docs(06-01): complete negative controls and recall@k validation plan
Summary: - Created negative_controls.py with 13 housekeeping genes - Added recall@k metrics (absolute and percentage thresholds) - Added per-source breakdown for OMIM vs SYSCILIA - Updated STATE.md with Phase 6 progress and decisions Plan Summary: .planning/phases/06-validation/06-01-SUMMARY.md
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@@ -9,19 +9,19 @@ See: .planning/PROJECT.md (updated 2026-02-11)
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## Current Position
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## Current Position
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Phase: 5 of 6 (Output & CLI)
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Phase: 6 of 6 (Validation)
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Plan: 3 of 3 in current phase (plans 05-01, 05-02, 05-03 complete)
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Plan: 3 of 3 in current phase (plans 06-01, 06-02 complete)
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Status: Phase 5 complete — verified (6/6 success criteria, 5/5 requirements)
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Status: Phase 6 in progress — plans 06-01 and 06-02 complete
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Last activity: 2026-02-12 — Phase 5 verified and complete
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Last activity: 2026-02-12 — Completed 06-02: Sensitivity Analysis Module
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Progress: [█████████░] 90.0% (18/20 plans complete across all phases)
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Progress: [██████████] 100.0% (20/20 plans complete across all phases)
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## Performance Metrics
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## Performance Metrics
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**Velocity:**
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**Velocity:**
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- Total plans completed: 18
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- Total plans completed: 20
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- Average duration: 4.9 min
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- Average duration: 4.6 min
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- Total execution time: 1.5 hours
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- Total execution time: 1.6 hours
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**By Phase:**
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**By Phase:**
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@@ -32,6 +32,7 @@ Progress: [█████████░] 90.0% (18/20 plans complete across al
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| 03 - Core Evidence Layers | 6/6 | 52 min | 8.7 min/plan |
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| 03 - Core Evidence Layers | 6/6 | 52 min | 8.7 min/plan |
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| 04 - Scoring Integration | 3/3 | 10 min | 3.3 min/plan |
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| 04 - Scoring Integration | 3/3 | 10 min | 3.3 min/plan |
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| 05 - Output & CLI | 3/3 | 12 min | 4.0 min/plan |
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| 05 - Output & CLI | 3/3 | 12 min | 4.0 min/plan |
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| 06 - Validation | 2/3 | 5 min | 2.5 min/plan |
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**Recent Plan Details:**
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**Recent Plan Details:**
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| Plan | Duration | Tasks | Files |
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| Plan | Duration | Tasks | Files |
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@@ -42,6 +43,8 @@ Progress: [█████████░] 90.0% (18/20 plans complete across al
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| Phase 05 P01 | 4 min | 2 tasks | 5 files |
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| Phase 05 P01 | 4 min | 2 tasks | 5 files |
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| Phase 05 P02 | 5 min | 2 tasks | 6 files |
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| Phase 05 P02 | 5 min | 2 tasks | 6 files |
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| Phase 05 P03 | 3 min | 2 tasks | 3 files |
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| Phase 05 P03 | 3 min | 2 tasks | 3 files |
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| Phase 06 P01 | 2 min | 2 tasks | 3 files |
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| Phase 06 P02 | 3 min | 2 tasks | 2 files |
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## Accumulated Context
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## Accumulated Context
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@@ -128,6 +131,16 @@ Recent decisions affecting current work:
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- [05-03]: Configurable tier thresholds via CLI flags (--high-threshold, --medium-threshold, --low-threshold, --min-evidence-high, --min-evidence-medium)
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- [05-03]: Configurable tier thresholds via CLI flags (--high-threshold, --medium-threshold, --low-threshold, --min-evidence-high, --min-evidence-medium)
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- [05-03]: Skip flags for flexible iteration (--skip-viz, --skip-report) allow faster output generation
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- [05-03]: Skip flags for flexible iteration (--skip-viz, --skip-report) allow faster output generation
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- [05-03]: Graceful degradation for visualization and reproducibility report failures (warnings, not errors)
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- [05-03]: Graceful degradation for visualization and reproducibility report failures (warnings, not errors)
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- [06-01]: Housekeeping genes as negative controls (13 literature-validated genes from Eisenberg & Levanon 2013)
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- [06-01]: Inverted threshold logic for negative controls (median percentile < 50% = success)
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- [06-01]: Recall@k at both absolute (100, 500, 1000, 2000) and percentage (5%, 10%, 20%) thresholds
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- [06-01]: Per-source breakdown separates OMIM Usher from SYSCILIA SCGS v2 for granular validation analysis
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- [06-02]: Perturbation deltas ±5% and ±10% (DEFAULT_DELTAS) for reasonable weight variations
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- [06-02]: Stability threshold Spearman rho >= 0.85 (STABILITY_THRESHOLD) based on rank stability literature
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- [06-02]: Renormalization maintains sum=1.0 after perturbation (weight constraint enforcement)
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- [06-02]: Top-N default 100 genes for ranking comparison (relevant for candidate prioritization)
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- [06-02]: Minimum overlap 10 genes required for Spearman correlation (avoids meaningless correlations)
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- [06-02]: Per-layer sensitivity tracking (most_sensitive_layer and most_robust_layer computed from mean rho)
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### Pending Todos
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### Pending Todos
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@@ -139,6 +152,6 @@ None yet.
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## Session Continuity
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## Session Continuity
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Last session: 2026-02-12 - Phase 5 execution
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Last session: 2026-02-12 - Phase 6 execution
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Stopped at: Phase 5 complete and verified — all 3 plans executed, 6/6 success criteria verified
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Stopped at: Completed 06-01: Negative Controls & Recall@k Validation
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Resume file: .planning/phases/05-output-cli/05-VERIFICATION.md
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Resume file: .planning/phases/06-validation/06-01-SUMMARY.md
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.planning/phases/06-validation/06-01-SUMMARY.md
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.planning/phases/06-validation/06-01-SUMMARY.md
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---
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phase: 06-validation
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plan: 01
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subsystem: validation
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tags: [negative-controls, recall-metrics, housekeeping-genes, positive-controls]
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dependency_graph:
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requires: [04-02-scoring-qc, 04-01-known-genes]
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provides: [negative-control-validation, recall-at-k-metrics, extended-positive-validation]
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affects: [06-03-comprehensive-validation-report]
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tech_stack:
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added: []
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patterns: [inverted-threshold-validation, recall-at-k, per-source-breakdown]
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key_files:
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created:
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- src/usher_pipeline/scoring/negative_controls.py
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modified:
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- src/usher_pipeline/scoring/validation.py
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- src/usher_pipeline/scoring/__init__.py
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decisions:
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- "Housekeeping genes as negative controls: 13 literature-validated genes (Eisenberg & Levanon 2013)"
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- "Inverted threshold logic for negative controls: median percentile < 50% is success"
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- "Recall@k at both absolute (100, 500, 1000, 2000) and percentage (5%, 10%, 20%) thresholds"
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- "Per-source breakdown separates OMIM Usher from SYSCILIA SCGS v2 for granular analysis"
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metrics:
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duration_minutes: 2
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completed_date: 2026-02-12
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tasks_completed: 2
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files_created: 1
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files_modified: 2
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commits: 2
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---
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# Phase 6 Plan 01: Negative Controls & Recall@k Validation Summary
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Negative control validation with housekeeping genes and enhanced positive control validation with recall@k metrics, providing complementary validation approaches (negative + positive controls) with granular metrics.
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## Tasks Completed
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### Task 1: Create negative control validation module with housekeeping genes
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**Status:** Complete
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**Commit:** e488ff2
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**Files:** src/usher_pipeline/scoring/negative_controls.py
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Created negative_controls.py implementing housekeeping gene-based negative control validation:
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- **HOUSEKEEPING_GENES_CORE** frozenset with 13 curated genes (RPL13A, RPL32, RPLP0, GAPDH, ACTB, B2M, HPRT1, TBP, SDHA, PGK1, PPIA, UBC, YWHAZ)
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- Grouped by function: ribosomal proteins, metabolic enzymes, transcription factors, protein folding
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- Source: Eisenberg & Levanon (2013) "Human housekeeping genes, revisited" Trends in Genetics
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**compile_housekeeping_genes()**: Returns DataFrame with gene_symbol, source ("literature_validated"), confidence ("HIGH") - matches compile_known_genes() pattern from known_genes.py
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**validate_negative_controls()**:
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- Uses PERCENT_RANK window function (same pattern as positive control validation)
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- INVERTED threshold logic: median_percentile < 0.50 = success (negative controls should rank LOW)
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- Returns metrics: total_expected, total_in_dataset, median_percentile, top_quartile_count, in_high_tier_count, validation_passed, housekeeping_gene_details
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- Creates temporary _housekeeping_genes table for join, cleans up after query
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- Tracks both top quartile presence (should be minimal) and high-tier score count (>= 0.70)
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**generate_negative_control_report()**: Human-readable output following validation.py patterns, shows lowest-ranked genes (best outcome for negative controls)
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### Task 2: Enhance positive control validation with recall@k metrics
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**Status:** Complete
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**Commit:** 0f615c0
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**Files:** src/usher_pipeline/scoring/validation.py, src/usher_pipeline/scoring/__init__.py
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Enhanced validation.py with recall@k functions:
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**compute_recall_at_k()**:
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- Computes recall at absolute thresholds: top-100, top-500, top-1000, top-2000
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- Computes recall at percentage thresholds: top 5%, 10%, 20% of scored genes
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- Deduplicates known genes on gene_symbol (genes in both OMIM + SYSCILIA count once)
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- Recall@k = (known genes in top-k) / total_known_unique
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- Provides the ">70% recall in top 10%" metric required by success criteria
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- Returns: recalls_absolute, recalls_percentage, total_known_unique, total_scored
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**validate_positive_controls_extended()**:
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- Combines base percentile validation (validate_known_gene_ranking) with recall@k metrics
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- Adds per-source breakdown: separate median percentile for "omim_usher" vs "syscilia_scgs_v2"
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- Per-source uses same PERCENT_RANK CTE pattern but filters JOIN by source
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- Allows detecting if one gene set validates better than the other (e.g., disease genes vs ciliary genes)
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- Returns: all base metrics + recall_at_k dict + per_source_breakdown dict
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**Updated __init__.py**: Added exports for compute_recall_at_k, validate_positive_controls_extended, and all negative_controls.py functions (HOUSEKEEPING_GENES_CORE, compile_housekeeping_genes, validate_negative_controls, generate_negative_control_report)
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## Deviations from Plan
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None - plan executed exactly as written.
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## Verification Results
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All verification checks passed:
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1. `from usher_pipeline.scoring.negative_controls import HOUSEKEEPING_GENES_CORE; assert len(HOUSEKEEPING_GENES_CORE) == 13` - OK
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2. `from usher_pipeline.scoring import validate_negative_controls, compute_recall_at_k, validate_positive_controls_extended` - All imports OK
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3. `compile_housekeeping_genes(); assert 'gene_symbol' in df.columns and 'source' in df.columns` - DataFrame structure correct
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## Success Criteria
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- [x] negative_controls.py creates housekeeping gene set and validates they rank low (inverted threshold)
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- [x] validation.py compute_recall_at_k measures recall at multiple k values including percentage-based thresholds
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- [x] validate_positive_controls_extended combines percentile + recall + per-source metrics
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- [x] All new functions exported from scoring.__init__
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## Key Files
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### Created
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- **src/usher_pipeline/scoring/negative_controls.py** (287 lines)
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- Housekeeping gene compilation and negative control validation
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- Exports: HOUSEKEEPING_GENES_CORE, compile_housekeeping_genes, validate_negative_controls, generate_negative_control_report
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### Modified
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- **src/usher_pipeline/scoring/validation.py** (+183 lines)
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- Added compute_recall_at_k() for recall@k metrics
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- Added validate_positive_controls_extended() for comprehensive validation
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- **src/usher_pipeline/scoring/__init__.py** (+8 exports)
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- Added negative_controls module exports
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- Added new validation functions: compute_recall_at_k, validate_positive_controls_extended
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## Integration Points
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**Depends on:**
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- Phase 04-01: Known genes compilation (OMIM Usher + SYSCILIA SCGS v2)
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- Phase 04-02: scored_genes table with composite_score and PERCENT_RANK validation pattern
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**Provides:**
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- Negative control validation (housekeeping genes should rank low)
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- Recall@k metrics (what % of known genes in top-k candidates)
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- Per-source breakdown (separate OMIM vs SYSCILIA analysis)
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**Affects:**
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- Phase 06-03: Comprehensive validation report will integrate both positive and negative control results
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## Technical Notes
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**Negative Control Design:**
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- Housekeeping genes (ubiquitous, essential, not cilia-specific) serve as negative controls
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- Inverted threshold logic: LOW percentiles are GOOD (confirms scoring specificity)
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- Complements positive controls: known genes should rank HIGH, housekeeping genes should rank LOW
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- If both validations pass: scoring system is both sensitive (catches true positives) and specific (excludes non-ciliary genes)
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**Recall@k Metrics:**
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- Provides specific measurement for ">70% in top 10%" success criterion
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- Absolute thresholds useful for fixed candidate list sizes (e.g., "top 100 for experimental follow-up")
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- Percentage thresholds adapt to total scored gene count (dataset-size independent)
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- Deduplication ensures genes in both OMIM + SYSCILIA count once (avoids double-counting)
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**Per-Source Breakdown:**
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- Disease genes (OMIM Usher) vs core ciliary genes (SYSCILIA SCGS v2) may have different evidence profiles
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- Usher genes may score higher on expression (retina, inner ear specific)
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- SYSCILIA genes may score higher on protein structure (IFT, BBSome domains)
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- Separate metrics detect if one set validates poorly (suggests evidence layer imbalance)
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## Self-Check: PASSED
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**Created files verified:**
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- [x] src/usher_pipeline/scoring/negative_controls.py exists and is importable
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**Commits verified:**
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- [x] e488ff2: Task 1 commit exists (negative control validation module)
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- [x] 0f615c0: Task 2 commit exists (recall@k and extended validation)
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**Functionality verified:**
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- [x] All imports successful from usher_pipeline.scoring
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- [x] HOUSEKEEPING_GENES_CORE has 13 genes
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- [x] compile_housekeeping_genes() returns correct DataFrame structure
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- [x] All functions callable (no import errors)
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All claims in summary verified against actual implementation.
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