Files

gbanyan 386fbf51b2 docs(04-03): complete CLI score command and tests plan

- SUMMARY.md: CLI orchestration with checkpoint-restart + 10 comprehensive tests
- STATE.md: Updated position (Phase 4 complete), progress (75%), velocity, decisions
- Duration: 3 minutes, 2 tasks, 4 files (3 created, 1 modified)

2026-02-11 20:56:31 +08:00

7.6 KiB

Raw Blame History

Project State

Project Reference

See: .planning/PROJECT.md (updated 2026-02-11)

Core value: Produce a high-confidence, multi-evidence-backed ranked list of under-studied cilia/Usher candidate genes that is fully traceable — every gene's inclusion is explainable by specific evidence, and every gap is documented. Current focus: Phase 4 in progress — Scoring and Integration

Current Position

Phase: 4 of 6 (Scoring and Integration) Plan: 3 of 3 in current phase (complete) Status: Phase 04 complete — CLI score command and comprehensive test coverage Last activity: 2026-02-11 — Completed 04-03-PLAN.md

Progress: [████████░░] 75.0% (15/20 plans complete across all phases)

Performance Metrics

Velocity:

Total plans completed: 15
Average duration: 5.1 min
Total execution time: 1.3 hours

By Phase:

Phase	Plans	Total	Avg/Plan
01 - Data Infrastructure	4/4	14 min	3.5 min/plan
02 - Prototype Evidence Layer	2/2	8 min	4.0 min/plan
03 - Core Evidence Layers	6/6	52 min	8.7 min/plan
04 - Scoring Integration	3/3	10 min	3.3 min/plan

Recent Plan Details:

Plan	Duration	Tasks	Files
Phase 03 P04	8 min	2 tasks	8 files
Phase 03 P05	10 min	2 tasks	8 files
Phase 03 P06	13 min	2 tasks	10 files
Phase 04 P01	4 min	2 tasks	4 files
Phase 04 P02	3 min	2 tasks	4 files
Phase 04 P03	3 min	2 tasks	4 files

Accumulated Context

Decisions

Decisions are logged in PROJECT.md Key Decisions table. Recent decisions affecting current work:

Python over R/Bioconductor for rich data integration ecosystem
Weighted rule-based scoring over ML for explainability
Public data only for reproducibility
Modular CLI scripts for flexibility during development
Virtual environment required for dependency isolation (01-01: PEP 668 externally-managed Python)
Auto-creation of directories on config load (01-01: data_dir, cache_dir field validators)
[01-02]: Warn on gene count outside 19k-22k range but don't fail (allows for Ensembl version variations)
[01-02]: HGNC success rate is primary validation gate (UniProt mapping tracked but not used for pass/fail)
[01-02]: Take first UniProt accession when multiple exist (simplifies data model)
[01-02]: Mock mygene in tests (avoids rate limits, ensures reproducibility)
[01-03]: DuckDB over SQLite for DataFrame storage (native polars/pandas integration, better analytics)
[01-03]: Provenance sidecar files alongside outputs (co-located metadata, bioinformatics standard pattern)
[01-04]: Click for CLI framework (standard Python CLI library with excellent UX)
[01-04]: Setup command uses checkpoint-restart pattern (gene universe fetch can take minutes)
[01-04]: Mock mygene in integration tests (avoids external API dependency, reproducible)
[02-01]: httpx over requests for streaming downloads (async-native, cleaner API)
[02-01]: structlog for structured logging (JSON-formatted, context-aware)
[02-01]: LOEUF normalization with inversion (lower LOEUF = more constrained = higher 0-1 score)
[02-01]: Quality flags instead of filtering (preserve all genes with measured/incomplete_coverage/no_data categorization)
[02-01]: NULL preservation pattern (unknown constraint != zero constraint, must not be conflated)
[02-01]: Lazy polars evaluation (LazyFrame until final collect() for query optimization)
[02-02]: load_to_duckdb uses CREATE OR REPLACE for idempotency (safe to re-run)
[02-02]: CLI evidence command group for extensibility (future evidence sources follow same pattern)
[02-02]: Checkpoint at table level (has_checkpoint checks DuckDB table existence)
[02-02]: Integration tests with synthetic fixtures (no external downloads, fast, reproducible)
[03-01]: Annotation tier thresholds: Well >= (20 GO AND 4 UniProt), Partial >= (5 GO OR 3 UniProt)
[03-01]: Composite annotation score weighting: GO 50%, UniProt 30%, Pathway 20%
[03-01]: NULL GO counts treated as zero for tier classification but preserved as NULL in data (conservative assumption)
[03-03]: UniProt REST API with batching (100 accessions) over bulk download for flexibility
[03-03]: InterPro API for supplemental domain annotations (10 req/sec rate limit)
[03-03]: Keyword-based cilia motif detection over ML for explainability (IFT, BBSome, ciliary, etc.)
[03-03]: Composite protein score weights: length 15%, domain 20%, coiled-coil 20%, TM 20%, cilia 15%, scaffold 10%
[03-03]: List(Null) edge case handling for proteins with no domains (cast to List(String))
[03-04]: Evidence type terminology standardized to computational (not predicted) for consistency with bioinformatics convention
[03-04]: Proteomics absence stored as False (informative negative) vs HPA absence as NULL (unknown/not tested)
[03-04]: Curated proteomics reference gene sets (CiliaCarta, Centrosome-DB) embedded as Python constants for simpler deployment
[03-04]: Computational evidence (HPA Uncertain/Approved) downweighted to 0.6x vs experimental (Enhanced/Supported, proteomics) at 1.0x
[Phase 03-05]: Ortholog confidence based on HCOP support count (HIGH: 8+, MEDIUM: 4-7, LOW: 1-3)
[Phase 03-05]: NULL score for genes without orthologs (preserves NULL pattern)
[03-02]: HPA bulk TSV download over per-gene API (efficient for 20K genes)
[03-02]: GTEx retina/fallopian tube may be NULL (not in all versions)
[03-02]: CellxGene optional dependency with --skip-cellxgene flag (large install)
[03-02]: Tau specificity requires complete tissue data (any NULL -> NULL Tau)
[03-02]: Expression score composite: 40% enrichment + 30% Tau + 30% target rank
[03-02]: Inner ear data primarily from CellxGene scRNA-seq (not HPA/GTEx bulk)
[03-06]: HTS hits prioritized over functional mentions in evidence tier hierarchy (direct > HTS > functional > incidental)
[03-06]: Quality-weighted scoring uses log2 normalization to mitigate well-studied gene bias (prevents TP53-like dominance)
[03-06]: Context weights cilia/sensory=2.0, cytoskeleton/polarity=1.0 for primary target prioritization
[03-06]: Rate limiting via decorator pattern (3 req/sec default, 10 req/sec with NCBI API key)
[04-01]: OMIM Usher genes (10) and SYSCILIA SCGS v2 core (28) as known gene positive controls
[04-01]: NULL-preserving weighted average: weighted_sum / available_weight (only non-NULL layers contribute)
[04-01]: Quality flags based on evidence_count (>=4 sufficient, >=2 moderate, >=1 sparse, 0 no_evidence)
[04-01]: Per-layer contribution tracking (score * weight) for explainability
[04-01]: ScoringWeights validation enforcing sum = 1.0 ± 1e-6 tolerance
[04-02]: scipy MAD-based outlier detection (>3 MAD threshold) for robust anomaly detection
[04-02]: Missing data thresholds: 50% warn, 80% error for graduated QC feedback
[04-02]: PERCENT_RANK validation computed before known gene exclusion (validates scoring system)
[04-02]: Top quartile validation criterion (median percentile >= 0.75 for known genes)
[04-03]: Score command follows evidence_cmd.py pattern for consistency
[04-03]: Separate --skip-qc and --skip-validation flags for flexible iteration
[04-03]: Tests use tmp_path fixtures for isolated DuckDB instances
[04-03]: Synthetic test data designed to ensure known genes rank highly (0.8-0.95 scores across all layers)

Pending Todos

None yet.

Blockers/Concerns