usher-exploring/.planning/phases/04-scoring-integration/04-02-SUMMARY.md

phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, duration, completed

phase	plan	subsystem	tags	requires	provides	affects	tech-stack	key-files	key-decisions	patterns-established	duration	completed
04-scoring-integration	02	scoring	qc validation quality-control outlier-detection positive-controls mad percentile-rank	phase plan provides 04-scoring-integration 01 scored_genes table, known_genes compilation phase provides 01-data-infrastructure PipelineStore, DuckDB persistence	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	04-03 candidate-ranking filtering quality-assessment	added patterns scipy>=1.14 for MAD-based outlier detection 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	created modified src/usher_pipeline/scoring/quality_control.py src/usher_pipeline/scoring/validation.py pyproject.toml src/usher_pipeline/scoring/__init__.py	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)	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	2m 54s	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

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Phase: 04-scoring-integration Plan: 02 Completed: 2026-02-11