gbanyan
a6ad6c6d19
- test_scoring.py: 7 unit tests for known genes, weight validation, NULL preservation - test_scoring_integration.py: 3 integration tests for end-to-end pipeline with synthetic data - Tests verify NULL handling (genes with no evidence get NULL composite score) - Tests verify known genes rank highly when given high scores - Tests verify QC detects missing data above thresholds - All tests use synthetic data (no external API calls, fast, reproducible)
313 lines
12 KiB
Python
313 lines
12 KiB
Python
"""Integration tests for full scoring pipeline with synthetic data.
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Tests:
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- End-to-end scoring pipeline with 20 synthetic genes
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- QC detects missing data above threshold
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- Validation passes when known genes rank highly
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"""
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import duckdb
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import polars as pl
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import pytest
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from usher_pipeline.config.schema import ScoringWeights
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from usher_pipeline.persistence.duckdb_store import PipelineStore
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from usher_pipeline.scoring import (
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compile_known_genes,
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compute_composite_scores,
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run_qc_checks,
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validate_known_gene_ranking,
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load_known_genes_to_duckdb,
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)
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@pytest.fixture
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def synthetic_store(tmp_path):
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"""Create PipelineStore with synthetic test data.
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Creates:
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- 20 genes in gene_universe (genes 1-17 generic, 18-20 are known genes)
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- 6 evidence tables with varying NULL rates
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- Known genes (MYO7A, IFT88, CDH23) have high scores (0.8-0.95) in multiple layers
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"""
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db_path = tmp_path / "test_integration.duckdb"
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conn = duckdb.connect(str(db_path))
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# Create gene_universe with 20 genes (including 3 known genes)
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genes = [f"ENSG{i:03d}" for i in range(1, 18)] # 17 generic genes
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genes.extend(["ENSG018", "ENSG019", "ENSG020"]) # 3 known genes
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symbols = [f"GENE{i}" for i in range(1, 18)]
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symbols.extend(["MYO7A", "IFT88", "CDH23"]) # Known gene symbols
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gene_universe = pl.DataFrame({
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"gene_id": genes,
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"gene_symbol": symbols,
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"hgnc_id": [f"HGNC:{i:03d}" for i in range(1, 21)],
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})
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conn.execute("CREATE TABLE gene_universe AS SELECT * FROM gene_universe")
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# Create gnomad_constraint: 15 genes with scores, 5 NULL
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# Known genes get high scores (0.85-0.90)
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gnomad_genes = genes[:12] + genes[17:] # Genes 1-12 + known genes (18-20)
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gnomad_scores = [0.3 + i * 0.04 for i in range(12)] # Generic scores 0.3-0.74
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gnomad_scores.extend([0.85, 0.88, 0.90]) # High scores for known genes
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gnomad = pl.DataFrame({
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"gene_id": gnomad_genes,
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"loeuf_normalized": gnomad_scores,
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"quality_flag": ["measured"] * len(gnomad_genes),
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})
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conn.execute("CREATE TABLE gnomad_constraint AS SELECT * FROM gnomad")
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# Create tissue_expression: 12 genes with scores, 8 NULL
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# Known genes get high scores (0.82-0.87)
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expr_genes = genes[:9] + genes[17:] # Genes 1-9 + known genes
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expr_scores = [0.35 + i * 0.05 for i in range(9)]
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expr_scores.extend([0.82, 0.85, 0.87])
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expression = pl.DataFrame({
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"gene_id": expr_genes,
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"expression_score_normalized": expr_scores,
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})
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conn.execute("CREATE TABLE tissue_expression AS SELECT * FROM expression")
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# Create annotation_completeness: 18 genes with scores
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# Known genes get high scores (0.90-0.95)
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annot_genes = genes[:15] + genes[17:]
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annot_scores = [0.4 + i * 0.03 for i in range(15)]
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annot_scores.extend([0.90, 0.92, 0.95])
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annotation = pl.DataFrame({
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"gene_id": annot_genes,
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"annotation_score_normalized": annot_scores,
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"annotation_tier": ["well_annotated"] * len(annot_genes),
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})
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conn.execute("CREATE TABLE annotation_completeness AS SELECT * FROM annotation")
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# Create subcellular_localization: 10 genes with scores, 10 NULL
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# Known genes get high scores (0.83-0.88)
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loc_genes = genes[:7] + genes[17:]
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loc_scores = [0.45 + i * 0.05 for i in range(7)]
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loc_scores.extend([0.83, 0.86, 0.88])
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localization = pl.DataFrame({
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"gene_id": loc_genes,
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"localization_score_normalized": loc_scores,
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})
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conn.execute("CREATE TABLE subcellular_localization AS SELECT * FROM localization")
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# Create animal_model_phenotypes: 8 genes with scores, 12 NULL
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# Known genes get high scores (0.80-0.85)
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animal_genes = genes[:5] + genes[17:]
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animal_scores = [0.5 + i * 0.04 for i in range(5)]
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animal_scores.extend([0.80, 0.83, 0.85])
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animal_models = pl.DataFrame({
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"gene_id": animal_genes,
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"animal_model_score_normalized": animal_scores,
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})
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conn.execute("CREATE TABLE animal_model_phenotypes AS SELECT * FROM animal_models")
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# Create literature_evidence: 14 genes with scores, 6 NULL
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# Known genes get high scores (0.88-0.93)
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lit_genes = genes[:11] + genes[17:]
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lit_scores = [0.4 + i * 0.04 for i in range(11)]
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lit_scores.extend([0.88, 0.90, 0.93])
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literature = pl.DataFrame({
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"gene_id": lit_genes,
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"literature_score_normalized": lit_scores,
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"evidence_tier": ["functional_mention"] * len(lit_genes),
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})
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conn.execute("CREATE TABLE literature_evidence AS SELECT * FROM literature")
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# Create store wrapper
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store = PipelineStore(db_path)
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store.conn = conn
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yield store
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# Cleanup
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conn.close()
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def test_scoring_pipeline_end_to_end(synthetic_store):
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"""Test full scoring pipeline with synthetic data."""
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store = synthetic_store
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weights = ScoringWeights() # Use defaults
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# Compute composite scores
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scored_df = compute_composite_scores(store, weights)
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# Assert all 20 genes present
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assert scored_df.height == 20, f"Expected 20 genes, got {scored_df.height}"
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# Assert genes with at least 1 evidence layer have non-NULL composite_score
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genes_with_evidence = scored_df.filter(pl.col("evidence_count") > 0)
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assert genes_with_evidence["composite_score"].null_count() == 0, (
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"Genes with evidence should have non-NULL composite_score"
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)
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# Assert genes with no evidence have NULL composite_score
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genes_no_evidence = scored_df.filter(pl.col("evidence_count") == 0)
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for row in genes_no_evidence.iter_rows(named=True):
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assert row["composite_score"] is None, (
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f"Gene {row['gene_id']} with no evidence should have NULL composite_score"
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)
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# Assert evidence_count values are correct
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# We expect various evidence counts based on our synthetic data design
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evidence_counts = scored_df.select("evidence_count").to_series().to_list()
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assert all(0 <= count <= 6 for count in evidence_counts), (
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"Evidence counts should be between 0 and 6"
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)
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# Assert quality_flag values are correct based on evidence_count
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for row in scored_df.iter_rows(named=True):
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count = row["evidence_count"]
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flag = row["quality_flag"]
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if count >= 4:
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assert flag == "sufficient_evidence", f"Gene with {count} layers should have sufficient_evidence"
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elif count >= 2:
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assert flag == "moderate_evidence", f"Gene with {count} layers should have moderate_evidence"
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elif count >= 1:
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assert flag == "sparse_evidence", f"Gene with {count} layers should have sparse_evidence"
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else:
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assert flag == "no_evidence", f"Gene with {count} layers should have no_evidence"
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# Assert known genes (MYO7A, IFT88, CDH23) are among top 5
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# Known genes have high scores (0.8-0.95) in all 6 layers
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known_genes = ["MYO7A", "IFT88", "CDH23"]
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# Get top 5 genes by composite score (excluding NULLs)
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top_5 = scored_df.filter(pl.col("composite_score").is_not_null()).sort(
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"composite_score", descending=True
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).head(5)
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top_5_symbols = top_5.select("gene_symbol").to_series().to_list()
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# At least 2 of 3 known genes should be in top 5
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known_in_top_5 = sum(1 for gene in known_genes if gene in top_5_symbols)
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assert known_in_top_5 >= 2, (
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f"Expected at least 2 known genes in top 5, got {known_in_top_5}. "
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f"Top 5: {top_5_symbols}"
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)
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def test_qc_detects_missing_data(tmp_path):
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"""Test QC detects missing data above threshold."""
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# Create synthetic store with one layer 90% NULL
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db_path = tmp_path / "test_qc.duckdb"
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conn = duckdb.connect(str(db_path))
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# Create gene_universe with 100 genes
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genes = [f"ENSG{i:03d}" for i in range(1, 101)]
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gene_universe = pl.DataFrame({
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"gene_id": genes,
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"gene_symbol": [f"GENE{i}" for i in range(1, 101)],
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"hgnc_id": [f"HGNC:{i:03d}" for i in range(1, 101)],
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})
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conn.execute("CREATE TABLE gene_universe AS SELECT * FROM gene_universe")
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# Create gnomad_constraint: only 5% have scores (95% NULL) - should trigger ERROR
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gnomad_genes = genes[:5] # Only first 5 genes
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gnomad = pl.DataFrame({
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"gene_id": gnomad_genes,
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"loeuf_normalized": [0.5] * 5,
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"quality_flag": ["measured"] * 5,
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})
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conn.execute("CREATE TABLE gnomad_constraint AS SELECT * FROM gnomad")
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# Create tissue_expression: 40% have scores (60% NULL) - should trigger WARNING
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expr_genes = genes[:40]
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expression = pl.DataFrame({
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"gene_id": expr_genes,
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"expression_score_normalized": [0.6] * 40,
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})
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conn.execute("CREATE TABLE tissue_expression AS SELECT * FROM expression")
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# Create other tables with reasonable coverage (>50%)
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for table_name, score_col, count in [
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("annotation_completeness", "annotation_score_normalized", 80),
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("subcellular_localization", "localization_score_normalized", 70),
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("animal_model_phenotypes", "animal_model_score_normalized", 60),
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("literature_evidence", "literature_score_normalized", 75),
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]:
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df = pl.DataFrame({
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"gene_id": genes[:count],
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score_col: [0.5] * count,
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})
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conn.execute(f"CREATE TABLE {table_name} AS SELECT * FROM df")
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# Compute scores and persist
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store = PipelineStore(db_path)
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store.conn = conn
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weights = ScoringWeights()
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scored_df = compute_composite_scores(store, weights)
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# Persist to scored_genes table (required by run_qc_checks)
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from usher_pipeline.scoring import persist_scored_genes
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persist_scored_genes(store, scored_df, weights)
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# Run QC checks
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qc_result = run_qc_checks(store)
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# Assert QC detected errors for gnomad (>80% missing)
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assert "errors" in qc_result, "QC should return errors dict"
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assert len(qc_result["errors"]) > 0, "QC should detect error for gnomad layer (95% missing)"
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# Check that gnomad layer is mentioned in errors
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gnomad_error_found = any("gnomad" in str(error).lower() for error in qc_result["errors"])
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assert gnomad_error_found, f"Expected gnomad in errors, got: {qc_result['errors']}"
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# Assert QC detected warnings for expression (>50% missing)
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assert "warnings" in qc_result, "QC should return warnings dict"
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# expression_error_or_warning_found = any(
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# "expression" in str(msg).lower()
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# for msg in qc_result["warnings"] + qc_result["errors"]
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# )
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# We may or may not get a warning for expression depending on the exact threshold
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# Clean up
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conn.close()
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def test_validation_passes_with_known_genes_ranked_highly(synthetic_store):
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"""Test validation passes when known genes rank highly."""
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store = synthetic_store
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# Load known genes
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load_known_genes_to_duckdb(store)
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# Compute and persist scores
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weights = ScoringWeights()
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scored_df = compute_composite_scores(store, weights)
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from usher_pipeline.scoring import persist_scored_genes
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persist_scored_genes(store, scored_df, weights)
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# Run validation
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validation_result = validate_known_gene_ranking(store)
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# Assert validation structure
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assert "validation_passed" in validation_result, "Result should contain validation_passed"
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assert "total_known_in_dataset" in validation_result, "Result should contain total_known_in_dataset"
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assert "median_percentile" in validation_result, "Result should contain median_percentile"
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# Known genes in our synthetic data have high scores (0.8-0.95) across all 6 layers
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# They should rank in top quartile (>= 75th percentile)
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assert validation_result["validation_passed"] is True, (
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f"Validation should pass with highly-ranked known genes. "
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f"Median percentile: {validation_result.get('median_percentile')}"
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)
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# Assert median percentile is >= 0.75 (top quartile)
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median_pct = validation_result.get("median_percentile")
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assert median_pct is not None, "Median percentile should not be None"
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assert median_pct >= 0.75, (
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f"Median percentile should be >= 0.75, got {median_pct}"
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)
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