kemal
af39d0ee6a
User guide: ArrayTrain section, v2 commands in the table, v2 accuracy rows, updated limitations. README status for v2.0.0. Version 2.0.0 (Python package); the Rust kernels are unchanged and stay simurg_core 1.1.0. uv.lock committed (uv lock --check clean). Full suite on the Pi, file by file under the memory guard: 29 files exit 0 (unit: 282 passed), ruff and mypy clean.
53 lines
3.4 KiB
Markdown
53 lines
3.4 KiB
Markdown
# SimurgArray
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Personal research toolkit for Illumina Infinium genotyping BeadChip data
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(IDAT → normalization → genotype calling → BAF/LRR → QC → VCF/PLINK; CN/LOH/mosaicism, PGx, cluster training,
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methylation control QC).
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- **SIMURGARRAY_MASTER_PLAN.md** — architecture, algorithms, roadmap
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- **SIMURGARRAY_FUNCTIONAL_SPEC.md** — 416 itemised requirements (CLI, inputs, outputs, QC, CN, PGx, methylation, edge cases)
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- `docs/validation/` — per-phase validation reports · `docs/known_unknowns.md` — findings log · `docs/research/` — background
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## Status
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**v2.0.0** (2026-09-30) — see `CHANGELOG.md`. v1: IDAT → normalization → GenCall-equivalent genotype calling →
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GTC v5 → sample/control QC with an HTML report → plus-strand VCF/BCF → PLINK 1/2, bedGraph, final report; plus a
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differential-testing harness. v2 adds cluster training (ArrayTrain), genome-wide CN/LOH/mosaic calling,
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pharmacogenomics (targeted CN, star alleles, phenotypes) and methylation control QC. Validation (`docs/validation/`):
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| Check | Result |
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|---|---|
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| 2141 vendor GTCs, 1000 Genomes Omni2.5 (`--preset autocall-1.6.2`) | normalization bit-identical; GTCs byte-identical in 2138/2141 |
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| GSA demo vs gtc2vcf, GTCtoVCF, plink2 | zero unexplained differences |
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| 1000 Genomes pedigrees (Omni2.5) | 717/717 parent–offspring pairs; Mendel error median 0.031 % |
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| 360 GSA arrays (GSE165845) vs NYGC 30x WGS / GIAB HG001 | concordance 0.9934 / 0.9985; replicates 0.9966 |
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| ArrayTrain EGT, GSE165845 chr22 vs 1000G 30x | 99.44 % concordance (vendor EGT 99.46 %), call rate 99.61 % |
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| CN, 360 GSE165845 arrays vs NYGC 30x SV | PASS precision DEL 44 % / DUP 57 % |
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| PGx, GSA v4 demo vs GeT-RM | 154/168 gene calls |
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| Methylation QC vs sesame | β r = 0.99998 |
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Commands: `simurg inspect`, `ingest`, `normalize`, `genotype`, `qc controls|samples|contamination|relatedness|loci|report|suggest-thresholds`,
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`gtc-to-vcf`, `export plink|bedgraph|finalreport|locus-summary`, `compare`, `train`, `cn build-model|call|annotate`,
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`pgx db build|coverage|copy-number|star-allele|phenotype`, `methyl qc|convert-manifest`. **User guide:
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`docs/user_guide.md`**; worked example: `examples/gsa_demo.sh`. Optional Rust kernels (byte-identical outputs,
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~80 GSA samples/min per server core, `docs/validation/P9.md`); wheels, Docker image and conda recipes
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(`docs/validation/P10.md`).
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## Quick start
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```bash
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uv venv && uv pip install -e ".[dev]"
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uv run pytest tests/unit # no downloads needed
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bash scripts/bootstrap_refs.sh # reference open-source repos → refs/ (oracles)
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bash scripts/fetch_data.sh p0 # ~1.4 GB of public test data → data/
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bash scripts/make_oracle_gtcs.sh # builds gtc2vcf, writes oracle GTCs
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bash scripts/make_oracle_qc.sh # locus-summary oracle (BeadArrayFiles, separate venv)
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bash scripts/fetch_data.sh p6 # GRCh38 FASTA (~0.9 GB download, 3.1 GB unpacked)
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bash scripts/make_oracle_vcf.sh # gtc2vcf oracle VCF for the P6 tests
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uv run pytest tests/oracle # real-data acceptance tests (add -m "not slow" to skip the long ones)
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uv run simurg inspect data/p0/gsa_v4_pgx/idats_Demo_36/idats_Demo_36/207895980019/207895980019_R03C01_Grn.idat
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bash examples/gsa_demo.sh results/gsa_demo 6 # end-to-end run on 6 demo samples
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```
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Vendor data files are downloaded for personal research use only and are never committed. See `THIRD_PARTY_NOTICES.md`.
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