76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""Compare `simurg methyl qc` numerics with the sesame oracle (scripts/v2/sesame_oracle.R) on the same probes.
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python scripts/v2/methyl_oracle_compare.py --manifest M.csv --sesame DIR --out summary.json PREFIX...
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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from pathlib import Path
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import numpy as np
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from simurg.formats.idat import read_idat
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from simurg.formats.methyl_manifest import read_methyl_manifest
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from simurg.signal import methyl as sig
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def _q(a: np.ndarray) -> dict[str, float]:
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a = a[np.isfinite(a)]
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if not len(a):
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return {}
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return {k: float(np.quantile(a, q)) for k, q in (("median", 0.5), ("p99", 0.99), ("max", 1.0))}
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--manifest", required=True)
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ap.add_argument("--sesame", required=True, type=Path)
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ap.add_argument("--out", required=True, type=Path)
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ap.add_argument("prefixes", nargs="+")
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a = ap.parse_args()
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man = read_methyl_manifest(a.manifest)
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pos = {p: i for i, p in enumerate(man.probe_ids)}
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res = {}
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for prefix in a.prefixes:
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s = sig.assemble(read_idat(prefix + "_Grn.idat"), read_idat(prefix + "_Red.idat"), man)
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p = sig.detection_p(s)
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n, fg, fr = sig.noob(s)
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b = sig.beta_values(n)
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rows = list(csv.DictReader((a.sesame / (Path(prefix).name + ".tsv")).open(), delimiter="\t"))
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idx = np.array([pos[r["probe_id"]] for r in rows])
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def col(name: str, rows: list[dict[str, str]] = rows) -> np.ndarray:
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return np.array([float(r[name]) if r[name] not in ("NA", "") else np.nan for r in rows])
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mask_s = np.array([r["mask"] == "TRUE" for r in rows])
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keep = ~mask_s & ~s.masked[idx]
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sp, sm, su, sb = col("pval"), col("m_noob"), col("u_noob"), col("beta_noob")
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op, om, ou, ob = p[idx], n.m[idx], n.u[idx], b[idx]
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ana = man.analytical()[idx] & keep
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res[Path(prefix).name] = {
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"probes": len(rows),
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"masked_sesame": int(mask_s.sum()),
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"masked_simurg": int(s.masked.sum()),
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"mask_agreement": float((mask_s == s.masked[idx]).mean()),
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"noob_fit": {"green": vars(fg), "red": vars(fr)},
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"pval_abs_diff": _q(np.abs(op - sp)[keep]),
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"pval_identical_fraction": float((op[keep] == sp[keep]).mean()),
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"detection_call_agreement_0.05": float(((op < 0.05) == (sp < 0.05))[keep].mean()),
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"prop_passing": {"simurg": float((op[ana] < 0.05).mean()), "sesame": float((sp[ana] < 0.05).mean())},
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"noob_m_rel_diff": _q(np.abs(om - sm)[keep] / np.maximum(np.abs(sm[keep]), 1)),
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"noob_u_rel_diff": _q(np.abs(ou - su)[keep] / np.maximum(np.abs(su[keep]), 1)),
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"beta_abs_diff": _q(np.abs(ob - sb)[keep]),
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"beta_corr": float(np.corrcoef(ob[keep & np.isfinite(sb)], sb[keep & np.isfinite(sb)])[0, 1]),
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}
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a.out.write_text(json.dumps(res, indent=1))
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print(json.dumps(res, indent=1))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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