v2 CN/LOH (WIP): cn build-model and cn call
Cyto model (.simurg-cyto: GC 1 kb/100 kb/1 Mb, PFB from the cluster file, optional sex-aware batch reference from normal GTCs; IN-051, CYTO-038); GC-wave correction (CYTO-003); smoothing (CYTO-004); HMM with constitutional, gain-LOH, mosaic and haploid states, forward-backward QUAL and Viterbi segments (CYTO-005/012); event statistics, effective size, post-filters, per-type thresholds, promotion/LowFraction, built-in centromere mask from UCSC cytoBand (CYTO-006..021); sex resolution (CYTO-024), ploidy; CNV VCF with bgzip+tabix (CYTO-028..031), segments parquet, cn_sample_summary.tsv and the cn_call_manifest.json sidecar.
This commit is contained in:
kemal committed
1 parent
579009271e
commit
e76b87939b
8 files changed
+1452
No files matched your search
@@ -0,0 +1,245 @@
|
||||
"""CN/LOH event extraction, statistics and filters — spec CYTO-006 … CYTO-021, CYTO-027. Pure functions, no I/O.
|
||||
|
||||
From a per-chromosome Viterbi path: runs of non-baseline states become events; statistics per CYTO-027; effective
|
||||
size per CYTO-010 (gaps > 150 × the chromosome's median spacing replaced by the median); QUAL from the mean posterior
|
||||
of the called state class (CYTO-012); mosaic fraction from the BAF split or, failing that, LRR (CYTO-018/019);
|
||||
post-filters in the order of CYTO-015; per-type thresholds (CYTO-007/008) and promotion/low fraction (CYTO-020/021).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
from simurg.cn.hmm import State
|
||||
|
||||
F64 = npt.NDArray[np.float64]
|
||||
I64 = npt.NDArray[np.int64]
|
||||
GAP_FACTOR = 150
|
||||
MIN_MF_PROBES = 20
|
||||
TYPES = ("del", "dup", "loh", "gainloh", "mdel", "mdup", "mloh")
|
||||
DEFAULTS = {"del": (10, 25_000), "dup": (10, 50_000), "loh": (500, 3_000_000), "gainloh": (300, 500_000),
|
||||
"mdel": (10, 25_000), "mdup": (10, 50_000), "mloh": (500, 3_000_000)} # fmt: skip
|
||||
CALL_KEY = {"DEL": "del", "DUP": "dup", "LOH": "loh", "GAINLOH": "gainloh", "MDEL": "mdel", "MDUP": "mdup",
|
||||
"MLOH": "mloh"} # fmt: skip
|
||||
|
||||
|
||||
@dataclass
|
||||
class Event:
|
||||
chrom: str
|
||||
start: int # first affected base S (1-based) = first probe position
|
||||
end: int # last affected base E = last probe position
|
||||
i0: int # probe index range in the chromosome arrays [i0, i1)
|
||||
i1: int
|
||||
call: str # DEL | DUP | LOH | GAINLOH | MDEL | MDUP | MLOH
|
||||
cn: int
|
||||
state: str
|
||||
quality: float = 0.0
|
||||
effective_size: int = 0
|
||||
nr: float = math.nan
|
||||
lrd: float = math.nan
|
||||
baf_dev: float = math.nan
|
||||
percent_het: float = math.nan
|
||||
lrr_median: float = math.nan
|
||||
mosaic_fraction: float | None = None
|
||||
high_fraction: bool = False
|
||||
filters: list[str] = field(default_factory=list)
|
||||
flags: list[str] = field(default_factory=list)
|
||||
|
||||
@property
|
||||
def mosaic(self) -> bool:
|
||||
return self.call.startswith("M")
|
||||
|
||||
@property
|
||||
def probes(self) -> int:
|
||||
return self.i1 - self.i0
|
||||
|
||||
@property
|
||||
def size(self) -> int:
|
||||
return self.end - self.start + 1
|
||||
|
||||
@property
|
||||
def svtype(self) -> str:
|
||||
return {"MDEL": "DEL", "MDUP": "DUP", "MLOH": "LOH", "GAINLOH": "LOH"}.get(self.call, self.call)
|
||||
|
||||
@property
|
||||
def loh_type(self) -> str | None:
|
||||
if self.call == "LOH":
|
||||
return "AOH"
|
||||
if self.call == "GAINLOH":
|
||||
return "AOH"
|
||||
if self.call == "MLOH":
|
||||
return "CNLOH"
|
||||
return None
|
||||
|
||||
@property
|
||||
def id_prefix(self) -> str:
|
||||
return {"DEL": "DEL", "DUP": "DUP", "LOH": "AOH", "GAINLOH": "AOH", "MDEL": "MosaicDEL", "MDUP": "MosaicDUP",
|
||||
"MLOH": "CNLOH"}[self.call] # fmt: skip
|
||||
|
||||
|
||||
def median_spacing(pos: I64) -> float:
|
||||
d = np.diff(np.sort(pos))
|
||||
d = d[d > 0]
|
||||
return float(np.median(d)) if len(d) else 1.0
|
||||
|
||||
|
||||
def effective_size(pos: I64, m: float) -> int:
|
||||
"""CYTO-010 for the probes of one event (sorted positions)."""
|
||||
if len(pos) < 2:
|
||||
return 1
|
||||
s = np.diff(pos).astype(np.float64)
|
||||
gap = s > GAP_FACTOR * m
|
||||
return int((pos[-1] - pos[0] + 1) - float((s[gap] - m).sum()))
|
||||
|
||||
|
||||
def extract(chrom: str, path: npt.NDArray[np.int32], states: list[State], pos: I64) -> list[Event]:
|
||||
"""Runs of non-baseline states, merged across runs of the same call type (mosaic fraction steps)."""
|
||||
out: list[Event] = []
|
||||
if len(path) == 0:
|
||||
return out
|
||||
br = np.flatnonzero(np.diff(path) != 0) + 1
|
||||
starts, ends = np.r_[0, br], np.r_[br, len(path)]
|
||||
for s, e in zip(starts.tolist(), ends.tolist(), strict=True):
|
||||
st = states[int(path[s])]
|
||||
if not st.call:
|
||||
continue
|
||||
if out and out[-1].i1 == s and out[-1].call == st.call and out[-1].cn == st.cn:
|
||||
out[-1].i1 = e
|
||||
out[-1].end = int(pos[e - 1])
|
||||
continue
|
||||
out.append(Event(chrom, int(pos[s]), int(pos[e - 1]), s, e, st.call, st.cn, st.name))
|
||||
return out
|
||||
|
||||
|
||||
def _mf_from_baf(call: str, baf: F64) -> float | None:
|
||||
b = baf[np.isfinite(baf) & (baf > 0.1) & (baf < 0.9)]
|
||||
if len(b) < MIN_MF_PROBES:
|
||||
return None
|
||||
d = float(np.median(np.abs(b - 0.5)))
|
||||
if call == "MDEL":
|
||||
f = 2 * d / (0.5 + d)
|
||||
elif call == "MDUP":
|
||||
f = 2 * d / (0.5 - d) if d < 0.5 else 1.0
|
||||
else:
|
||||
f = 2 * d
|
||||
return float(min(1.0, max(0.0, f)))
|
||||
|
||||
|
||||
def _mf_from_lrr(call: str, lrr: F64, gamma: float) -> float | None:
|
||||
x = lrr[np.isfinite(lrr)]
|
||||
if len(x) < MIN_MF_PROBES or gamma <= 0:
|
||||
return None
|
||||
f = abs(2 ** (float(np.median(x)) / gamma) - 1)
|
||||
return float(min(1.0, max(0.0, f)))
|
||||
|
||||
|
||||
def annotate_stats(
|
||||
ev: Event, pos: I64, lrr: F64, baf: F64, geno: npt.NDArray[np.uint8], post: F64, states: list[State], m: float,
|
||||
gamma: float, haploid: bool,
|
||||
) -> None: # fmt: skip
|
||||
"""CYTO-010/012/018/027 statistics in place (``lrr`` unsmoothed)."""
|
||||
sl = slice(ev.i0, ev.i1)
|
||||
x = lrr[sl]
|
||||
fx = x[np.isfinite(x)]
|
||||
ev.nr = float(np.median(2.0**fx)) if len(fx) else math.nan
|
||||
ev.lrd = float(np.std(fx)) if len(fx) > 1 else math.nan
|
||||
ev.lrr_median = float(np.median(fx)) if len(fx) else math.nan
|
||||
g = geno[sl]
|
||||
called = g > 0
|
||||
ev.percent_het = float(100.0 * (g == 2).sum() / called.sum()) if called.any() else math.nan
|
||||
b = baf[sl][(g == 2) & np.isfinite(baf[sl])]
|
||||
ev.baf_dev = float(np.std(b)) if len(b) > 1 else math.nan
|
||||
ev.effective_size = effective_size(pos[sl], m)
|
||||
cls = [j for j, st in enumerate(states) if st.call == ev.call]
|
||||
p = float(post[sl][:, cls].sum(axis=1).mean()) if cls else 0.0
|
||||
ev.quality = 60.0 if p >= 1.0 else float(min(60, max(0, round(-10 * math.log10(max(1e-300, 1 - p))))))
|
||||
if ev.mosaic:
|
||||
mf = None if haploid else _mf_from_baf(ev.call, baf[sl])
|
||||
ev.mosaic_fraction = mf if mf is not None else _mf_from_lrr(ev.call, x, gamma)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Thresholds:
|
||||
probes: dict[str, int]
|
||||
size_bp: dict[str, int]
|
||||
max_mosaic_fraction: float = 1.0
|
||||
min_mosaic_fraction: float = 0.10
|
||||
filter_qual: int = 10
|
||||
lrr_direction_min_frac: float = 0.6
|
||||
|
||||
@classmethod
|
||||
def defaults(cls) -> Thresholds:
|
||||
return cls({k: v[0] for k, v in DEFAULTS.items()}, {k: v[1] for k, v in DEFAULTS.items()})
|
||||
|
||||
def as_header(self, extra: dict[str, object]) -> str:
|
||||
parts = []
|
||||
for k in ("del", "dup", "loh", "gainloh", "mdel", "mdup", "mloh"):
|
||||
parts += [f"min_{k}_probes={self.probes[k]}", f"min_{k}_size_bp={self.size_bp[k]}"]
|
||||
parts += [f"{k}={v}" for k, v in extra.items()]
|
||||
return ";".join(parts)
|
||||
|
||||
|
||||
def post_filter(events: list[Event], lrr_by_event: list[F64], th: Thresholds, counts: dict[str, int]) -> list[Event]:
|
||||
"""CYTO-015 steps 1–3 and CYTO-020 promotion; per-type thresholds are applied by :func:`apply_thresholds`."""
|
||||
kept: list[Event] = []
|
||||
for ev, x in zip(events, lrr_by_event, strict=True):
|
||||
fx = x[np.isfinite(x)]
|
||||
if ev.call in ("DEL", "MDEL") and len(fx) and (fx < 0).mean() < th.lrr_direction_min_frac:
|
||||
counts["lrr_direction"] = counts.get("lrr_direction", 0) + 1
|
||||
continue
|
||||
if ev.call in ("DUP", "MDUP", "GAINLOH") and len(fx) and (fx > 0).mean() < th.lrr_direction_min_frac:
|
||||
counts["lrr_direction"] = counts.get("lrr_direction", 0) + 1
|
||||
continue
|
||||
kept.append(ev)
|
||||
# short LOH between copy-neutral neighbours (no other event adjacent on either side)
|
||||
out: list[Event] = []
|
||||
for i, ev in enumerate(kept):
|
||||
if ev.call in ("LOH", "MLOH") and ev.size < 500_000:
|
||||
left = kept[i - 1] if i > 0 and kept[i - 1].chrom == ev.chrom else None
|
||||
right = kept[i + 1] if i + 1 < len(kept) and kept[i + 1].chrom == ev.chrom else None
|
||||
if not (left and left.i1 == ev.i0) and not (right and right.i0 == ev.i1):
|
||||
counts["short_loh"] = counts.get("short_loh", 0) + 1
|
||||
continue
|
||||
out.append(ev)
|
||||
merged: list[Event] = []
|
||||
for ev in out:
|
||||
prev = merged[-1] if merged else None
|
||||
if (prev and prev.chrom == ev.chrom and prev.call == ev.call and prev.cn == ev.cn
|
||||
and ev.i0 - prev.i1 <= 3 and (ev.start - prev.end) <= 0.1 * (ev.end - prev.start + 1)): # fmt: skip
|
||||
prev.i1, prev.end = ev.i1, ev.end
|
||||
counts["merged"] = counts.get("merged", 0) + 1
|
||||
continue
|
||||
merged.append(ev)
|
||||
for ev in merged: # CYTO-020 promotion
|
||||
if ev.mosaic and ev.mosaic_fraction is not None and ev.mosaic_fraction >= th.max_mosaic_fraction:
|
||||
ev.call = {"MDEL": "DEL", "MDUP": "DUP", "MLOH": "LOH"}[ev.call]
|
||||
ev.high_fraction = True
|
||||
return merged
|
||||
|
||||
|
||||
def apply_thresholds(events: list[Event], th: Thresholds, counts: dict[str, int]) -> list[Event]:
|
||||
out = []
|
||||
for ev in events:
|
||||
k = CALL_KEY[ev.call]
|
||||
if ev.probes < th.probes[k] or ev.effective_size < th.size_bp[k]:
|
||||
counts["below_threshold"] = counts.get("below_threshold", 0) + 1
|
||||
continue
|
||||
out.append(ev)
|
||||
return out
|
||||
|
||||
|
||||
def set_filters(events: list[Event], th: Thresholds, sample_noisy: bool) -> None:
|
||||
"""CYTO-013 FILTER values."""
|
||||
for ev in events:
|
||||
f = []
|
||||
if ev.quality < th.filter_qual:
|
||||
f.append(f"Q{th.filter_qual}")
|
||||
if sample_noisy:
|
||||
f.append("SampleQuality")
|
||||
if ev.mosaic and ev.mosaic_fraction is not None and ev.mosaic_fraction < th.min_mosaic_fraction:
|
||||
f.append("LowFraction")
|
||||
ev.filters = f or ["PASS"]
|
||||
Reference in new issue
Block a user