PGx CN: pgx copy-number train|call (CLI-031/032, PCN-001..017, IN-050), region BED v1, demo identities (K-028)
- simurg/pgx/cn.py: per-region CN mixture, mu_c = delta + gamma*log2((c+1)/3), free lower CN-0 component, Hardy-Weinberg-constrained weights over haplotype copy numbers, EM from a grid of label starts. - pipeline/pgx_cn.py: model file (.simurg-pgxcn), batch rules (PCN-004), per-sample CN VCF + tbi, batch and sample summaries. - The GSA v4 PGx demo arrays are 30 CEPH/1000G individuals (scripts/pgx/demo36_identity.tsv); against NYGC 30x SV RD_CN 224/240 region calls agree (GSTM1, UGT2B17 30/30), in-sample (docs/validation/V2_pgx_cn.md).
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"""Targeted PGx copy number (spec PCN-001 … PCN-017, CLI-031/032, IN-050)."""
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from __future__ import annotations
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import gzip
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from pathlib import Path
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import numpy as np
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from typer.testing import CliRunner
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from simurg.cli.main import app
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from simurg.formats.gtc import Gtc, write_gtc
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from simurg.pgx.cn import TEMPLATE, call_cn, fit_region, hwe_weights, parse_regions
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from simurg.pipeline.pgx_cn import default_regions
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from simurg.synth.files import SynthLocus, encode_bpm
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def _cluster_values(rng: np.random.Generator, cns: np.ndarray, delta: float, gamma: float, sd: float,
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cn0_drop: float = 0.0) -> np.ndarray: # fmt: skip
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x = delta + gamma * TEMPLATE[cns] + rng.normal(0, sd, len(cns))
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return np.where(cns == 0, x + cn0_drop, x)
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def _hwe_cns(rng: np.random.Generator, n: int, f: list[float]) -> np.ndarray:
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return rng.choice(len(f), n, p=f) + rng.choice(len(f), n, p=f)
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def test_hwe_weights() -> None: # PCN-009 [OURS]
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w = hwe_weights(np.array([0.3, 0.6, 0.1, 0.0]))
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assert abs(w.sum() - 1) < 1e-12
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assert np.allclose(w[:4], [0.09, 0.36, 0.06 + 0.36, 0.12])
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def test_labels_identified() -> None: # PCN-009: GSTM1-like, GSTT1-like and one-cluster regions
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rng = np.random.default_rng(1)
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cns = _hwe_cns(rng, 60, [0.7, 0.3]) # common deletion: mostly CN 0 and 1
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x = _cluster_values(rng, cns, 0.1, 0.22, 0.02)
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f = fit_region(x)
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assert np.mean([call_cn(v, f)[0] for v in x] == cns) > 0.95
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cns = _hwe_cns(rng, 60, [0.45, 0.55])
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x = _cluster_values(rng, cns, 0.3, 0.55, 0.02, cn0_drop=-1.2) # CN 0 far below the template
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f = fit_region(x)
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assert f.cn0_shift < -0.5
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assert np.mean([call_cn(v, f)[0] for v in x] == cns) > 0.95
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cns = np.full(40, 2)
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cns[:2] = 1
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x = _cluster_values(rng, cns, -0.05, 0.25, 0.015)
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f = fit_region(x)
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assert [call_cn(v, f)[0] for v in x] == cns.tolist()
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def test_packaged_regions() -> None: # PCN-001
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name, text = default_regions()
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regs = parse_regions(text)
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assert name.endswith(".v1.bed") and len(regs) == 10
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assert {r.gene for r in regs} == {"CYP2D6", "CYP2A6", "GSTM1", "GSTT1", "UGT2B17", "SULT1A1", "CYP2E1"}
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assert next(r for r in regs if r.gene == "GSTT1").chrom == "chr22_KI270879v1_alt"
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def _dataset(tmp: Path, n_samples: int = 40) -> tuple[Path, Path, Path, Path]:
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rng = np.random.default_rng(5)
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loci = []
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for c in ("1", "2"):
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for k in range(1500):
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loci.append(SynthLocus(f"cnv{c}_{k}", c, 2000 * (k + 1), "[A/G]", len(loci) + 1, 0, 0))
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bpm = tmp / "m.bpm"
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bpm.write_bytes(encode_bpm(loci))
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fa = tmp / "g.fa"
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body = {c: "".join(rng.choice(list("ACGT"), 1500 * 2000 + 10_000)) for c in ("1", "2")}
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fa.write_text("".join(f">{c}\n" + "\n".join(s[i : i + 60] for i in range(0, len(s), 60)) + "\n"
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for c, s in body.items())) # fmt: skip
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regions = tmp / "regions.bed"
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regions.write_text("# test regions\n1\t100000\t160000\tGENEA\tGENEA\n2\t300000\t340000\tGENEB\tGENEB\n"
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"3\t1000\t5000\tGENEC\tGENEC\n") # fmt: skip
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n = len(loci)
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pos = np.array([lc.pos for lc in loci])
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chrom = np.array([lc.chrom for lc in loci])
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in_a = (chrom == "1") & (pos > 100_000) & (pos <= 160_000)
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in_b = (chrom == "2") & (pos > 300_000) & (pos <= 340_000)
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cn_a = _hwe_cns(rng, n_samples, [0.5, 0.5])
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gtcs = tmp / "gtcs"
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truth = {}
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for i in range(n_samples):
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sid = f"2000000000{i:02d}_R01C01"
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lrr = rng.normal(0, 0.08, n)
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lrr[in_a] += 0.24 * TEMPLATE[cn_a[i]] + (-0.2 if cn_a[i] == 0 else 0.0)
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cn_b = 3 if i == 0 else 2
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lrr[in_b] += 0.25 * TEMPLATE[cn_b]
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truth[sid] = (int(cn_a[i]), cn_b)
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g = Gtc(num_snps=n, genotypes=np.ones(n, np.uint8), genotype_scores=np.full(n, 0.9, np.float32),
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b_allele_freqs=np.zeros(n, np.float32), logr_ratios=lrr.astype(np.float32),
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raw_x=np.full(n, 1000, np.uint16), raw_y=np.full(n, 1000, np.uint16),
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base_calls=np.zeros((n, 2), np.uint8), gender=b"F", call_rate=0.995, logr_dev=0.12,
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cluster_file="c.egt") # fmt: skip
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(gtcs / sid).mkdir(parents=True)
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write_gtc(g, gtcs / sid / f"{sid}.gtc")
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(tmp / "truth.txt").write_text("\n".join(f"{k}\t{a}\t{b}" for k, (a, b) in truth.items()))
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return bpm, fa, gtcs, regions
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def test_pgx_cn_train_and_call(tmp_path: Path) -> None: # CLI-031/032, PCN-002/004/011/012/013, IN-050
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bpm, fa, gtcs, regions = _dataset(tmp_path)
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run = CliRunner()
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r = run.invoke(app, ["pgx", "copy-number", "train", "--gtcs", str(gtcs), "--bpm", str(bpm), "--fasta", str(fa),
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"--out", str(tmp_path / "model"), "--regions", str(regions), "--quiet"]) # fmt: skip
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assert r.exit_code == 2 and "E_PGX_TRAIN_TOO_SMALL" in r.output # 40 < 96 (PCN-005)
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r = run.invoke(app, ["pgx", "copy-number", "train", "--gtcs", str(gtcs), "--bpm", str(bpm), "--fasta", str(fa),
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"--out", str(tmp_path / "model"), "--regions", str(regions), "--min-samples", "30",
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"--min-passing", "30", "--quiet"]) # fmt: skip
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assert r.exit_code == 0, r.output
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model = tmp_path / "model" / "m.simurg-pgxcn"
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assert model.exists() and (tmp_path / "model" / "train_exclusions.tsv").exists()
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out = tmp_path / "call"
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r = run.invoke(app, ["pgx", "copy-number", "call", "--gtcs", str(gtcs), "--model", str(model), "--fasta", str(fa),
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"--out", str(out), "--min-batch", "50", "--quiet"]) # fmt: skip
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assert r.exit_code == 2 and "E_PGX_BATCH_TOO_SMALL" in r.output
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r = run.invoke(app, ["pgx", "copy-number", "call", "--gtcs", str(gtcs), "--model", str(model), "--fasta", str(fa),
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"--out", str(out), "--quiet"]) # fmt: skip
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assert r.exit_code == 0, r.output
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truth = {ln.split("\t")[0]: (int(ln.split("\t")[1]), int(ln.split("\t")[2]))
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for ln in (tmp_path / "truth.txt").read_text().splitlines()} # fmt: skip
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ok_a = ok_b = 0
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for sid, (a, b) in truth.items():
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with gzip.open(out / f"{sid}.pgx_cn.vcf.gz", "rt") as fh:
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text = fh.read()
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assert (out / f"{sid}.pgx_cn.vcf.gz.tbi").exists()
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recs = [ln.split("\t") for ln in text.splitlines() if not ln.startswith("#")]
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assert [x[7].split("REGION=")[1].split(";")[0] for x in recs] == ["GENEA", "GENEB", "GENEC"] # PCN-002
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for x in recs: # PCN-011: POS = S − 1, ID coordinates = POS + 1 … END
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end = int(x[7].split("END=")[1].split(";")[0])
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s_, e_ = x[2].split(":")[-1].split("-")
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assert int(s_) == int(x[1]) + 1 and int(e_) == end and x[8] == "CN:NR"
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genec = recs[2]
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assert genec[6] == "NoCall" and genec[9].startswith(".:")
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ok_a += int(recs[0][9].split(":")[0]) == a
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ok_b += int(recs[1][9].split(":")[0]) == b
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assert ok_a >= 0.95 * len(truth) and ok_b >= 0.9 * len(truth), (ok_a, ok_b)
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rows = [ln.split("\t") for ln in (out / "pgx_cn_batch_summary.tsv").read_text().splitlines()]
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assert rows[0] == ["region", "gene", "n_gain", "n_loss", "n_neutral", "n_nocall", "n_filtered"]
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assert [r_[0] for r_ in rows[1:]] == ["GENEA", "GENEB", "GENEC", "ALL"]
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for r_ in rows[1:-1]: # PCN-012: the four counts add up to N
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assert sum(int(v) for v in r_[2:6]) == len(truth)
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head = (out / "pgx_cn_sample_summary.tsv").read_text().splitlines()[0].split("\t")
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assert head[:6] == ["sample_id", "sample_name", "sample_folder", "logr_dev", "passed_batch_qc",
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"used_in_reference"] and head[-1] == "flags" # fmt: skip
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