File size: 2,872 Bytes
a974167 58313cb a974167 58313cb a974167 58313cb a974167 58313cb a974167 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | """Command-line interface: hmoe-da predict input.h5ad -o output.h5ad"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
def _cmd_predict(args) -> int:
from .io import read_adata
from .model import predict
ad = read_adata(args.input)
print(f"loaded {args.input}: {ad.shape}")
if args.layer:
if args.layer not in ad.layers:
print(f"ERROR: layer {args.layer!r} not in adata.layers ({list(ad.layers)})",
file=sys.stderr)
return 2
ad.X = ad.layers[args.layer]
print(f"using layer {args.layer!r} as counts")
elif args.use_raw:
if ad.raw is None:
print("ERROR: --use-raw given but adata.raw is None", file=sys.stderr)
return 2
ad = ad.raw.to_adata()
print("using adata.raw as counts")
ad = predict(
ad,
include_gad2=not args.exclude_gad2,
assume_raw=args.assume_raw,
key_added=args.key,
)
obs_cols = [c for c in ad.obs.columns if c.startswith(args.key)]
print("\nper-cell results written to adata.obs:")
for c in obs_cols:
print(f" {c}")
print("\nsubtype counts:")
print(ad.obs[f"{args.key}_subtype"].value_counts().to_string())
out = args.output
ad.write_h5ad(out)
print(f"\nwrote {out}")
if args.csv:
df = ad.obs[obs_cols].copy()
df.index.name = "cell"
df.to_csv(args.csv)
print(f"wrote {args.csv}")
return 0
def main(argv=None) -> int:
p = argparse.ArgumentParser(
prog="hmoe-da",
description="Assign dopaminergic-neuron subtypes to cells in an sc/snRNA-seq dataset.",
)
sub = p.add_subparsers(dest="command", required=True)
pp = sub.add_parser("predict", help="predict subtypes for an .h5ad file")
pp.add_argument("input", help="input .h5ad or .rds (Seurat) with RAW COUNTS (.h5ad: in .X, or use --layer/--use-raw)")
pp.add_argument("-o", "--output", required=True, help="output .h5ad path")
pp.add_argument("--csv", help="also write per-cell labels to this CSV path")
pp.add_argument("--exclude-gad2", action="store_true",
help="exclude the Gad2 family: reassign Gad2-called cells to their best non-Gad2 subtype")
pp.add_argument("--layer", help="use adata.layers[LAYER] as the count matrix")
pp.add_argument("--use-raw", action="store_true", help="use adata.raw as the count matrix")
pp.add_argument("--assume-raw", action="store_true",
help="skip the raw-counts sanity check (use if you are sure .X is counts)")
pp.add_argument("--key", default="hmoe", help="prefix for output columns (default: hmoe)")
pp.set_defaults(func=_cmd_predict)
args = p.parse_args(argv)
return args.func(args)
if __name__ == "__main__":
raise SystemExit(main())
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