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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 | #!/usr/bin/env python
"""Expression level vs degradation rate: are they independent?
Tests the degree to which gamma is decorrelated from expression level.
High decorrelation validates that gamma captures post-transcriptional
regulation beyond what's explained by expression abundance.
"""
from _common import *
OUT = output_dir("11_expression_vs_gamma")
def run(name, loader, cluster_key):
print(f"\n{'=' * 60}\n{name.upper()}: Expression vs gamma\n{'=' * 60}")
adata = run_analytical(loader)
from scipy.sparse import issparse
X = adata.X
if issparse(X):
X = np.asarray(X.todense())
X = np.asarray(X, dtype=np.float32)
gamma = adata.layers["gamma"]
gamma_med = np.median(gamma, axis=0)
expr_mean = X.mean(axis=0)
# Filter to expressed genes with gamma > 0
valid = (gamma_med > 0) & (expr_mean > 0) & np.isfinite(gamma_med) & np.isfinite(expr_mean)
g, e = gamma_med[valid], expr_mean[valid]
r_expr_gamma, p = stats.spearmanr(np.log1p(e), np.log1p(g))
print(f" Expression vs gamma: r={r_expr_gamma:.4f} (p={p:.2e}, n={len(g)})")
# Does controlling for expression improve half-life correlation?
_, hl_human = load_halflife_refs()
g_hl, h_hl, names = match_halflife(adata, hl_human)
# Get expression for matched genes
name_to_idx = {n: i for i, n in enumerate(adata.var_names)}
expr_matched = np.array([expr_mean[name_to_idx[n]] for n in names])
r_raw, _ = stats.spearmanr(g_hl, h_hl)
# Partial correlation: gamma vs half-life, controlling for expression
# Residualize gamma and halflife on expression
from numpy.polynomial.polynomial import polyfit, polyval
log_e = np.log1p(expr_matched)
log_g = np.log1p(g_hl)
log_h = np.log1p(h_hl)
# Residualize
coef_g = polyfit(log_e, log_g, 1)
resid_g = log_g - polyval(log_e, coef_g)
coef_h = polyfit(log_e, log_h, 1)
resid_h = log_h - polyval(log_e, coef_h)
r_partial, p_partial = stats.spearmanr(resid_g, resid_h)
print(f" Raw gamma-halflife: r={r_raw:.4f}")
print(f" Partial (ctrl expression): r={r_partial:.4f} (p={p_partial:.2e})")
print(f" Expression confound: Δr={abs(r_raw) - abs(r_partial):.4f}")
# Variance decomposition: how much gamma variance is explained by expression?
from sklearn.linear_model import LinearRegression
lr = LinearRegression().fit(np.log1p(e).reshape(-1, 1), np.log1p(g))
r2_expr = lr.score(np.log1p(e).reshape(-1, 1), np.log1p(g))
print(f" R² of gamma ~ expression: {r2_expr:.4f} ({r2_expr*100:.1f}% explained)")
print(f" Residual gamma variance: {1 - r2_expr:.4f} ({(1-r2_expr)*100:.1f}% independent)")
results = {
"r_expr_gamma": float(r_expr_gamma),
"r_raw_halflife": float(r_raw),
"r_partial_halflife": float(r_partial),
"expression_confound": float(abs(r_raw) - abs(r_partial)),
"r2_gamma_from_expression": float(r2_expr),
"n_genes": int(len(g)),
}
save_json(results, f"{name}_expr_vs_gamma", OUT)
# Figure
fig, axes = plt.subplots(1, 3, figsize=(15, 4.5))
axes[0].scatter(np.log1p(e), np.log1p(g), alpha=0.05, s=3, c="steelblue")
axes[0].set_xlabel("log(1 + mean expression)")
axes[0].set_ylabel("log(1 + median gamma)")
axes[0].set_title(f"Expression vs gamma (r={r_expr_gamma:.3f})")
bars = axes[1].bar(["Raw", "Partial\n(ctrl expr)"],
[abs(r_raw), abs(r_partial)],
color=["steelblue", "darkorange"], alpha=0.7)
axes[1].set_ylabel("|Spearman r| with half-life")
axes[1].set_title(f"Expression confound (Δr={abs(r_raw)-abs(r_partial):.3f})")
# Pie: variance decomposition
axes[2].pie([r2_expr, 1 - r2_expr],
labels=[f"Expression\n({r2_expr*100:.0f}%)",
f"Independent\n({(1-r2_expr)*100:.0f}%)"],
colors=["lightcoral", "lightsteelblue"],
autopct="%1.0f%%", startangle=90)
axes[2].set_title("Gamma variance decomposition")
fig.suptitle(f"{name}: Expression vs degradation", y=1.02)
fig.tight_layout()
save_fig(fig, f"{name}_expr_vs_gamma", OUT)
return results
def main():
set_figure_style()
for name, loader, ck in DATASETS:
run(name, loader, ck)
if __name__ == "__main__":
main()
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