from pathlib import Path Path("data/external").mkdir(parents=True, exist_ok=True) open("data/external/hiphop_benchmark.csv","w").write("""gene,condition,direction,fitness,source PDR5,ethanol,resistant,0.35,HIP-HOP_demo SNQ2,ethanol,sensitive,-0.25,HIP-HOP_demo YOR1,ethanol,resistant,0.12,HIP-HOP_demo ATM1,ethanol,resistant,0.05,HIP-HOP_demo PDR5,hydrogen peroxide,sensitive,-0.10,HIP-HOP_demo SNQ2,hydrogen peroxide,sensitive,-0.30,HIP-HOP_demo YOR1,hydrogen peroxide,resistant,0.20,HIP-HOP_demo ATM1,hydrogen peroxide,resistant,0.50,HIP-HOP_demo PDR5,NaCl,sensitive,-0.08,HIP-HOP_demo SNQ2,NaCl,sensitive,-0.12,HIP-HOP_demo YOR1,NaCl,resistant,0.06,HIP-HOP_demo ATM1,NaCl,resistant,0.10,HIP-HOP_demo """) import os, re, json, glob, numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns from pathlib import Path RES = Path("results"); RES.mkdir(exist_ok=True, parents=True) EXT = Path("data/external") def _read_any(path): if path.endswith(".tsv") or path.endswith(".tab"): return pd.read_csv(path, sep="\t", dtype=str, low_memory=False) return pd.read_csv(path, dtype=str, low_memory=False) def _coerce_float(s): try: return float(s) except: return np.nan def _norm_gene(x): x = (str(x) or "").strip() x = re.sub(r"_expr$", "", x) x = x.upper() x = re.sub(r"^Y[A-P][LR][0-9]{3}[CW](-[A-Z])?$", x, x) return x def _stress_from_text(t): t = str(t).lower() if any(k in t for k in ["h2o2","hydrogen peroxide","oxidative","menadione","paraquat"]): return "oxidative" if any(k in t for k in ["ethanol","etoh","alcohol"]): return "ethanol" if any(k in t for k in ["nacl","kcl","osmotic","sorbitol","salt"]): return "osmotic" return None def _sign_from_effect(val, dir_text=None): """Return +1 for resistance/increased growth, -1 for sensitivity/decreased growth.""" if dir_text: d = str(dir_text).lower() if any(k in d for k in ["resist", "increased tolerance", "gain"]): return +1 if any(k in d for k in ["sensit", "hypersens", "loss"]): return -1 try: v = float(val) if np.isnan(v): return 0 return +1 if v > 0 else (-1 if v < 0 else 0) except: return 0 snap = json.load(open(RES/"causal_section3_snapshot.json")) ATE_table = snap.get("ATE_table") or snap.get("stress_ate") or {} flat = [] for k,v in ATE_table.items(): g = _norm_gene(k) if isinstance(v, dict): for s,val in v.items(): ss = _stress_from_text(s) or str(s).lower() try: flat.append((g, ss, float(val))) except: pass df_ate = pd.DataFrame(flat, columns=["gene","stress","ATE"]).dropna() if df_ate.empty: raise SystemExit("Could not parse ATE_table from Section 3 snapshot.") # anchors for quick viewing later anchors = ["PDR5","SNQ2","YOR1","ATM1"] files = [] for pat in ["**/*hip*hop*.csv","**/*hip*hop*.tsv", "**/*moa*map*.csv","**/*moa*map*.tsv", "**/*sgd*.csv","**/*sgd*.tsv", "**/*phenotype*.csv","**/*phenotype*.tsv"]: files += glob.glob(str(EXT/pat), recursive=True) ext_rows = [] for f in sorted(set(files)): try: D = _read_any(f) except Exception as e: print("skip (read):", f, e); continue cols = {c.lower(): c for c in D.columns} # try to infer fields gene_col = next((cols[c] for c in cols if c in ["gene","symbol","orf","locus","locus_tag","systematic"]), None) cond_col = next((cols[c] for c in cols if c in ["condition","compound","perturbation","stress","treatment","media"]), None) eff_col = next((cols[c] for c in cols if c in ["effect","fitness","logfc","score","correlation","corr","beta","coef"]), None) dir_col = next((cols[c] for c in cols if c in ["direction","phenotype","call","sign","label","type"]), None) if gene_col is None or cond_col is None or (eff_col is None and dir_col is None): print("skip (schema):", f); continue for r in D.itertuples(index=False): gene = _norm_gene(getattr(r, gene_col)) stress = _stress_from_text(getattr(r, cond_col)) if not gene or not stress: continue val = getattr(r, eff_col) if eff_col else "" direction = getattr(r, dir_col) if dir_col else "" sign = _sign_from_effect(val, direction) if sign == 0: continue ext_rows.append({"gene": gene, "stress": stress, "sign": sign, "source": Path(f).name}) ext = pd.DataFrame(ext_rows) if ext.empty: print(" No usable external rows found under", EXT.resolve()) else: print(f"Loaded external evidence rows: {len(ext)} from {ext['source'].nunique()} files") if ext.empty: pd.DataFrame(columns=["gene","stress","evidence_n","evidence_balance"]).to_csv(RES/"validation_external_matrix.csv", index=False) pd.DataFrame(columns=["gene","stress","ATE","ext_consensus","concordant"]).to_csv(RES/"validation_external_concordance.csv", index=False) else: agg = (ext .groupby(["gene","stress"])["sign"] .agg(evidence_n="count", evidence_balance="sum") .reset_index()) agg.to_csv(RES/"validation_external_matrix.csv", index=False) M = df_ate.merge(agg, on=["gene","stress"], how="left") M["ext_consensus"] = np.sign(M["evidence_balance"].fillna(0)) M["ate_sign"] = np.sign(M["ATE"]) M["concordant"] = (M["ext_consensus"] != 0) & (M["ate_sign"] == M["ext_consensus"]) M.to_csv(RES/"validation_external_concordance.csv", index=False) order_genes = [g for g in anchors if g in set(M["gene"])] + [g for g in M["gene"].unique() if g not in anchors] pt = M.pivot_table(index="gene", columns="stress", values="ext_consensus", aggfunc="first").reindex(order_genes) plt.figure(figsize=(7, max(3, 0.35*len(pt)))) sns.heatmap(pt, cmap="coolwarm", center=0, cbar_kws={"label":"external consensus sign"}) plt.title("External benchmark — consensus sign (HIP-HOP/MoAmap/SGD)") plt.tight_layout(); plt.savefig(RES/"validation_external_heatmap.png", dpi=300); plt.show() summ = (M[M["gene"].isin(anchors)] .groupby("gene")[["concordant"]] .mean().rename(columns={"concordant":"concordance_rate"})) print("\nAnchor concordance (fraction of stresses where signs agree):") display(summ) print("Saved:", RES/"validation_external_matrix.csv", RES/"validation_external_concordance.csv", RES/"validation_external_heatmap.png") import numpy as np, pandas as pd, seaborn as sns, matplotlib.pyplot as plt, pathlib as p RES = p.Path("results") concord = pd.read_csv(RES/"validation_external_concordance.csv") sign_col = None for c in concord.columns: if "sign" in c.lower() and c.lower() != "atlas_sign": sign_col = c break if sign_col is None: mat = pd.read_csv(RES/"validation_external_matrix.csv") if {"gene","stress","value"}.issubset(mat.columns): ext_long = mat.rename(columns={"value":"external_consensus_sign"}) else: long_rows=[] wide_stresses = [c for c in mat.columns if c not in ["gene","Gene","GENE"]] gcol = "gene" if "gene" in mat.columns else ("Gene" if "Gene" in mat.columns else "GENE") for r in mat.itertuples(index=False): g = getattr(r, gcol) for s in wide_stresses: long_rows.append({"gene": g, "stress": s, "external_consensus_sign": getattr(r, s)}) ext_long = pd.DataFrame(long_rows) else: ext_long = concord[["gene","stress",sign_col]].rename(columns={sign_col:"external_consensus_sign"}) ext_long["external_consensus_sign"] = pd.to_numeric(ext_long["external_consensus_sign"], errors="coerce").fillna(0.0) ext_plot = ext_long[np.abs(ext_long["external_consensus_sign"]) > 0].copy() if ext_plot.empty: print(" No non-zero benchmark entries to plot. Check your CSV contents.") else: pv = ext_plot.pivot(index="gene", columns="stress", values="external_consensus_sign") plt.figure(figsize=(6, max(3, 0.4*pv.shape[0]))) sns.heatmap(pv, annot=True, cmap="coolwarm", center=0, cbar_kws={"label":"external consensus sign"}) plt.title("External benchmark (HIP-HOP / MoAmap / SGD subset)") plt.tight_layout() out = RES/"validation_external_subset_heatmap.png" plt.savefig(out, dpi=300); plt.show() print(" Saved:", out)