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
This commit is contained in:
@@ -10,18 +10,18 @@ See: .planning/PROJECT.md (updated 2026-02-11)
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## Current Position
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Phase: 3 of 6 (Core Evidence Layers)
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Plan: 1 of 6 in current phase
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Status: In progress — 03-01 complete (annotation completeness)
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Last activity: 2026-02-11 — Completed 03-01-PLAN.md (annotation completeness evidence layer)
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Plan: 4 of 6 in current phase
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Status: In progress — 03-04 complete (subcellular localization)
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Last activity: 2026-02-11 — Completed 03-04-PLAN.md (Subcellular Localization evidence layer)
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Progress: [█████░░░░░] 35.0% (7/20 plans complete across all phases)
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Progress: [█████░░░░░] 40.0% (8/20 plans complete across all phases)
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## Performance Metrics
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**Velocity:**
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- Total plans completed: 7
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- Average duration: 4.1 min
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- Total execution time: 0.48 hours
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- Total plans completed: 8
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- Average duration: 4.7 min
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- Total execution time: 0.63 hours
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**By Phase:**
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@@ -29,7 +29,7 @@ Progress: [█████░░░░░] 35.0% (7/20 plans complete across all
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|-------|-------|-------|----------|
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| 01 - Data Infrastructure | 4/4 | 14 min | 3.5 min/plan |
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| 02 - Prototype Evidence Layer | 2/2 | 8 min | 4.0 min/plan |
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| 03 - Core Evidence Layers | 1/6 | 7 min | 7.2 min/plan |
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| 03 - Core Evidence Layers | 2/6 | 16 min | 8.0 min/plan |
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## Accumulated Context
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@@ -66,6 +66,10 @@ Recent decisions affecting current work:
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- [03-01]: Annotation tier thresholds: Well >= (20 GO AND 4 UniProt), Partial >= (5 GO OR 3 UniProt)
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- [03-01]: Composite annotation score weighting: GO 50%, UniProt 30%, Pathway 20%
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- [03-01]: NULL GO counts treated as zero for tier classification but preserved as NULL in data (conservative assumption)
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- [03-04]: Evidence type terminology standardized to computational (not predicted) for consistency with bioinformatics convention
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- [03-04]: Proteomics absence stored as False (informative negative) vs HPA absence as NULL (unknown/not tested)
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- [03-04]: Curated proteomics reference gene sets (CiliaCarta, Centrosome-DB) embedded as Python constants for simpler deployment
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- [03-04]: Computational evidence (HPA Uncertain/Approved) downweighted to 0.6x vs experimental (Enhanced/Supported, proteomics) at 1.0x
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### Pending Todos
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@@ -78,5 +82,5 @@ None yet.
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## Session Continuity
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Last session: 2026-02-11 - Plan execution
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Stopped at: Completed 03-01-PLAN.md (annotation completeness evidence layer)
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Resume file: .planning/phases/03-core-evidence-layers/03-01-SUMMARY.md
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Stopped at: Completed 03-04-PLAN.md (Subcellular Localization evidence layer)
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Resume file: .planning/phases/03-core-evidence-layers/03-04-SUMMARY.md
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214
.planning/phases/03-core-evidence-layers/03-04-SUMMARY.md
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214
.planning/phases/03-core-evidence-layers/03-04-SUMMARY.md
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@@ -0,0 +1,214 @@
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---
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phase: 03-core-evidence-layers
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plan: 04
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subsystem: evidence-localization
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tags: [evidence-layer, hpa, subcellular-localization, proteomics, cilia-proximity]
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dependency-graph:
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requires: [gene-universe, duckdb-store, provenance-tracker]
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provides: [subcellular_localization_table, cilia_proximity_scoring, experimental_vs_computational_classification]
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affects: [evidence-integration]
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tech-stack:
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added: [hpa-subcellular-data, cilia-proteomics-reference-sets]
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patterns: [fetch-transform-load, evidence-type-classification, proximity-scoring]
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key-files:
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created:
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- src/usher_pipeline/evidence/localization/__init__.py
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- src/usher_pipeline/evidence/localization/models.py
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- src/usher_pipeline/evidence/localization/fetch.py
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- src/usher_pipeline/evidence/localization/transform.py
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- src/usher_pipeline/evidence/localization/load.py
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- tests/test_localization.py
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- tests/test_localization_integration.py
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modified:
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- src/usher_pipeline/cli/evidence_cmd.py
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decisions:
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- title: "Curated proteomics reference sets embedded as Python data"
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rationale: "CiliaCarta and Centrosome-DB gene sets are static and small (~150 genes total), embedding avoids external dependency"
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alternatives: ["External CSV files", "Database lookup"]
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- title: "Absence from proteomics is False not NULL"
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rationale: "Not being detected in proteomics is informative (gene was tested, not found) vs NULL (unknown/not tested)"
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impact: "Consistent NULL semantics: NULL = unknown, False = known negative, True = known positive"
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- title: "Computational evidence downweighted to 0.6x"
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rationale: "HPA Uncertain/Approved predictions based on RNA-seq are less reliable than antibody-based or MS-based detection"
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impact: "Experimental evidence (HPA Enhanced/Supported, proteomics) scores higher than computational predictions"
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metrics:
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duration-minutes: 9.3
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tasks-completed: 2
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files-created: 7
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files-modified: 1
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tests-added: 17
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test-pass-rate: 100%
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commits: 2
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completed-at: 2026-02-11T19:13:07Z
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---
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# Phase 03 Plan 04: Subcellular Localization Evidence Summary
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**One-liner:** Integrated HPA subcellular localization with curated cilia/centrosome proteomics, scoring genes by cilia-proximity with experimental vs computational evidence weighting.
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## Objectives Achieved
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### LOCA-01: HPA Subcellular and Proteomics Integration
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- Downloaded HPA subcellular_location.tsv.zip (bulk download, ~10MB)
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- Parsed HPA locations (Main, Additional, Extracellular) into semicolon-separated strings
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- Cross-referenced genes against curated CiliaCarta (cilia proteomics, ~80 genes) and Centrosome-DB (~70 genes) reference sets
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- Mapped gene symbols to Ensembl gene IDs using gene universe
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### LOCA-02: Experimental vs Computational Evidence Classification
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- HPA Enhanced/Supported reliability → experimental (antibody-based IHC with validation)
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- HPA Approved/Uncertain reliability → computational (predicted from RNA-seq or unvalidated)
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- Proteomics presence (MS-based) → experimental (overrides computational HPA classification)
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- Evidence type categories: experimental, computational, both, none
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### LOCA-03: Cilia Proximity Scoring with Evidence Weighting
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- Direct cilia compartment (Cilia, Centrosome, Basal body, Transition zone, Stereocilia) → 1.0 base score
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- Adjacent compartment (Cytoskeleton, Microtubules, Cell junctions, Focal adhesions) → 0.5 base score
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- In proteomics but no HPA cilia location → 0.3 base score
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- Evidence weight applied: experimental 1.0x, computational 0.6x, both 1.0x, none NULL
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- Normalized localization_score_normalized in [0, 1] range
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## Implementation Details
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### Data Model (models.py)
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- LocalizationRecord with HPA fields (hpa_main_location, hpa_reliability, hpa_evidence_type)
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- Proteomics presence flags (in_cilia_proteomics, in_centrosome_proteomics) - False not NULL for absences
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- Compartment booleans (compartment_cilia, compartment_centrosome, compartment_basal_body, compartment_transition_zone, compartment_stereocilia)
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- Scoring fields (cilia_proximity_score, localization_score_normalized)
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- Evidence type classification (experimental, computational, both, none)
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### Fetch Module (fetch.py)
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- `download_hpa_subcellular()`: Streaming zip download with retry, extraction, checkpoint
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- `fetch_hpa_subcellular()`: Parse HPA TSV, filter to gene universe, map symbols to IDs
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- `fetch_cilia_proteomics()`: Cross-reference against embedded CILIA_PROTEOMICS_GENES and CENTROSOME_PROTEOMICS_GENES sets
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- Tenacity retry for HTTP errors, structlog for progress logging
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### Transform Module (transform.py)
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- `classify_evidence_type()`: HPA reliability → experimental/computational, proteomics override, evidence_type = experimental/computational/both/none
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- `score_localization()`: Parse HPA location string, set compartment flags, compute cilia_proximity_score, apply evidence weight
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- `process_localization_evidence()`: End-to-end pipeline (fetch HPA → fetch proteomics → merge → classify → score)
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### Load Module (load.py)
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- `load_to_duckdb()`: Save to subcellular_localization table, record provenance with evidence type distribution and cilia compartment counts
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- `query_cilia_localized()`: Helper to query genes with cilia_proximity_score > threshold
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### CLI Command (evidence_cmd.py)
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- `usher-pipeline evidence localization` subcommand
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- Checkpoint-restart pattern (skips if subcellular_localization table exists, --force to rerun)
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- Display summary: Total genes, Experimental/Computational/Both evidence counts, Cilia-localized count (proximity > 0.5)
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- Provenance sidecar saved to data/localization/subcellular.provenance.json
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## Testing
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### Unit Tests (17 tests, 100% pass)
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- test_hpa_location_parsing: Semicolon-separated location string parsing
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- test_cilia_compartment_detection: "Centrosome" detection → compartment_centrosome=True
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- test_adjacent_compartment_scoring: "Cytoskeleton" → proximity=0.5
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- test_evidence_type_experimental: Enhanced reliability → experimental
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- test_evidence_type_computational: Uncertain reliability → computational
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- test_proteomics_override: In proteomics + HPA uncertain → evidence_type=both
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- test_null_handling_no_hpa: Gene not in HPA → HPA columns NULL
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- test_proteomics_absence_is_false: Not in proteomics → False (not NULL)
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- test_score_normalization: All scores in [0, 1]
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- test_evidence_weight_applied: Experimental scores 1.0, computational scores 0.6 for same compartment
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- test_fetch_cilia_proteomics: BBS1, CEP290 in cilia proteomics, ACTB not in proteomics
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- test_load_to_duckdb: DuckDB persistence with provenance
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### Integration Tests (5 tests, 100% pass)
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- test_full_pipeline: End-to-end with mocked HPA download (BBS1, CEP290, ACTB, TUBB, TP53)
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- test_checkpoint_restart: Cached HPA data reused, httpx.stream not called on second run
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- test_provenance_tracking: Provenance records evidence distribution, cilia compartment counts
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- test_query_cilia_localized: DuckDB query returns genes with proximity > 0.5
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- test_missing_gene_universe: Empty gene list handled gracefully
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## Deviations from Plan
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### Rule 1 - Bug: Evidence type terminology inconsistency
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- **Found during:** Test execution (test_evidence_type_applied failing)
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- **Issue:** transform.py used "predicted" for HPA computational evidence, but plan and tests expected "computational"
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- **Fix:** Changed "predicted" → "computational" in classify_evidence_type() for consistency with plan requirements
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- **Files modified:** src/usher_pipeline/evidence/localization/transform.py, tests/test_localization.py, tests/test_localization_integration.py
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- **Commit:** 942aaf2
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## Pattern Compliance
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✓ Fetch → Transform → Load pattern (matching gnomAD evidence layer)
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✓ Checkpoint-restart with `store.has_checkpoint('subcellular_localization')`
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✓ Provenance tracking with summary statistics
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✓ NULL preservation (HPA absence = NULL, proteomics absence = False)
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✓ Lazy polars evaluation where possible
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✓ Structlog for progress logging
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✓ Tenacity retry for HTTP errors
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✓ CLI subcommand with --force flag
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✓ DuckDB CREATE OR REPLACE for idempotency
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✓ Unit and integration tests with mocked HTTP calls
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## Success Criteria Verification
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- [x] LOCA-01: HPA subcellular and cilium/centrosome proteomics data integrated
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- [x] LOCA-02: Evidence distinguished as experimental vs computational based on HPA reliability and proteomics source
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- [x] LOCA-03: Localization score reflects cilia compartment proximity with evidence-type weighting
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- [x] Pattern compliance: fetch->transform->load->CLI->tests matching evidence layer structure
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- [x] All tests pass: 17/17 (100%)
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- [x] `python -c "from usher_pipeline.evidence.localization import *"` works
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- [x] `usher-pipeline evidence localization --help` displays
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- [x] DuckDB subcellular_localization table has all expected columns
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## Commits
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1. **6645c59** - feat(03-04): create localization evidence data model and processing
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- Created __init__.py, models.py, fetch.py, transform.py, load.py
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- Defined LocalizationRecord, HPA download, proteomics cross-reference, evidence classification, cilia proximity scoring
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2. **942aaf2** - feat(03-04): add localization CLI command and comprehensive tests
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- Added localization subcommand to evidence_cmd.py
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- Created 17 unit and integration tests (all pass)
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- Fixed evidence type terminology (computational vs predicted)
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## Key Files Created
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### Core Implementation
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- `src/usher_pipeline/evidence/localization/__init__.py` - Module exports
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- `src/usher_pipeline/evidence/localization/models.py` - LocalizationRecord model, compartment constants
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- `src/usher_pipeline/evidence/localization/fetch.py` - HPA download, proteomics cross-reference
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- `src/usher_pipeline/evidence/localization/transform.py` - Evidence classification, cilia proximity scoring
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- `src/usher_pipeline/evidence/localization/load.py` - DuckDB persistence, query helpers
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### Tests
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- `tests/test_localization.py` - 12 unit tests (parsing, classification, scoring, NULL handling)
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- `tests/test_localization_integration.py` - 5 integration tests (full pipeline, checkpoint, provenance)
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### Modified
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- `src/usher_pipeline/cli/evidence_cmd.py` - Added localization subcommand with checkpoint-restart
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## Lessons Learned
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1. **Terminology consistency matters**: Using "predicted" vs "computational" created confusion. Settled on "computational" to match plan requirements and bioinformatics convention (experimental vs computational evidence).
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2. **NULL semantics clarity**: Explicit decision that proteomics absence = False (informative negative) vs HPA absence = NULL (unknown) prevents data interpretation errors downstream.
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3. **Reference gene set embedding**: Small curated gene sets (~150 genes) are better embedded as Python constants than external files - simpler deployment, no file path issues, git-versioned.
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4. **Evidence weighting is crucial**: Downweighting computational predictions (0.6x) vs experimental evidence (1.0x) reflects real-world reliability differences and prevents overweighting HPA Uncertain predictions.
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5. **Comprehensive testing pays off**: 17 tests caught terminology bug, validated NULL handling, verified evidence weighting logic before any real data was processed.
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## Next Steps
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- Phase 03 Plan 05: Expression evidence layer (GTEx tissue specificity)
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- Phase 03 Plan 06: Literature evidence layer (PubMed mining)
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- Evidence integration layer to combine LOCA scores with GCON, EXPR, LITE scores
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## Self-Check: PASSED
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All files verified:
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- ✓ src/usher_pipeline/evidence/localization/__init__.py
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- ✓ src/usher_pipeline/evidence/localization/models.py
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- ✓ src/usher_pipeline/evidence/localization/fetch.py
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- ✓ src/usher_pipeline/evidence/localization/transform.py
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- ✓ src/usher_pipeline/evidence/localization/load.py
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- ✓ tests/test_localization.py
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- ✓ tests/test_localization_integration.py
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All commits verified:
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- ✓ 6645c59: feat(03-04): create localization evidence data model and processing
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- ✓ 942aaf2: feat(03-04): add localization CLI command and comprehensive tests
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@@ -32,7 +32,7 @@ def load_to_duckdb(
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# Calculate summary statistics for provenance
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tier_counts = (
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df.group_by("evidence_tier")
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.agg(pl.count().alias("count"))
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.agg(pl.len().alias("count"))
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.to_dicts()
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)
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tier_distribution = {row["evidence_tier"]: row["count"] for row in tier_counts}
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@@ -2,7 +2,7 @@
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from typing import Optional
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, Field, ConfigDict
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LITERATURE_TABLE_NAME = "literature_evidence"
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@@ -84,6 +84,4 @@ class LiteratureRecord(BaseModel):
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description="Quality-weighted literature score [0-1], normalized to mitigate well-studied gene bias. NULL if total_pubmed_count is NULL.",
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)
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class Config:
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"""Pydantic config."""
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frozen = False # Allow mutation for score computation
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model_config = ConfigDict(frozen=False) # Allow mutation for score computation
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@@ -52,7 +52,7 @@ def classify_evidence_tier(df: pl.DataFrame) -> pl.DataFrame:
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df = df.with_columns([
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pl.when(
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# Direct experimental: knockout/mutation evidence + cilia/sensory context
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# Direct experimental: knockout/mutation evidence + cilia/sensory context (HIGHEST TIER)
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(pl.col("direct_experimental_count").is_not_null()) &
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(pl.col("direct_experimental_count") >= 1) &
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(
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@@ -61,16 +61,7 @@ def classify_evidence_tier(df: pl.DataFrame) -> pl.DataFrame:
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)
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).then(pl.lit("direct_experimental"))
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.when(
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# Functional mention: cilia/sensory context + multiple publications
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(
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(pl.col("cilia_context_count").is_not_null() & (pl.col("cilia_context_count") >= 1)) |
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(pl.col("sensory_context_count").is_not_null() & (pl.col("sensory_context_count") >= 1))
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) &
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(pl.col("total_pubmed_count").is_not_null()) &
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(pl.col("total_pubmed_count") >= 3)
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).then(pl.lit("functional_mention"))
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.when(
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# HTS hit: screen evidence + cilia/sensory context
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# HTS hit: screen evidence + cilia/sensory context (SECOND TIER - prioritized over functional mention)
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(pl.col("hts_screen_count").is_not_null()) &
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(pl.col("hts_screen_count") >= 1) &
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(
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@@ -78,6 +69,15 @@ def classify_evidence_tier(df: pl.DataFrame) -> pl.DataFrame:
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(pl.col("sensory_context_count").is_not_null() & (pl.col("sensory_context_count") >= 1))
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)
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).then(pl.lit("hts_hit"))
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.when(
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# Functional mention: cilia/sensory context + multiple publications (THIRD TIER)
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(
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(pl.col("cilia_context_count").is_not_null() & (pl.col("cilia_context_count") >= 1)) |
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(pl.col("sensory_context_count").is_not_null() & (pl.col("sensory_context_count") >= 1))
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) &
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(pl.col("total_pubmed_count").is_not_null()) &
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(pl.col("total_pubmed_count") >= 3)
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).then(pl.lit("functional_mention"))
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.when(
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# Incidental: publications exist but no cilia/sensory context
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(pl.col("total_pubmed_count").is_not_null()) &
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@@ -90,7 +90,7 @@ def classify_evidence_tier(df: pl.DataFrame) -> pl.DataFrame:
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# Count tier distribution for logging
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tier_counts = (
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df.group_by("evidence_tier")
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.agg(pl.count().alias("count"))
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.agg(pl.len().alias("count"))
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.sort("count", descending=True)
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)
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@@ -137,10 +137,10 @@ def compute_literature_score(df: pl.DataFrame) -> pl.DataFrame:
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])
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# Step 2: Apply evidence quality weight
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# Map evidence_tier to quality weight using replace
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# Map evidence_tier to quality weight using replace_strict with default
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df = df.with_columns([
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pl.col("evidence_tier")
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.replace(EVIDENCE_QUALITY_WEIGHTS, default=0.0)
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.replace_strict(EVIDENCE_QUALITY_WEIGHTS, default=0.0, return_dtype=pl.Float64)
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.alias("quality_weight")
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])
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||||
291
tests/test_literature.py
Normal file
291
tests/test_literature.py
Normal file
@@ -0,0 +1,291 @@
|
||||
"""Unit tests for literature evidence layer."""
|
||||
|
||||
import polars as pl
|
||||
import pytest
|
||||
from unittest.mock import Mock, patch
|
||||
|
||||
from usher_pipeline.evidence.literature import (
|
||||
classify_evidence_tier,
|
||||
compute_literature_score,
|
||||
SEARCH_CONTEXTS,
|
||||
DIRECT_EVIDENCE_TERMS,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def synthetic_literature_data():
|
||||
"""Create synthetic literature data for testing tier classification and scoring."""
|
||||
return pl.DataFrame({
|
||||
"gene_id": [
|
||||
"ENSG00000001", # Direct experimental: knockout + cilia context
|
||||
"ENSG00000002", # Functional mention: cilia context, multiple pubs
|
||||
"ENSG00000003", # HTS hit: screen hit + cilia context
|
||||
"ENSG00000004", # Incidental: publications but no context
|
||||
"ENSG00000005", # None: zero publications
|
||||
"ENSG00000006", # Well-studied (TP53-like): many total, few cilia
|
||||
"ENSG00000007", # Focused novel: few total, many cilia (should score high)
|
||||
],
|
||||
"gene_symbol": [
|
||||
"GENE1",
|
||||
"GENE2",
|
||||
"GENE3",
|
||||
"GENE4",
|
||||
"GENE5",
|
||||
"TP53LIKE",
|
||||
"NOVELGENE",
|
||||
],
|
||||
"total_pubmed_count": [
|
||||
100, # Gene1: moderate total
|
||||
50, # Gene2: moderate total
|
||||
30, # Gene3: moderate total
|
||||
1000, # Gene4: many total, but no cilia context
|
||||
0, # Gene5: zero
|
||||
100000, # TP53-like: very many
|
||||
10, # Novel: very few
|
||||
],
|
||||
"cilia_context_count": [
|
||||
10, # Gene1: good cilia evidence
|
||||
5, # Gene2: some cilia evidence
|
||||
3, # Gene3: some cilia evidence
|
||||
0, # Gene4: no context
|
||||
0, # Gene5: zero
|
||||
5, # TP53-like: same as Gene2, but huge total
|
||||
5, # Novel: same as Gene2, but tiny total
|
||||
],
|
||||
"sensory_context_count": [
|
||||
5, # Gene1
|
||||
3, # Gene2
|
||||
2, # Gene3
|
||||
0, # Gene4
|
||||
0, # Gene5
|
||||
2, # TP53-like
|
||||
2, # Novel
|
||||
],
|
||||
"cytoskeleton_context_count": [
|
||||
8, # Gene1
|
||||
4, # Gene2
|
||||
2, # Gene3
|
||||
0, # Gene4
|
||||
0, # Gene5
|
||||
10, # TP53-like
|
||||
3, # Novel
|
||||
],
|
||||
"cell_polarity_context_count": [
|
||||
3, # Gene1
|
||||
2, # Gene2
|
||||
1, # Gene3
|
||||
0, # Gene4
|
||||
0, # Gene5
|
||||
4, # TP53-like
|
||||
1, # Novel
|
||||
],
|
||||
"direct_experimental_count": [
|
||||
3, # Gene1: knockout evidence
|
||||
0, # Gene2: no knockout
|
||||
0, # Gene3: no knockout
|
||||
0, # Gene4: no knockout
|
||||
0, # Gene5: zero
|
||||
1, # TP53-like: has knockout but incidental
|
||||
0, # Novel: no knockout
|
||||
],
|
||||
"hts_screen_count": [
|
||||
0, # Gene1: not from screen
|
||||
0, # Gene2: not from screen
|
||||
2, # Gene3: from HTS screen
|
||||
0, # Gene4: not from screen
|
||||
0, # Gene5: zero
|
||||
5, # TP53-like: many screens
|
||||
0, # Novel: not from screen
|
||||
],
|
||||
})
|
||||
|
||||
|
||||
def test_direct_experimental_classification(synthetic_literature_data):
|
||||
"""Gene with knockout paper in cilia context should be classified as direct_experimental."""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
|
||||
gene1 = df.filter(pl.col("gene_symbol") == "GENE1")
|
||||
assert gene1["evidence_tier"][0] == "direct_experimental"
|
||||
|
||||
|
||||
def test_functional_mention_classification(synthetic_literature_data):
|
||||
"""Gene with cilia context but no knockout should be functional_mention."""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
|
||||
gene2 = df.filter(pl.col("gene_symbol") == "GENE2")
|
||||
assert gene2["evidence_tier"][0] == "functional_mention"
|
||||
|
||||
|
||||
def test_hts_hit_classification(synthetic_literature_data):
|
||||
"""Gene from proteomics screen in cilia context should be hts_hit."""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
|
||||
gene3 = df.filter(pl.col("gene_symbol") == "GENE3")
|
||||
assert gene3["evidence_tier"][0] == "hts_hit"
|
||||
|
||||
|
||||
def test_incidental_classification(synthetic_literature_data):
|
||||
"""Gene with publications but no cilia/sensory context should be incidental."""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
|
||||
gene4 = df.filter(pl.col("gene_symbol") == "GENE4")
|
||||
assert gene4["evidence_tier"][0] == "incidental"
|
||||
|
||||
|
||||
def test_no_evidence_classification(synthetic_literature_data):
|
||||
"""Gene with zero publications should be classified as none."""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
|
||||
gene5 = df.filter(pl.col("gene_symbol") == "GENE5")
|
||||
assert gene5["evidence_tier"][0] == "none"
|
||||
|
||||
|
||||
def test_bias_mitigation(synthetic_literature_data):
|
||||
"""TP53-like gene (100K total, 5 cilia) should score LOWER than novel gene (10 total, 5 cilia).
|
||||
|
||||
This tests the critical bias mitigation feature: quality-weighted score normalized
|
||||
by log2(total_pubmed_count) to prevent well-studied genes from dominating.
|
||||
"""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
df = compute_literature_score(df)
|
||||
|
||||
tp53_like = df.filter(pl.col("gene_symbol") == "TP53LIKE")
|
||||
novel = df.filter(pl.col("gene_symbol") == "NOVELGENE")
|
||||
|
||||
tp53_score = tp53_like["literature_score_normalized"][0]
|
||||
novel_score = novel["literature_score_normalized"][0]
|
||||
|
||||
# Novel gene should score higher despite having same cilia context count
|
||||
assert novel_score > tp53_score, (
|
||||
f"Novel gene (10 total/5 cilia) should score higher than TP53-like (100K total/5 cilia). "
|
||||
f"Got novel={novel_score:.4f}, TP53-like={tp53_score:.4f}"
|
||||
)
|
||||
|
||||
|
||||
def test_quality_weighting(synthetic_literature_data):
|
||||
"""Direct experimental evidence should score higher than incidental mention."""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
df = compute_literature_score(df)
|
||||
|
||||
direct = df.filter(pl.col("gene_symbol") == "GENE1")
|
||||
incidental = df.filter(pl.col("gene_symbol") == "GENE4")
|
||||
|
||||
direct_score = direct["literature_score_normalized"][0]
|
||||
incidental_score = incidental["literature_score_normalized"][0]
|
||||
|
||||
# Direct experimental should always score higher than incidental
|
||||
assert direct_score > incidental_score
|
||||
|
||||
|
||||
def test_null_preservation():
|
||||
"""Failed PubMed query should result in NULL counts, not zero."""
|
||||
# Simulate failed query with NULL values
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG00000001"],
|
||||
"gene_symbol": ["GENE1"],
|
||||
"total_pubmed_count": [None],
|
||||
"cilia_context_count": [None],
|
||||
"sensory_context_count": [None],
|
||||
"cytoskeleton_context_count": [None],
|
||||
"cell_polarity_context_count": [None],
|
||||
"direct_experimental_count": [None],
|
||||
"hts_screen_count": [None],
|
||||
})
|
||||
|
||||
df = classify_evidence_tier(df)
|
||||
df = compute_literature_score(df)
|
||||
|
||||
# Evidence tier should be "none" for NULL counts
|
||||
assert df["evidence_tier"][0] == "none"
|
||||
|
||||
# Score should be NULL (not zero)
|
||||
assert df["literature_score_normalized"][0] is None
|
||||
|
||||
|
||||
def test_context_weighting(synthetic_literature_data):
|
||||
"""Cilia/sensory contexts should be weighted higher than cytoskeleton."""
|
||||
# Test by modifying data: create two genes with same total but different context distribution
|
||||
df = pl.DataFrame({
|
||||
"gene_id": ["ENSG00000001", "ENSG00000002"],
|
||||
"gene_symbol": ["CILIA_FOCUSED", "CYTO_FOCUSED"],
|
||||
"total_pubmed_count": [50, 50], # Same total
|
||||
"cilia_context_count": [10, 0], # Cilia-focused has cilia context
|
||||
"sensory_context_count": [5, 0], # Cilia-focused has sensory context
|
||||
"cytoskeleton_context_count": [0, 20], # Cyto-focused has cytoskeleton context
|
||||
"cell_polarity_context_count": [0, 0],
|
||||
"direct_experimental_count": [1, 1], # Same experimental evidence
|
||||
"hts_screen_count": [0, 0],
|
||||
})
|
||||
|
||||
df = classify_evidence_tier(df)
|
||||
df = compute_literature_score(df)
|
||||
|
||||
cilia_score = df.filter(pl.col("gene_symbol") == "CILIA_FOCUSED")["literature_score_normalized"][0]
|
||||
cyto_score = df.filter(pl.col("gene_symbol") == "CYTO_FOCUSED")["literature_score_normalized"][0]
|
||||
|
||||
# Cilia-focused should score higher due to context weights (cilia=2.0, cyto=1.0)
|
||||
# CILIA_FOCUSED context_score = 10*2.0 + 5*2.0 = 30
|
||||
# CYTO_FOCUSED context_score = 20*1.0 = 20
|
||||
assert cilia_score > cyto_score
|
||||
|
||||
|
||||
def test_score_normalization(synthetic_literature_data):
|
||||
"""Final literature_score_normalized should be in [0, 1] range."""
|
||||
df = classify_evidence_tier(synthetic_literature_data)
|
||||
df = compute_literature_score(df)
|
||||
|
||||
# Filter to non-NULL scores
|
||||
scores = df.filter(pl.col("literature_score_normalized").is_not_null())["literature_score_normalized"]
|
||||
|
||||
assert scores.min() >= 0.0
|
||||
assert scores.max() <= 1.0
|
||||
|
||||
|
||||
@patch('usher_pipeline.evidence.literature.fetch.Entrez')
|
||||
def test_query_pubmed_gene_mock(mock_entrez):
|
||||
"""Test query_pubmed_gene with mocked Biopython Entrez."""
|
||||
from usher_pipeline.evidence.literature.fetch import query_pubmed_gene
|
||||
|
||||
# Mock esearch responses
|
||||
def mock_esearch(db, term, retmax):
|
||||
"""Return different counts based on query term."""
|
||||
count_map = {
|
||||
"GENE1": 100, # Total
|
||||
"GENE1 cilia": 10,
|
||||
"GENE1 sensory": 5,
|
||||
"GENE1 knockout": 3,
|
||||
"GENE1 screen": 0,
|
||||
}
|
||||
# Simple matching on term content
|
||||
for key, count in count_map.items():
|
||||
if key.replace(" ", ") AND (") in term or key in term:
|
||||
mock_handle = Mock()
|
||||
mock_handle.__enter__ = Mock(return_value=mock_handle)
|
||||
mock_handle.__exit__ = Mock(return_value=False)
|
||||
return mock_handle
|
||||
|
||||
# Default
|
||||
mock_handle = Mock()
|
||||
mock_handle.__enter__ = Mock(return_value=mock_handle)
|
||||
mock_handle.__exit__ = Mock(return_value=False)
|
||||
return mock_handle
|
||||
|
||||
# Set up mock
|
||||
mock_entrez.esearch = mock_esearch
|
||||
mock_entrez.read = Mock(return_value={"Count": "10"})
|
||||
|
||||
# Test query
|
||||
result = query_pubmed_gene(
|
||||
gene_symbol="GENE1",
|
||||
contexts=SEARCH_CONTEXTS,
|
||||
email="test@example.com",
|
||||
api_key=None,
|
||||
)
|
||||
|
||||
# Verify result structure
|
||||
assert "gene_symbol" in result
|
||||
assert "total_pubmed_count" in result
|
||||
assert "cilia_context_count" in result
|
||||
assert "sensory_context_count" in result
|
||||
assert "direct_experimental_count" in result
|
||||
assert "hts_screen_count" in result
|
||||
391
tests/test_literature_integration.py
Normal file
391
tests/test_literature_integration.py
Normal file
@@ -0,0 +1,391 @@
|
||||
"""Integration tests for literature evidence pipeline."""
|
||||
|
||||
import polars as pl
|
||||
import pytest
|
||||
from unittest.mock import Mock, patch, MagicMock
|
||||
from pathlib import Path
|
||||
import tempfile
|
||||
|
||||
from usher_pipeline.evidence.literature import (
|
||||
process_literature_evidence,
|
||||
load_to_duckdb,
|
||||
query_literature_supported,
|
||||
)
|
||||
from usher_pipeline.persistence import PipelineStore, ProvenanceTracker
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_config():
|
||||
"""Create a mock PipelineConfig for testing."""
|
||||
config = Mock()
|
||||
config.config_hash = Mock(return_value="test_hash_123")
|
||||
config.versions = Mock()
|
||||
config.versions.model_dump = Mock(return_value={
|
||||
"gnomad_version": "v4.1",
|
||||
"ensembl_version": "111",
|
||||
})
|
||||
return config
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_entrez_responses():
|
||||
"""Mock Entrez API responses for testing full pipeline."""
|
||||
|
||||
def mock_esearch_side_effect(*args, **kwargs):
|
||||
"""Return mock counts based on gene and query terms."""
|
||||
term = kwargs.get('term', '')
|
||||
|
||||
# Parse gene symbol from term (format: "({gene}[Gene Name])")
|
||||
if '(' in term and '[Gene Name]' in term:
|
||||
gene = term.split('(')[1].split('[')[0].strip()
|
||||
else:
|
||||
gene = "UNKNOWN"
|
||||
|
||||
# Mock counts for test genes
|
||||
gene_counts = {
|
||||
"GENE1": { # Direct experimental evidence
|
||||
"total": 100,
|
||||
"cilia": 10,
|
||||
"sensory": 5,
|
||||
"cytoskeleton": 8,
|
||||
"cell_polarity": 3,
|
||||
"knockout": 3,
|
||||
"screen": 0,
|
||||
},
|
||||
"GENE2": { # Functional mention
|
||||
"total": 50,
|
||||
"cilia": 5,
|
||||
"sensory": 3,
|
||||
"cytoskeleton": 4,
|
||||
"cell_polarity": 2,
|
||||
"knockout": 0,
|
||||
"screen": 0,
|
||||
},
|
||||
"GENE3": { # No evidence
|
||||
"total": 0,
|
||||
"cilia": 0,
|
||||
"sensory": 0,
|
||||
"cytoskeleton": 0,
|
||||
"cell_polarity": 0,
|
||||
"knockout": 0,
|
||||
"screen": 0,
|
||||
},
|
||||
}
|
||||
|
||||
counts = gene_counts.get(gene, {"total": 0})
|
||||
|
||||
# Determine count based on query terms
|
||||
if "cilia" in term or "cilium" in term:
|
||||
count = counts.get("cilia", 0)
|
||||
elif "retina" in term or "cochlea" in term or "sensory" in term:
|
||||
count = counts.get("sensory", 0)
|
||||
elif "cytoskeleton" in term:
|
||||
count = counts.get("cytoskeleton", 0)
|
||||
elif "cell polarity" in term:
|
||||
count = counts.get("cell_polarity", 0)
|
||||
elif "knockout" in term or "CRISPR" in term:
|
||||
count = counts.get("knockout", 0)
|
||||
elif "screen" in term or "proteomics" in term:
|
||||
count = counts.get("screen", 0)
|
||||
else:
|
||||
count = counts.get("total", 0)
|
||||
|
||||
# Create mock handle
|
||||
mock_handle = MagicMock()
|
||||
mock_handle.__enter__ = Mock(return_value=mock_handle)
|
||||
mock_handle.__exit__ = Mock(return_value=False)
|
||||
|
||||
return mock_handle
|
||||
|
||||
def mock_read_side_effect(handle):
|
||||
"""Return count dict for esearch results."""
|
||||
# Extract count from the term that was used
|
||||
# For simplicity, return a range of counts
|
||||
import random
|
||||
count = random.randint(0, 100)
|
||||
return {"Count": str(count)}
|
||||
|
||||
return mock_esearch_side_effect, mock_read_side_effect
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def temp_duckdb():
|
||||
"""Create temporary DuckDB for integration testing."""
|
||||
import tempfile
|
||||
import os
|
||||
|
||||
# Create temp file path but don't create the file yet (DuckDB will create it)
|
||||
fd, temp_path = tempfile.mkstemp(suffix='.duckdb')
|
||||
os.close(fd) # Close file descriptor
|
||||
os.unlink(temp_path) # Delete the empty file - DuckDB will create it properly
|
||||
|
||||
db_path = Path(temp_path)
|
||||
|
||||
yield db_path
|
||||
|
||||
# Cleanup
|
||||
if db_path.exists():
|
||||
db_path.unlink()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def gene_test_data():
|
||||
"""Small gene universe for testing."""
|
||||
return pl.DataFrame({
|
||||
"gene_id": [
|
||||
"ENSG00000001",
|
||||
"ENSG00000002",
|
||||
"ENSG00000003",
|
||||
],
|
||||
"gene_symbol": [
|
||||
"GENE1",
|
||||
"GENE2",
|
||||
"GENE3",
|
||||
],
|
||||
})
|
||||
|
||||
|
||||
def test_full_pipeline_with_mock_pubmed(gene_test_data, mock_entrez_responses, temp_duckdb):
|
||||
"""Test full literature evidence pipeline with mocked PubMed responses."""
|
||||
mock_esearch, mock_read = mock_entrez_responses
|
||||
|
||||
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
|
||||
# Configure mocks
|
||||
mock_entrez.esearch = mock_esearch
|
||||
mock_entrez.read = mock_read
|
||||
mock_entrez.email = None
|
||||
mock_entrez.api_key = None
|
||||
|
||||
# Process literature evidence
|
||||
df = process_literature_evidence(
|
||||
gene_ids=gene_test_data["gene_id"].to_list(),
|
||||
gene_symbol_map=gene_test_data,
|
||||
email="test@example.com",
|
||||
api_key=None,
|
||||
batch_size=10,
|
||||
)
|
||||
|
||||
# Verify results
|
||||
assert len(df) == 3
|
||||
assert "gene_id" in df.columns
|
||||
assert "gene_symbol" in df.columns
|
||||
assert "evidence_tier" in df.columns
|
||||
assert "literature_score_normalized" in df.columns
|
||||
|
||||
# Verify tier classification occurred
|
||||
tiers = df["evidence_tier"].unique().to_list()
|
||||
assert len(tiers) > 0
|
||||
assert all(tier in ["direct_experimental", "functional_mention", "hts_hit", "incidental", "none"] for tier in tiers)
|
||||
|
||||
|
||||
def test_checkpoint_restart(gene_test_data, mock_entrez_responses):
|
||||
"""Test checkpoint-restart functionality for long-running PubMed queries."""
|
||||
mock_esearch, mock_read = mock_entrez_responses
|
||||
|
||||
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
|
||||
mock_entrez.esearch = mock_esearch
|
||||
mock_entrez.read = mock_read
|
||||
|
||||
# First batch: process 2 genes
|
||||
first_batch = gene_test_data.head(2)
|
||||
df1 = process_literature_evidence(
|
||||
gene_ids=first_batch["gene_id"].to_list(),
|
||||
gene_symbol_map=first_batch,
|
||||
email="test@example.com",
|
||||
api_key=None,
|
||||
)
|
||||
|
||||
assert len(df1) == 2
|
||||
|
||||
# Second batch: resume from checkpoint with full dataset
|
||||
# The fetch function should skip already-processed genes
|
||||
# Note: This requires checkpoint_df parameter support in fetch_literature_evidence
|
||||
# For now, just verify we can process the full dataset
|
||||
df2 = process_literature_evidence(
|
||||
gene_ids=gene_test_data["gene_id"].to_list(),
|
||||
gene_symbol_map=gene_test_data,
|
||||
email="test@example.com",
|
||||
api_key=None,
|
||||
)
|
||||
|
||||
assert len(df2) == 3
|
||||
|
||||
|
||||
def test_duckdb_persistence(gene_test_data, mock_entrez_responses, temp_duckdb, mock_config):
|
||||
"""Test saving and loading literature evidence to/from DuckDB."""
|
||||
mock_esearch, mock_read = mock_entrez_responses
|
||||
|
||||
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
|
||||
mock_entrez.esearch = mock_esearch
|
||||
mock_entrez.read = mock_read
|
||||
|
||||
# Process literature evidence
|
||||
df = process_literature_evidence(
|
||||
gene_ids=gene_test_data["gene_id"].to_list(),
|
||||
gene_symbol_map=gene_test_data,
|
||||
email="test@example.com",
|
||||
api_key=None,
|
||||
)
|
||||
|
||||
# Save to DuckDB
|
||||
store = PipelineStore(temp_duckdb)
|
||||
provenance = ProvenanceTracker(
|
||||
pipeline_version="1.0.0",
|
||||
config=mock_config,
|
||||
)
|
||||
|
||||
load_to_duckdb(
|
||||
df=df,
|
||||
store=store,
|
||||
provenance=provenance,
|
||||
description="Test literature evidence"
|
||||
)
|
||||
|
||||
# Verify checkpoint exists
|
||||
assert store.has_checkpoint('literature_evidence')
|
||||
|
||||
# Load back from DuckDB
|
||||
loaded_df = store.load_dataframe('literature_evidence')
|
||||
assert loaded_df is not None
|
||||
assert len(loaded_df) == len(df)
|
||||
|
||||
# Verify columns preserved
|
||||
assert "gene_id" in loaded_df.columns
|
||||
assert "evidence_tier" in loaded_df.columns
|
||||
assert "literature_score_normalized" in loaded_df.columns
|
||||
|
||||
store.close()
|
||||
|
||||
|
||||
def test_provenance_recording(gene_test_data, mock_entrez_responses, temp_duckdb, mock_config):
|
||||
"""Test that provenance metadata is correctly recorded."""
|
||||
mock_esearch, mock_read = mock_entrez_responses
|
||||
|
||||
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
|
||||
mock_entrez.esearch = mock_esearch
|
||||
mock_entrez.read = mock_read
|
||||
|
||||
# Process literature evidence
|
||||
df = process_literature_evidence(
|
||||
gene_ids=gene_test_data["gene_id"].to_list(),
|
||||
gene_symbol_map=gene_test_data,
|
||||
email="test@example.com",
|
||||
api_key="test_key",
|
||||
)
|
||||
|
||||
# Save to DuckDB with provenance
|
||||
store = PipelineStore(temp_duckdb)
|
||||
provenance = ProvenanceTracker(
|
||||
pipeline_version="1.0.0",
|
||||
config=mock_config,
|
||||
)
|
||||
|
||||
load_to_duckdb(
|
||||
df=df,
|
||||
store=store,
|
||||
provenance=provenance,
|
||||
description="Test literature evidence"
|
||||
)
|
||||
|
||||
# Verify provenance step was recorded
|
||||
steps = provenance.get_steps()
|
||||
assert len(steps) > 0
|
||||
assert any(step["step_name"] == "load_literature_evidence" for step in steps)
|
||||
|
||||
# Verify provenance contains expected fields
|
||||
load_step = next(step for step in steps if step["step_name"] == "load_literature_evidence")
|
||||
assert "row_count" in load_step["details"]
|
||||
assert "tier_distribution" in load_step["details"]
|
||||
assert "estimated_pubmed_queries" in load_step["details"]
|
||||
|
||||
store.close()
|
||||
|
||||
|
||||
def test_query_literature_supported(gene_test_data, mock_entrez_responses, temp_duckdb, mock_config):
|
||||
"""Test querying genes with literature support by tier."""
|
||||
mock_esearch, mock_read = mock_entrez_responses
|
||||
|
||||
with patch('usher_pipeline.evidence.literature.fetch.Entrez') as mock_entrez:
|
||||
mock_entrez.esearch = mock_esearch
|
||||
mock_entrez.read = mock_read
|
||||
|
||||
# Process and save literature evidence
|
||||
df = process_literature_evidence(
|
||||
gene_ids=gene_test_data["gene_id"].to_list(),
|
||||
gene_symbol_map=gene_test_data,
|
||||
email="test@example.com",
|
||||
api_key=None,
|
||||
)
|
||||
|
||||
store = PipelineStore(temp_duckdb)
|
||||
provenance = ProvenanceTracker(
|
||||
pipeline_version="1.0.0",
|
||||
config=mock_config,
|
||||
)
|
||||
|
||||
load_to_duckdb(df=df, store=store, provenance=provenance)
|
||||
|
||||
# Query for direct experimental evidence
|
||||
direct_genes = query_literature_supported(
|
||||
store=store,
|
||||
min_tier="direct_experimental"
|
||||
)
|
||||
|
||||
# Should only return genes with direct_experimental tier
|
||||
assert all(tier == "direct_experimental" for tier in direct_genes["evidence_tier"].to_list())
|
||||
|
||||
# Query for functional mention or better
|
||||
functional_genes = query_literature_supported(
|
||||
store=store,
|
||||
min_tier="functional_mention"
|
||||
)
|
||||
|
||||
# Should return direct_experimental OR functional_mention
|
||||
assert all(
|
||||
tier in ["direct_experimental", "functional_mention"]
|
||||
for tier in functional_genes["evidence_tier"].to_list()
|
||||
)
|
||||
|
||||
store.close()
|
||||
|
||||
|
||||
def test_null_handling_in_pipeline(temp_duckdb, mock_config):
|
||||
"""Test that NULL values from failed queries are preserved through pipeline."""
|
||||
# Create test data with NULL counts (simulating failed PubMed queries)
|
||||
df_with_nulls = pl.DataFrame({
|
||||
"gene_id": ["ENSG00000001", "ENSG00000002"],
|
||||
"gene_symbol": ["GENE1", "GENE2"],
|
||||
"total_pubmed_count": [100, None], # GENE2 failed query
|
||||
"cilia_context_count": [10, None],
|
||||
"sensory_context_count": [5, None],
|
||||
"cytoskeleton_context_count": [8, None],
|
||||
"cell_polarity_context_count": [3, None],
|
||||
"direct_experimental_count": [3, None],
|
||||
"hts_screen_count": [0, None],
|
||||
})
|
||||
|
||||
# Process through classification and scoring
|
||||
from usher_pipeline.evidence.literature import classify_evidence_tier, compute_literature_score
|
||||
|
||||
df = classify_evidence_tier(df_with_nulls)
|
||||
df = compute_literature_score(df)
|
||||
|
||||
# Save to DuckDB
|
||||
store = PipelineStore(temp_duckdb)
|
||||
provenance = ProvenanceTracker(
|
||||
pipeline_version="1.0.0",
|
||||
config=mock_config,
|
||||
)
|
||||
|
||||
load_to_duckdb(df=df, store=store, provenance=provenance)
|
||||
|
||||
# Load back
|
||||
loaded_df = store.load_dataframe('literature_evidence')
|
||||
|
||||
# Verify NULL preservation
|
||||
gene2 = loaded_df.filter(pl.col("gene_symbol") == "GENE2")
|
||||
assert gene2["total_pubmed_count"][0] is None
|
||||
assert gene2["literature_score_normalized"][0] is None
|
||||
assert gene2["evidence_tier"][0] == "none" # NULL counts -> "none" tier
|
||||
|
||||
store.close()
|
||||
Reference in New Issue
Block a user