feat(04-02): implement QC checks for scoring results

- Add scipy>=1.14 dependency for MAD-based outlier detection
- Create quality_control.py with 4 QC functions
- compute_missing_data_rates: NULL rate detection with warn/error thresholds
- compute_distribution_stats: mean/median/std per layer with anomaly detection
- detect_outliers: MAD-based robust outlier detection (>3 MAD)
- run_qc_checks: orchestrator with composite score percentiles

pyproject.toml

@@ -36,6 +36,7 @@ dependencies = [
     "httpx>=0.28",
     "structlog>=25.0",
     "biopython>=1.84",
+    "scipy>=1.14",
 ]
 
 [project.optional-dependencies]