kemalandClaude Opus 5.5
c1c2c30c87
Methylation CSV manifest reader (MET-002/003), raw M/U assembly with bead masking (MET-005), pOOBAH detection p-values, noob background correction, linear/nonlinear dye bias, beta and M-values (MET-010..012), the 21 methylation control metrics (MET-006, bs-conv red-bg set option), per-sample ctrl/cgs tables, summary tsv/xlsx/html with the detection histogram and a beta PCA, pcs.tsv.gz, controls_raw/controls_qc.tsv; methylation datasets load in qc report (MET-014); qc controls --array-type methylation; minfi-style Sentrix_ID/Sentrix_Position sheet keys. Vendor EPIC manifests carry a 5th field on some control lines: >= 4 fields accepted. Runs on the public minfiDataEPIC set (3 x NA12878). Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
396 lines
17 KiB
Python
396 lines
17 KiB
Python
"""Phase P5 QC unit tests: control metrics (QC-001…QC-008), thresholds (QC-020…QC-025), status (QC-030…QC-033),
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contamination (plan §9.3), relatedness (plan §9.4), locus summary (OUT-072), suggestions (QC-026)."""
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from __future__ import annotations
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import math
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from pathlib import Path
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import numpy as np
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import pytest
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from simurg.core.errors import SimurgError
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from simurg.qc.contamination import _median_exact, _median_reference, b_allele_frequency, baf_regress, ks_ksmall
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from simurg.qc.controls import (
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beads_from_definitions,
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canonical_id,
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compute_control_metrics,
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excluded_from_report,
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normalise_name,
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)
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from simurg.qc.locus_summary import gs_percentile_columns, summarize_block
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from simurg.qc.relatedness import degree, mendel_errors, pair_stats, select_sites
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from simurg.qc.status import control_status, functional_status, het_zscores, sample_flags
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from simurg.qc.suggest import suggest
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from simurg.qc.thresholds import auto_profile, parse_cli_pairs, profile_values, resolve_thresholds
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GSA_CONTROLS = [
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("1", "Staining", "Red", "DNP (High)"),
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("2", "Staining", "Purple", "DNP (Bgnd)"),
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("3", "Staining", "Green", "Biotin (High)"),
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("4", "Staining", "Blue", "Biotin (Bgnd)"),
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("5", "Extension", "Red", "Extension (A)"),
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("6", "Extension", "Purple", "Extension (T)"),
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("7", "Extension", "Green", "Extension (C)"),
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("8", "Extension", "Blue", "Extension (G)"),
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("9", "Target Removal", "Green", "Target Removal"),
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("10", "Hybridization", "Green", "Hyb (High)"),
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("11", "Hybridization", "Blue", "Hyb (Medium)"),
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("12", "Hybridization", "Black", "Hyb (Low)"),
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("13", "Stringency", "Red", "String (PM)"),
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("14", "Stringency", "Purple", "String (MM)"),
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("15:15:15", "Non-Specific Binding", "Red", "NSB (Bgnd)"),
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("16", "Non-Specific Binding", "Purple", "NSB (Bgnd)"),
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("17", "Non-Polymorphic", "Red", "NP(A)"),
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("18", "Non-Polymorphic", "Purple", "NP(T)"),
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("19", "Non-Polymorphic", "Green", "NP(C)"),
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("20", "Non-Polymorphic", "Blue", "NP(G)"),
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("21", "Restoration", "Green", "Restore"),
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]
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def _beads(): # type: ignore[no-untyped-def]
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return beads_from_definitions(
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[{"address": a, "category": c, "color": col, "name": n} for a, c, col, n in GSA_CONTROLS]
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)
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# ------------------------------------------------------------------------------------------------ controls
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@pytest.mark.parametrize(
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("name", "cid"),
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[
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("Extension (A)", "ext_A"),
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("Biotin (High)", "biotin_high"),
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("DNP (Bgnd)", "dnp_bkg"),
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("NP (A)", "np_A"),
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("NP(A)", "np_A"),
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("String (PM)", "string_pm"),
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("NSB (Bgnd) Red", "nsb_bkg"),
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("Hyb ( High )", "hyb_high"),
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("Target Removal", "target_removal_1"),
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("Restore", "restoration"),
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("GT Mismatch 1 (PM)", "gtmm_1_pm"),
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("Something else", ""),
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],
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)
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def test_canonical_names(name: str, cid: str) -> None: # QC-002
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assert canonical_id(name) == cid
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def test_normalise_and_exclusion() -> None: # QC-002, QC-010
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assert normalise_name(" NP (G) 1 ") == "np(g) 1"
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b = beads_from_definitions([{"address": "1", "category": "NEGATIVE", "color": "Red", "name": "x"}])[0]
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assert excluded_from_report(b)
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def test_bead_expansion_dedups_addresses() -> None: # QC-001
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beads = _beads()
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assert len(beads) == len(GSA_CONTROLS) # "15:15:15" is one bead type
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assert beads[14].address == 15
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def test_control_metrics_formulas() -> None: # QC-003, QC-004
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beads = _beads()
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g = np.zeros(len(beads))
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r = np.zeros(len(beads))
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val = {b.name + b.color: i for i, b in enumerate(beads)}
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def put(name: str, color: str, green: float, red: float) -> None:
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g[val[name + color]], r[val[name + color]] = green, red
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put("DNP (High)", "Red", 100, 8000)
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put("DNP (Bgnd)", "Purple", 90, 800)
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put("Biotin (High)", "Green", 9000, 50)
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put("Biotin (Bgnd)", "Blue", 900, 50)
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put("Extension (A)", "Red", 300, 9000)
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put("Extension (T)", "Purple", 400, 8000)
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put("Extension (C)", "Green", 7000, 200)
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put("Extension (G)", "Blue", 6000, 250)
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put("Target Removal", "Green", 1700, 10)
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put("Hyb (High)", "Green", 6000, 10)
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put("Hyb (Medium)", "Blue", 3000, 10)
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put("Hyb (Low)", "Black", 1000, 10)
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put("String (PM)", "Red", 10, 3000)
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put("String (MM)", "Purple", 10, 1000)
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put("NSB (Bgnd)", "Red", 500, 400)
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put("NSB (Bgnd)", "Purple", 700, 300)
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put("NP(A)", "Red", 100, 5000)
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put("NP(T)", "Purple", 120, 6000)
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put("NP(C)", "Green", 4000, 300)
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put("NP(G)", "Blue", 5000, 200)
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put("Restore", "Green", 1360, 0)
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q = compute_control_metrics(beads, g, r, bg_offset=3000)
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v = q.values
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bkg_grn, bkg_red = 400.0, 250.0
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assert v["restoration"] == pytest.approx(1360 / (bkg_grn + 3000))
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assert v["staining_green"] == pytest.approx(10.0)
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assert v["staining_red"] == pytest.approx(10.0)
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assert v["extension_green"] == pytest.approx(6000 / 400)
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assert v["extension_red"] == pytest.approx(8000 / 250)
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assert v["hyb_high_med"] == pytest.approx(2.0)
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assert v["hyb_med_low"] == pytest.approx(3.0)
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assert v["target_removal_1"] == pytest.approx(3400 / 1700)
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assert v["stringency"] == pytest.approx(3.0)
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assert v["nsb_green"] == pytest.approx((bkg_grn + 3000) / 700) # max over all NSB beads
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assert v["nsb_red"] == pytest.approx((bkg_red + 3000) / 400) # NSB red uses bkg_red
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assert v["np_green"] == pytest.approx(4000 / 120)
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assert v["np_red"] == pytest.approx(5000 / 300)
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assert not q.missing and not q.zero_divisor
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assert "target_removal_2" not in v # QC-007: not a genotyping column
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def test_control_missing_and_zero_divisor() -> None: # QC-007, QC-008
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beads = [b for b in _beads() if b.canonical != "restoration"]
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g = np.ones(len(beads))
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r = np.ones(len(beads))
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i = next(k for k, b in enumerate(beads) if b.canonical == "string_mm")
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r[i] = 0
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q = compute_control_metrics(beads, g, r)
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assert math.isnan(q.values["restoration"]) and q.missing["restoration"] == ["restoration"]
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assert q.zero_divisor == ["stringency"] and q.values["stringency"] == math.inf # QC-008: signal / 0 passes
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r[i - 1] = 0
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q = compute_control_metrics(beads, g, r)
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assert math.isnan(q.values["stringency"]) # 0 / 0 is NaN (flagged)
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def test_absent_addresses_are_skipped() -> None: # QC-008, K-021: two bead-pool addresses, one on the chip
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beads = beads_from_definitions(
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[
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{"address": "5:105", "category": "Staining", "color": "Green", "name": "Biotin (High)"},
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{"address": "6:106", "category": "Staining", "color": "Blue", "name": "Biotin (Bgnd)"},
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]
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)
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g = np.array([np.nan, 900.0, 100.0, np.nan])
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q = compute_control_metrics(beads, g, np.zeros(4))
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assert q.values["staining_green"] == pytest.approx(9.0)
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q = compute_control_metrics(beads, np.array([np.nan, 900.0, np.nan, np.nan]), np.zeros(4))
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assert q.missing["staining_green"] == ["biotin_bkg"]
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def test_methylation_metric_set() -> None: # QC-006: methylation metrics are computed (v2); missing ones are NaN
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q = compute_control_metrics(_beads(), np.ones(21), np.ones(21), "methylation")
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assert len(q.values) == 21 and "stringency" not in q.values and "bs_conv_2" in q.missing
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# ------------------------------------------------------------------------------------------------ thresholds
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def test_builtin_and_precedence(tmp_path: Path) -> None: # QC-023
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th = resolve_thresholds()
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assert th.get("callRate").value == 0.98 and th.get("logRDev").value == 0.20
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assert not th.get("stainingGreen").enabled
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cfg = tmp_path / "t.yaml"
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cfg.write_text("qc:\n profile: genotyping\n report:\n callRate: 0.90\n npRed: null\n")
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th = resolve_thresholds(config=cfg, cli={"callRate": "0.95"})
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assert th.get("callRate").value == 0.95 and th.get("callRate").source == "cli"
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assert th.get("npRed").value is None and th.get("npRed").source == "config" # QC-022 disabled
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assert th.get("stringency").value == 2 and th.get("stringency").source == "profile:genotyping"
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def test_yaml_spellings_and_aliases(tmp_path: Path) -> None: # QC-020
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cfg = tmp_path / "t.yml"
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cfg.write_text("QC:\n Report:\n tgaControl: 1\n stringency: {threshold: 2, direction: above}\n")
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th = resolve_thresholds(config=cfg)
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assert th.get("assayControl").value == 1
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assert th.get("stringency").direction == "above"
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@pytest.mark.parametrize(
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("content", "suffix", "code"),
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[
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(None, ".yaml", "E_QC_CONFIG_MISSING"),
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("qc: {report: {}}", ".txt", "E_QC_CONFIG_EXT"),
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("qc: [unclosed", ".yaml", "E_QC_CONFIG_PARSE"),
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("report: {callRate: 1}", ".yaml", "E_QC_CONFIG_STRUCTURE"),
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("qc: {report: {bogus: 1}}", ".yaml", "E_QC_THRESHOLD_KEY"),
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("qc: {report: {callRate: high}}", ".yaml", "E_QC_THRESHOLD_VALUE"),
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("qc: {report: {callRate: 1.5}}", ".yaml", "E_QC_THRESHOLD_RANGE"),
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("qc: {report: {npRed: -1}}", ".yaml", "E_QC_THRESHOLD_RANGE"),
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("qc: {profile: nope, report: {}}", ".yaml", "E_QC_PROFILE"),
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],
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)
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def test_config_validation(tmp_path: Path, content: str | None, suffix: str, code: str) -> None: # QC-021
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p = tmp_path / f"c{suffix}"
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if content is not None:
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p.write_text(content)
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with pytest.raises(SimurgError) as e:
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resolve_thresholds(config=p)
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assert e.value.code == code
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def test_profiles() -> None: # QC-024, QC-025
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assert profile_values("genotyping_example")["hybGreenHighMed"] == 1.25
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assert profile_values("methylation_epic+ffpe")["restoration"] == 1
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assert profile_values("methylation_msa")["bisulfiteConversionIIBg"] == 0.5
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assert auto_profile("genotyping", "EX") == "genotyping_ex"
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assert auto_profile("genotyping", "LCG") == "genotyping"
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assert auto_profile("methylation", manifest_name="MSA-48v1") == "methylation_msa"
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with pytest.raises(SimurgError):
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parse_cli_pairs(["callRate"])
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# ------------------------------------------------------------------------------------------------ status
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def test_status_axes() -> None: # QC-030, QC-031
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th = resolve_thresholds(profile="genotyping")
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ok = {"status": "OK", "autosomal_call_rate": 0.98, "logr_dev": 0.2} # equality passes
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assert functional_status(ok, th) == "PASS"
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assert functional_status({**ok, "logr_dev": 0.21}, th) == "FAIL"
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assert functional_status({**ok, "logr_dev": float("nan")}, th) == "FAIL"
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assert functional_status({**ok, "status": "FAILED"}, th) == "FAIL"
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assert functional_status(None, th) == "NA"
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vals = {"np_red": 4.0, "stringency": 1.0, "staining_green": 9.0, "restoration": 0.1}
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st, codes = control_status(vals, [], th)
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assert st == "FLAGGED" and codes == ["stringency", "npRed"] # QC-004 order; restoration disabled
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st, codes = control_status({**vals, "np_red": 6, "stringency": 3}, ["np_red"], th)
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assert st == "CLEAR"
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def test_flags() -> None: # QC-033
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th = resolve_thresholds()
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row = {
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"status": "OK",
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"autosomal_call_rate": 0.9,
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"logr_dev": 0.3,
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"sex_check": "mismatch",
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"contamination": 0.05,
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"TGA_norm_r": 0.5,
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}
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f = sample_flags(row, th, het_z=4.0, probe_columns=["TGA_norm_r"], duplicates=["x"], unexpected_relatedness=True)
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assert f == [
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"low_call_rate",
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"high_lrr_sd",
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"sex_mismatch",
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"het_outlier",
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"contaminated",
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"assay_control_fail",
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"duplicate_of:x",
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"unexpected_relatedness",
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]
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z = het_zscores([0.1, 0.2, 0.3, float("nan")])
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assert z[1] == pytest.approx(0.0, abs=1e-12) and math.isnan(z[3])
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# ------------------------------------------------------------------------------------------------ contamination
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def test_ksmall_matches_selection() -> None:
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rng = np.random.default_rng(0)
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for n in range(1, 40):
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a = rng.random(n).tolist()
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assert ks_ksmall(list(a), n // 2) == sorted(a)[n // 2]
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def test_median_modes() -> None:
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b = np.array([[0.1, 0.3, 0.2, 0.4, 0.9]], dtype=np.float32)
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m = np.array([[True, True, True, True, False]])
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assert _median_exact(b, m)[0] == np.float32((np.float32(0.2) + np.float32(0.3)) * np.float32(0.5))
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assert np.isfinite(_median_reference(b[0][m[0]]))
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def _simulate(alpha: float, n: int = 60, m: int = 20000, seed: int = 1): # type: ignore[no-untyped-def]
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"""Genotypes and BAF of n samples; sample 0 is contaminated with fraction alpha."""
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rng = np.random.default_rng(seed)
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p = rng.uniform(0.1, 0.9, m)
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g = rng.binomial(2, p[:, None], (m, n))
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baf = g / 2 + rng.normal(0, 0.02, (m, n))
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other = rng.binomial(2, p) / 2
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baf[:, 0] = (1 - alpha) * g[:, 0] / 2 + alpha * other + rng.normal(0, 0.02, m)
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return (g + 1).astype(np.uint8), baf.astype(np.float32)
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@pytest.mark.parametrize("median", ["reference", "exact"])
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def test_baf_regress_recovers_alpha(median: str) -> None: # plan §9.3
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gt, baf = _simulate(0.1)
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res = baf_regress(gt, baf, median=median)
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assert res.estimate[0] == pytest.approx(0.1, abs=0.015)
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assert np.all(np.abs(res.estimate[1:]) < 0.01)
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assert res.n_sites > 0 and res.n_hom[0] > 0
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def test_af_threshold_is_float32() -> None:
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gt = np.array([[1] * 9 + [3]], dtype=np.uint8) # B frequency 0.1 exactly → excluded (0.1f > 0.1)
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assert b_allele_frequency(gt)[0] == 0.1
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assert baf_regress(gt, np.zeros_like(gt, dtype=np.float32)).n_sites == 0
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# ------------------------------------------------------------------------------------------------ relatedness
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def test_king_duplicate_parent_unrelated() -> None:
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rng = np.random.default_rng(3)
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m = 20000
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p = rng.uniform(0.05, 0.95, m)
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hap = lambda: (rng.random(m) < p).astype(np.int8) # noqa: E731
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f1, f2, m1, m2 = hap(), hap(), hap(), hap()
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father, mother = f1 + f2, m1 + m2
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child = np.where(rng.random(m) < 0.5, f1, f2) + np.where(rng.random(m) < 0.5, m1, m2)
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unrel = hap() + hap()
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G = np.stack([father, mother, child, child.copy(), unrel], axis=1).astype(np.uint8) + 1
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ps = pair_stats(G)
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assert ps.kinship[2, 3] == pytest.approx(0.5, abs=0.01)
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assert ps.kinship[0, 2] == pytest.approx(0.25, abs=0.03)
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assert abs(ps.kinship[0, 4]) < 0.03 and abs(ps.kinship[0, 1]) < 0.03
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assert ps.concordance[2, 3] == 1.0
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assert degree(0.5) == "duplicate" and degree(0.25) == "first" and degree(0.0) == "unrelated"
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e, n = mendel_errors(G[:, 2], G[:, 0], G[:, 1])
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assert e == 0 and n == m
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e, _ = mendel_errors(G[:, 4], G[:, 0], G[:, 1])
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assert e > 1000
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def test_mendel_rules() -> None:
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c = np.array([1, 2, 3, 2, 1, 0], np.uint8)
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f = np.array([1, 1, 1, 3, 3, 1], np.uint8)
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mo = np.array([1, 1, 3, 3, 2, 1], np.uint8)
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assert mendel_errors(c, f, mo) == (4, 5) # AA×AA→AB, AA×BB→BB, BB×BB→AB, BB×AB→AA; no-call skipped
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assert mendel_errors(c, f, None) == (2, 5) # duo: opposite homozygotes only (BB/AA, AA/BB)
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def test_select_sites() -> None:
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counts = np.array([[0, 50, 0, 50], [10, 45, 0, 45], [0, 100, 0, 0], [0, 99, 1, 0]], np.int64)
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idx = select_sites(counts, np.array([True, True, True, False]))
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assert idx.tolist() == [0] # 1: call rate 0.9; 2: monomorphic; 3: not usable
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# ------------------------------------------------------------------------------------------------ locus summary
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def test_gs_percentile_matches_scalar_reference() -> None:
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def ref(scores: list[float], p: int) -> float: # BeadArrayFiles ScoreStatistics.percentile
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n = len(scores)
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if n == 0:
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return math.nan
|
||
idx = int(n * p / 100)
|
||
if n * p / 100.0 - idx < 0.5:
|
||
idx -= 1
|
||
if idx < 0:
|
||
return scores[0]
|
||
if idx >= n - 1:
|
||
return scores[-1]
|
||
x1, x2 = 100 * (idx + 0.5) / float(n), 100 * (idx + 1 + 0.5) / float(n)
|
||
y1, y2 = float(scores[idx]), float(scores[idx + 1])
|
||
return y1 + (y2 - y1) / (x2 - x1) * (p - x1)
|
||
|
||
rng = np.random.default_rng(5)
|
||
cols = [np.sort(rng.random(k).astype(np.float32)) for k in range(0, 25)]
|
||
mat = np.full((25, len(cols)), np.inf, np.float32)
|
||
for j, c in enumerate(cols):
|
||
mat[: len(c), j] = c
|
||
counts = np.array([len(c) for c in cols])
|
||
for p in (10, 50):
|
||
got = gs_percentile_columns(mat, counts, p)
|
||
want = [ref(c.tolist(), p) for c in cols]
|
||
np.testing.assert_array_equal(got, np.array(want))
|
||
|
||
|
||
def test_summarize_block_edge_cases() -> None:
|
||
g = np.array([[0, 1, 1, 2], [0, 1, 3, 2], [0, 1, 2, 2]], np.uint8) # samples × loci
|
||
s = np.full(g.shape, 0.8, np.float32)
|
||
st = summarize_block(g, s)
|
||
assert st["chi_test_p100"][0] == 0 and st["het_excess"][0] == 0 # no calls
|
||
assert st["chi_test_p100"][1] == 1 and st["het_excess"][1] == 0 # monomorphic
|
||
assert st["het_excess"][3] > 0.9 and st["chi_test_p100"][3] < 1e-10 # all AB: not monomorphic in the reference
|
||
assert np.isnan(st["gc50"][0]) and st["gc50"][1] == pytest.approx(0.8)
|
||
assert st["minor_freq"][2] == pytest.approx(0.5)
|
||
|
||
|
||
# ------------------------------------------------------------------------------------------------ suggest
|
||
def test_suggest_thresholds() -> None: # QC-026
|
||
rng = np.random.default_rng(2)
|
||
vals = {"npRed": list(np.exp(rng.normal(np.log(10), 0.1, 200))), "logRDev": [0.1] * 10}
|
||
sugs = {s.key: s for s in suggest(vals, resolve_thresholds(), k=4)}
|
||
assert sugs["npRed"].value == pytest.approx(10 * math.exp(-0.4), rel=0.1)
|
||
assert sugs["logRDev"].value is None and "fewer" in sugs["logRDev"].note
|