Evaluate Enrichment

Evaluating enrichment against the curated benchmark

This scores an enrichment run's GO terms against external ground truth: the curator-validated CORE terms in curation/genesets/*.yaml. Unlike the evals/go_iba_impact_* analyses (which compare annotation variants to each other, no ground truth), this is a real precision/recall measurement.

Read first: evals/iba_vs_benchmark/README.md — the full pipeline, the current headline numbers, and the guardrail. This skill is the runbook.

Pipeline

All generated artifacts live under a scratch dir (e.g. /tmp/iba_eval/) and are NOT committed; rerun to regenerate.

1. Build queries.gmt — the member genes of every evaluable set.

   • MSigDB sets: fetched from mygeneset.info (_id = set name) → a queries.msigdb base. See scripts/fetch_mygeneset_query.py.
   • LIT: sets: curation/genesets/lit_members.gmt (captured from primary sources).
   • queries.gmt = queries.msigdb + lit_members.gmt. Each line: <gene_set_name>\t<desc>\tGENE1\tGENE2…. The first field must equal the YAML gene_set_name (the scorer joins on it).

2. Prepare annotation variants — downloads GO + goa_human.gaf.gz, writes a per-variant evidence-filtered gene_terms.tsv, shared terms.tsv/closure.tsv, a common background, and a genesets-rs config per variant (Bonferroni, max_p_adjust 0.05):

   python3 scripts/prepare_go_eval.py --out-dir /tmp/iba_eval \
     --variants all,iba,iba_iea,no_contributes_to
   

   Place queries.gmt (step 1) at /tmp/iba_eval/queries.gmt.

3. Run enrichment per variant → <variant>/results.tsv:

   for v in all iba iba_iea no_contributes_to; do
     genesets-rs run /tmp/iba_eval/$v/config.yaml
   done
   

4. Score vs the gold:

   uv run --project python/genesets-workflows --extra curation \
     python scripts/score_method_vs_benchmark.py --eval-dir /tmp/iba_eval \
     --out /tmp/iba_eval/benchmark_scores.tsv
   

Metrics (per variant)

• recall_core — recovered / total CORE terms (category in core_process/core_component). The headline; tool-independent of recovery_status.
• recall_confirm (n=…) / recall_mechan (n=…) — recall split by the insight tag. Mechanistic insight runs ~2× harder to recover than confirmatory; read recall_mechan alongside its (small) denominator.
• gap_recovered — count of annotation_gap CORE terms a variant recovered: a DISAGREEMENT between the curator's gap prediction and a tool run. A review item, NOT an automatic gold edit.

For per-set diagnostics (which terms a set recovered, by insight/recovery_status), load the gold via genesets_workflows.curation.model.load_interpretation and the hits from <variant>/results.tsv (query_id → target_id); see the inline scripts in evals/iba_vs_benchmark/README.md.

Adding new sets to the eval

When the curate-geneset skill adds sets, fold them in before re-scoring: append MSigDB memberships to queries.msigdb and LIT: memberships are already in lit_members.gmt; rebuild queries.gmt = queries.msigdb + lit_members.gmt; re-run steps 3–4. The GO+GOA download (step 2) can be reused.

The guardrail — the eval measures, it never refits the gold

category (biology) is the scored authority and stays fixed. recovery_status and insight are curator judgments checked against GOA facts during curation — never auto-updated to match whatever a method + GOA snapshot + p-threshold recovers. Refitting them would make recall-vs-gold circular (the tool couldn't be "wrong" because truth would be redefined to match it) and forfeit the gold's independence across methods and GOA versions. gap_recovered disagreements are inputs to deliberate curator review, not relabels. If you find yourself editing curation/genesets/*.yaml to raise a recall number, stop — that is the failure mode this guardrail exists to prevent.

Calibration note: even all-GOA recovers only ~56% of CORE terms under Bonferroni, so core + annotation_supported means "the genes carry it", not "it always reaches genome-wide significance".