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4e2940e | 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 | #!/usr/bin/env python
"""Beta contribution: does multiplying by beta improve gamma estimates?
Compares:
- gamma = beta * Mu/Ms (scPTR)
- gamma_naive = Mu/Ms (no beta)
- gamma_scvelo = regression slope (scVelo SS)
If beta doesn't help, scPTR is literally the same as Mu/Ms ratio.
"""
from _common import *
OUT = output_dir("34_beta_contribution")
def main():
set_figure_style()
_, hl_human = load_halflife_refs()
hl_mouse, _ = load_halflife_refs()
all_results = {}
for ds_name, loader, ck in DATASETS:
print(f"\n{'=' * 60}\n{ds_name.upper()}: Beta Contribution\n{'=' * 60}")
adata = loader()
scptr.pp.filter_genes(adata)
scptr.pp.normalize_layers(adata)
scptr.pp.neighbors(adata, n_neighbors=30)
scptr.pp.smooth_layers(adata)
from scipy.sparse import issparse
Mu = adata.layers["Mu"]
Ms = adata.layers["Ms"]
if issparse(Mu):
Mu = np.asarray(Mu.todense())
if issparse(Ms):
Ms = np.asarray(Ms.todense())
Mu = np.asarray(Mu, dtype=float)
Ms = np.asarray(Ms, dtype=float)
# ── Naive gamma: Mu/Ms ───────────────────────────────────────
gamma_naive = np.where(Ms > 0.01, Mu / Ms, 0)
# Clip like scPTR
for g in range(gamma_naive.shape[1]):
col = gamma_naive[:, g]
pos = col[col > 0]
if len(pos) > 0:
cap = np.percentile(pos, 99)
gamma_naive[:, g] = np.clip(col, 0, cap)
adata_naive = adata.copy()
adata_naive.layers["gamma"] = gamma_naive.astype(np.float32)
# ── scPTR gamma: beta * Mu/Ms ────────────────────────────────
scptr.tl.estimate_beta(adata)
scptr.tl.estimate_gamma(adata)
# ── Compare half-life ─────────────────────────────────────────
r_scptr_m, n_m = halflife_spearman(adata, hl_mouse)
r_scptr_h, n_h = halflife_spearman(adata, hl_human)
r_naive_m, n_nm = halflife_spearman(adata_naive, hl_mouse)
r_naive_h, n_nh = halflife_spearman(adata_naive, hl_human)
print(f"\n {'Method':<25} {'Mouse r':>10} {'Human r':>10}")
print(" " + "-" * 50)
print(f" {'Naive (Mu/Ms)':<25} {r_naive_m:>10.4f} {r_naive_h:>10.4f}")
print(f" {'scPTR (beta*Mu/Ms)':<25} {r_scptr_m:>10.4f} {r_scptr_h:>10.4f}")
print(f" {'Beta improvement':<25} {abs(r_scptr_m)-abs(r_naive_m):>10.4f} {abs(r_scptr_h)-abs(r_naive_h):>10.4f}")
# ── Compare PT states ─────────────────────────────────────────
from sklearn.metrics import silhouette_score
from sklearn.decomposition import PCA
labels = adata.obs[ck].astype("category").cat.codes.values
pca_scptr = PCA(n_components=10).fit_transform(adata.layers["gamma"])
pca_naive = PCA(n_components=10).fit_transform(gamma_naive)
sil_scptr = silhouette_score(pca_scptr, labels, sample_size=min(2000, len(labels)))
sil_naive = silhouette_score(pca_naive, labels, sample_size=min(2000, len(labels)))
print(f"\n Silhouette (cell-type in gamma PCA):")
print(f" Naive: {sil_naive:.4f}")
print(f" scPTR: {sil_scptr:.4f}")
# ── Beta distribution ─────────────────────────────────────────
beta = adata.var["beta"].values
print(f"\n Beta: median={np.median(beta):.4f}, CV={np.std(beta)/np.mean(beta):.4f}")
print(f" If CV≈0, beta is constant → no contribution")
print(f" Actual CV={np.std(beta)/np.mean(beta):.2f} → {'substantial' if np.std(beta)/np.mean(beta) > 0.5 else 'modest'} gene-specific effect")
# ── Correlation between naive and scPTR gamma ─────────────────
med_naive = np.median(gamma_naive, axis=0)
med_scptr = np.median(adata.layers["gamma"], axis=0)
valid = (med_naive > 0) & (med_scptr > 0)
r_agree, _ = stats.spearmanr(med_naive[valid], med_scptr[valid])
print(f"\n Naive vs scPTR gamma agreement: r={r_agree:.4f}")
all_results[ds_name] = {
"naive_mouse": float(r_naive_m), "naive_human": float(r_naive_h),
"scptr_mouse": float(r_scptr_m), "scptr_human": float(r_scptr_h),
"beta_improvement_mouse": float(abs(r_scptr_m) - abs(r_naive_m)),
"beta_improvement_human": float(abs(r_scptr_h) - abs(r_naive_h)),
"sil_naive": float(sil_naive), "sil_scptr": float(sil_scptr),
"beta_cv": float(np.std(beta) / np.mean(beta)),
"naive_scptr_agreement": float(r_agree),
}
save_json(all_results, "beta_contribution", OUT)
# Figure
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
for i, (ds, res) in enumerate(all_results.items()):
x = i * 3
axes[0].bar(x, abs(res["naive_human"]), 0.8, color="gray", alpha=0.7,
label="Naive (Mu/Ms)" if i == 0 else "")
axes[0].bar(x + 1, abs(res["scptr_human"]), 0.8, color="darkorange", alpha=0.7,
label="scPTR (beta*Mu/Ms)" if i == 0 else "")
axes[0].set_xticks([0.5, 3.5])
axes[0].set_xticklabels(list(all_results.keys()))
axes[0].set_ylabel("|r| with half-life (human)")
axes[0].set_title("Beta contribution to half-life r")
axes[0].legend()
for i, (ds, res) in enumerate(all_results.items()):
x = i * 3
axes[1].bar(x, res["sil_naive"], 0.8, color="gray", alpha=0.7)
axes[1].bar(x + 1, res["sil_scptr"], 0.8, color="darkorange", alpha=0.7)
axes[1].set_xticks([0.5, 3.5])
axes[1].set_xticklabels(list(all_results.keys()))
axes[1].set_ylabel("Silhouette score")
axes[1].set_title("Beta contribution to PT state quality")
fig.suptitle("Does beta estimation improve gamma?", y=1.02)
fig.tight_layout()
save_fig(fig, "beta_contribution", OUT)
if __name__ == "__main__":
main()
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