kemal
b699d2c188
- compare/core: EXACT/NEAR/DISCREPANCY/UNKNOWN with the plan §16.3 tolerances (abs, float32 ULP), NaN = NaN, locus classes (assay, variant type, chromosome group, intensity-only); fields absent on both sides are not applicable - compare/records: GTC vs GTC (per-locus arrays, transforms per bin, controls, sample statistics; metadata listed, not counted) and VCF vs VCF (records keyed incl. ID; SNV/MNV/indel, merged, multi-allelic classes) - simurg compare A B (files or folders paired by name): summary.json, discrepancies.tsv.gz (never truncated), self-contained dashboard.html; --tolerance, --fail-on-discrepancy (exit 3) - scripts/nightly.py + install_nightly.sh (crontab 02:30): unit tests, GSA vs gtc2vcf (GTC + VCF), VCF regression vs golden, Omni2.5 subset vs vendor GTCs; first run PASS on every step - tests/unit/test_compare.py; docs/validation/P7.md, README
224 lines
8.2 KiB
Python
224 lines
8.2 KiB
Python
"""GTC-vs-GTC and VCF-vs-VCF comparison on parsed data (spec CLI-038; plan §16.3)."""
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from __future__ import annotations
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import math
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from collections.abc import Mapping, Sequence
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from dataclasses import dataclass, field
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from typing import Any
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import numpy as np
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import numpy.typing as npt
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from numpy.lib import recfunctions as rfn
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from simurg.compare.core import (
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DISCREPANCY,
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EXACT,
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GTC_TOLERANCES,
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UNKNOWN,
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VCF_EXACT_FIELDS,
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VCF_FLOAT_TOL,
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FieldResult,
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Tolerance,
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categorize,
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compare_locus_field,
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compare_scalar,
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tol_text,
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)
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GTC_LOCUS_FIELDS = ("raw_x", "raw_y", "genotypes", "base_calls", "genotype_scores", "b_allele_freqs", "logr_ratios")
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GTC_SCALARS = ("call_rate", "logr_dev", "gc10", "gc50", "gender", "num_calls", "num_no_calls", "num_intensity_only")
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GTC_METADATA = ("sample_name", "sample_plate", "sample_well", "cluster_file", "snp_manifest", "imaging_date",
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"autocall_date", "autocall_version", "slide_identifier") # fmt: skip
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@dataclass
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class Comparison:
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"""Results of one pair (one sample)."""
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label: str
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kind: str # "gtc" | "vcf"
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fields: list[FieldResult] = field(default_factory=list)
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metadata: dict[str, list[str]] = field(default_factory=dict) # field -> [a, b] when they differ
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notes: list[str] = field(default_factory=list)
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def compare_gtc(
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a: Any,
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b: Any,
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label: str,
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classes: Mapping[str, npt.NDArray[np.bool_]],
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names: Sequence[str],
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tolerances: Mapping[str, Tolerance] = GTC_TOLERANCES,
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) -> Comparison:
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"""Two parsed GTCs of the same manifest."""
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res = Comparison(label, "gtc")
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ctx = {"pair": label}
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if a.num_snps != b.num_snps:
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res.notes.append(f"locus count differs: {a.num_snps} vs {b.num_snps}")
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return res
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for f in GTC_LOCUS_FIELDS:
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va, vb = getattr(a, f, None), getattr(b, f, None)
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if (va is None or len(va) == 0) and (vb is None or len(vb) == 0):
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continue # absent on both sides (e.g. BAF/LRR in GTC v3): not applicable
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res.fields.append(compare_locus_field(f, va, vb, tolerances[f], classes, names, ctx))
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# normalization transforms: one row per bin
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ta, tb = a.normalization_transforms, b.normalization_transforms
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fr = FieldResult("normalization_transforms", tol_text(tolerances["normalization_transforms"]))
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if len(ta) != len(tb) or not len(ta):
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fr.add("bins", np.full(max(len(ta), len(tb), 1), UNKNOWN, np.int8))
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else:
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ua = rfn.structured_to_unstructured(ta).astype(np.float32)
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ub = rfn.structured_to_unstructured(tb).astype(np.float32)
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cats = categorize(ua, ub, tolerances["normalization_transforms"]).max(axis=1)
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fr.add("bins", cats)
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for i in np.flatnonzero(cats == DISCREPANCY).tolist():
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fr.discrepancies.append({**ctx, "field": "normalization_transforms", "locus": f"bin{i}",
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"a": str(ua[i].tolist()), "b": str(ub[i].tolist())}) # fmt: skip
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res.fields.append(fr)
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# controls: one row per control bead
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for f in ("controls_x", "controls_y"):
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ca, cb = getattr(a, f), getattr(b, f)
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fr = FieldResult(f, tol_text(tolerances[f]))
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fr.add("controls", categorize(ca, cb, tolerances[f]) if len(ca) == len(cb) else np.array([UNKNOWN], np.int8))
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res.fields.append(fr)
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for f in GTC_SCALARS:
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res.fields.append(compare_scalar(f, _scalar(a, f), _scalar(b, f), tolerances[f], ctx))
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for f in ("percentiles_x", "percentiles_y"):
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pa, pb = getattr(a, f), getattr(b, f)
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fr = FieldResult(f, tol_text(tolerances[f]))
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fr.add("sample", categorize(pa, pb, tolerances[f]) if len(pa) == len(pb) else np.array([UNKNOWN], np.int8))
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res.fields.append(fr)
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for f in GTC_METADATA:
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va, vb = str(getattr(a, f, "")), str(getattr(b, f, ""))
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if va != vb:
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res.metadata[f] = [va, vb]
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if a.version != b.version:
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res.notes.append(f"GTC versions differ: {a.version} vs {b.version} (fields absent in one are UNKNOWN)")
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return res
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def _scalar(g: Any, f: str) -> Any:
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if f == "gender":
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return g.gender_char
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v = getattr(g, f, None)
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if isinstance(v, float) and math.isnan(v):
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return v
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return v
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# ------------------------------------------------------------------------------------------------ VCF
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@dataclass
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class VcfRecord:
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chrom: str
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pos: int
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vid: str
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ref: str
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alt: str
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fields: dict[str, str] # FORMAT key -> value of the compared sample
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def vcf_classes(r: VcfRecord) -> list[str]:
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c = r.chrom[3:] if r.chrom.lower().startswith("chr") else r.chrom
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grp = "autosome" if c.isdigit() else {"X": "X", "Y": "Y", "MT": "MT", "M": "MT"}.get(c, "other")
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alts = r.alt.split(",")
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if all(len(x) == len(r.ref) == 1 for x in alts):
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vtype = "SNV"
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elif all(len(x) == len(r.ref) for x in alts):
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vtype = "MNV"
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else:
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vtype = "indel"
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out = ["all", f"chrom={grp}", f"type={vtype}"]
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if "," in r.vid or ";" in r.vid:
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out.append("merged_site")
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if len(alts) > 1:
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out.append("multiallelic")
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return out
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def _gt_norm(gt: str) -> str:
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parts = gt.replace("|", "/").split("/")
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if any(p == "." for p in parts):
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return "." if len(parts) == 1 else "./."
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return "/".join(sorted(parts, key=int))
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def _cat_scalar(va: str, vb: str, tol: Tolerance) -> int:
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if va == vb:
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return EXACT
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x, y = _num(va), _num(vb)
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if math.isnan(x) and math.isnan(y):
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return EXACT
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if math.isnan(x) or math.isnan(y):
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return DISCREPANCY
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if x == y:
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return EXACT
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if tol.abs is not None and abs(x - y) <= tol.abs:
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return 1 # NEAR
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return DISCREPANCY
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def compare_vcf_records(
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a: Mapping[tuple[str, int, str, str, str], VcfRecord],
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b: Mapping[tuple[str, int, str, str, str], VcfRecord],
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label: str,
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float_tol: Tolerance = VCF_FLOAT_TOL,
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) -> Comparison:
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"""Records keyed by (CHROM, POS, REF, ALT, ID); FORMAT fields of one sample on each side."""
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res = Comparison(label, "vcf")
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ctx = {"pair": label}
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keys = list(a) + [k for k in b if k not in a]
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fields = sorted({f for r in a.values() for f in r.fields} | {f for r in b.values() for f in r.fields})
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counts: dict[str, dict[str, list[int]]] = {f: {} for f in ["record", *fields]}
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disc: dict[str, list[dict[str, Any]]] = {f: [] for f in ["record", *fields]}
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tols = {f: (Tolerance() if f in VCF_EXACT_FIELDS else float_tol) for f in fields}
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def bump(f: str, classes: list[str], cat: int) -> None:
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per = counts[f]
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for cls in classes:
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c = per.get(cls)
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if c is None:
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c = per[cls] = [0, 0, 0, 0]
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c[cat] += 1
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for k in keys:
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ra, rb = a.get(k), b.get(k)
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r = ra or rb
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assert r is not None
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classes = vcf_classes(r)
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where = f"{r.chrom}:{r.pos}:{r.ref}>{r.alt} {r.vid}"
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if ra is None or rb is None:
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bump("record", classes, UNKNOWN)
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disc["record"].append({**ctx, "field": "record", "locus": where,
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"a": "present" if ra else "absent", "b": "present" if rb else "absent"}) # fmt: skip
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continue
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bump("record", classes, EXACT)
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for f in fields:
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va, vb = ra.fields.get(f), rb.fields.get(f)
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if va is None and vb is None:
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continue # the field does not apply to this record on either side (e.g. BAF of merged sites)
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if va is None or vb is None:
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cat = UNKNOWN
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elif f == "GT":
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cat = EXACT if _gt_norm(va) == _gt_norm(vb) else DISCREPANCY
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else:
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cat = _cat_scalar(va, vb, tols[f])
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bump(f, classes, cat)
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if cat == DISCREPANCY:
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disc[f].append({**ctx, "field": f, "locus": where, "a": va, "b": vb})
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for f in ["record", *fields]:
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fr = FieldResult(
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f, "present in both" if f == "record" else ("exact" if f in VCF_EXACT_FIELDS else tol_text(float_tol))
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)
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fr.counts = counts[f]
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fr.discrepancies = disc[f]
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res.fields.append(fr)
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return res
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def _num(v: str) -> float:
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try:
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return float(v)
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except ValueError:
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return math.nan
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