File size: 4,714 Bytes
141bacd | 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 83 84 85 86 87 88 89 90 91 | """dingwall marker deep-dive: single rank_genes_groups call cross-referenced against canonical panels."""
from pathlib import Path
import warnings, json, numpy as np, pandas as pd, anndata as ad, scanpy as sc, scipy.sparse as sp
warnings.filterwarnings("ignore"); sc.settings.verbosity = 0
import os as _os
from pathlib import Path as _Path
PANDA_ROOT = _Path(_os.environ.get("PANDA_ROOT", str(_Path(__file__).resolve().parents[2])))
ROOT = Path(str(PANDA_ROOT))
OUT = ROOT / "discovery/pan_skin/marker"
OUT.mkdir(parents=True, exist_ok=True)
PANELS = {
"eden-dermal-niche": ["S100a4", "Twist2", "Prrx1", "Pdgfra", "Fap", "Fn1"],
"eccrine-secretory": ["Dcd", "Aqp5", "Muc7", "Cst6", "Krt7"],
"eccrine-ductal": ["Krt77", "Krt5", "Krt14", "Cldn6", "Grhl3"],
"basal-multipotent": ["Krt5", "Krt14", "Trp63", "Itgb4", "Sox2"],
"hair-placode": ["Shh", "Sox9", "Lhx2", "Foxi3", "Wnt10a"],
"melanocyte": ["Dct", "Mlana", "Tyrp1", "Pmel", "Sox10"],
"endothelial": ["Pecam1", "Cdh5", "Kdr", "Flt1"],
"spinous": ["Krt10", "Krt1", "Dsp"],
"basal-IFE": ["Krt5", "Krt14", "Krt15", "Col17a1"],
"immune": ["Ptprc", "Cd68", "Cd3d", "Cd19"],
"fibroblast": ["Col1a1", "Dcn", "Pdgfra"],
}
CKO_GSMS = {"GSM6833482", "GSM6833483"} # CORRECTED: 480/481 are rttaControl (WT), not cKO
WT_GSMS = {"GSM6833478", "GSM6833479", "GSM6833480", "GSM6833481"} # CORRECTED: 4 Cre-neg controls per GEO metadata
print("[load] Dingwall raw + predictions", flush=True)
raw = ad.read_h5ad(ROOT / "data/raw/GSE220977_combined.h5ad")
pred_df = pd.read_csv(ROOT / "discovery/pan_skin/marker/dingwall_predictions.csv")
common = raw.obs_names.intersection(pd.Index(pred_df["cell_id"].astype(str)))
raw = raw[list(common)].copy()
pred_map = dict(zip(pred_df["cell_id"].astype(str), pred_df["pred_label"]))
raw.obs["pred_label"] = pd.Categorical([pred_map.get(c, "unknown") for c in raw.obs_names])
raw.obs["genotype"] = np.where(raw.obs["sample"].astype(str).isin(list(CKO_GSMS)), "En1-cKO",
np.where(raw.obs["sample"].astype(str).isin(list(WT_GSMS)), "WT", "other"))
print(f"[align] {raw.n_obs} cells across {raw.obs['pred_label'].nunique()} classes", flush=True)
# subset to classes with >=30 cells for stable Wilcoxon
counts = raw.obs["pred_label"].value_counts()
keep_cls = counts[counts >= 30].index.tolist()
raw = raw[raw.obs["pred_label"].isin(keep_cls)].copy()
raw.obs["pred_label"] = raw.obs["pred_label"].astype(str).astype("category")
print(f"[filter] kept {raw.n_obs} cells × {len(keep_cls)} classes", flush=True)
sc.pp.normalize_total(raw, target_sum=1e4); sc.pp.log1p(raw)
# single-call with groupby is much faster than per-class loop
print("[wilcoxon] single-call across all predicted classes...", flush=True)
sc.tl.rank_genes_groups(raw, groupby="pred_label", method="wilcoxon", n_genes=25, use_raw=False)
print("[wilcoxon] done", flush=True)
rows = []
for cls in raw.uns["rank_genes_groups"]["names"].dtype.names:
mask = raw.obs["pred_label"] == cls
if mask.sum() < 30: continue
genes = list(raw.uns["rank_genes_groups"]["names"][cls][:20])
pvals = [float(x) for x in raw.uns["rank_genes_groups"]["pvals_adj"][cls][:20]]
logfc = [float(x) for x in raw.uns["rank_genes_groups"]["logfoldchanges"][cls][:20]]
top_str = ",".join([f"{g}(LFC{lf:+.1f})" for g, lf in zip(genes[:10], logfc[:10])])
panel_hits = {}
for pname, plist in PANELS.items():
hits = [g for g in plist if g in genes[:20]]
panel_hits[pname] = f"{len(hits)}/{len(plist)}: {','.join(hits)}"
gt = raw.obs["genotype"][mask]
ncko = int((gt == "En1-cKO").sum()); nwt = int((gt == "WT").sum())
frac_cko = ncko / max(1, ncko + nwt)
best_panel = max(panel_hits.items(),
key=lambda x: int(x[1].split("/")[0]) / (int(x[1].split(":")[0].split("/")[1]) + 1e-6))
rows.append({
"predicted_class": cls,
"n_cells": int(mask.sum()),
"top_wilcoxon_markers": top_str,
"min_p_adj_top5": min(pvals[:5], default=float("nan")),
"best_canonical_panel_match": best_panel[0],
"recovery": best_panel[1],
"n_En1_cKO": ncko,
"n_WT": nwt,
"frac_En1_cKO": frac_cko,
})
df = pd.DataFrame(rows).sort_values("n_cells", ascending=False)
df.to_csv(OUT / "90_dingwall_marker_deep_dive.csv", index=False)
print(f"[write] {OUT}/90_dingwall_marker_deep_dive.csv ({len(df)} classes)", flush=True)
print()
print(df[["predicted_class", "n_cells", "best_canonical_panel_match", "recovery", "frac_En1_cKO"]].to_string(index=False))
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