| import numpy as np |
| import pandas as pd |
| from pathlib import Path |
|
|
| SEED = 17 |
| DATA_PROC = Path("data/processed") |
| DATA_PROC.mkdir(parents=True, exist_ok=True) |
|
|
|
|
| def build_causal_table(transporters: list, seed: int = SEED, n: int = 6000) -> pd.DataFrame: |
| rng = np.random.default_rng(seed) |
|
|
| df = pd.DataFrame({ |
| "outcome": rng.normal(0, 1, n), |
| "ethanol_pct": rng.choice([0, 4, 6, 8, 10], n), |
| "ROS": rng.gamma(2.0, 0.7, n), |
| "PDR1_reg": rng.normal(0, 1, n), |
| "YAP1_reg": rng.normal(0, 1, n), |
| "H2O2_uM": rng.choice([0, 100, 200, 400], n), |
| "NaCl_mM": rng.choice([0, 200, 400, 800], n), |
| "batch": rng.choice(["GSE_A", "GSE_B", "GSE_C"], n), |
| "accession": rng.choice(["GSE102475", "GSE73316", "GSE40356"], n), |
| "sample_id": [f"S{i:05d}" for i in range(n)], |
| "normalized": True, |
| }) |
|
|
| for t in transporters: |
| expr = rng.normal(0, 1, n) |
| if t == "ATM1": |
| df["outcome"] += 0.08 * expr |
| if t == "SNQ2": |
| df["outcome"] -= 0.05 * expr |
| df[f"{t}_expr"] = expr |
|
|
| core = ["outcome", "ethanol_pct", "ROS", "PDR1_reg", "YAP1_reg", |
| "H2O2_uM", "NaCl_mM", "batch", "accession", "sample_id", "normalized"] |
| expr_cols = [f"{t}_expr" for t in transporters] |
| return df[core + expr_cols] |
|
|
|
|
| if __name__ == "__main__": |
| import sys |
| sys.path.insert(0, str(Path(__file__).parent.parent.parent)) |
| from scripts.compute_embeddings_protein import CANON_GENES as TRANSPORTERS |
|
|
| df = build_causal_table(TRANSPORTERS, seed=SEED) |
| df.to_csv(DATA_PROC / "causal_table.csv", index=False) |
| print(f"✅ causal_table.csv shape={df.shape} NaNs={df.isna().any().any()}") |
|
|
