--- phase: 04-scoring-integration plan: 02 subsystem: scoring tags: [qc, validation, quality-control, outlier-detection, positive-controls, mad, percentile-rank] # Dependency graph requires: - phase: 04-scoring-integration plan: 01 provides: scored_genes table, known_genes compilation - phase: 01-data-infrastructure provides: PipelineStore, DuckDB persistence provides: - Quality control checks for missing data, distributions, outliers - MAD-based robust outlier detection per evidence layer - Positive control validation against known gene rankings - PERCENT_RANK window function validation - QC orchestrator with composite score statistics affects: [04-03, candidate-ranking, filtering, quality-assessment] # Tech tracking tech-stack: added: - scipy>=1.14 for MAD-based outlier detection patterns: - MAD-based robust outlier detection (>3 MAD threshold) - PERCENT_RANK window function for percentile ranking - Threshold-based classification (warn/error levels) - Temporary table pattern for DuckDB joins key-files: created: - src/usher_pipeline/scoring/quality_control.py - src/usher_pipeline/scoring/validation.py modified: - pyproject.toml - src/usher_pipeline/scoring/__init__.py key-decisions: - "scipy.stats.median_abs_deviation for MAD computation (robust to outliers)" - "Missing data thresholds: 50% warn, 80% error" - "Outlier threshold: >3 MAD from median per layer" - "PERCENT_RANK computed across ALL genes before exclusion (validates scoring system)" - "Top quartile validation: median percentile >= 0.75 for known genes" - "Temporary table pattern for known gene join (avoids external temp files)" patterns-established: - "QC orchestrator pattern: run_qc_checks combines all checks with pass/fail boolean" - "Composite score percentiles (p10, p25, p50, p75, p90) for distribution analysis" - "Human-readable validation reports with formatted tables" - "NULL-aware statistics: only compute on non-NULL values per layer" # Metrics duration: 2m 54s completed: 2026-02-11 --- # Phase 04 Plan 02: Quality Control and Positive Control Validation Summary **MAD-based outlier detection and PERCENT_RANK validation ensuring scoring system credibility before candidate ranking** ## Performance - **Duration:** 2 minutes 54 seconds (174 seconds) - **Started:** 2026-02-11T12:45:12Z - **Completed:** 2026-02-11T12:48:06Z - **Tasks:** 2 - **Files modified:** 4 ## Accomplishments - Added scipy>=1.14 dependency for robust statistical methods - Implemented compute_missing_data_rates() with warn (>50%) and error (>80%) thresholds - Created compute_distribution_stats() detecting no variation (std < 0.01) and out-of-range values - Built detect_outliers() using MAD-based detection (>3 MAD from median per layer) - Developed run_qc_checks() orchestrator with composite score percentiles (p10-p90) - Implemented validate_known_gene_ranking() using PERCENT_RANK window function - Created generate_validation_report() with human-readable formatted output - Exported run_qc_checks, validate_known_gene_ranking, generate_validation_report from scoring module ## Task Commits Each task was committed atomically: 1. **Task 1: Quality control checks for scoring results** - `ba2f97a` (feat) 2. **Task 2: Positive control validation against known gene rankings** - `70a5d6e` (feat) ## Files Created/Modified - `src/usher_pipeline/scoring/quality_control.py` - QC checks with MAD-based outlier detection - `src/usher_pipeline/scoring/validation.py` - Known gene ranking validation with PERCENT_RANK - `pyproject.toml` - Added scipy>=1.14 dependency - `src/usher_pipeline/scoring/__init__.py` - Export QC and validation functions ## Decisions Made 1. **scipy for MAD computation:** Used scipy.stats.median_abs_deviation with scale="normal" for robust outlier detection. MAD is less sensitive to outliers than standard deviation, making it ideal for detecting anomalous genes in scored datasets. 2. **Missing data threshold classification:** 50% missing = warning (may still be usable), 80% missing = error (too sparse to trust). This dual-threshold approach allows for graduated QC feedback. 3. **Outlier threshold calibration:** >3 MAD from median flags outliers. This is a standard robust statistics threshold balancing sensitivity and specificity. Layers with MAD=0 (no variation) skip outlier detection. 4. **PERCENT_RANK validation timing:** Validation computes percentile ranks BEFORE known gene exclusion to validate the scoring system itself (not post-filtering artifacts). Uses temporary table pattern for efficient DuckDB join. 5. **Top quartile validation criterion:** Median percentile >= 0.75 ensures known cilia/Usher genes rank in top 25% of all genes. This threshold confirms scoring logic prioritizes cilia-relevant features. 6. **Composite score percentiles:** Included p10, p25, p50, p75, p90 in run_qc_checks() for distribution analysis. These percentiles help identify skewness, outliers, and score concentration patterns. ## Deviations from Plan None - plan executed exactly as written. ## Issues Encountered None. Both verification tests passed on first attempt. ## User Setup Required None - scipy installed automatically in virtual environment. ## Next Phase Readiness Ready for Phase 04 Plan 03 (ranked candidate list generation and filtering): - QC checks available via run_qc_checks() for post-scoring validation - Positive control validation confirms scoring system works correctly - Outlier detection identifies anomalous genes per layer - Composite score percentiles provide distribution context for threshold selection No blockers. Next plan can implement: - Known gene exclusion filtering - Quality flag filtering (sufficient_evidence threshold) - Composite score ranking - Top-N candidate selection with provenance ## Self-Check: PASSED All claimed files and commits verified: - src/usher_pipeline/scoring/quality_control.py - FOUND - src/usher_pipeline/scoring/validation.py - FOUND - pyproject.toml - FOUND - Commit ba2f97a (Task 1) - FOUND - Commit 70a5d6e (Task 2) - FOUND --- *Phase: 04-scoring-integration* *Plan: 02* *Completed: 2026-02-11*