kemalandClaude Opus 5.5
baac9af5c5
noise-outlier exclusion + robustness test (PCN-017/EDGE-026), platform from the CSV manifest (CLI-032) Also: star-allele calls are checked for one manifest/build per run (PGX-011), duplications at the CN cap are named xN (PGX-022), and tandem names attach supporting variants to their full copy. Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
62 lines
3.7 KiB
Markdown
62 lines
3.7 KiB
Markdown
# V2 — Targeted PGx copy number (PCN-001 … PCN-017)
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Status: **implemented, interim validation** (2026-09-29). `simurg pgx copy-number train|call` (CLI-031/032), model
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file `.simurg-pgxcn` (IN-050), region BED v1 built from UCSC ncbiRefSeq (`scripts/pgx/build_cn_regions.py`,
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packaged as `simurg/pgx/data/pgx_cn_regions.GRCh38.v1.bed`), per-sample `*.pgx_cn.vcf.gz` + `.tbi`,
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`pgx_cn_batch_summary.tsv`, `pgx_cn_sample_summary.tsv`. Unit tests: `tests/unit/test_pgx_cn.py`.
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## Data
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* **Arrays:** the GSA v4 PGx demo (`idats_Demo_36`, 36 arrays), genotyped by SimurgArray. Its CNV probes cover every
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region (51–357 probes; GSTT1: 160 probes on `chr22_KI270879v1_alt` in the GRCh38 manifest).
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* **Identities:** the demo arrays are 30 CEPH / 1000 Genomes individuals (6 replicate arrays). Identified by
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genotype concordance with the 3202-sample 1000G 30x panel on 4647 chr22 SNPs: best match 0.992–0.996, next best
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≤ 0.91 (relatives) — `scripts/pgx/demo36_identity.tsv`.
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* **Truth:** NYGC 30x SV callset (1KGP_3202 GATK-SV/svtools freeze V3), RD_CN per individual
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(`scripts/pgx/validate_cn_truth.py`).
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* The GSE165845 GSA-MD v1 arrays (360 arrays, CN/LOH validation) have 0–9 probes per PGx region: not usable here.
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## Result (30 individuals, model trained on the same 36 arrays with `--min-samples 30`)
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| Region | Concordance with NYGC RD_CN | Mismatches (truth → call) |
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|---|---|---|
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| GSTM1 (whole gene) | **30/30** | – |
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| UGT2B17 (whole gene) | **30/30** | – |
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| SULT1A1 | 29/30 | 2→1 |
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| CYP2A6.exon.1 | 29/30 | 3→2 |
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| CYP2A6.intron.7 | 28/30 | 3→2, 1→2 |
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| CYP2D6.p5 | 28/30 | 4→3, 2→1 |
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| CYP2D6.intron.2 | 25/30 | 3→2 ×3, 4→3, 2→1 |
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| CYP2D6.exon.9 | 25/30 | 3→2 ×3, 4→2, 2→1 |
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| **all** | **224/240 (93.3 %)** | |
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GSTT1 (no SV truth on the alt contig): CN 0/1/2 = 5/16/9, in Hardy–Weinberg proportions (null allele 0.43; CEU
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literature ≈ 0.4–0.5). Details: `V2_pgx_cn_demo36.json`.
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* CYP2D6 truth is one gene-level record; the exon-9 / intron-2 "misses" at truth CN 3 are expected for tandem
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structures such as `*68+*4` or `*36+*10`, where not every sub-region is duplicated. Star-allele truth (GeT-RM)
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is the right yardstick and follows with the star-allele caller.
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* PCN-017 target (≥ 95 % per region) is met for GSTM1, UGT2B17, SULT1A1, CYP2A6.exon.1; not (yet) shown for the
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CYP2D6 sub-regions.
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## Additions
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* PCN-006: a warning when the batch's IDAT scan dates span more than 14 days (no sample-sheet input in `pgx
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copy-number call`, so the lot/batch-column check does not apply).
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* PCN-010: `pgx_cn_baf_evidence.tsv` — per region the log-likelihood of CN 0 … 5 from the informative SNP BAFs
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(binomial weights from the training cohort's B-allele frequency); `--bafs-in-cn` adds it to the CN posterior
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(experimental, off by default). On the demo it agrees poorly with the LRR calls (e.g. GSTM1 2/36): the SNPs in
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these regions do not separate the CN states here, so it stays informational.
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* PCN-009 / EDGE-026: training excludes samples whose genome-wide level **or** noise is an outlier (robust |z| > 5);
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`test_outlier_robustness` injects 10 % noisy, shifted arrays and requires ≥ 99 % unchanged calls for the others.
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* CLI-032: the platform (LCG/EX) comes from the CSV manifest's `Assay Format` (`--csv`), or `--platform`; the demo
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model records `EX`.
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## Caveats (K-028)
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* Trained and evaluated on the same 30 individuals (the spec requires ≥ 96 training samples; the priors were
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chosen while looking at this truth). This is an optimistic, in-sample figure until an independent cohort is
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available.
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* Deviations from PCN-009: reference from the training cohort (not the calling batch); no GC-correction step; CN
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model = compressed-intensity template + HWE-constrained weights (`simurg/pgx/cn.py`).
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