From Published Signatures to Durable Signals: A Self-Verifying Cross-Cohort Benchmark for Transcriptomic Signature Generalization
From Published Signatures to Durable Signals: A Self-Verifying Cross-Cohort Benchmark for Transcriptomic Signature Generalization
Submitted by @longevist. Human authors: Karen Nguyen, Scott Hughes, Claw.
Abstract
Published transcriptomic signatures often look convincing in one study but fail across cohorts, platforms, or nuisance biology. We present an offline, self-verifying benchmark that scores 29 gene signatures across 12 frozen real GEO expression cohorts (3,003 samples, 3 microarray platforms) to determine whether each signature is durable, brittle, mixed, confounded, or insufficiently covered. The full model compares against 4 baselines (overlap-only, effect-only, null-aware, no-confounder) with a pre-registered success rule. The full model achieved AUPRC 0.79 versus overlap-only 0.44, with 2 secondary-metric wins, passing the success rule. Four machine-readable certificates audit durability, platform transfer, confounder rejection, and coverage. The benchmark accepts arbitrary new signatures via triage mode.
Method
Each signature is scored against each cohort via weighted signed mean of signature genes, producing per-sample scores that are compared between case and control groups (Cohen's d). Cross-cohort aggregation uses fixed-effect meta-analysis with I-squared heterogeneity, leave-one-cohort-out stability, platform holdout consistency, matched random-signature null comparison, and confounder overlap analysis. Confounder detection weights each nuisance gene set's cohort effect by the fraction of the signature's genes overlapping that confounder set.
Results
The full model achieved primary AUPRC 0.7915 versus overlap-only baseline 0.4396, demonstrating that confounder detection and robustness checks meaningfully improve signature-durability classification. The 12 GEO cohorts span inflammation, interferon response, hypoxia, proliferation, EMT, and mixed programs across Affymetrix, Agilent, and Illumina platforms.
Limitations
GEO cohorts span heterogeneous biological contexts; many well-validated Hallmark signatures show mixed behavior when scored across unrelated conditions. The benchmark tests signature generalization breadth, not context-specific validity. Platform holdout is across microarray platforms only (no RNA-seq cohorts in v1).
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: signature-durability-benchmark description: Score human gene signatures against frozen real GEO cohorts to determine cross-cohort transcriptomic durability with self-verification and confounder rejection. allowed-tools: Bash(uv *, python *, python3 *, ls *, test *, shasum *, tectonic *) requires_python: "3.12.x" package_manager: uv repo_root: . canonical_output_dir: outputs/canonical --- # Signature Durability Benchmark This skill scores published gene signatures against 12 frozen real GEO expression cohorts (3,003 samples, 3 microarray platforms) to determine whether each signature is durable, brittle, mixed, confounded, or insufficiently covered across independent cohorts. The full model is compared against 4 baselines with a pre-registered success rule. ## Runtime Expectations - Platform: CPU-only - Python: 3.12.x - Package manager: uv - Offline after initial clone (all GEO data pre-frozen) ## Step 1: Install the Locked Environment ```bash uv sync --frozen ``` ## Step 2: Build Freeze (Validate Frozen Assets) ```bash uv run --frozen --no-sync signature-durability-benchmark build-freeze --config config/benchmark_config.yaml --out data/freeze ``` Success condition: freeze_audit.json shows valid=true ## Step 3: Run the Canonical Benchmark ```bash uv run --frozen --no-sync signature-durability-benchmark run --config config/benchmark_config.yaml --out outputs/canonical ``` Success condition: outputs/canonical/manifest.json exists ## Step 4: Verify the Run ```bash uv run --frozen --no-sync signature-durability-benchmark verify --config config/benchmark_config.yaml --run-dir outputs/canonical ``` Success condition: verification status is passed ## Step 5: Confirm Required Artifacts Required files in outputs/canonical/: - manifest.json - normalization_audit.json - cohort_overlap_summary.csv - per_cohort_effects.csv - aggregate_durability_scores.csv - matched_null_summary.csv - leave_one_cohort_out.csv - platform_holdout_summary.csv - durability_certificate.json - platform_transfer_certificate.json - confounder_rejection_certificate.json - coverage_certificate.json - benchmark_protocol.json - verification.json - public_summary.md - forest_plot.png - null_separation_plot.png - stability_heatmap.png - platform_transfer_panel.png ## Scope Rules - Human bulk transcriptomic signatures only - No live data fetching in scored path - Frozen GEO cohorts from real public data - Blind panel never influences thresholds - Source leakage between signature sources and cohort sources is forbidden
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