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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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | """zero-shot HSC PANDA on nestorowa 2016 smart-seq2 as unlabeled discovery target."""
from __future__ import annotations
from pathlib import Path
import warnings, json, sys, pickle
warnings.filterwarnings("ignore")
import numpy as np, pandas as pd, anndata as ad, scanpy as sc, scipy.sparse as sp
import torch
import os as _os
from pathlib import Path as _Path
PANDA_ROOT = _Path(_os.environ.get("PANDA_ROOT", str(_Path(__file__).resolve().parents[2])))
sys.path.insert(0, str(PANDA_ROOT))
from panda.model import PANDAEncoder
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
CKPT = Path(str(PANDA_ROOT / "checkpoints/hematopoiesis"))
HARM = Path(str(PANDA_ROOT / "data/corpus/hematopoiesis/harmonized"))
OUT = Path(str(PANDA_ROOT / "discovery/hematopoiesis/marker"))
def load_nestorowa():
p = Path(str(PANDA_ROOT / "data/corpus/hematopoiesis/tier_a/nestorowa_GSE81682_counts.txt.gz"))
df = pd.read_csv(p, sep="\t", index_col=0, compression="gzip")
# rows = ENSMUSG, cols = cells; convert IDs to symbols via mygene
import mygene
print(f"[nestorowa] converting {len(df)} ENSMUSG IDs to symbols via mygene …", flush=True)
mg = mygene.MyGeneInfo()
ids = df.index.astype(str).tolist()
res = mg.querymany(ids, scopes="ensembl.gene", fields="symbol",
species="mouse", returnall=False, verbose=False)
id2sym = {}
for r in res:
if "symbol" in r and "query" in r and not r.get("notfound", False):
id2sym[r["query"]] = r["symbol"]
syms_series = pd.Series(df.index.astype(str)).map(id2sym).values
syms = pd.Series(syms_series, index=df.index)
keep = syms.notna()
print(f"[nestorowa] mapped {int(keep.sum())}/{len(df)} genes", flush=True)
df = df.loc[keep]
df.index = syms[keep].values
df = df.groupby(df.index).sum()
X = sp.csr_matrix(df.values.T.astype(np.float32))
obs = pd.DataFrame(index=df.columns.astype(str))
obs["dataset"] = "nestorowa_GSE81682"
obs["sample"] = obs.index.str.split("_").str[0]
var = pd.DataFrame({"gene_symbol": df.index.astype(str).values},
index=df.index.astype(str))
a = ad.AnnData(X=X, obs=obs, var=var)
a.var_names_make_unique()
return a
def project(a, shared_hvgs, mu, sig):
G = len(shared_hvgs); n = a.n_obs
hvg2i = {g: i for i, g in enumerate(shared_hvgs)}
common = [g for g in a.var_names.astype(str) if g in hvg2i]
frac = len(common) / G
print(f"[proj] {len(common)}/{G} HVGs present ({frac:.1%})", flush=True)
a_c = a[:, common].copy()
sc.pp.normalize_total(a_c, target_sum=1e4)
sc.pp.log1p(a_c)
X = a_c.X.toarray().astype(np.float32) if sp.issparse(a_c.X) else a_c.X.astype(np.float32)
Xf = np.zeros((n, G), dtype=np.float32)
cols = [hvg2i[g] for g in common]
Xf[:, cols] = X
Xz = np.clip((Xf - mu.astype(np.float32)) / sig.astype(np.float32), -10, 10)
return Xz, frac
def main():
ck = torch.load(CKPT / "panda_final.pt", map_location=DEVICE, weights_only=False)
classes = ck["classes"]; datasets = ck["datasets"]
model = PANDAEncoder(n_pca=50, n_classes=len(classes),
n_datasets=len(datasets)).to(DEVICE).eval()
model.load_state_dict(ck["model"])
protos = ck["prototypes"]
protos = protos / (np.linalg.norm(protos, axis=1, keepdims=True) + 1e-8)
print(f"[model] classes: {classes}", flush=True)
stats = np.load(HARM / "corpus_stats.npz", allow_pickle=True)
shared_hvgs = [str(g) for g in stats["shared_hvgs"]]
mu, sig = stats["mean"], stats["std"]
with open(HARM / "pca_basis.pkl", "rb") as f: pca = pickle.load(f)
a = load_nestorowa()
print(f"[target] Nestorowa shape: {a.shape}", flush=True)
Xz, frac = project(a, shared_hvgs, mu, sig)
Xpca = pca.transform(Xz).astype(np.float32)
all_z = []
with torch.no_grad():
for i in range(0, a.n_obs, 4096):
xb = torch.from_numpy(Xpca[i:i+4096]).to(DEVICE)
aux = torch.zeros(len(xb), 2, device=DEVICE)
out = model(xb, aux, lam_dann=0.0)
all_z.append(out["z"].cpu().numpy())
Z = np.concatenate(all_z, axis=0)
cos = Z @ protos.T
pred_ix = cos.argmax(axis=1)
conf = cos.max(axis=1)
entropy = -(np.exp(cos / 0.07) / np.exp(cos / 0.07).sum(axis=1, keepdims=True) *
np.log(np.exp(cos / 0.07) / np.exp(cos / 0.07).sum(axis=1, keepdims=True) + 1e-12)
).sum(axis=1)
a.obs["pred_label"] = np.array([classes[i] for i in pred_ix], dtype=object)
a.obs["pred_conf"] = conf.astype(np.float32)
a.obs["pred_entropy"] = entropy.astype(np.float32)
print(f"\n[nestorowa] predicted class distribution:")
print(a.obs["pred_label"].value_counts())
print(f"\n[nestorowa] pred_conf: p10={np.percentile(conf,10):.3f}, "
f"p50={np.percentile(conf,50):.3f}, p90={np.percentile(conf,90):.3f}")
print(f"[nestorowa] shared-HVG fraction: {frac:.1%}")
a.obs.to_csv(OUT / "63_nestorowa_predictions.csv")
print(f"\n[nestorowa] wrote predictions to 63_nestorowa_predictions.csv")
# cluster bottom-decile confidence cells for novel-population DE
thr = np.percentile(conf, 10)
mask = conf <= thr
print(f"\n[nestorowa] bottom-decile confidence: {int(mask.sum())} cells (thr={thr:.3f})",
flush=True)
if mask.sum() >= 30:
sub_low = ad.AnnData(X=Z[mask].astype(np.float32))
sc.pp.neighbors(sub_low, use_rep="X", n_neighbors=10)
sc.tl.leiden(sub_low, resolution=0.5, key_added="cluster")
print(f"[nestorowa] novel clusters: {sub_low.obs['cluster'].nunique()}")
a_low = a[mask].copy()
a_low.obs["cluster"] = sub_low.obs["cluster"].values
try:
sc.tl.rank_genes_groups(a_low, "cluster", method="wilcoxon", n_genes=10, use_raw=False)
rows = []
for cl in sorted(a_low.obs["cluster"].unique()):
names = a_low.uns["rank_genes_groups"]["names"][cl]
lfc = a_low.uns["rank_genes_groups"]["logfoldchanges"][cl]
for g, l in zip(names[:8], lfc[:8]):
rows.append({"cluster": cl, "gene": g, "logfc": round(float(l), 3)})
pd.DataFrame(rows).to_csv(OUT / "63_nestorowa_novel_markers.csv", index=False)
print(f"[nestorowa] novel markers saved")
except Exception as e:
print(f"[nestorowa] DE failed: {e}")
if __name__ == "__main__":
main()
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