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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | """3-panel UMAP: dingwall (En1 genotype), dahlin (Kit genotype), veres (stage). 8k cells/panel."""
from pathlib import Path
import warnings, sys, pickle, json
warnings.filterwarnings("ignore")
import numpy as np, pandas as pd, anndata as ad, torch, scanpy as sc, scipy.sparse as sp
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import umap
from pathlib import Path as _P_root
ROOT = _P_root(__file__).resolve().parents[2]
ROOT_STR = str(ROOT)
sys.path.insert(0, ROOT_STR)
from panda import PANDAEncoder
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
FIG = Path(f"{ROOT_STR}/figures")
FIG.mkdir(exist_ok=True)
RS = 42
SAMPLE_N = 8000
def get_projection(ckpt_dir, data_a, shared_hvgs, mu, sig, pca):
"""128-d PANDA projections for the target AnnData."""
ck = torch.load(ckpt_dir / "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)
G = len(shared_hvgs); hvg2i = {g: i for i, g in enumerate(shared_hvgs)}
common = [g for g in data_a.var_names.astype(str) if g in hvg2i]
a_c = data_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((data_a.n_obs, 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)
Xpca = pca.transform(Xz).astype(np.float32)
all_z = []
with torch.no_grad():
for i in range(0, data_a.n_obs, 4096):
xb = torch.from_numpy(Xpca[i:i+4096]).to(DEVICE)
aux = torch.zeros(len(xb), 2, device=DEVICE)
all_z.append(model(xb, aux, lam_dann=0.0)["z"].cpu().numpy())
Z = np.concatenate(all_z, axis=0)
cos = Z @ protos.T
pred = np.array([classes[i] for i in cos.argmax(axis=1)], dtype=object)
return Z, pred
def umap_it(Z, seed=RS):
reducer = umap.UMAP(n_neighbors=30, min_dist=0.3, random_state=seed,
metric="cosine", n_components=2)
return reducer.fit_transform(Z)
def load_sharon_stages():
from pathlib import Path as _P
SHARON_DIR = _P(f"{ROOT_STR}/data/corpus/pancreas/held_out_unlabeled/sharon_extract")
parts, stages = [], []
for meta_file in sorted(SHARON_DIR.glob("*.cell_metadata.tsv.gz")):
counts_file = str(meta_file).replace("cell_metadata", "processed_counts")
if not _P(counts_file).exists(): continue
meta = pd.read_csv(meta_file, sep="\t", compression="gzip")
counts = pd.read_csv(counts_file, sep="\t", compression="gzip", index_col=0)
counts.columns = [c[0].upper() + c[1:].lower() if len(c) > 1 else c
for c in counts.columns.astype(str)]
counts = counts.T.groupby(level=0).sum().T
obs = meta.set_index("library.barcode")
obs = obs.loc[obs.index.intersection(counts.index)]
counts_al = counts.loc[obs.index]
X = sp.csr_matrix(counts_al.values.astype(np.float32))
obs["dataset"] = "sharon"
var = pd.DataFrame({"gene_symbol": counts_al.columns}, index=counts_al.columns)
a = ad.AnnData(X=X, obs=obs, var=var); a.var_names_make_unique()
parts.append(a)
return ad.concat(parts, join="outer", label="_batch")
def load_dahlin():
from pathlib import Path as _P
D_DIR = _P(f"{ROOT_STR}/data/corpus/hematopoiesis/held_out_unlabeled/dahlin_extract")
GT = {"SIGAB1":"WT","SIGAC1":"WT","SIGAD1":"WT","SIGAF1":"WT","SIGAG1":"WT",
"SIGAH1":"WT","SIGAG8":"Kit_W41","SIGAH8":"Kit_W41"}
parts = []
for f in sorted(D_DIR.glob("*.txt.gz")):
sample = f.name.split("_")[1].split(".")[0]
df = pd.read_csv(f, sep="\t", compression="gzip", index_col=0)
X = sp.csr_matrix(df.values.T.astype(np.float32))
obs = pd.DataFrame(index=[f"{sample}_{bc}" for bc in df.columns.astype(str)])
obs["sample"] = sample; obs["genotype"] = GT.get(sample, "unknown")
var = pd.DataFrame(index=df.index.astype(str))
parts.append(ad.AnnData(X=X, obs=obs, var=var))
a = ad.concat(parts, join="outer", label="_batch")
import mygene
mg = mygene.MyGeneInfo()
ids = a.var_names.astype(str).tolist()
res = mg.querymany(ids, scopes="ensembl.gene", fields="symbol", species="mouse",
verbose=False)
id2sym = {r["query"]: r["symbol"] for r in res if "symbol" in r}
syms = pd.Series(a.var_names.astype(str)).map(id2sym).values
keep = pd.notna(syms)
a = a[:, keep].copy(); a.var_names = syms[keep]; a.var_names_make_unique()
return a
def main():
fig, axes = plt.subplots(1, 3, figsize=(17, 5.3))
# ---- Panel A: Dingwall (skin) ----
print("[fig] Dingwall UMAP …", flush=True)
p = ad.read_h5ad(f"{ROOT_STR}/discovery/pan_skin/marker/50_aldrich_projections.h5ad")
Z = np.asarray(p.obsm["Z_projection"])
rng = np.random.default_rng(RS)
idx = rng.choice(len(Z), size=min(SAMPLE_N, len(Z)), replace=False)
Z_a = Z[idx]
emb = umap_it(Z_a)
genotype = p.obs["genotype"].values[idx]
ax = axes[0]
for g, c in zip(["WT", "En1-cKO"], ["#2b83ba", "#d7191c"]):
m = genotype == g
ax.scatter(emb[m, 0], emb[m, 1], s=2, alpha=0.5, c=c,
label=f"{g} (n={int(m.sum())})")
ax.set_title(f"(a) Dingwall skin (n={SAMPLE_N}) — En1 genotype", fontsize=10)
ax.set_xlabel("UMAP 1"); ax.set_ylabel("UMAP 2")
ax.legend(markerscale=6, frameon=False, fontsize=9)
# ---- Panel B: Dahlin (HSC) ----
print("[fig] Dahlin UMAP …", flush=True)
cache_d = Path(f"{ROOT_STR}/figures/_cache_dahlin_umap.npz")
if cache_d.exists():
c = np.load(cache_d, allow_pickle=True)
emb2 = c["emb"]; gt_d = c["gt"]
else:
stats_h = np.load(f"{ROOT_STR}/data/corpus/hematopoiesis/harmonized/corpus_stats.npz",
allow_pickle=True)
shared_hvgs_h = [str(g) for g in stats_h["shared_hvgs"]]
pca_h = pickle.load(open(f"{ROOT_STR}/data/corpus/hematopoiesis/harmonized/pca_basis.pkl","rb"))
a_d = load_dahlin()
rng2 = np.random.default_rng(RS)
idx2 = rng2.choice(a_d.n_obs, size=min(SAMPLE_N, a_d.n_obs), replace=False)
a_d_sub = a_d[idx2].copy()
Z_d, pred_d = get_projection(Path(f"{ROOT_STR}/checkpoints/hematopoiesis"),
a_d_sub, shared_hvgs_h, stats_h["mean"], stats_h["std"], pca_h)
emb2 = umap_it(Z_d)
gt_d = a_d_sub.obs["genotype"].values
np.savez(cache_d, emb=emb2, gt=np.asarray(gt_d, dtype=object))
ax = axes[1]
for g, c in zip(["WT", "Kit_W41"], ["#2b83ba", "#d7191c"]):
m = gt_d == g
ax.scatter(emb2[m, 0], emb2[m, 1], s=2, alpha=0.5, c=c,
label=f"{g} (n={int(m.sum())})")
ax.set_title(f"(b) Dahlin HSC (n={SAMPLE_N}) — Kit genotype", fontsize=10)
ax.set_xlabel("UMAP 1"); ax.set_ylabel("UMAP 2")
ax.legend(markerscale=6, frameon=False, fontsize=9)
# ---- Panel C: Veres (pancreas) ----
print("[fig] Veres UMAP …", flush=True)
stats_p = np.load(f"{ROOT_STR}/data/corpus/pancreas/harmonized/corpus_stats.npz",
allow_pickle=True)
shared_hvgs_p = [str(g) for g in stats_p["shared_hvgs"]]
pca_p = pickle.load(open(f"{ROOT_STR}/data/corpus/pancreas/harmonized/pca_basis.pkl","rb"))
a_s = load_sharon_stages()
stage_num = pd.to_numeric(a_s.obs["Stage"], errors="coerce")
keep_st = stage_num.notna().values
a_s = a_s[keep_st].copy()
a_s.obs["Stage_int"] = stage_num[keep_st].astype(int).values
rng3 = np.random.default_rng(RS)
idx3 = rng3.choice(a_s.n_obs, size=min(SAMPLE_N, a_s.n_obs), replace=False)
a_s_sub = a_s[idx3].copy()
Z_s, pred_s = get_projection(Path(f"{ROOT_STR}/checkpoints/pancreas"),
a_s_sub, shared_hvgs_p, stats_p["mean"], stats_p["std"], pca_p)
emb3 = umap_it(Z_s)
stage = a_s_sub.obs["Stage_int"].values
ax = axes[2]
stage_colors = {3: "#fdae61", 4: "#f8b0d1", 5: "#7570b3", 6: "#d7191c"}
for s in sorted(np.unique(stage)):
m = stage == s
ax.scatter(emb3[m, 0], emb3[m, 1], s=2, alpha=0.5,
c=stage_colors.get(s, "#666"), label=f"Stage {s} (n={int(m.sum())})")
ax.set_title(f"(c) Veres hPSC (n={SAMPLE_N}) — differentiation stage", fontsize=10)
ax.set_xlabel("UMAP 1"); ax.set_ylabel("UMAP 2")
ax.legend(markerscale=6, frameon=False, fontsize=9)
plt.tight_layout()
plt.savefig(FIG / "fig6_multi_umap.pdf", bbox_inches="tight", dpi=100)
plt.close()
print(f"[fig] wrote {FIG}/fig6_multi_umap.pdf")
if __name__ == "__main__":
main()
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