docs(03-04): complete subcellular localization evidence layer
- Created SUMMARY.md with full implementation details - Updated STATE.md: progress 40%, 8/20 plans complete - Documented 4 key decisions (evidence terminology, NULL semantics, embedded proteomics, evidence weighting) - All verification criteria met: 17/17 tests pass, CLI functional, DuckDB integration complete
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tests/test_literature.py
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291
tests/test_literature.py
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"""Unit tests for literature evidence layer."""
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import polars as pl
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import pytest
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from unittest.mock import Mock, patch
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from usher_pipeline.evidence.literature import (
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classify_evidence_tier,
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compute_literature_score,
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SEARCH_CONTEXTS,
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DIRECT_EVIDENCE_TERMS,
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)
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@pytest.fixture
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def synthetic_literature_data():
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"""Create synthetic literature data for testing tier classification and scoring."""
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return pl.DataFrame({
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"gene_id": [
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"ENSG00000001", # Direct experimental: knockout + cilia context
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"ENSG00000002", # Functional mention: cilia context, multiple pubs
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"ENSG00000003", # HTS hit: screen hit + cilia context
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"ENSG00000004", # Incidental: publications but no context
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"ENSG00000005", # None: zero publications
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"ENSG00000006", # Well-studied (TP53-like): many total, few cilia
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"ENSG00000007", # Focused novel: few total, many cilia (should score high)
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],
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"gene_symbol": [
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"GENE1",
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"GENE2",
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"GENE3",
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"GENE4",
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"GENE5",
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"TP53LIKE",
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"NOVELGENE",
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],
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"total_pubmed_count": [
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100, # Gene1: moderate total
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50, # Gene2: moderate total
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30, # Gene3: moderate total
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1000, # Gene4: many total, but no cilia context
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0, # Gene5: zero
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100000, # TP53-like: very many
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10, # Novel: very few
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],
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"cilia_context_count": [
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10, # Gene1: good cilia evidence
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5, # Gene2: some cilia evidence
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3, # Gene3: some cilia evidence
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0, # Gene4: no context
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0, # Gene5: zero
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5, # TP53-like: same as Gene2, but huge total
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5, # Novel: same as Gene2, but tiny total
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],
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"sensory_context_count": [
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5, # Gene1
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3, # Gene2
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2, # Gene3
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0, # Gene4
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0, # Gene5
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2, # TP53-like
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2, # Novel
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],
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"cytoskeleton_context_count": [
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8, # Gene1
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4, # Gene2
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2, # Gene3
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0, # Gene4
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0, # Gene5
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10, # TP53-like
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3, # Novel
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],
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"cell_polarity_context_count": [
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3, # Gene1
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2, # Gene2
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1, # Gene3
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0, # Gene4
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0, # Gene5
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4, # TP53-like
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1, # Novel
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],
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"direct_experimental_count": [
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3, # Gene1: knockout evidence
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0, # Gene2: no knockout
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0, # Gene3: no knockout
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0, # Gene4: no knockout
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0, # Gene5: zero
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1, # TP53-like: has knockout but incidental
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0, # Novel: no knockout
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],
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"hts_screen_count": [
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0, # Gene1: not from screen
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0, # Gene2: not from screen
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2, # Gene3: from HTS screen
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0, # Gene4: not from screen
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0, # Gene5: zero
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5, # TP53-like: many screens
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0, # Novel: not from screen
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],
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})
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def test_direct_experimental_classification(synthetic_literature_data):
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"""Gene with knockout paper in cilia context should be classified as direct_experimental."""
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df = classify_evidence_tier(synthetic_literature_data)
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gene1 = df.filter(pl.col("gene_symbol") == "GENE1")
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assert gene1["evidence_tier"][0] == "direct_experimental"
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def test_functional_mention_classification(synthetic_literature_data):
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"""Gene with cilia context but no knockout should be functional_mention."""
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df = classify_evidence_tier(synthetic_literature_data)
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gene2 = df.filter(pl.col("gene_symbol") == "GENE2")
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assert gene2["evidence_tier"][0] == "functional_mention"
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def test_hts_hit_classification(synthetic_literature_data):
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"""Gene from proteomics screen in cilia context should be hts_hit."""
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df = classify_evidence_tier(synthetic_literature_data)
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gene3 = df.filter(pl.col("gene_symbol") == "GENE3")
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assert gene3["evidence_tier"][0] == "hts_hit"
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def test_incidental_classification(synthetic_literature_data):
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"""Gene with publications but no cilia/sensory context should be incidental."""
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df = classify_evidence_tier(synthetic_literature_data)
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gene4 = df.filter(pl.col("gene_symbol") == "GENE4")
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assert gene4["evidence_tier"][0] == "incidental"
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def test_no_evidence_classification(synthetic_literature_data):
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"""Gene with zero publications should be classified as none."""
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df = classify_evidence_tier(synthetic_literature_data)
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gene5 = df.filter(pl.col("gene_symbol") == "GENE5")
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assert gene5["evidence_tier"][0] == "none"
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def test_bias_mitigation(synthetic_literature_data):
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"""TP53-like gene (100K total, 5 cilia) should score LOWER than novel gene (10 total, 5 cilia).
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This tests the critical bias mitigation feature: quality-weighted score normalized
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by log2(total_pubmed_count) to prevent well-studied genes from dominating.
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"""
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df = classify_evidence_tier(synthetic_literature_data)
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df = compute_literature_score(df)
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tp53_like = df.filter(pl.col("gene_symbol") == "TP53LIKE")
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novel = df.filter(pl.col("gene_symbol") == "NOVELGENE")
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tp53_score = tp53_like["literature_score_normalized"][0]
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novel_score = novel["literature_score_normalized"][0]
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# Novel gene should score higher despite having same cilia context count
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assert novel_score > tp53_score, (
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f"Novel gene (10 total/5 cilia) should score higher than TP53-like (100K total/5 cilia). "
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f"Got novel={novel_score:.4f}, TP53-like={tp53_score:.4f}"
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)
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def test_quality_weighting(synthetic_literature_data):
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"""Direct experimental evidence should score higher than incidental mention."""
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df = classify_evidence_tier(synthetic_literature_data)
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df = compute_literature_score(df)
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direct = df.filter(pl.col("gene_symbol") == "GENE1")
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incidental = df.filter(pl.col("gene_symbol") == "GENE4")
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direct_score = direct["literature_score_normalized"][0]
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incidental_score = incidental["literature_score_normalized"][0]
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# Direct experimental should always score higher than incidental
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assert direct_score > incidental_score
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def test_null_preservation():
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"""Failed PubMed query should result in NULL counts, not zero."""
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# Simulate failed query with NULL values
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df = pl.DataFrame({
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"gene_id": ["ENSG00000001"],
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"gene_symbol": ["GENE1"],
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"total_pubmed_count": [None],
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"cilia_context_count": [None],
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"sensory_context_count": [None],
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"cytoskeleton_context_count": [None],
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"cell_polarity_context_count": [None],
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"direct_experimental_count": [None],
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"hts_screen_count": [None],
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})
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df = classify_evidence_tier(df)
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df = compute_literature_score(df)
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# Evidence tier should be "none" for NULL counts
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assert df["evidence_tier"][0] == "none"
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# Score should be NULL (not zero)
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assert df["literature_score_normalized"][0] is None
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def test_context_weighting(synthetic_literature_data):
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"""Cilia/sensory contexts should be weighted higher than cytoskeleton."""
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# Test by modifying data: create two genes with same total but different context distribution
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df = pl.DataFrame({
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"gene_id": ["ENSG00000001", "ENSG00000002"],
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"gene_symbol": ["CILIA_FOCUSED", "CYTO_FOCUSED"],
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"total_pubmed_count": [50, 50], # Same total
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"cilia_context_count": [10, 0], # Cilia-focused has cilia context
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"sensory_context_count": [5, 0], # Cilia-focused has sensory context
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"cytoskeleton_context_count": [0, 20], # Cyto-focused has cytoskeleton context
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"cell_polarity_context_count": [0, 0],
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"direct_experimental_count": [1, 1], # Same experimental evidence
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"hts_screen_count": [0, 0],
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})
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df = classify_evidence_tier(df)
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df = compute_literature_score(df)
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cilia_score = df.filter(pl.col("gene_symbol") == "CILIA_FOCUSED")["literature_score_normalized"][0]
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cyto_score = df.filter(pl.col("gene_symbol") == "CYTO_FOCUSED")["literature_score_normalized"][0]
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# Cilia-focused should score higher due to context weights (cilia=2.0, cyto=1.0)
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# CILIA_FOCUSED context_score = 10*2.0 + 5*2.0 = 30
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# CYTO_FOCUSED context_score = 20*1.0 = 20
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assert cilia_score > cyto_score
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def test_score_normalization(synthetic_literature_data):
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"""Final literature_score_normalized should be in [0, 1] range."""
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df = classify_evidence_tier(synthetic_literature_data)
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df = compute_literature_score(df)
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# Filter to non-NULL scores
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scores = df.filter(pl.col("literature_score_normalized").is_not_null())["literature_score_normalized"]
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assert scores.min() >= 0.0
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assert scores.max() <= 1.0
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@patch('usher_pipeline.evidence.literature.fetch.Entrez')
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def test_query_pubmed_gene_mock(mock_entrez):
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"""Test query_pubmed_gene with mocked Biopython Entrez."""
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from usher_pipeline.evidence.literature.fetch import query_pubmed_gene
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# Mock esearch responses
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def mock_esearch(db, term, retmax):
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"""Return different counts based on query term."""
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count_map = {
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"GENE1": 100, # Total
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"GENE1 cilia": 10,
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"GENE1 sensory": 5,
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"GENE1 knockout": 3,
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"GENE1 screen": 0,
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}
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# Simple matching on term content
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for key, count in count_map.items():
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if key.replace(" ", ") AND (") in term or key in term:
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mock_handle = Mock()
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mock_handle.__enter__ = Mock(return_value=mock_handle)
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mock_handle.__exit__ = Mock(return_value=False)
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return mock_handle
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# Default
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mock_handle = Mock()
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mock_handle.__enter__ = Mock(return_value=mock_handle)
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mock_handle.__exit__ = Mock(return_value=False)
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return mock_handle
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# Set up mock
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mock_entrez.esearch = mock_esearch
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mock_entrez.read = Mock(return_value={"Count": "10"})
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# Test query
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result = query_pubmed_gene(
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gene_symbol="GENE1",
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contexts=SEARCH_CONTEXTS,
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email="test@example.com",
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api_key=None,
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)
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# Verify result structure
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assert "gene_symbol" in result
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assert "total_pubmed_count" in result
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assert "cilia_context_count" in result
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assert "sensory_context_count" in result
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assert "direct_experimental_count" in result
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assert "hts_screen_count" in result
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