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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | #!/usr/bin/env python
"""Gene-gene gamma correlation structure.
Do genes with correlated degradation rates share RBP regulators?
Identifies co-degradation modules and tests for shared eCLIP RBP targets.
"""
from _common import *
from sklearn.cluster import AgglomerativeClustering
OUT = output_dir("09_gamma_coexpression")
def run(name, loader, cluster_key):
print(f"\n{'=' * 60}\n{name.upper()}: Gamma co-regulation\n{'=' * 60}")
adata_an = run_analytical(loader)
gamma = adata_an.layers["gamma"]
# Per-gene median gamma
gamma_med = np.median(gamma, axis=0)
active = gamma_med > 0.01
gamma_active = gamma[:, active]
gene_names = adata_an.var_names[active].tolist()
print(f" Active genes (median gamma > 0.01): {len(gene_names)}")
# Gene-gene correlation matrix (Spearman on per-cell gamma)
# Subsample cells for speed
rng = np.random.RandomState(42)
n_sub = min(1000, gamma_active.shape[0])
idx = rng.choice(gamma_active.shape[0], n_sub, replace=False)
gamma_sub = gamma_active[idx]
# Correlation matrix
n_genes = gamma_sub.shape[1]
if n_genes > 2000:
# Too many — take top 500 by variance
var = gamma_sub.var(axis=0)
top = np.argsort(var)[::-1][:500]
gamma_sub = gamma_sub[:, top]
gene_names = [gene_names[i] for i in top]
n_genes = 500
print(f" Computing {n_genes}x{n_genes} correlation matrix...")
corr = np.corrcoef(gamma_sub.T)
corr = np.nan_to_num(corr)
# Cluster genes into co-degradation modules
n_clusters = min(10, max(2, n_genes // 50))
clust = AgglomerativeClustering(n_clusters=n_clusters, metric="precomputed",
linkage="average")
dist = 1 - np.abs(corr)
np.fill_diagonal(dist, 0)
labels = clust.fit_predict(dist)
module_sizes = pd.Series(labels).value_counts().sort_index()
print(f" Found {n_clusters} co-degradation modules: {module_sizes.to_dict()}")
# For each module: check eCLIP RBP enrichment
eclip = pd.read_csv(DATA_DIR / "eclip_targets.csv")
eclip_by_rbp = eclip.groupby("rbp")["target_gene"].apply(lambda x: set(x.str.upper())).to_dict()
module_results = []
for mod_id in range(n_clusters):
mod_genes = [gene_names[i] for i in range(len(gene_names)) if labels[i] == mod_id]
mod_upper = set(g.upper() for g in mod_genes)
# Which RBPs target this module?
rbp_counts = {}
for rbp, targets in eclip_by_rbp.items():
n_hit = len(mod_upper & targets)
if n_hit > 0:
rbp_counts[rbp] = n_hit
top_rbp = sorted(rbp_counts.items(), key=lambda x: x[1], reverse=True)[:3]
# Mean within-module correlation
mod_idx = [i for i in range(len(gene_names)) if labels[i] == mod_id]
if len(mod_idx) > 1:
sub_corr = corr[np.ix_(mod_idx, mod_idx)]
mean_corr = (sub_corr.sum() - len(mod_idx)) / (len(mod_idx) * (len(mod_idx) - 1))
else:
mean_corr = 1.0
module_results.append({
"module": mod_id,
"n_genes": len(mod_genes),
"mean_within_corr": float(mean_corr),
"top_rbps": [(r, c) for r, c in top_rbp],
"example_genes": mod_genes[:10],
})
if top_rbp:
rbp_str = ", ".join(f"{r}({c})" for r, c in top_rbp)
print(f" Module {mod_id}: {len(mod_genes)} genes, r={mean_corr:.3f}, RBPs: {rbp_str}")
# Mean between-module correlation (should be lower)
between_corrs = []
for i in range(n_clusters):
for j in range(i + 1, n_clusters):
idx_i = [k for k in range(len(gene_names)) if labels[k] == i]
idx_j = [k for k in range(len(gene_names)) if labels[k] == j]
if idx_i and idx_j:
between = corr[np.ix_(idx_i, idx_j)]
between_corrs.append(between.mean())
mean_within = np.mean([m["mean_within_corr"] for m in module_results])
mean_between = np.mean(between_corrs) if between_corrs else 0
print(f"\n Mean within-module correlation: {mean_within:.4f}")
print(f" Mean between-module correlation: {mean_between:.4f}")
print(f" Ratio: {mean_within / max(abs(mean_between), 1e-8):.2f}x")
results = {
"n_active_genes": len(gene_names),
"n_modules": n_clusters,
"mean_within_corr": float(mean_within),
"mean_between_corr": float(mean_between),
"modules": module_results,
}
save_json(results, f"{name}_gamma_coexpression", OUT)
# Figure: heatmap of correlation matrix (clustered)
order = np.argsort(labels)
corr_ordered = corr[np.ix_(order, order)]
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
im = axes[0].imshow(corr_ordered, cmap="RdBu_r", vmin=-0.5, vmax=0.5, aspect="auto")
plt.colorbar(im, ax=axes[0])
axes[0].set_title(f"{name}: Gamma correlation ({n_genes} genes)")
# Module size bar
axes[1].bar(range(n_clusters),
[m["mean_within_corr"] for m in module_results],
color="steelblue", alpha=0.7)
axes[1].axhline(mean_between, color="red", ls="--", label=f"Between={mean_between:.3f}")
axes[1].set_xlabel("Module")
axes[1].set_ylabel("Mean within-module correlation")
axes[1].set_title("Co-degradation module structure")
axes[1].legend()
fig.tight_layout()
save_fig(fig, f"{name}_gamma_corr", 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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