File size: 4,373 Bytes
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 | #!/usr/bin/env python
"""Validate PT-specific genes against eCLIP RBP binding data.
Tests whether genes identified as post-transcriptionally regulated
by DeepPTR's z_PT latent are confirmed RBP targets in ENCODE eCLIP.
"""
from _common import *
OUT = output_dir("03_eclip_validation")
def load_eclip():
eclip = pd.read_csv(DATA_DIR / "eclip_targets.csv")
return eclip
def load_pt_genes(dataset_name):
adv_file = PROJECT_ROOT / "output" / "deep_advantages" / "results" / f"{dataset_name}_advantages.json"
if not adv_file.exists():
return []
with open(adv_file) as f:
adv = json.load(f)
return adv.get("disentanglement", {}).get("pt_specific_genes", [])
def load_gene_list(filename):
with open(DATA_DIR / filename) as f:
return set(line.strip().upper() for line in f if line.strip())
def main():
set_figure_style()
eclip = load_eclip()
eclip_targets = set(eclip["target_gene"].str.upper())
eclip_by_rbp = eclip.groupby("rbp")["target_gene"].apply(lambda x: set(x.str.upper())).to_dict()
are_genes = load_gene_list("are_genes.txt")
nmd_genes = load_gene_list("nmd_genes.txt")
all_results = {}
for name, loader, ck in DATASETS:
print(f"\n{'=' * 60}\n{name.upper()}\n{'=' * 60}")
pt_genes = load_pt_genes(name)
if not pt_genes:
print(" No PT-specific genes found")
continue
pt_upper = set(g.upper() for g in pt_genes)
# eCLIP overlap
in_eclip = pt_upper & eclip_targets
frac = len(in_eclip) / max(len(pt_upper), 1)
print(f" PT genes: {len(pt_genes)}, in eCLIP: {len(in_eclip)} ({frac*100:.0f}%)")
if in_eclip:
print(f" Validated: {sorted(in_eclip)}")
# Per-RBP
rbp_hits = {}
for rbp, targets in eclip_by_rbp.items():
overlap = pt_upper & targets
if overlap:
rbp_hits[rbp] = sorted(overlap)
print(f"\n Top RBPs:")
for rbp in sorted(rbp_hits, key=lambda x: len(rbp_hits[x]), reverse=True)[:10]:
print(f" {rbp}: {len(rbp_hits[rbp])} — {rbp_hits[rbp][:5]}")
# Fisher's exact: PT genes vs random background for eCLIP enrichment
# Background: use all genes from analytical pipeline
adata_an = run_analytical(loader)
all_upper = set(g.upper() for g in adata_an.var_names)
bg_in_eclip = all_upper & eclip_targets
# 2x2 table: [PT∩eCLIP, PT∩¬eCLIP; ¬PT∩eCLIP, ¬PT∩¬eCLIP]
a = len(in_eclip)
b = len(pt_upper) - a
c = len(bg_in_eclip) - a
d = len(all_upper) - len(pt_upper) - c
if min(a, b, c, d) >= 0:
odds, fisher_p = stats.fisher_exact([[a, b], [c, d]], alternative="greater")
print(f"\n Fisher's exact (PT enriched for eCLIP?): OR={odds:.2f}, p={fisher_p:.4f}")
else:
odds, fisher_p = np.nan, np.nan
# ARE/NMD overlap
are_overlap = pt_upper & are_genes
nmd_overlap = pt_upper & nmd_genes
print(f" ARE overlap: {len(are_overlap)}, NMD overlap: {len(nmd_overlap)}")
all_results[name] = {
"n_pt_genes": len(pt_genes),
"n_in_eclip": len(in_eclip),
"frac_in_eclip": frac,
"fisher_odds": float(odds) if np.isfinite(odds) else None,
"fisher_p": float(fisher_p) if np.isfinite(fisher_p) else None,
"validated_genes": sorted(in_eclip),
"top_rbps": {k: v for k, v in sorted(rbp_hits.items(), key=lambda x: len(x[1]), reverse=True)[:10]},
"are_overlap": sorted(are_overlap),
"nmd_overlap": sorted(nmd_overlap),
}
save_json(all_results, "eclip_validation", OUT)
# Summary figure: RBP target counts
for name, res in all_results.items():
rbps = res.get("top_rbps", {})
if not rbps:
continue
fig, ax = plt.subplots(figsize=(8, 4))
rbp_names = list(rbps.keys())[:10]
counts = [len(rbps[r]) for r in rbp_names]
ax.barh(rbp_names, counts, color="darkorange", alpha=0.7)
ax.set_xlabel("Number of PT-specific gene targets")
ax.set_title(f"{name}: RBPs targeting PT-specific genes")
fig.tight_layout()
save_fig(fig, f"{name}_rbp_targets", OUT)
if __name__ == "__main__":
main()
|