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9f9fb84 9471dea 9f9fb84 | 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 | import numpy as np, pandas as pd
from pathlib import Path
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
DATA = Path("data/processed"); RES = Path("results"); RES.mkdir(exist_ok=True, parents=True)
C = pd.read_csv(DATA/"causal_table.csv")
anchors = ["PDR5_expr","SNQ2_expr","YOR1_expr","ATM1_expr"]
regs = ["YAP1_reg","PDR1_reg"]
rows=[]
for t in anchors:
df = C[[t,"outcome"]+regs].dropna().copy()
for r in regs:
Z = df[[t, r]].astype(float).values
Z = Z - Z.mean(0, keepdims=True)
T = Z[:,0:1]; R = Z[:,1:2]
X = np.hstack([T, R, T*R])
y = df["outcome"].astype(float).values
nuis = ["ethanol_pct","ROS","NaCl_mM","H2O2_uM"]
W = df.join(C[nuis], how="left")[nuis].astype(float).values
W = W - W.mean(0, keepdims=True)
Xfull = np.hstack([X, W])
mdl = LinearRegression().fit(Xfull, y)
beta_T, beta_R, beta_TR = mdl.coef_[0], mdl.coef_[1], mdl.coef_[2]
rows.append([t.replace("_expr",""), r, beta_T, beta_TR])
INT = pd.DataFrame(rows, columns=["transporter","regulator","main_T","interaction_TxReg"])
INT.to_csv(RES/"validation_interactions.csv", index=False)
print(" Saved:", RES/"validation_interactions.csv")
display(INT.sort_values(["transporter","regulator"])) |