bio-workflows-biomarker-pipeline

Step 1: Data Preparation

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

expr = pd.read_csv('expression.csv', index_col=0)
meta = pd.read_csv('metadata.csv', index_col=0)

X = expr.T  # samples x genes
y = meta.loc[X.index, 'condition'].values

## Fit scaler on training only to prevent data leakage

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

QC Checkpoint 1: Check class balance, sample counts per group

• Minimum 10 samples per class recommended
• Classes should be reasonably balanced (ratio <3:1)

Option A: Boruta (All-Relevant Selection)

from boruta import BorutaPy
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif