| |
| """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) |
|
|
| |
| 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)}") |
|
|
| |
| 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]}") |
|
|
| |
| |
| adata_an = run_analytical(loader) |
| all_upper = set(g.upper() for g in adata_an.var_names) |
| bg_in_eclip = all_upper & eclip_targets |
|
|
| |
| 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_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) |
|
|
| |
| 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() |
|
|
