else 'Unknown'}"
+ ),
+ },
+ ]
+ return pd.DataFrame(rows)
+
+
+def _state_markdown(st: Dict[str, Any]) -> str:
+ """Episode banner from ``WebInterfaceManager.get_state()`` (PathwayState fields)."""
+ if not st:
+ return "*No state yet — reset an episode.*"
+ eid = str(st.get("episode_id") or "")
+ eid_short = f"`{eid[:10]}…`" if len(eid) > 10 else f"`{eid}`"
+ pipe = "counts + PyDESeq2" if st.get("pipeline_mode") else "legacy lists"
+ strict = "strict" if st.get("strict_mode") else "lenient"
+ de_ok = "✓" if st.get("de_run") else "○"
+ ora_ok = "✓" if st.get("enrichment_run") else "○"
+ ex_u = int(st.get("expert_calls_used") or 0)
+ ex_b = int(st.get("expert_budget") or 0)
+ expert = f"{ex_u} / {ex_b}" if ex_b > 0 else "off"
+ done = "**Episode ended.** Reset to start over." if st.get("is_done") else ""
+ conds = st.get("conditions") or []
+ cond_line = ", ".join(f"`{c}`" for c in conds[:12]) if conds else "—"
+ vref = st.get("validated_reference")
+ valt = st.get("validated_alternate")
+ val_line = ""
+ if vref and valt:
+ val_line = f"\n\n**Validated contrast (for DE if fields empty):** `{vref}` → `{valt}`"
+ des = "✓" if st.get("design_understood") else "○"
+ return (
+ f"**Episode** {eid_short} · step **{st.get('step_count', 0)}** · {pipe} · {strict}\n\n"
+ f"**Conditions in case:** {cond_line}\n\n"
+ f"**Pipeline:** Design {des} · DE {de_ok} · ORA {ora_ok} · **Expert calls** {expert}"
+ f"{val_line}\n\n"
+ f"{done}"
+ ).strip()
+
+
+def _observation_to_tables(
+ data: Dict[str, Any],
+) -> Tuple[str, pd.DataFrame, pd.DataFrame, pd.DataFrame, str, str, str]:
+ """Markdown summary, DE df, ORA df, compare df, overlap/ambiguity, trace, raw JSON."""
+ obs = data.get("observation") or {}
+ if not isinstance(obs, dict):
+ obs = {}
+
+ msg = obs.get("message", "") or ""
+ reward = obs.get("reward")
+ done = obs.get("done")
+ lines = [
+ f"**Message:** {msg}",
+ f"**Reward:** `{reward}` · **Done:** `{done}`",
+ ]
+ ac = obs.get("available_conditions") or []
+ if ac:
+ lines.append("**Conditions (from last step):** " + ", ".join(f"`{c}`" for c in ac[:20]))
+
+ ed = obs.get("experiment_design")
+ if isinstance(ed, dict) and ed:
+ lines.append(
+ "**Experiment design (structured):**\n```json\n"
+ + json.dumps(ed, indent=2, default=str)[:8000]
+ + "\n```"
+ )
+
+ comparison = obs.get("pathway_comparison") or {}
+ if comparison.get("comparison_scope") == "enrichr_overlap_genes":
+ lines.append(
+ "**Comparison scope:** Enrichr overlap genes only; "
+ "exclusive/shared support is relative to the returned query overlaps, "
+ "and full pathway membership is unknown."
+ )
+
+ md = "\n\n".join(lines)
+
+ de_rows = obs.get("de_genes") or []
+ if de_rows and isinstance(de_rows, list):
+ df_de = pd.DataFrame(de_rows[:200])
+ else:
+ top = obs.get("top_genes") or []
+ if top:
+ df_de = pd.DataFrame({"gene": top})
+ else:
+ df_de = pd.DataFrame(
+ {"info": ["No DE table yet — run differential expression."]}
+ )
+
+ pe = obs.get("pathway_enrichment") or []
+ if pe and isinstance(pe, list):
+ rows_flat = []
+ for r in pe[:80]:
+ if not isinstance(r, dict):
+ continue
+ rows_flat.append(
+ {
+ "pathway": r.get("pathway", ""),
+ "p_value": r.get("p_value"),
+ "q_value": r.get("q_value"),
+ "overlap_count": r.get("overlap_count"),
+ "pathway_size": r.get("pathway_size"),
+ "gene_ratio": r.get("gene_ratio", ""),
+ }
+ )
+ df_pe = (
+ pd.DataFrame(rows_flat)
+ if rows_flat
+ else pd.DataFrame({"info": ["No ORA results — run pathway enrichment."]})
+ )
+ else:
+ tp = obs.get("top_pathways") or []
+ if tp:
+ df_pe = pd.DataFrame({"pathway": tp})
+ else:
+ df_pe = pd.DataFrame(
+ {"info": ["No ORA table yet — run pathway enrichment."]}
+ )
+
+ df_cmp = _pathway_comparison_df(obs)
+
+ ov = obs.get("overlap_summary") or {}
+ amb = obs.get("statistical_ambiguity") or {}
+ extra = []
+ if ov:
+ extra.append(
+ "**Overlap across top pathways:**\n```json\n"
+ + json.dumps(ov, indent=2)[:4000]
+ + "\n```"
+ )
+ if amb:
+ extra.append(
+ "**Statistical ambiguity:**\n```json\n"
+ + json.dumps(amb, indent=2)[:2000]
+ + "\n```"
+ )
+ extra_txt = "\n\n".join(extra) if extra else "*No overlap / ambiguity data yet.*"
+
+ em = obs.get("expert_message")
+ if em:
+ extra_txt = (extra_txt + f"\n\n**Expert:** {em}").strip()
+
+ trace = obs.get("trace_path") or ""
+ trace_md = (
+ f"**HTML episode trace:** `{trace}`\n\n"
+ f"Open the file locally to audit each step in a browser."
+ if trace
+ else "*Trace file path appears after environment steps.*"
+ )
+
+ raw = json.dumps(data, indent=2, default=str)
+ return md, df_de, df_pe, df_cmp, extra_txt, trace_md, raw
+
+
+def _response(
+ data: Dict[str, Any],
+ web_manager: Any,
+ status: str,
+ update_contrast: bool,
+) -> Tuple[Any, ...]:
+ """Shared outputs for all steps; optionally refresh contrast textboxes from state."""
+ md, df_de, df_pe, df_cmp, extra, trace_md, raw = _observation_to_tables(data)
+ st = web_manager.get_state()
+ state_md = _state_markdown(st if isinstance(st, dict) else {})
+ conds = (st or {}).get("conditions") or []
+ if update_contrast and conds:
+ design = (data.get("observation") or {}).get("experiment_design") or {}
+ contrast = design.get("default_contrast") or {}
+ ref_v = str(contrast.get("reference") or conds[0])
+ alt_v = str(contrast.get("alternate") or (conds[1] if len(conds) > 1 else conds[0]))
+ cref, calt = gr.update(value=ref_v), gr.update(value=alt_v)
+ else:
+ cref, calt = gr.update(), gr.update()
+ return (
+ md,
+ df_de,
+ df_pe,
+ df_cmp,
+ extra,
+ trace_md,
+ raw,
+ state_md,
+ status,
+ cref,
+ calt,
+ )
+
+
+def build_pathway_gradio_app(
+ web_manager: Any,
+ action_fields: List[Dict[str, Any]],
+ metadata: Optional[EnvironmentMetadata],
+ is_chat_env: bool,
+ title: str,
+ quick_start_md: str,
+) -> gr.Blocks:
+ """
+ Second tab (**Visualization**) for pathway_analysis_env: interactive pathway lab.
+
+ Uses ``web_manager.env.set_case_file`` before reset, and ``step_environment`` with
+ structured ``PathwayAction`` payloads.
+ """
+ case_choices = _list_case_files()
+ saved_runs = _list_saved_runs()
+ display = metadata.name if metadata else title
+
+ async def do_reset(case_file: str):
+ try:
+ if hasattr(web_manager.env, "set_case_file"):
+ web_manager.env.set_case_file(case_file)
+ data = await web_manager.reset_environment()
+ return _response(
+ data,
+ web_manager,
+ f"Loaded case `{case_file}` and reset.",
+ update_contrast=True,
+ )
+ except Exception as e:
+ empty = pd.DataFrame({"error": [str(e)]})
+ z = gr.update()
+ return (
+ "",
+ empty,
+ empty,
+ empty,
+ "",
+ "",
+ "",
+ f"*Error:* `{e}`",
+ str(e),
+ z,
+ z,
+ )
+
+ async def step_inspect():
+ data = await web_manager.step_environment({"action_type": "inspect_dataset"})
+ return _response(
+ data,
+ web_manager,
+ "Inspect complete.",
+ update_contrast=False,
+ )
+
+ async def step_understand(cond_a: str, cond_b: str):
+ payload: Dict[str, Any] = {
+ "action_type": "understand_experiment_design",
+ "condition_a": (cond_a or "").strip() or None,
+ "condition_b": (cond_b or "").strip() or None,
+ }
+ data = await web_manager.step_environment(payload)
+ return _response(
+ data,
+ web_manager,
+ "Understand experiment design complete.",
+ update_contrast=False,
+ )
+
+ async def step_de(cond_a: str, cond_b: str):
+ payload: Dict[str, Any] = {
+ "action_type": "run_differential_expression",
+ "condition_a": (cond_a or "").strip() or None,
+ "condition_b": (cond_b or "").strip() or None,
+ }
+ data = await web_manager.step_environment(payload)
+ return _response(
+ data,
+ web_manager,
+ "Differential expression complete.",
+ update_contrast=False,
+ )
+
+ async def step_ora():
+ data = await web_manager.step_environment(
+ {"action_type": "run_pathway_enrichment"}
+ )
+ return _response(
+ data,
+ web_manager,
+ "ORA complete.",
+ update_contrast=False,
+ )
+
+ async def step_compare(pa: str, pb: str):
+ data = await web_manager.step_environment(
+ {
+ "action_type": "compare_pathways",
+ "pathway_a": (pa or "").strip(),
+ "pathway_b": (pb or "").strip(),
+ }
+ )
+ return _response(
+ data,
+ web_manager,
+ "Compare complete.",
+ update_contrast=False,
+ )
+
+ async def step_expert(q: str):
+ data = await web_manager.step_environment(
+ {
+ "action_type": "ask_expert",
+ "expert_question": (q or "").strip() or None,
+ }
+ )
+ return _response(
+ data,
+ web_manager,
+ "Expert step complete.",
+ update_contrast=False,
+ )
+
+ async def step_submit(hyp: str):
+ data = await web_manager.step_environment(
+ {"action_type": "submit_answer", "hypothesis": (hyp or "").strip()}
+ )
+ return _response(
+ data,
+ web_manager,
+ "Answer submitted.",
+ update_contrast=False,
+ )
+
+ with gr.Blocks(title=f"{display} — Pathway lab") as blocks:
+ gr.Markdown(
+ f"# Pathway lab\n\n"
+ f"**Agent-style flow:** **(1) Groups & design** — how many conditions and samples per group. "
+ f"**(2) DGE** — pick reference vs alternate, then differential expression. "
+ f"**(3) Pathways** — ORA, compare, submit hypothesis. "
+ f"Buttons: Understand design → Inspect → Run DE → Run ORA → … Use **Playground** for raw actions.\n\n"
+ f"---"
+ )
+
+ with gr.Row(equal_height=True):
+ with gr.Column(scale=2):
+ gr.Markdown("**Case & episode**")
+ with gr.Row():
+ case_dd = gr.Dropdown(
+ choices=case_choices,
+ value=case_choices[0] if case_choices else None,
+ label="Case JSON (`data/`)",
+ scale=2,
+ )
+ reset_btn = gr.Button("Reset episode", variant="primary", scale=1)
+ with gr.Column(scale=3):
+ out_state = gr.Markdown(label="Episode state")
+ out_status = gr.Textbox(label="Last action", max_lines=2)
+
+ gr.Markdown("### Contrast (PyDESeq2 pipeline cases)")
+ gr.Markdown(
+ "*Reference* = baseline condition, *alternate* = treatment. "
+ "Reset fills these from the case when possible; edit if needed. "
+ "**Understand design:** leave both empty for a structured summary only, or fill both to validate the contrast (used by **Run DE** when those fields are left empty)."
+ )
+ with gr.Row():
+ cond_ref = gr.Textbox(
+ label="Reference condition",
+ placeholder="e.g. control",
+ lines=1,
+ )
+ cond_alt = gr.Textbox(
+ label="Alternate condition",
+ placeholder="e.g. treated",
+ lines=1,
+ )
+
+ gr.Markdown("#### Workflow")
+ with gr.Row():
+ btn_ud = gr.Button("0 · Understand design", variant="secondary")
+ btn_ins = gr.Button("1 · Inspect", variant="secondary")
+ btn_de = gr.Button("2 · Run DE", variant="primary")
+ btn_ora = gr.Button("3 · Run ORA", variant="primary")
+ with gr.Row():
+ pw_a = gr.Textbox(label="Pathway A", placeholder="MAPK signaling", scale=1)
+ pw_b = gr.Textbox(label="Pathway B", placeholder="PI3K-Akt", scale=1)
+ btn_cmp = gr.Button("4 · Compare", scale=0, min_width=120)
+ with gr.Row():
+ hyp = gr.Textbox(
+ label="Hypothesis (pathway name)",
+ placeholder="True activated pathway",
+ scale=2,
+ )
+ btn_sub = gr.Button("5 · Submit", variant="stop", scale=0, min_width=120)
+
+ with gr.Accordion("Expert (optional, budgeted per case)", open=False):
+ with gr.Row():
+ ex_q = gr.Textbox(
+ label="Question / note",
+ placeholder="Optional — uses expert budget",
+ scale=3,
+ )
+ btn_ex = gr.Button("Ask expert", scale=0, min_width=120)
+
+ gr.Markdown("### Results")
+ out_md = gr.Markdown()
+ with gr.Tabs():
+ with gr.Tab("DE genes"):
+ out_de = gr.Dataframe(
+ label="Differential expression",
+ interactive=False,
+ wrap=True,
+ )
+ with gr.Tab("ORA"):
+ out_ora = gr.Dataframe(
+ label="Pathway enrichment",
+ interactive=False,
+ wrap=True,
+ )
+ with gr.Tab("Compare"):
+ out_cmp = gr.Dataframe(
+ label="Pathway vs pathway",
+ interactive=False,
+ wrap=True,
+ )
+ with gr.Tab("Saved run (GSE235417)"):
+ gr.Markdown(
+ "Browse exported run artifacts under `envs/pathway_analysis_env/outputs//` "
+ "(e.g. `gse235417`). This view does **not** re-run DESeq2; it only loads saved JSON."
+ )
+ run_dd = gr.Dropdown(
+ choices=saved_runs,
+ value="gse235417" if "gse235417" in saved_runs else (saved_runs[0] if saved_runs else None),
+ label="Saved run folder (`outputs/`)",
+ )
+ load_btn = gr.Button("Load saved results", variant="primary")
+ run_md = gr.Markdown()
+ run_de = gr.Dataframe(label="Saved DE (top 200)", interactive=False, wrap=True)
+ run_enr = gr.Dataframe(label="Saved enrichment (top 50)", interactive=False, wrap=True)
+ with gr.Tab("Overlap & ambiguity"):
+ out_extra = gr.Markdown()
+ with gr.Tab("Trace"):
+ out_trace = gr.Markdown()
+ with gr.Tab("Raw JSON"):
+ out_raw = gr.Code(label="Wire payload", language="json", interactive=False)
+
+ ui_outputs = [
+ out_md,
+ out_de,
+ out_ora,
+ out_cmp,
+ out_extra,
+ out_trace,
+ out_raw,
+ out_state,
+ out_status,
+ cond_ref,
+ cond_alt,
+ ]
+
+ reset_btn.click(fn=do_reset, inputs=[case_dd], outputs=ui_outputs)
+ btn_ud.click(fn=step_understand, inputs=[cond_ref, cond_alt], outputs=ui_outputs)
+ btn_ins.click(fn=step_inspect, outputs=ui_outputs)
+ btn_de.click(fn=step_de, inputs=[cond_ref, cond_alt], outputs=ui_outputs)
+ btn_ora.click(fn=step_ora, outputs=ui_outputs)
+ btn_cmp.click(fn=step_compare, inputs=[pw_a, pw_b], outputs=ui_outputs)
+ btn_sub.click(fn=step_submit, inputs=[hyp], outputs=ui_outputs)
+ btn_ex.click(fn=step_expert, inputs=[ex_q], outputs=ui_outputs)
+
+ def do_load_saved(run_name: str):
+ run = _load_saved_run(run_name or "")
+ md, df_de, df_enr = _saved_run_to_tables(run)
+ return md, df_de, df_enr
+
+ load_btn.click(fn=do_load_saved, inputs=[run_dd], outputs=[run_md, run_de, run_enr])
+
+ return blocks
diff --git a/envs/pathway_analysis_env/server/pathway_environment.py b/envs/pathway_analysis_env/server/pathway_environment.py
new file mode 100644
index 0000000000..2af8942b91
--- /dev/null
+++ b/envs/pathway_analysis_env/server/pathway_environment.py
@@ -0,0 +1,1003 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Pathway analysis environment: PyDESeq2 DE, Fisher ORA, overlap-aware tools,
+optional expert budget, HTML episode trace.
+"""
+
+from __future__ import annotations
+
+import html
+import json
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from openenv.core.env_server import Environment
+
+from ..models import PathwayAction, PathwayObservation, PathwayState
+from . import failure_codes as FC
+from .analysis import (
+ build_sample_metadata,
+ compare_pathways_detail,
+ counts_dict_to_samples_by_genes,
+ filter_counts_by_minimum_total,
+ gseapy_available,
+ load_counts_csv_as_samples_by_genes,
+ load_author_de_table_csv,
+ merge_analysis_options,
+ enrichr_ora,
+ ora_fisher,
+ overlap_genes_across_top_pathways,
+ pick_de_query_genes,
+ pydeseq2_available,
+ run_deseq2_contrast,
+ top_hits_statistically_close,
+ validate_counts_case,
+)
+
+DATA_DIR = Path(__file__).resolve().parent.parent / "data"
+OUTPUT_TRACE_DIR = Path(__file__).resolve().parent.parent / "outputs" / "pathway_traces"
+
+
+def load_case(case_name: str = "toy_case_001.json") -> Dict[str, Any]:
+ with open(DATA_DIR / case_name, "r", encoding="utf-8") as f:
+ return json.load(f)
+
+
+def _legacy_de_rows(top_names: List[str]) -> List[Dict[str, Any]]:
+ """Synthetic DE rows for legacy JSON-only cases."""
+ rows: List[Dict[str, Any]] = []
+ for i, name in enumerate(top_names):
+ rows.append(
+ {
+ "gene": name,
+ "baseMean": 500.0,
+ "log2FoldChange": 2.0 - i * 0.1,
+ "lfcSE": 0.2,
+ "pvalue": 1e-6,
+ "padj": 0.01,
+ "significant": True,
+ }
+ )
+ return rows
+
+
+def _write_html_trace(
+ episode_id: str,
+ steps: List[Dict[str, Any]],
+ case_id: str,
+) -> str:
+ OUTPUT_TRACE_DIR.mkdir(parents=True, exist_ok=True)
+ path = OUTPUT_TRACE_DIR / f"{episode_id}.html"
+ rows_html = []
+ for s in steps:
+ rows_html.append(
+ "| {} | {} | {} |
".format(
+ html.escape(str(s.get("step", ""))),
+ html.escape(json.dumps(s.get("detail", {}), indent=2)[:8000]),
+ html.escape(str(s.get("message", ""))[:2000]),
+ )
+ )
+ body = f"""
+Pathway trace {html.escape(episode_id)}
+
+
+Pathway analysis episode
+case: {html.escape(case_id)} episode: {html.escape(episode_id)}
+Generated {html.escape(datetime.now(timezone.utc).isoformat())}
+| Step | Detail | Message |
|---|
+{"".join(rows_html)}
+"""
+ path.write_text(body, encoding="utf-8")
+ return str(path)
+
+
+def _safe_case_id(case: Dict[str, Any]) -> str:
+ """Best-effort case identifier for trace rendering."""
+ try:
+ cid = case.get("case_id")
+ except Exception:
+ cid = None
+ return str(cid or "unknown_case")
+
+
+class PathwayEnvironment(Environment[PathwayAction, PathwayObservation, PathwayState]):
+ """
+ Pathway inference with optional **pipeline Mode A** (counts + metadata in JSON),
+ or **legacy** toy fixtures (static gene/pathway lists).
+ """
+
+ def __init__(self, case_file: str = "toy_case_001.json"):
+ super().__init__()
+ self._case_file = case_file
+ self._case: Dict[str, Any] = {}
+ self._state = PathwayState()
+ self._de_rows: List[Dict[str, Any]] = []
+ self._ora_rows: List[Dict[str, Any]] = []
+ self._query_genes: List[str] = []
+ self._trace_steps: List[Dict[str, Any]] = []
+ self._universe_genes: List[str] = []
+ self.reset()
+
+ def set_case_file(self, case_file: str) -> None:
+ """Switch JSON case before ``reset()`` (used by the Gradio Pathway lab tab)."""
+ self._case_file = case_file
+
+ def reset(
+ self,
+ seed: Optional[int] = None,
+ episode_id: Optional[str] = None,
+ **kwargs: Any,
+ ) -> PathwayObservation:
+ self._case = load_case(self._case_file)
+ eid = episode_id or str(uuid.uuid4())
+ strict = bool(kwargs.get("strict", self._case.get("strict_mode", False)))
+ pipeline = (
+ (
+ "counts" in self._case
+ or "counts_file" in self._case
+ or "de_table_file" in self._case
+ )
+ and "sample_ids" in self._case
+ and "sample_metadata" in self._case
+ )
+ self._de_rows = []
+ self._ora_rows = []
+ self._query_genes = []
+ self._trace_steps = []
+ self._universe_genes = []
+ self._state = PathwayState(
+ episode_id=eid,
+ step_count=0,
+ true_pathway=self._case["true_pathway"],
+ conditions=list(self._case.get("conditions", [])),
+ pipeline_mode=pipeline,
+ strict_mode=strict,
+ expert_budget=max(0, int(self._case.get("expert_budget", 0))),
+ expert_calls_used=0,
+ legacy_mode=not pipeline,
+ )
+ mode = "legacy"
+ if pipeline:
+ if "de_table_file" in self._case:
+ mode = "author_de_table"
+ elif "counts_file" in self._case or "counts" in self._case:
+ mode = "counts_matrix"
+ else:
+ mode = "pipeline_unknown"
+ msg = (
+ "Dataset loaded (pipeline: counts/metadata)."
+ if mode == "counts_matrix"
+ else (
+ "Dataset loaded (pipeline: author DE table; enrichment only, not DESeq2-from-counts)."
+ if mode == "author_de_table"
+ else "Toy dataset loaded (legacy static lists)."
+ )
+ )
+ self._trace(
+ "reset",
+ {
+ "case_id": self._case.get("case_id"),
+ "pipeline": pipeline,
+ "mode": mode,
+ "strict": strict,
+ },
+ msg,
+ )
+ trace_path = _write_html_trace(
+ eid, self._trace_steps, _safe_case_id(self._case)
+ )
+ return PathwayObservation(
+ message=msg
+ + " Use understand_experiment_design, inspect, run DE, enrichment, compare, or submit.",
+ available_conditions=self._state.conditions,
+ experiment_design=self._experiment_design_dict(),
+ metadata={
+ "case_id": self._case["case_id"],
+ "pipeline_mode": pipeline,
+ "pipeline_data_mode": mode,
+ },
+ trace_path=trace_path,
+ )
+
+ def _trace(self, kind: str, detail: Dict[str, Any], message: str) -> None:
+ s = self._state
+ self._trace_steps.append(
+ {
+ "step": s.step_count,
+ "kind": kind,
+ "detail": detail,
+ "message": message,
+ }
+ )
+
+ def _refresh_trace_file(self) -> str:
+ eid = self._state.episode_id or "unknown"
+ return _write_html_trace(
+ eid, self._trace_steps, _safe_case_id(self._case)
+ )
+
+ def _fail_strict(
+ self, reason: str, failure_code: str = FC.STRICT_TERMINATION
+ ) -> PathwayObservation:
+ self._state.is_done = True
+ self._trace(
+ "strict_failure",
+ {"reason": reason, "failure_code": failure_code},
+ reason,
+ )
+ tp = self._refresh_trace_file()
+ return PathwayObservation(
+ message=reason,
+ done=True,
+ reward=-3.0,
+ metadata={
+ "strict_failure": True,
+ "reason": reason,
+ "failure_code": failure_code,
+ },
+ trace_path=tp,
+ )
+
+ def step(
+ self,
+ action: PathwayAction,
+ timeout_s: Optional[float] = None,
+ **kwargs: Any,
+ ) -> PathwayObservation:
+ s = self._state
+
+ if s.is_done:
+ obs = PathwayObservation(
+ message="Episode already finished; call reset() for a new episode.",
+ done=True,
+ reward=0.0,
+ metadata={
+ "error": "episode_done",
+ "failure_code": FC.EPISODE_ALREADY_DONE,
+ "step_count": s.step_count,
+ },
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ s.step_count += 1
+
+ if action.action_type == "inspect_dataset":
+ meta = self._case.get("sample_metadata") or {}
+ obs = PathwayObservation(
+ message="Sample metadata and conditions are available for contrast specification.",
+ available_conditions=s.conditions,
+ reward=0.05,
+ metadata={
+ "step_count": s.step_count,
+ "sample_metadata": meta,
+ "sample_ids": self._case.get("sample_ids", []),
+ "pydeseq2_available": pydeseq2_available(),
+ "experiment_metadata": self._case.get("experiment_metadata"),
+ },
+ )
+ self._trace("inspect_dataset", {"conditions": s.conditions}, obs.message)
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ if action.action_type == "understand_experiment_design":
+ return self._step_understand_experiment_design(action)
+
+ if action.action_type == "run_differential_expression":
+ return self._step_de(action)
+
+ if action.action_type == "run_pathway_enrichment":
+ return self._step_enrichment(action)
+
+ if action.action_type == "compare_pathways":
+ return self._step_compare(action)
+
+ if action.action_type == "ask_expert":
+ return self._step_expert(action)
+
+ if action.action_type == "submit_answer":
+ return self._step_submit(action)
+
+ obs = PathwayObservation(
+ message=f"Unknown action_type: {action.action_type}",
+ reward=-0.2,
+ metadata={
+ "step_count": s.step_count,
+ "failure_code": FC.UNKNOWN_ACTION_TYPE,
+ "action_type": action.action_type,
+ },
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ def _experiment_design_dict(self) -> Dict[str, Any]:
+ case = self._case
+ s = self._state
+ sample_ids = list(case.get("sample_ids") or [])
+ smd = case.get("sample_metadata") or {}
+ per: Dict[str, int] = {}
+ for sid in sample_ids:
+ c = smd.get(sid)
+ if c is not None:
+ per[c] = per.get(c, 0) + 1
+ conds = list(s.conditions)
+ return {
+ "case_id": case.get("case_id"),
+ "pipeline_mode": s.pipeline_mode,
+ "conditions": conds,
+ "n_groups": len(conds),
+ "n_samples": len(sample_ids),
+ "samples_per_condition": per,
+ "sample_ids": sample_ids,
+ "default_contrast": case.get("default_contrast"),
+ "experiment_metadata": case.get("experiment_metadata"),
+ "agent_workflow": (
+ "(1) Groups: use conditions + samples_per_condition to see how many groups and "
+ "replicates exist. (2) DGE: pick reference vs alternate for DESeq2 "
+ "(validate via understand_experiment_design or pass to run_differential_expression). "
+ "(3) Pathways: run_pathway_enrichment then compare/submit."
+ ),
+ "design_note": (
+ "Reference = baseline (denominator of log2 fold change); alternate = comparison arm "
+ "for DGE. Optionally set condition_a / condition_b here to validate before "
+ "run_differential_expression."
+ ),
+ }
+
+ def _validate_contrast_proposal(
+ self, ref: str, alt: str
+ ) -> Optional[Tuple[str, str]]:
+ """Return (error_message, failure_code) if invalid; None if valid for DESeq2."""
+ conds = set(self._state.conditions)
+ if ref not in conds or alt not in conds:
+ return (
+ "Reference and alternate must be among the case `conditions`.",
+ FC.DESIGN_INVALID_CONTRAST_NAMES,
+ )
+ if ref == alt:
+ return (
+ "Reference and alternate must be two different conditions.",
+ FC.DESIGN_INVALID_CONTRAST_NAMES,
+ )
+ sample_ids = list(self._case.get("sample_ids") or [])
+ smd = self._case.get("sample_metadata") or {}
+ if not sample_ids:
+ return None
+ per: Dict[str, int] = {}
+ for sid in sample_ids:
+ c = smd.get(sid)
+ if c is not None:
+ per[c] = per.get(c, 0) + 1
+ if per.get(ref, 0) < 1 or per.get(alt, 0) < 1:
+ return (
+ "Each contrast arm must have at least one sample in `sample_metadata`.",
+ FC.DESIGN_INSUFFICIENT_SAMPLES_PER_ARM,
+ )
+ return None
+
+ def _step_understand_experiment_design(
+ self, action: PathwayAction
+ ) -> PathwayObservation:
+ s = self._state
+ design = self._experiment_design_dict()
+ ref_in = (action.condition_a or "").strip()
+ alt_in = (action.condition_b or "").strip()
+ has_both = bool(ref_in and alt_in)
+ has_partial = bool(ref_in or alt_in) and not has_both
+
+ if has_partial:
+ obs = PathwayObservation(
+ message=(
+ "Provide both reference (condition_a) and alternate (condition_b) to "
+ "validate a contrast, or leave both empty for a design summary only."
+ ),
+ available_conditions=s.conditions,
+ experiment_design=design,
+ reward=-0.02,
+ metadata={
+ "step_count": s.step_count,
+ "validation": "incomplete",
+ "failure_code": FC.DESIGN_PARTIAL_CONTRAST,
+ },
+ )
+ self._trace(
+ "understand_experiment_design",
+ {"validation": "incomplete"},
+ obs.message,
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ if not has_both:
+ s.design_understood = True
+ msg = (
+ "Design summary: you have the groups (conditions) and sample counts per group. "
+ "Next, choose reference vs alternate for DGE (differential expression), then pathway "
+ "steps. Re-run this action with both conditions set to validate your contrast."
+ )
+ obs = PathwayObservation(
+ message=msg,
+ available_conditions=s.conditions,
+ experiment_design=design,
+ reward=0.05,
+ metadata={"step_count": s.step_count, "validation": "summary_only"},
+ )
+ self._trace("understand_experiment_design", {"mode": "summary"}, msg)
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ invalid = self._validate_contrast_proposal(ref_in, alt_in)
+ if invalid:
+ err, fcode = invalid
+ s.validated_reference = None
+ s.validated_alternate = None
+ s.design_understood = True
+ obs = PathwayObservation(
+ message=err,
+ available_conditions=s.conditions,
+ experiment_design=design,
+ reward=-0.05,
+ metadata={
+ "step_count": s.step_count,
+ "validation": "invalid",
+ "failure_code": fcode,
+ },
+ )
+ self._trace(
+ "understand_experiment_design",
+ {"validation": "invalid", "proposal": [ref_in, alt_in]},
+ err,
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ s.validated_reference = ref_in
+ s.validated_alternate = alt_in
+ s.design_understood = True
+ design["validated_contrast"] = {"reference": ref_in, "alternate": alt_in}
+ msg = (
+ f"DGE contrast chosen: reference=`{ref_in}`, alternate=`{alt_in}` "
+ f"({len(s.conditions)} groups in study). "
+ "run_differential_expression will use this pair when DE omits conditions; "
+ "explicit DE fields override. Then run pathway enrichment."
+ )
+ obs = PathwayObservation(
+ message=msg,
+ available_conditions=s.conditions,
+ experiment_design=design,
+ reward=0.08,
+ metadata={"step_count": s.step_count, "validation": "valid"},
+ )
+ self._trace(
+ "understand_experiment_design",
+ {"validation": "valid", "contrast": [ref_in, alt_in]},
+ msg,
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ def _resolve_de_contrast(
+ self, action: PathwayAction
+ ) -> tuple[Optional[str], Optional[str]]:
+ """DESeq2 contrast: explicit action fields beat validated design, then default_contrast."""
+ dc = self._case.get("default_contrast") or {}
+ ar = (action.condition_a or "").strip()
+ ab = (action.condition_b or "").strip()
+ if ar or ab:
+ return ar or None, ab or None
+ if self._state.validated_reference or self._state.validated_alternate:
+ return self._state.validated_reference, self._state.validated_alternate
+ return dc.get("reference"), dc.get("alternate")
+
+ def _step_de(self, action: PathwayAction) -> PathwayObservation:
+ s = self._state
+ if s.legacy_mode:
+ names = list(self._case.get("top_genes", []))
+ self._de_rows = _legacy_de_rows(names)
+ self._query_genes = names
+ s.de_run = True
+ self._trace("de", {"legacy": True, "genes": names}, "Legacy DE")
+ obs = PathwayObservation(
+ message="Differential expression complete (legacy fixture).",
+ top_genes=names,
+ de_genes=self._de_rows,
+ reward=0.25,
+ metadata={"step_count": s.step_count, "legacy": True},
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ if "de_table_file" not in self._case and not pydeseq2_available():
+ if s.strict_mode:
+ return self._fail_strict(
+ "PyDESeq2 is not installed; strict mode terminates.",
+ FC.DE_PYDESeq2_UNAVAILABLE,
+ )
+ return PathwayObservation(
+ message="PyDESeq2 is not installed; cannot run DE on counts.",
+ reward=-0.5,
+ metadata={
+ "error": "missing_pydeseq2",
+ "failure_code": FC.DE_PYDESeq2_UNAVAILABLE,
+ },
+ )
+
+ ref, alt = self._resolve_de_contrast(action)
+ if not ref or not alt:
+ msg = "Specify condition_a (reference) and condition_b (alternate) for DESeq2."
+ if s.strict_mode:
+ return self._fail_strict(msg, FC.DE_MISSING_CONTRAST)
+ return PathwayObservation(
+ message=msg,
+ reward=-0.3,
+ metadata={"error": "contrast", "failure_code": FC.DE_MISSING_CONTRAST},
+ )
+
+ sample_ids = self._case["sample_ids"]
+ smd = self._case["sample_metadata"]
+ try:
+ meta_df = build_sample_metadata(sample_ids, smd)
+ except ValueError as exc:
+ if s.strict_mode:
+ return self._fail_strict(str(exc), FC.CASE_SAMPLE_METADATA_MISMATCH)
+ return PathwayObservation(
+ message=str(exc),
+ reward=-0.5,
+ metadata={"failure_code": FC.CASE_SAMPLE_METADATA_MISMATCH},
+ )
+
+ invalid = self._validate_contrast_proposal(ref, alt)
+ if invalid:
+ msg, code = invalid
+ if s.strict_mode:
+ return self._fail_strict(msg, code)
+ return PathwayObservation(
+ message=msg, reward=-0.3, metadata={"failure_code": code}
+ )
+
+ if "de_table_file" in self._case:
+ try:
+ # Precomputed tables only support the case's configured default pair.
+ dc = self._case.get("default_contrast") or {}
+ if (ref, alt) != (dc.get("reference"), dc.get("alternate")):
+ msg = (
+ "Author DE tables only support the configured default_contrast; "
+ "no reversal or refitting is available."
+ )
+ if s.strict_mode:
+ return self._fail_strict(
+ msg, FC.DE_UNSUPPORTED_PRECOMPUTED_CONTRAST
+ )
+ return PathwayObservation(
+ message=msg,
+ reward=-0.3,
+ metadata={
+ "failure_code": FC.DE_UNSUPPORTED_PRECOMPUTED_CONTRAST
+ },
+ )
+ opts = merge_analysis_options(self._case)
+ de_rows = load_author_de_table_csv(
+ DATA_DIR / str(self._case["de_table_file"]),
+ gene_column=str(
+ self._case.get("de_table_gene_column") or "Gene,name"
+ ),
+ log2fc_column=str(
+ self._case.get("de_table_log2fc_column") or "log2FoldChange"
+ ),
+ pvalue_column=str(
+ self._case.get("de_table_pvalue_column") or "pvalue"
+ ),
+ padj_column=str(self._case.get("de_table_padj_column") or "padj"),
+ )
+ padj_alpha = float(opts["padj_alpha"])
+ for r in de_rows:
+ try:
+ pv = float(r.get("padj"))
+ except (TypeError, ValueError):
+ pv = 1.0
+ r["significant"] = bool(pv <= padj_alpha)
+ self._de_rows = de_rows
+ self._query_genes = pick_de_query_genes(
+ de_rows,
+ padj_alpha=padj_alpha,
+ direction=str(opts["de_query_direction"]),
+ min_abs_log2fc=float(opts["min_abs_log2fc"]),
+ )
+ self._universe_genes = [] # unknown without counts
+ s.de_run = True
+ top_names = [r["gene"] for r in de_rows[:50]]
+ self._trace(
+ "de",
+ {
+ "precomputed": True,
+ "source": "author_de_table",
+ "contrast": [ref, alt],
+ "n_sig": sum(1 for r in de_rows if r.get("significant")),
+ "n_rows": len(de_rows),
+ },
+ "Differential expression loaded (author-provided table).",
+ )
+ obs = PathwayObservation(
+ message="Differential expression loaded from author table.",
+ top_genes=top_names,
+ de_genes=self._de_rows[:200],
+ reward=0.25,
+ metadata={
+ "step_count": s.step_count,
+ "precomputed": True,
+ "source": "author_de_table",
+ },
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ except (ValueError, OSError) as exc:
+ if s.strict_mode:
+ return self._fail_strict(str(exc), FC.DE_INVALID_AUTHOR_TABLE)
+ return PathwayObservation(
+ message=str(exc),
+ reward=-0.5,
+ metadata={
+ "error": "author_table_invalid",
+ "failure_code": FC.DE_INVALID_AUTHOR_TABLE,
+ },
+ )
+
+ try:
+ if "counts_file" in self._case:
+ counts_df = load_counts_csv_as_samples_by_genes(
+ DATA_DIR / str(self._case["counts_file"]),
+ sample_ids=sample_ids,
+ )
+ else:
+ counts = self._case["counts"]
+ v_err = validate_counts_case(self._case)
+ if v_err:
+ raise ValueError(v_err)
+ counts_df = counts_dict_to_samples_by_genes(counts, sample_ids)
+ except (ValueError, OSError) as exc:
+ if s.strict_mode:
+ return self._fail_strict(str(exc), FC.DE_INVALID_COUNTS_MATRIX)
+ return PathwayObservation(
+ message=str(exc),
+ reward=-0.5,
+ metadata={
+ "error": "counts_or_metadata_invalid",
+ "failure_code": FC.DE_INVALID_COUNTS_MATRIX,
+ },
+ )
+
+ opts = merge_analysis_options(self._case)
+ counts_df, n_genes_in, n_genes_filt = filter_counts_by_minimum_total(
+ counts_df, int(opts["min_total_count"])
+ )
+ if n_genes_filt < 5:
+ msg = (
+ f"After min_total_count={opts['min_total_count']} prefilter, "
+ f"only {n_genes_filt} genes remain (need ≥5 for stable DESeq2)."
+ )
+ if s.strict_mode:
+ return self._fail_strict(msg, FC.DE_TOO_FEW_GENES_AFTER_FILTER)
+ return PathwayObservation(
+ message=msg,
+ reward=-0.5,
+ metadata={
+ "error": "too_few_genes_after_filter",
+ "failure_code": FC.DE_TOO_FEW_GENES_AFTER_FILTER,
+ },
+ )
+
+ rows, err = run_deseq2_contrast(
+ counts_df,
+ meta_df,
+ alt,
+ ref,
+ padj_alpha=float(opts["padj_alpha"]),
+ )
+ if err:
+ if s.strict_mode:
+ return self._fail_strict(err, FC.DE_DESEQ2_FAILED)
+ return PathwayObservation(
+ message=err,
+ reward=-0.5,
+ metadata={"error": err, "failure_code": FC.DE_DESEQ2_FAILED},
+ )
+
+ self._universe_genes = list(counts_df.columns)
+ self._de_rows = rows
+ self._query_genes = pick_de_query_genes(
+ rows,
+ padj_alpha=float(opts["padj_alpha"]),
+ direction=str(opts["de_query_direction"]),
+ min_abs_log2fc=float(opts["min_abs_log2fc"]),
+ )
+ s.de_run = True
+ top_names = [r["gene"] for r in rows[:50]]
+ self._trace(
+ "de",
+ {
+ "contrast": [ref, alt],
+ "n_sig": sum(1 for r in rows if r["significant"]),
+ "genes_in_matrix": n_genes_in,
+ "genes_after_prefilter": n_genes_filt,
+ },
+ "DESeq2 complete",
+ )
+ obs = PathwayObservation(
+ message="Differential expression complete (PyDESeq2).",
+ top_genes=top_names,
+ de_genes=rows[:200],
+ reward=0.35,
+ metadata={
+ "step_count": s.step_count,
+ "contrast": [ref, alt],
+ "genes_in_matrix": n_genes_in,
+ "genes_after_prefilter": n_genes_filt,
+ "analysis_options": {
+ k: opts[k]
+ for k in (
+ "min_total_count",
+ "padj_alpha",
+ "de_query_direction",
+ "min_abs_log2fc",
+ )
+ },
+ },
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ def _step_enrichment(self, action: PathwayAction) -> PathwayObservation:
+ s = self._state
+ if not self._de_rows and not s.legacy_mode:
+ msg = "Run differential expression before enrichment."
+ return PathwayObservation(
+ message=msg,
+ reward=-0.2,
+ metadata={"failure_code": FC.ORA_DE_PREREQUISITE},
+ )
+
+ pathways = self._case.get("pathway_genes") or {}
+ if s.legacy_mode:
+ names = list(self._case.get("top_pathways", []))
+ s.enrichment_run = True
+ fake = [
+ {
+ "pathway": n,
+ "p_value": 0.001,
+ "q_value": 0.01,
+ "overlap_genes": list(self._case.get("top_genes", []))[:2],
+ "overlap_count": 2,
+ "pathway_size": 10,
+ "de_in_universe": len(self._query_genes),
+ "gene_ratio": "2/10",
+ }
+ for n in names
+ ]
+ self._ora_rows = fake
+ amb = top_hits_statistically_close(fake)
+ ov = overlap_genes_across_top_pathways(fake)
+ self._trace("ora", {"legacy": True}, "Legacy ORA")
+ obs = PathwayObservation(
+ message="Pathway enrichment complete (legacy fixture).",
+ top_pathways=names,
+ pathway_enrichment=fake,
+ statistical_ambiguity=amb,
+ overlap_summary=ov,
+ reward=0.45,
+ metadata={"legacy": True},
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ opts = merge_analysis_options(self._case)
+ universe = self._universe_genes
+ if not universe:
+ if "counts" in self._case:
+ universe = list(self._case["counts"].keys())
+ else:
+ universe = []
+ query = action.gene_list if action.gene_list else self._query_genes
+ if not query:
+ query = pick_de_query_genes(
+ self._de_rows,
+ padj_alpha=float(opts["padj_alpha"]),
+ direction=str(opts["de_query_direction"]),
+ min_abs_log2fc=float(opts["min_abs_log2fc"]),
+ )
+ if not query and self._de_rows:
+ query = [r["gene"] for r in self._de_rows[:50]]
+
+ enrichr_libs = self._case.get("enrichr_libraries")
+ if enrichr_libs:
+ if not gseapy_available():
+ msg = "gseapy not installed; cannot run Enrichr enrichment."
+ if s.strict_mode:
+ return self._fail_strict(msg, FC.ORA_GSEAPY_UNAVAILABLE)
+ return PathwayObservation(
+ message=msg,
+ reward=-0.3,
+ metadata={
+ "error": "missing_gseapy",
+ "failure_code": FC.ORA_GSEAPY_UNAVAILABLE,
+ },
+ )
+ ora, err = enrichr_ora(
+ query,
+ libraries=list(enrichr_libs),
+ background=universe or None,
+ top_k=100,
+ )
+ if err:
+ if s.strict_mode:
+ return self._fail_strict(err, FC.ORA_ENRICHR_FAILED)
+ return PathwayObservation(
+ message=err,
+ reward=-0.3,
+ metadata={
+ "error": "enrichr_failed",
+ "failure_code": FC.ORA_ENRICHR_FAILED,
+ },
+ )
+ else:
+ if not pathways:
+ msg = "Case has no pathway_genes (and no enrichr_libraries); cannot run ORA."
+ if s.strict_mode:
+ return self._fail_strict(msg, FC.ORA_NO_PATHWAY_DEFINITIONS)
+ return PathwayObservation(
+ message=msg,
+ reward=-0.3,
+ metadata={
+ "error": "no_pathways",
+ "failure_code": FC.ORA_NO_PATHWAY_DEFINITIONS,
+ },
+ )
+ ora = ora_fisher(
+ query,
+ pathways,
+ universe,
+ min_pathway_genes=int(opts["ora_min_pathway_genes"]),
+ )
+ self._ora_rows = ora
+ s.enrichment_run = True
+ top_names = [r["pathway"] for r in ora[:20]]
+ amb = top_hits_statistically_close(ora)
+ ov = overlap_genes_across_top_pathways(ora)
+ self._trace("ora", {"n_pathways": len(ora)}, "ORA complete")
+ obs = PathwayObservation(
+ message="Over-representation analysis complete.",
+ top_pathways=top_names,
+ pathway_enrichment=ora[:50],
+ statistical_ambiguity=amb,
+ overlap_summary=ov,
+ reward=0.5,
+ metadata={
+ "query_genes": len(query),
+ "ora_universe_size": len(universe),
+ "ora_min_pathway_genes": int(opts["ora_min_pathway_genes"]),
+ },
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ def _step_compare(self, action: PathwayAction) -> PathwayObservation:
+ s = self._state
+ a = (action.pathway_a or "").strip()
+ b = (action.pathway_b or "").strip()
+ if not a or not b:
+ return PathwayObservation(
+ message="Provide pathway_a and pathway_b.",
+ reward=-0.1,
+ metadata={
+ "error": "missing_names",
+ "failure_code": FC.COMPARE_MISSING_PATHWAY_NAMES,
+ },
+ )
+ pathways = self._case.get("pathway_genes") or {}
+ if s.legacy_mode:
+ # infer dummy pathways from top_pathways list
+ pathways = {
+ p: self._case.get("top_genes", [])
+ for p in self._case.get("top_pathways", [])
+ }
+ query = self._query_genes or list(self._case.get("top_genes", []))
+ enrichr_support = bool(self._case.get("enrichr_libraries")) and not s.legacy_mode
+ if enrichr_support:
+ pathways = {r["pathway"]: r["overlap_genes"] for r in self._ora_rows}
+ # Enrichr returns query overlap, not full pathway membership.
+ query = [g for genes in pathways.values() for g in genes]
+ if a not in pathways or b not in pathways:
+ return PathwayObservation(
+ message="Both pathways must have definitions or completed enrichment hits.",
+ reward=-0.1,
+ metadata={"error": "unknown_pathway"},
+ )
+ detail = compare_pathways_detail(a, b, pathways, query)
+ if enrichr_support:
+ detail["pathway_a_size"] = None
+ detail["pathway_b_size"] = None
+ detail["comparison_scope"] = "enrichr_overlap_genes"
+ self._trace("compare_pathways", detail, f"Compared {a} vs {b}")
+ obs = PathwayObservation(
+ message=f"Pathway comparison: {a} vs {b}.",
+ pathway_comparison=detail,
+ reward=0.15,
+ metadata={"step_count": s.step_count},
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ def _step_expert(self, action: PathwayAction) -> PathwayObservation:
+ s = self._state
+ budget = s.expert_budget
+ if budget <= 0:
+ return PathwayObservation(
+ message="Expert calls are disabled for this case.",
+ reward=-0.1,
+ metadata={
+ "expert": "disabled",
+ "failure_code": FC.EXPERT_DISABLED,
+ },
+ )
+ if s.expert_calls_used >= budget:
+ return PathwayObservation(
+ message="Expert call budget exhausted.",
+ reward=-0.5,
+ metadata={
+ "expert": "exhausted",
+ "failure_code": FC.EXPERT_BUDGET_EXHAUSTED,
+ },
+ )
+ s.expert_calls_used += 1
+ penalty = float(self._case.get("expert_penalty", 0.3))
+ hint = self._case.get(
+ "expert_hint",
+ "Focus on pathways with strongest overlap support and check genes shared across top hits.",
+ )
+ self._trace(
+ "ask_expert",
+ {"question": action.expert_question, "call": s.expert_calls_used},
+ "Expert consulted",
+ )
+ obs = PathwayObservation(
+ message="Expert guidance (penalized).",
+ expert_message=str(hint),
+ reward=-penalty,
+ metadata={"expert_calls_remaining": budget - s.expert_calls_used},
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ def _step_submit(self, action: PathwayAction) -> PathwayObservation:
+ s = self._state
+ proposed = (action.hypothesis or "").strip().lower()
+ correct = proposed == s.true_pathway.lower()
+ s.is_done = True
+ self._trace(
+ "submit",
+ {"hypothesis": action.hypothesis, "correct": correct},
+ "Episode end",
+ )
+ meta: Dict[str, Any] = {"correct": correct, "step_count": s.step_count}
+ if not correct:
+ meta["failure_code"] = FC.SUBMIT_INCORRECT_HYPOTHESIS
+ obs = PathwayObservation(
+ message="Answer submitted.",
+ done=True,
+ reward=2.0 if correct else -1.0,
+ metadata=meta,
+ )
+ obs.trace_path = self._refresh_trace_file()
+ return obs
+
+ @property
+ def state(self) -> PathwayState:
+ return self._state
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diff --git a/tests/envs/test_pathway_analysis_env.py b/tests/envs/test_pathway_analysis_env.py
new file mode 100644
index 0000000000..271800ac86
--- /dev/null
+++ b/tests/envs/test_pathway_analysis_env.py
@@ -0,0 +1,262 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Tests for pathway_analysis_env (DE, ORA, compare, expert, trace)."""
+
+from __future__ import annotations
+
+import json
+
+import pytest
+from pathway_analysis_env.models import PathwayAction
+from pathway_analysis_env.server.analysis import (
+ adjust_pvalues_bh,
+ build_sample_metadata,
+ ora_fisher,
+ overlap_genes_across_top_pathways,
+ pydeseq2_available,
+ run_deseq2_contrast,
+ validate_counts_case,
+)
+from pathway_analysis_env.server.pathway_environment import (
+ DATA_DIR,
+ load_case,
+ PathwayEnvironment,
+)
+
+requires_pydeseq2 = pytest.mark.skipif(
+ not pydeseq2_available(),
+ reason="PyDESeq2 required for pathway pipeline tests",
+)
+
+
+def test_set_case_file():
+ env = PathwayEnvironment(case_file="toy_case_001.json")
+ env.set_case_file("toy_case_legacy.json")
+ assert env._case_file == "toy_case_legacy.json"
+
+
+def test_load_pipeline_case():
+ case = load_case("toy_case_001.json")
+ assert "counts" in case
+ assert "pathway_genes" in case
+ assert case["true_pathway"] == "MAPK signaling"
+
+
+def test_load_gse235417_case_is_pipeline_mode():
+ case = load_case("gse235417_case.json")
+ assert "counts_file" in case
+ assert "sample_ids" in case
+ assert "sample_metadata" in case
+ assert case["default_contrast"]["reference"] == "baseline"
+ assert case["default_contrast"]["alternate"] == "resistant"
+
+
+@requires_pydeseq2
+def test_deseq2_mapk_case():
+ case = json.loads((DATA_DIR / "toy_case_001.json").read_text(encoding="utf-8"))
+ from pathway_analysis_env.server.analysis import (
+ build_sample_metadata,
+ counts_dict_to_samples_by_genes,
+ )
+
+ cdf = counts_dict_to_samples_by_genes(case["counts"], case["sample_ids"])
+ meta = build_sample_metadata(case["sample_ids"], case["sample_metadata"])
+ rows, err = run_deseq2_contrast(
+ cdf,
+ meta,
+ case["default_contrast"]["alternate"],
+ case["default_contrast"]["reference"],
+ )
+ assert err is None
+ top = [r["gene"] for r in rows[:5]]
+ assert "DUSP6" in top or "FOS" in top
+
+
+def test_ora_fisher_structure():
+ universe = ["A", "B", "C", "D", "E", "F"]
+ pathways = {"P1": ["A", "B", "C"], "P2": ["C", "D"]}
+ de = ["A", "B", "C"]
+ ora = ora_fisher(de, pathways, universe, min_pathway_genes=2)
+ assert len(ora) == 2
+ assert ora[0]["pathway"] in ("P1", "P2")
+ assert "q_value" in ora[0]
+
+
+def test_adjust_pvalues_bh_matches_scipy():
+ ps = [0.01, 0.05, 0.1]
+ q = adjust_pvalues_bh(ps)
+ assert len(q) == 3
+ assert all(0.0 <= x <= 1.0 for x in q)
+
+
+def test_validate_counts_case():
+ assert validate_counts_case({}) is None
+ bad = {
+ "counts": {"G1": [1, 2], "G2": [1]},
+ "sample_ids": ["a", "b"],
+ }
+ assert validate_counts_case(bad) is not None
+
+
+def test_build_sample_metadata_missing_sample():
+ with pytest.raises(ValueError, match="missing"):
+ build_sample_metadata(["S1", "S2"], {"S1": "a"})
+
+
+def test_understand_experiment_design_summary():
+ env = PathwayEnvironment(case_file="toy_case_001.json")
+ env.reset()
+ obs = env.step(PathwayAction(action_type="understand_experiment_design"))
+ assert obs.experiment_design
+ assert obs.experiment_design.get("samples_per_condition")
+ assert env.state.design_understood is True
+ assert env.state.validated_reference is None
+
+
+@requires_pydeseq2
+def test_understand_validated_contrast_matches_explicit_de():
+ env = PathwayEnvironment(case_file="toy_case_001.json")
+ env.reset()
+ u = env.step(
+ PathwayAction(
+ action_type="understand_experiment_design",
+ condition_a="control",
+ condition_b="treated",
+ )
+ )
+ assert u.experiment_design and u.experiment_design.get("validated_contrast")
+ assert env.state.validated_reference == "control"
+ assert env.state.validated_alternate == "treated"
+ a = env.step(
+ PathwayAction(
+ action_type="run_differential_expression",
+ condition_a="control",
+ condition_b="treated",
+ )
+ )
+ env2 = PathwayEnvironment(case_file="toy_case_001.json")
+ env2.reset()
+ env2.step(
+ PathwayAction(
+ action_type="understand_experiment_design",
+ condition_a="control",
+ condition_b="treated",
+ )
+ )
+ b = env2.step(PathwayAction(action_type="run_differential_expression"))
+ assert a.de_genes and b.de_genes
+ assert [r.get("gene") for r in a.de_genes[:10]] == [
+ r.get("gene") for r in b.de_genes[:10]
+ ]
+
+
+def test_no_step_after_episode_done():
+ env = PathwayEnvironment(case_file="toy_case_legacy.json")
+ env.reset()
+ env.step(PathwayAction(action_type="submit_answer", hypothesis="MAPK signaling"))
+ late = env.step(PathwayAction(action_type="inspect_dataset"))
+ assert late.done
+ assert late.metadata.get("error") == "episode_done"
+ assert late.metadata.get("failure_code") == "episode_already_done"
+
+
+def test_overlap_summary():
+ ora = [
+ {
+ "pathway": "a",
+ "p_value": 0.01,
+ "overlap_genes": ["G1", "G2"],
+ },
+ {
+ "pathway": "b",
+ "p_value": 0.02,
+ "overlap_genes": ["G2", "G3"],
+ },
+ ]
+ ov = overlap_genes_across_top_pathways(ora, top_k=2)
+ assert "G2" in ov["genes_supporting_multiple_top_pathways"]
+
+
+@requires_pydeseq2
+def test_episode_pipeline_success():
+ env = PathwayEnvironment(case_file="toy_case_001.json")
+ obs0 = env.reset(episode_id="ep-test-1")
+ assert obs0.metadata.get("pipeline_mode") is True
+ assert obs0.trace_path
+
+ a = PathwayAction(
+ action_type="run_differential_expression",
+ condition_a="control",
+ condition_b="treated",
+ )
+ obs1 = env.step(a)
+ assert obs1.de_genes
+ assert obs1.top_genes
+
+ b = PathwayAction(action_type="run_pathway_enrichment")
+ obs2 = env.step(b)
+ assert obs2.pathway_enrichment
+ assert "MAPK signaling" in obs2.top_pathways[:3]
+ assert obs2.overlap_summary is not None
+
+ c = PathwayAction(
+ action_type="compare_pathways",
+ pathway_a="MAPK signaling",
+ pathway_b="ERK cascade",
+ )
+ obs3 = env.step(c)
+ assert obs3.pathway_comparison
+ assert "shared_de_support" in obs3.pathway_comparison
+
+ d = PathwayAction(action_type="ask_expert", expert_question="hint?")
+ obs4 = env.step(d)
+ assert obs4.expert_message
+ assert obs4.reward is not None and obs4.reward < 0
+
+ e = PathwayAction(action_type="submit_answer", hypothesis="MAPK signaling")
+ obs5 = env.step(e)
+ assert obs5.done
+ assert obs5.metadata.get("correct") is True
+
+
+def test_legacy_fixture():
+ env = PathwayEnvironment(case_file="toy_case_legacy.json")
+ obs0 = env.reset()
+ assert obs0.metadata.get("pipeline_mode") is False
+ env.step(PathwayAction(action_type="run_differential_expression"))
+ env.step(PathwayAction(action_type="run_pathway_enrichment"))
+ fin = env.step(
+ PathwayAction(action_type="submit_answer", hypothesis="MAPK signaling")
+ )
+ assert fin.done
+ assert fin.metadata.get("correct") is True
+
+
+@requires_pydeseq2
+def test_strict_invalid_counts_matrix():
+ env = PathwayEnvironment(case_file="toy_case_001.json")
+ env.reset(strict=True)
+ env._case["counts"]["DUSP6"] = [1, 2]
+ obs = env.step(
+ PathwayAction(
+ action_type="run_differential_expression",
+ condition_a="control",
+ condition_b="treated",
+ )
+ )
+ assert obs.done and obs.metadata.get("strict_failure") is True
+ assert obs.metadata.get("failure_code") == "de_invalid_counts_matrix"
+
+
+@requires_pydeseq2
+def test_strict_mode_missing_contrast():
+ env = PathwayEnvironment(case_file="toy_case_no_default.json")
+ env.reset(strict=True)
+ obs = env.step(PathwayAction(action_type="run_differential_expression"))
+ assert obs.done is True
+ assert obs.metadata.get("strict_failure") is True
+ assert obs.metadata.get("failure_code") == "de_missing_contrast"
diff --git a/tests/envs/test_pathway_benchmark.py b/tests/envs/test_pathway_benchmark.py
new file mode 100644
index 0000000000..502bc1dc77
--- /dev/null
+++ b/tests/envs/test_pathway_benchmark.py
@@ -0,0 +1,78 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+import sys
+from pathlib import Path
+
+import pandas as pd
+import pytest
+from pathway_analysis_env.bench import geo_openenv_deseq2 as bench
+from pathway_analysis_env.server import analysis
+
+
+@pytest.mark.parametrize("retained", [4, 5])
+def test_benchmark_prefilter_and_background(tmp_path, monkeypatch, retained):
+ repo = Path(__file__).resolve().parents[2]
+ (tmp_path / "data").mkdir()
+ counts = {f"G{i}": [5, 5] for i in range(retained)}
+ counts["low"] = [1, 1]
+ pd.DataFrame(counts, index=["c", "t"]).T.to_csv(tmp_path / "data/counts.csv")
+ case = {
+ "counts_file": "counts.csv",
+ "sample_ids": ["c", "t"],
+ "sample_metadata": {
+ "c": {"condition": "control"},
+ "t": {"condition": "treated"},
+ },
+ "default_contrast": {"reference": "control", "alternate": "treated"},
+ "analysis_options": {"min_total_count": 10},
+ }
+ case_path = tmp_path / "case.json"
+ case_path.write_text(json.dumps(case))
+ out = tmp_path / "results"
+ monkeypatch.chdir(repo)
+ monkeypatch.setattr(
+ sys,
+ "argv",
+ [
+ "bench",
+ "--repo-root",
+ str(repo),
+ "--env-dir",
+ str(tmp_path),
+ "--case-json",
+ str(case_path),
+ "--out-dir",
+ str(out),
+ ],
+ )
+ calls = []
+
+ def fit(counts, *args, **kwargs):
+ calls.append("fit")
+ assert list(counts.columns) == [f"G{i}" for i in range(retained)]
+ return [
+ {"gene": "G0", "padj": 0.01, "log2FoldChange": 2, "significant": True}
+ ], None
+
+ def enrich(query, *, background, **kwargs):
+ calls.append("enrich")
+ assert background == [f"G{i}" for i in range(retained)]
+ return [], None
+
+ monkeypatch.setattr(analysis, "run_deseq2_contrast", fit)
+ monkeypatch.setattr(analysis, "enrichr_ora", enrich)
+ if retained < 5:
+ with pytest.raises(RuntimeError, match="only 4 genes remain"):
+ bench.main()
+ assert calls == []
+ assert not out.exists()
+ else:
+ bench.main()
+ assert calls == ["fit", "enrich"]
+ summary = json.loads((out / "summary.json").read_text())
+ assert summary["genes_in_matrix"] == retained + 1
+ assert summary["genes_after_prefilter"] == retained
diff --git a/tests/envs/test_pathway_counts_pipeline.py b/tests/envs/test_pathway_counts_pipeline.py
new file mode 100644
index 0000000000..ce04506240
--- /dev/null
+++ b/tests/envs/test_pathway_counts_pipeline.py
@@ -0,0 +1,192 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Local fixtures for the true-counts FASTQ cache boundary."""
+
+import pytest
+from pathway_analysis_env.bench import run_true_counts_pipeline as pipeline
+
+
+@pytest.mark.parametrize("mate", [1, 2])
+@pytest.mark.parametrize("cached", [False, True])
+def test_incomplete_pair_is_never_used_as_single_end(
+ tmp_path, monkeypatch, mate, cached
+):
+ sample = pipeline.Sample("sample", "SRR1", "control")
+ partial = tmp_path / f"SRR1_{mate}.fastq"
+ if cached:
+ partial.write_text("@read\nACGT\n+\n!!!!\n")
+
+ calls = []
+
+ def fake_run(cmd):
+ calls.append(cmd)
+ partial.write_text("@read\nACGT\n+\n!!!!\n")
+
+ monkeypatch.setattr(pipeline, "_run", fake_run)
+ with pytest.raises(RuntimeError, match="Incomplete paired FASTQ"):
+ pipeline._fasterq_dump_one(sample, out_dir=tmp_path, threads_per_job=1)
+ assert len(calls) == (0 if cached else 1)
+ assert partial.exists() # Preserve the cache for diagnosis/recovery.
+
+
+@pytest.mark.parametrize("paired", [False, True])
+@pytest.mark.parametrize("cached", [False, True])
+def test_complete_fastqs_keep_their_layout(tmp_path, monkeypatch, paired, cached):
+ sample = pipeline.Sample("sample", "SRR1", "control")
+ names = ["SRR1_1.fastq", "SRR1_2.fastq"] if paired else ["SRR1.fastq"]
+ paths = [tmp_path / name for name in names]
+ calls = []
+
+ def create_files():
+ for path in paths:
+ path.write_text("@read\nACGT\n+\n!!!!\n")
+
+ def fake_run(cmd):
+ calls.append(cmd)
+ create_files()
+
+ if cached:
+ create_files()
+ monkeypatch.setattr(pipeline, "_run", fake_run)
+ result = pipeline._fasterq_dump_one(sample, out_dir=tmp_path, threads_per_job=2)
+ assert result == ("sample", "SRR1", (paths[0], paths[1] if paired else None))
+ assert len(calls) == (0 if cached else 1)
+
+
+def test_single_fastq_does_not_mask_incomplete_pair(tmp_path, monkeypatch):
+ for name in ("SRR1.fastq", "SRR1_1.fastq"):
+ (tmp_path / name).write_text("@read\nACGT\n+\n!!!!\n")
+
+ def unexpected_run(cmd):
+ pytest.fail(
+ "Incomplete cached outputs should fail before invoking fasterq-dump"
+ )
+
+ monkeypatch.setattr(pipeline, "_run", unexpected_run)
+ with pytest.raises(RuntimeError, match="Incomplete paired FASTQ"):
+ pipeline._fasterq_dump_one(
+ pipeline.Sample("sample", "SRR1", "control"),
+ out_dir=tmp_path,
+ threads_per_job=1,
+ )
+
+
+@pytest.mark.parametrize("paired", [False, True])
+def test_featurecounts_layout_flags(tmp_path, monkeypatch, paired):
+ output = tmp_path / "counts.txt"
+ calls = []
+
+ def run(cmd):
+ calls.append(cmd)
+ output.write_text("fixture")
+
+ monkeypatch.setattr(pipeline, "_run", run)
+ pipeline.quantify_featurecounts(
+ gtf=tmp_path / "genes.gtf",
+ bams={"b": tmp_path / "b.bam", "a": tmp_path / "a.bam"},
+ out_path=output,
+ threads=2,
+ paired_end=paired,
+ )
+ assert ("-p" in calls[0]) is paired
+ assert ("--countReadPairs" in calls[0]) is paired
+ assert calls[0][-2:] == [str(tmp_path / "a.bam"), str(tmp_path / "b.bam")]
+
+
+@pytest.mark.parametrize("layout", ["single", "paired", "mixed"])
+@pytest.mark.parametrize("parallelism", [1, 2])
+def test_star_main_preserves_fastq_layout(tmp_path, monkeypatch, layout, parallelism):
+ import json
+ import sys
+
+ samples = tmp_path / "samples.json"
+ samples.write_text(
+ json.dumps(
+ [
+ {"sample_id": sid, "sra": sid, "condition": "control"}
+ for sid in ("a", "b")
+ ]
+ )
+ )
+ index = tmp_path / "index"
+ index.mkdir()
+ (index / "genomeParameters.txt").touch()
+ fastqs = {
+ sid: (
+ tmp_path / f"{sid}_1.fastq",
+ tmp_path / f"{sid}_2.fastq"
+ if layout == "paired" or (layout == "mixed" and sid == "a")
+ else None,
+ )
+ for sid in ("a", "b")
+ }
+ monkeypatch.setattr(pipeline, "download_fastq", lambda *a, **k: fastqs)
+ monkeypatch.setattr(
+ sys,
+ "argv",
+ [
+ "pipeline",
+ "--samples-json",
+ str(samples),
+ "--out-dir",
+ str(tmp_path / "out"),
+ "--gtf",
+ str(tmp_path / "genes.gtf"),
+ "--star-index",
+ str(index),
+ "--quant-parallelism",
+ str(parallelism),
+ ],
+ )
+ calls = []
+
+ def align(*, sample_id, fastq2, **kwargs):
+ calls.append("align")
+ assert (fastq2 is not None) is (layout == "paired")
+ return tmp_path / f"{sample_id}.bam"
+
+ def run(cmd):
+ calls.append(cmd)
+ pipeline.Path(cmd[cmd.index("-o") + 1]).write_text("fixture")
+
+ monkeypatch.setattr(pipeline, "align_star", align)
+ monkeypatch.setattr(pipeline, "_run", run)
+ monkeypatch.setattr(
+ pipeline, "featurecounts_to_openenv_counts", lambda **kwargs: None
+ )
+ if layout == "mixed":
+ with pytest.raises(ValueError, match="uniform"):
+ pipeline.main()
+ assert calls == []
+ else:
+ pipeline.main()
+ assert ("-p" in calls[-1]) is (layout == "paired")
+ assert ("--countReadPairs" in calls[-1]) is (layout == "paired")
+
+
+def test_plain_and_gzip_gtf_produce_identical_gene_maps(tmp_path):
+ import gzip
+
+ text = (
+ "# annotation\n"
+ 'chr1\ttest\ttranscript\t1\t20\t.\t+\t.\tgene_id "G1"; transcript_id "T1"; gene_name "ABC";\n'
+ 'chr1\ttest\texon\t1\t10\t.\t+\t.\tgene_id "G1"; transcript_id "T1"; gene_name "ABC";\n'
+ 'chr1\ttest\texon\t30\t40\t.\t+\t.\tgene_id "G2"; transcript_id "T2";\n'
+ )
+ plain = tmp_path / "genes.gtf"
+ plain.write_text(text)
+ compressed = tmp_path / "genes.gtf.gz"
+ compressed.write_bytes(gzip.compress(text.encode()))
+ outputs = []
+ for i, source in enumerate((plain, compressed)):
+ # Both STAR's input preparation and Salmon's mapping accept either form.
+ star_input = pipeline._maybe_decompress_to(source, out_dir=tmp_path / "star")
+ assert star_input.read_text() == text
+ result = pipeline.build_tx2gene_from_gtf(
+ gtf_gz=source, out_tsv=tmp_path / f"map{i}.tsv"
+ )
+ outputs.append(result.read_text())
+ assert outputs == ["T1\tABC\nT2\tG2\n"] * 2
diff --git a/tests/envs/test_pathway_numeric_ui.py b/tests/envs/test_pathway_numeric_ui.py
new file mode 100644
index 0000000000..b73e1395ae
--- /dev/null
+++ b/tests/envs/test_pathway_numeric_ui.py
@@ -0,0 +1,138 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Offline regressions for DE serialization and the UI's contrast direction."""
+
+import json
+from types import SimpleNamespace
+
+import pandas as pd
+import pytest
+from pathway_analysis_env.models import PathwayObservation
+from pathway_analysis_env.server import analysis, gradio_ui
+
+
+def test_author_de_unknown_statistics_are_json_null(tmp_path):
+ table = tmp_path / "de.csv"
+ table.write_text(
+ "gene;log2FoldChange;pvalue;padj\n"
+ "TP53;2;0.001;0.01\n"
+ "MISSING;NaN;inf;NaN\n"
+ "INFINITE;-inf;-inf;inf\n"
+ )
+ rows = analysis.load_author_de_table_csv(table)
+ payload = PathwayObservation(de_genes=rows).model_dump()
+ json.dumps(payload, allow_nan=False)
+ by_gene = {row["gene"]: row for row in rows}
+ assert by_gene["TP53"]["baseMean"] is None
+ assert by_gene["TP53"]["log2FoldChange"] == 2.0
+ for gene in ("MISSING", "INFINITE"):
+ assert by_gene[gene]["log2FoldChange"] is None
+ assert by_gene[gene]["pvalue"] is None
+ assert by_gene[gene]["padj"] == 1.0
+
+
+def test_deseq_nonfinite_statistics_serialize_and_sort(monkeypatch):
+ result = pd.DataFrame(
+ {
+ "baseMean": [10.0, float("nan"), float("inf")],
+ "log2FoldChange": [2.0, float("nan"), float("-inf")],
+ "lfcSE": [0.2, float("inf"), float("nan")],
+ "pvalue": [0.001, float("nan"), float("inf")],
+ "padj": [0.01, float("nan"), float("-inf")],
+ },
+ index=["TP53", "MISSING", "INFINITE"],
+ )
+ monkeypatch.setattr(analysis, "_PYDESQ2_AVAILABLE", True)
+ monkeypatch.setattr(
+ analysis, "DeseqDataSet", lambda **kwargs: SimpleNamespace(deseq2=lambda: None)
+ )
+ monkeypatch.setattr(
+ analysis,
+ "DeseqStats",
+ lambda *args, **kwargs: SimpleNamespace(
+ summary=lambda: None, results_df=result
+ ),
+ )
+ rows, error = analysis.run_deseq2_contrast(
+ pd.DataFrame(),
+ pd.DataFrame({"condition": ["control", "treated"]}),
+ "treated",
+ "control",
+ )
+ assert error is None
+ json.dumps(PathwayObservation(de_genes=rows).model_dump(), allow_nan=False)
+ assert rows[0]["gene"] == "TP53"
+ for row in rows[1:]:
+ assert all(
+ row[key] is None
+ for key in ("baseMean", "log2FoldChange", "lfcSE", "pvalue")
+ )
+ assert row["padj"] == 1.0
+ assert not row["significant"]
+ assert analysis.pick_de_query_genes(rows, direction="up") == ["TP53"]
+
+
+@pytest.mark.parametrize("update", [True, False])
+def test_ui_reset_preserves_default_contrast_direction(update):
+ manager = SimpleNamespace(get_state=lambda: {"conditions": ["FAR", "control"]})
+ data = {
+ "observation": {
+ "experiment_design": {
+ "default_contrast": {"reference": "control", "alternate": "FAR"}
+ }
+ }
+ }
+ outputs = gradio_ui._response(data, manager, "reset", update_contrast=update)
+ if update:
+ assert outputs[-2]["value"] == "control"
+ assert outputs[-1]["value"] == "FAR"
+ else:
+ assert "value" not in outputs[-2]
+ assert "value" not in outputs[-1]
+
+
+def test_counts_like_reset_supplies_ui_contrast(monkeypatch):
+ from pathway_analysis_env.server import pathway_environment
+
+ monkeypatch.setattr(pathway_environment, "_write_html_trace", lambda *args: "")
+ env = pathway_environment.PathwayEnvironment(
+ case_file="gse227102_t47d_counts_like_case.json"
+ )
+ observation = env.reset()
+ manager = SimpleNamespace(get_state=lambda: env.state.model_dump())
+ outputs = gradio_ui._response(
+ {"observation": observation.model_dump()},
+ manager,
+ "reset",
+ update_contrast=True,
+ )
+ assert outputs[-2]["value"] == "control"
+ assert outputs[-1]["value"] == "FAR"
+
+
+@pytest.mark.parametrize("sizes", [(None, None), (None, 3), (2, None), (2, 3), (0, 0)])
+def test_comparison_ui_preserves_unknown_and_known_sizes(sizes):
+ a_size, b_size = sizes
+ comparison = analysis.compare_pathways_detail(
+ "P1", "P2", {"P1": ["A", "B"], "P2": ["B", "C"]}, ["A", "B", "C"]
+ )
+ comparison.update(pathway_a_size=a_size, pathway_b_size=b_size)
+ obs = PathwayObservation(pathway_comparison=comparison)
+ outputs = gradio_ui._response(
+ {"observation": obs.model_dump()},
+ SimpleNamespace(get_state=lambda: {}),
+ "Compare complete.",
+ update_contrast=False,
+ )
+ table = outputs[3]
+ assert table["count"].tolist()[:3] == [1, 1, 1]
+ assert table["genes (preview)"].tolist()[:3] == ["A", "C", "B"]
+ expected = "Unknown" if None in sizes else sum(sizes)
+ assert table.iloc[3]["count"] == expected
+ for label, size in zip(("A", "B"), sizes):
+ rendered = "Unknown" if size is None else str(size)
+ assert f"{label} size={rendered}" in table.iloc[3]["genes (preview)"]
+ assert json.loads(outputs[6])["observation"]["pathway_comparison"] == comparison
diff --git a/tests/envs/test_pathway_packaging.py b/tests/envs/test_pathway_packaging.py
new file mode 100644
index 0000000000..17327fae57
--- /dev/null
+++ b/tests/envs/test_pathway_packaging.py
@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: BSD-3-Clause
+"""Exercise data loading from a built artifact, outside the source checkout."""
+
+import gzip
+import json
+import os
+import shutil
+import subprocess
+import sys
+import zipfile
+from pathlib import Path
+
+import pytest
+
+
+ENV_DIR = Path(__file__).resolve().parents[2] / "envs" / "pathway_analysis_env"
+
+
+def test_wheel_contains_nested_case_assets(tmp_path):
+ pytest.importorskip("setuptools")
+ pytest.importorskip("wheel")
+ source = tmp_path / "source"
+ shutil.copytree(
+ ENV_DIR,
+ source,
+ ignore=shutil.ignore_patterns(
+ ".venv", "build", "*.egg-info", "__pycache__", "outputs"
+ ),
+ )
+ # External GEO matrices are not checked in. Synthetic values test packaging
+ # and parsing only, at the paths referenced by the real case definitions.
+ for case_file in (source / "data").rglob("*.json"):
+ case = json.loads(case_file.read_text())
+ if "counts_file" in case:
+ asset = source / "data" / case["counts_file"]
+ contents = (
+ "gene,"
+ + ",".join(case["sample_ids"])
+ + "\nTP53,"
+ + ",".join(["10"] * len(case["sample_ids"]))
+ + "\n"
+ )
+ elif "de_table_file" in case:
+ asset = source / "data" / case["de_table_file"]
+ contents = "Gene,name;log2FoldChange;pvalue;padj\nTP53;2;0.001;0.01\n"
+ else:
+ continue
+ asset.parent.mkdir(parents=True, exist_ok=True)
+ with gzip.open(asset, "wt") as stream:
+ stream.write(contents)
+ (source / "data" / "geo_eval" / "plain.csv").write_text("gene,sample\nTP53,10\n")
+ built = subprocess.run(
+ [
+ sys.executable,
+ "-c",
+ "from setuptools.build_meta import build_wheel; build_wheel('dist')",
+ ],
+ cwd=source,
+ capture_output=True,
+ text=True,
+ )
+ assert built.returncode == 0, built.stdout + built.stderr
+ installed = tmp_path / "installed"
+ with zipfile.ZipFile(next((source / "dist").glob("*.whl"))) as wheel:
+ wheel.extractall(installed)
+ shutil.rmtree(source)
+ probe = subprocess.run(
+ [
+ sys.executable,
+ "-c",
+ """
+import json
+from pathway_analysis_env.server.pathway_environment import DATA_DIR, load_case
+from pathway_analysis_env.server.analysis import (
+ load_counts_csv_as_samples_by_genes, load_author_de_table_csv,
+)
+assert str(DATA_DIR).startswith(INSTALLED)
+nested = 'geo_eval/gse128911_mda_mb_134_vi_fulvestrant_vs_dmso/gse128911_case.json'
+assert load_case(nested)['case_id'].startswith('GSE128911')
+counts = tables = 0
+for path in DATA_DIR.rglob('*.json'):
+ case = json.loads(path.read_text())
+ if 'counts_file' in case:
+ frame = load_counts_csv_as_samples_by_genes(
+ DATA_DIR / case['counts_file'], sample_ids=case['sample_ids'],
+ )
+ assert frame.shape == (len(case['sample_ids']), 1)
+ counts += 1
+ elif 'de_table_file' in case:
+ rows = load_author_de_table_csv(DATA_DIR / case['de_table_file'])
+ assert rows[0]['gene'] == 'TP53'
+ tables += 1
+assert counts == 4 and tables == 2, (counts, tables)
+assert (DATA_DIR / 'geo_eval/plain.csv').read_text().startswith('gene,sample')
+""".replace("INSTALLED", repr(str(installed))),
+ ],
+ cwd=tmp_path,
+ env={
+ **os.environ,
+ "PYTHONPATH": os.pathsep.join(
+ [str(installed), str(ENV_DIR.parents[1] / "src")]
+ ),
+ },
+ capture_output=True,
+ text=True,
+ )
+ assert probe.returncode == 0, probe.stdout + probe.stderr
diff --git a/tests/envs/test_pathway_reference_download.py b/tests/envs/test_pathway_reference_download.py
new file mode 100644
index 0000000000..ed26dab8c7
--- /dev/null
+++ b/tests/envs/test_pathway_reference_download.py
@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: BSD-3-Clause
+"""Reference download failures use tiny local payloads; no network or genome data."""
+
+import gzip
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+from pathway_analysis_env.bench import download_hg38_gencode as download
+
+
+NAMES = (
+ "GRCh38.primary_assembly.genome.fa.gz",
+ "gencode.v47.annotation.gtf.gz",
+ "gencode.v47.transcripts.fa.gz",
+)
+VALID = gzip.compress(b">fixture\nACGT\n")
+BAD_CRC = VALID[:-8] + bytes([VALID[-8] ^ 1]) + VALID[-7:]
+
+
+@pytest.fixture
+def invoke(tmp_path, monkeypatch):
+ monkeypatch.setattr(sys, "argv", ["download", "--out-dir", str(tmp_path)])
+ return download.main
+
+
+@pytest.mark.parametrize(
+ "failure", ["http", "interrupted", "html", "truncated", "crc", "empty"]
+)
+def test_failed_download_is_not_published(tmp_path, monkeypatch, invoke, failure):
+ unrelated = tmp_path / "keep.txt"
+ unrelated.write_text("keep")
+ calls = []
+
+ def run(cmd):
+ calls.append(cmd)
+ output = Path(cmd[cmd.index("-o") + 1])
+ output.write_bytes(
+ {
+ "http": b"503",
+ "interrupted": VALID[:12],
+ "html": b"error",
+ "truncated": VALID[:-4],
+ "crc": BAD_CRC,
+ "empty": gzip.compress(b""),
+ }[failure]
+ )
+ if failure in ("http", "interrupted"):
+ raise subprocess.CalledProcessError(22 if failure == "http" else 18, cmd)
+
+ monkeypatch.setattr(download, "_run", run)
+ with pytest.raises((subprocess.CalledProcessError, OSError, EOFError, ValueError)):
+ invoke()
+ assert len(calls) == 1
+ assert list(tmp_path.iterdir()) == [unrelated]
+
+
+def test_success_is_validated_then_cached(tmp_path, monkeypatch, invoke):
+ calls = []
+
+ def run(cmd):
+ calls.append(cmd)
+ assert "--fail" in cmd
+ assert int(cmd[cmd.index("--retry") + 1]) > 0
+ output = Path(cmd[cmd.index("-o") + 1])
+ assert output.parent == tmp_path
+ assert output.name not in NAMES
+ assert not any((tmp_path / name).exists() for name in NAMES[len(calls) - 1 :])
+ output.write_bytes(VALID)
+
+ monkeypatch.setattr(download, "_run", run)
+ invoke()
+ invoke()
+ assert len(calls) == 3 # Valid references are reused without network traffic.
+ assert {p.name for p in tmp_path.iterdir()} == set(NAMES)
+ assert all((tmp_path / name).read_bytes() == VALID for name in NAMES)
+
+
+@pytest.mark.parametrize("payload", [b"404", VALID[:-4], BAD_CRC, b""])
+def test_corrupt_cache_fails_safely_without_downloading(
+ tmp_path, monkeypatch, invoke, payload
+):
+ cached = tmp_path / NAMES[0]
+ cached.write_bytes(payload)
+ unrelated = tmp_path / "unrelated.gz"
+ unrelated.write_bytes(b"preserve")
+
+ def unexpected(cmd):
+ pytest.fail(
+ "Corrupt cache requires explicit recovery, never a silent huge download"
+ )
+
+ monkeypatch.setattr(download, "_run", unexpected)
+ with pytest.raises(ValueError, match="cached reference"):
+ invoke()
+ assert cached.read_bytes() == payload
+ assert unrelated.read_bytes() == b"preserve"
diff --git a/tests/envs/test_pathway_review_regressions.py b/tests/envs/test_pathway_review_regressions.py
new file mode 100644
index 0000000000..f0d87444ae
--- /dev/null
+++ b/tests/envs/test_pathway_review_regressions.py
@@ -0,0 +1,268 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import pytest
+from pathway_analysis_env.models import PathwayAction
+from pathway_analysis_env.server import pathway_environment as pe
+
+
+@pytest.fixture
+def env(tmp_path, monkeypatch):
+ monkeypatch.setattr(pe, "OUTPUT_TRACE_DIR", tmp_path / "traces")
+ monkeypatch.setattr(pe, "pydeseq2_available", lambda: True)
+ return pe.PathwayEnvironment()
+
+
+@pytest.mark.parametrize("strict", [False, True])
+def test_author_table_without_pydeseq2(env, tmp_path, monkeypatch, strict):
+ table = tmp_path / "author.csv"
+ table.write_text("Gene,name;log2FoldChange;pvalue;padj\nTP53;2;0.001;0.01\n")
+ env._case["de_table_file"] = str(table)
+ env._state.strict_mode = strict
+ monkeypatch.setattr(pe, "pydeseq2_available", lambda: False)
+ obs = env.step(PathwayAction(action_type="run_differential_expression"))
+ assert obs.metadata.get("precomputed") is True
+ assert obs.de_genes[0]["gene"] == "TP53"
+ assert not obs.done
+
+
+@pytest.mark.parametrize(
+ "fields",
+ [
+ {"condition_a": "treated"},
+ {"condition_b": "control"},
+ {"condition_a": "control", "condition_b": "control"},
+ {"condition_a": "missing", "condition_b": "treated"},
+ ],
+)
+@pytest.mark.parametrize("strict", [False, True])
+def test_invalid_de_contrast_never_fits(env, monkeypatch, fields, strict):
+ def unexpected(*args, **kwargs):
+ pytest.fail("invalid contrast reached fitting")
+
+ monkeypatch.setattr(pe, "run_deseq2_contrast", unexpected)
+ env._state.strict_mode = strict
+ obs = env.step(PathwayAction(action_type="run_differential_expression", **fields))
+ assert obs.metadata["failure_code"]
+ assert obs.reward < 0
+ assert obs.done is strict
+ assert not env.state.de_run
+
+
+def test_contrast_pair_precedence(env, monkeypatch):
+ seen = []
+
+ def fit(counts, metadata, alt, ref, **kwargs):
+ seen.append((ref, alt))
+ return [], None
+
+ monkeypatch.setattr(pe, "run_deseq2_contrast", fit)
+ env.step(PathwayAction(action_type="run_differential_expression"))
+ env._state.validated_reference = "treated"
+ env._state.validated_alternate = "control"
+ env.step(PathwayAction(action_type="run_differential_expression"))
+ env.step(
+ PathwayAction(
+ action_type="run_differential_expression",
+ condition_a="control",
+ condition_b="treated",
+ )
+ )
+ assert seen == [
+ ("control", "treated"),
+ ("treated", "control"),
+ ("control", "treated"),
+ ]
+
+
+@pytest.mark.parametrize("strict", [False, True])
+def test_metadata_failure_code(env, strict):
+ env._state.strict_mode = strict
+ env._case["sample_metadata"].pop(env._case["sample_ids"][0])
+ obs = env.step(PathwayAction(action_type="run_differential_expression"))
+ assert obs.metadata["failure_code"] == "case_sample_metadata_mismatch"
+ assert obs.done is strict
+
+
+def test_compare_enrichr_support(env, monkeypatch):
+ env._case.pop("pathway_genes")
+ env._case["enrichr_libraries"] = ["mock_library"]
+ env._state.de_run = True
+ env._query_genes = ["A", "B", "C"]
+ env._de_rows = [
+ {"gene": g, "padj": 0.01, "log2FoldChange": 2} for g in env._query_genes
+ ]
+ monkeypatch.setattr(pe, "gseapy_available", lambda: True)
+ monkeypatch.setattr(
+ pe,
+ "enrichr_ora",
+ lambda *a, **k: (
+ [
+ {
+ "pathway": "P1",
+ "overlap_genes": ["A", "B"],
+ "p_value": 0.01,
+ "q_value": 0.02,
+ },
+ {
+ "pathway": "P2",
+ "overlap_genes": ["B", "C"],
+ "p_value": 0.02,
+ "q_value": 0.03,
+ },
+ ],
+ None,
+ ),
+ )
+ env.step(PathwayAction(action_type="run_pathway_enrichment"))
+ obs = env.step(
+ PathwayAction(action_type="compare_pathways", pathway_a="P1", pathway_b="P2")
+ )
+ d = obs.pathway_comparison
+ assert d["exclusive_to_a"] == ["A"]
+ assert d["exclusive_to_b"] == ["C"]
+ assert d["shared_de_support"] == ["B"]
+ assert d["pathway_a_size"] is None # hits do not give full pathway membership
+ assert d["comparison_scope"] == "enrichr_overlap_genes"
+
+ from types import SimpleNamespace
+
+ from pathway_analysis_env.server import gradio_ui
+
+ outputs = gradio_ui._response(
+ {"observation": obs.model_dump()},
+ SimpleNamespace(get_state=lambda: env.state.model_dump()),
+ "Compare complete.",
+ update_contrast=False,
+ )
+ table = outputs[3]
+ assert table["count"].tolist() == [1, 1, 1, "Unknown"]
+ assert table["genes (preview)"].tolist()[:3] == ["A", "C", "B"]
+ assert "Enrichr overlap genes only" in outputs[0]
+ assert "full pathway membership is unknown" in outputs[0]
+
+
+def test_compare_unknown_pathways_does_not_reward(env):
+ obs = env.step(
+ PathwayAction(
+ action_type="compare_pathways", pathway_a="absent", pathway_b="missing"
+ )
+ )
+ assert obs.reward < 0
+ assert obs.pathway_comparison is None
+
+
+@pytest.mark.parametrize("strict", [False, True])
+def test_missing_counts_file_is_failure(env, tmp_path, strict):
+ env._case["counts_file"] = str(tmp_path / "missing.csv.gz")
+ env._state.strict_mode = strict
+ obs = env.step(PathwayAction(action_type="run_differential_expression"))
+ assert obs.metadata["failure_code"] == "de_invalid_counts_matrix"
+ assert obs.done is strict
+ assert not env.state.de_run
+
+
+@pytest.mark.parametrize("strict", [False, True])
+def test_author_table_rejects_reversed_contrast(env, tmp_path, strict):
+ table = tmp_path / "author.csv"
+ table.write_text("Gene,name;log2FoldChange;pvalue;padj\nTP53;2;0.001;0.01\n")
+ env._case["de_table_file"] = str(table)
+ env._state.strict_mode = strict
+ obs = env.step(
+ PathwayAction(
+ action_type="run_differential_expression",
+ condition_a="treated",
+ condition_b="control",
+ )
+ )
+ assert obs.metadata["failure_code"] == "de_unsupported_precomputed_contrast"
+ assert obs.done is strict
+ assert not env.state.de_run
+ assert not obs.de_genes
+
+
+def test_author_table_caps_observation_but_keeps_analysis_rows(env, tmp_path):
+ table = tmp_path / "author.csv"
+ table.write_text(
+ "Gene,name;log2FoldChange;pvalue;padj\n"
+ + "".join(f"G{i};2;0.001;0.01\n" for i in range(251))
+ )
+ env._case["de_table_file"] = str(table)
+ obs = env.step(PathwayAction(action_type="run_differential_expression"))
+ assert len(obs.de_genes) == 200
+ assert len(env._de_rows) == 251
+ assert len(env._query_genes) == 200 # existing query selection cap
+
+
+@pytest.mark.parametrize("strict", [False, True])
+@pytest.mark.parametrize("failure", ["missing", "runtime", "definitions"])
+def test_enrichment_failure_codes(env, monkeypatch, strict, failure):
+ env._state.strict_mode = strict
+ env._case.pop("pathway_genes")
+ env._case["enrichr_libraries"] = [] if failure == "definitions" else ["mock"]
+ env._de_rows = [{"gene": "TP53", "padj": 0.01, "log2FoldChange": 2}]
+ monkeypatch.setattr(pe, "gseapy_available", lambda: failure != "missing")
+ monkeypatch.setattr(pe, "enrichr_ora", lambda *a, **k: ([], "service failed"))
+ obs = env.step(PathwayAction(action_type="run_pathway_enrichment"))
+ assert (
+ obs.metadata["failure_code"]
+ == {
+ "missing": "ora_gseapy_unavailable",
+ "runtime": "ora_enrichr_failed",
+ "definitions": "ora_no_pathway_definitions",
+ }[failure]
+ )
+ assert obs.done is strict
+ assert not env.state.enrichment_run
+
+
+@pytest.mark.parametrize("mode", ["validated_reverse", "missing_default"])
+def test_author_table_rejects_unsupported_implicit_contrast(env, mode):
+ env._case["de_table_file"] = "unused.csv"
+ env._state.validated_reference = "treated"
+ env._state.validated_alternate = "control"
+ if mode == "missing_default":
+ env._case.pop("default_contrast")
+ obs = env.step(PathwayAction(action_type="run_differential_expression"))
+ assert obs.metadata["failure_code"] == "de_unsupported_precomputed_contrast"
+ assert not env.state.de_run
+
+
+@pytest.mark.parametrize("strict", [False, True])
+@pytest.mark.parametrize("source", ["author", "counts"])
+@pytest.mark.parametrize("failure", ["missing", "unreadable", "invalid"])
+def test_de_source_failures_have_distinct_codes(
+ env, tmp_path, monkeypatch, strict, source, failure
+):
+ path = tmp_path / "input.csv"
+ if failure == "unreadable":
+ from pathway_analysis_env.server import analysis
+
+ path.write_text("unreadable fixture")
+
+ def denied(*args, **kwargs):
+ raise PermissionError(f"Cannot read {path}")
+
+ # Exercise the actual loaders with a deterministic read failure, even as root.
+ monkeypatch.setattr(analysis.pd, "read_csv", denied)
+ elif failure == "invalid":
+ path.write_text("wrong,columns\nfoo,bar\n")
+ env._case[f"{'de_table' if source == 'author' else 'counts'}_file"] = str(path)
+ env._state.strict_mode = strict
+
+ def unexpected(*args, **kwargs):
+ pytest.fail("failed source must not fall back to fitting counts")
+
+ monkeypatch.setattr(pe, "run_deseq2_contrast", unexpected)
+ obs = env.step(PathwayAction(action_type="run_differential_expression"))
+ expected = (
+ "de_invalid_author_table" if source == "author" else "de_invalid_counts_matrix"
+ )
+ assert obs.metadata["failure_code"] == expected
+ assert obs.reward < 0
+ assert obs.done is strict
+ assert obs.metadata.get("strict_failure", False) is strict
+ assert not env.state.de_run
+ assert not obs.de_genes
diff --git a/tests/envs/test_pathway_saved_runs.py b/tests/envs/test_pathway_saved_runs.py
new file mode 100644
index 0000000000..c2ce58ba95
--- /dev/null
+++ b/tests/envs/test_pathway_saved_runs.py
@@ -0,0 +1,71 @@
+"""Saved-run reads must remain inside the outputs directory."""
+
+import json
+
+import pytest
+from pathway_analysis_env.server import gradio_ui
+
+
+@pytest.fixture
+def outputs(tmp_path, monkeypatch):
+ root = tmp_path / "outputs"
+ root.mkdir()
+ monkeypatch.setattr(gradio_ui, "_OUTPUTS_DIR", root)
+ return root
+
+
+def write_run(path):
+ path.mkdir(exist_ok=True)
+ for name, value in (
+ ("summary.json", {"case_id": "safe"}),
+ ("de_top200.json", []),
+ ("enrichment_top50.json", []),
+ ):
+ (path / name).write_text(json.dumps(value))
+
+
+def test_load_saved_run(outputs):
+ write_run(outputs / "run1")
+ result = gradio_ui._load_saved_run("run1")
+ assert result == {
+ "run": "run1",
+ "summary": {"case_id": "safe"},
+ "de": [],
+ "enrichment": [],
+ }
+
+
+@pytest.mark.parametrize(
+ "kind",
+ [
+ "parent",
+ "absolute",
+ "directory_symlink",
+ "summary.json",
+ "de_top200.json",
+ "enrichment_top50.json",
+ ],
+)
+def test_saved_run_rejects_escape_before_reading(outputs, monkeypatch, kind):
+ outside = outputs.parent / "outside"
+ write_run(outside)
+ run_name = "run1"
+ if kind == "parent":
+ run_name = "../outside"
+ elif kind == "absolute":
+ run_name = str(outside)
+ elif kind == "directory_symlink":
+ (outputs / run_name).symlink_to(outside, target_is_directory=True)
+ else:
+ write_run(outputs / run_name)
+ (outputs / run_name / kind).unlink()
+ (outputs / run_name / kind).symlink_to(outside / kind)
+
+ def unexpected_read(*args, **kwargs):
+ pytest.fail("Rejected saved run must not read any files")
+
+ monkeypatch.setattr(type(outputs), "read_text", unexpected_read)
+ result = gradio_ui._load_saved_run(run_name)
+ assert result["summary_error"] == "Invalid run name"
+ assert result["summary"] == {}
+ assert result["de"] == result["enrichment"] == []
diff --git a/tests/envs/test_pathway_serving.py b/tests/envs/test_pathway_serving.py
new file mode 100644
index 0000000000..21d26c1829
--- /dev/null
+++ b/tests/envs/test_pathway_serving.py
@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: BSD-3-Clause
+"""Runtime coverage for the Pathway app's serving contract."""
+
+import importlib
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import yaml
+from fastapi.testclient import TestClient
+from pathway_analysis_env.models import PathwayState
+
+
+ENV_DIR = Path(__file__).resolve().parents[2] / "envs" / "pathway_analysis_env"
+
+
+def test_declared_app_imports_from_environment_root():
+ target = yaml.safe_load((ENV_DIR / "openenv.yaml").read_text())["app"]
+ result = subprocess.run(
+ [
+ sys.executable,
+ "-c",
+ "from uvicorn.importer import import_from_string; "
+ f"import_from_string({target!r})",
+ ],
+ cwd=ENV_DIR,
+ env={
+ **os.environ,
+ "ENABLE_WEB_INTERFACE": "false",
+ "PYTHONPATH": os.pathsep.join(
+ [str(ENV_DIR.parent), str(ENV_DIR.parents[1] / "src")]
+ ),
+ },
+ capture_output=True,
+ text=True,
+ )
+ assert result.returncode == 0, result.stderr
+
+
+def test_http_serves_pathway_state(monkeypatch, tmp_path):
+ monkeypatch.setenv("ENABLE_WEB_INTERFACE", "false")
+ environment = importlib.import_module(
+ "pathway_analysis_env.server.pathway_environment"
+ )
+ monkeypatch.setattr(environment, "OUTPUT_TRACE_DIR", tmp_path)
+ app_module = importlib.import_module("pathway_analysis_env.server.app")
+ with TestClient(app_module.app) as client:
+ schema = client.get("/schema")
+ state = client.get("/state")
+ assert schema.status_code == state.status_code == 200
+ assert schema.json()["state"] == PathwayState.model_json_schema()
+ assert set(state.json()) == set(PathwayState.model_fields)
+ assert state.json()["pipeline_mode"] is True
+ assert state.json()["de_run"] is False