bio-workflows-biomarker-pipeline

name	bio-workflows-biomarker-pipeline
description	End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.
tool_type	python
primary_tool	sklearn
workflow	true
depends_on	["machine-learning/biomarker-discovery","machine-learning/model-validation","machine-learning/omics-classifiers","machine-learning/prediction-explanation"]
qc_checkpoints	[{"after_selection":"Selected features >5 and <200, stability >0.6"},{"after_cv":"Nested CV AUC >0.7, fold variance <0.1"},{"after_interpretation":"Top 20 SHAP features: >50% overlap with selected features"}]

Version Compatibility

Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scikit-learn 1.4+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Biomarker Discovery Pipeline

"Build a validated biomarker panel from my omics data" → Orchestrate feature selection (Boruta/LASSO), nested cross-validation classifier training, and SHAP interpretation to produce a robust, validated biomarker signature.

Complete pipeline from expression data to validated biomarker panels with classifier.

Workflow Overview

Expression matrix + Metadata
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    v
[1. Data Preparation] -----> StandardScaler, train/test split
    |
    v
[2. Feature Selection] ----> Boruta or LASSO stability selection
    |
    v
[3. Model Training] -------> RandomForest/XGBoost with nested CV
    |
    v
[4. Model Interpretation] -> SHAP values, feature importance
    |
    v
[5. Validation] -----------> Hold-out test, bootstrap CI
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    v
Validated biomarker panel + classifier

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

# test_size=0.2: Standard 80/20 split; use 0.3 for <100 samples
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# 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)

Step 2: Feature Selection

Option A: Boruta (All-Relevant Selection)

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

# Pre-filter if >10k features
if X_train_scaled.shape[1] > 10000:
    selector = SelectKBest(f_classif, k=5000)
    selector.fit(X_train_scaled, y_train)
    X_train_filt = X_train_scaled[:, selector.get_support()]
    feature_mask = selector.get_support()
else:
    X_train_filt = X_train_scaled
    feature_mask = None

# max_depth=5: Shallow trees for stable importances
rf = RandomForestClassifier(n_estimators=100, max_depth=5, n_jobs=-1, random_state=42)
# max_iter=100: Usually sufficient; 200 if many tentative
boruta = BorutaPy(rf, n_estimators='auto', max_iter=100, random_state=42, verbose=0)
boruta.fit(X_train_filt, y_train)

selected_idx = boruta.support_
print(f'Selected {selected_idx.sum()} features')

Option B: LASSO Stability Selection

from sklearn.linear_model import LogisticRegressionCV
import numpy as np

# n_bootstrap=100: Quick; use 500 for publication
n_bootstrap = 100
stability_scores = np.zeros(X_train_scaled.shape[1])

for i in range(n_bootstrap):
    idx = np.random.choice(len(y_train), size=len(y_train), replace=True)
    # Cs=10: 10 regularization values to search
    model = LogisticRegressionCV(penalty='l1', solver='saga', Cs=10, cv=3, random_state=i, max_iter=1000)
    model.fit(X_train_scaled[idx], y_train[idx])
    stability_scores += (model.coef_[0] != 0).astype(int)

stability_scores /= n_bootstrap
# stability_threshold=0.6: Standard; 0.8 for strict
selected_idx = stability_scores > 0.6
print(f'Selected {selected_idx.sum()} features (stability >0.6)')

QC Checkpoint 2:

• Selected features: 5-200 range
• Too few (<5): lower threshold, increase iterations
• Too many (>200): increase threshold, add pre-filtering

Step 3: Model Training with Nested CV

from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.ensemble import RandomForestClassifier

X_train_sel = X_train_scaled[:, selected_idx]
X_test_sel = X_test_scaled[:, selected_idx]

# outer_cv=5: Standard for performance estimation
outer_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

# n_estimators=100: Sufficient for most omics
clf = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
cv_scores = cross_val_score(clf, X_train_sel, y_train, cv=outer_cv, scoring='roc_auc')

print(f'Nested CV AUC: {cv_scores.mean():.3f} +/- {cv_scores.std():.3f}')

QC Checkpoint 3:

• AUC >0.7 acceptable, >0.8 good
• Fold variance <0.1 (stable performance)
• Check for overfitting: train AUC should not be >>test AUC

Step 4: Model Interpretation

import shap
import matplotlib.pyplot as plt

clf.fit(X_train_sel, y_train)

# SHAP v0.47+: call explainer directly
explainer = shap.TreeExplainer(clf)
shap_values = explainer(X_train_sel)

# Beeswarm: shows importance AND direction
shap.plots.beeswarm(shap_values, max_display=20, show=False)
plt.tight_layout()
plt.savefig('shap_beeswarm.png', dpi=150, bbox_inches='tight')
plt.close()

# Extract top features
import numpy as np
mean_shap = np.abs(shap_values.values).mean(axis=0)
top_shap_idx = np.argsort(mean_shap)[-20:]

QC Checkpoint 4:

• Top 20 SHAP features should have >50% overlap with selected features
• SHAP directions should be biologically plausible

Step 5: Final Validation

from sklearn.metrics import roc_auc_score, classification_report
import numpy as np

y_prob = clf.predict_proba(X_test_sel)[:, 1]
test_auc = roc_auc_score(y_test, y_prob)
print(f'Hold-out test AUC: {test_auc:.3f}')

# Bootstrap CI for AUC
# n_bootstrap=1000: Standard for publication-quality CI
n_bootstrap = 1000
boot_aucs = []
for i in range(n_bootstrap):
    idx = np.random.choice(len(y_test), size=len(y_test), replace=True)
    boot_aucs.append(roc_auc_score(y_test[idx], y_prob[idx]))

ci_lower, ci_upper = np.percentile(boot_aucs, [2.5, 97.5])
print(f'95% CI: [{ci_lower:.3f}, {ci_upper:.3f}]')

print(classification_report(y_test, clf.predict(X_test_sel)))

Parameter Recommendations

Step	Parameter	Recommendation
Split	test_size	0.2 (standard), 0.3 for small datasets
Boruta	max_iter	100 (sufficient), 200 if tentative features
LASSO	n_bootstrap	100 (quick), 500 for publication
LASSO	stability_threshold	0.6 (standard), 0.8 for strict
Nested CV	outer_folds	5 (standard), 10 for small datasets
Nested CV	inner_folds	3 (sufficient for tuning)
RF	n_estimators	100-500
XGBoost	learning_rate	0.1 (conservative)

Troubleshooting

Issue	Likely Cause	Solution
No features selected	Too strict threshold	Lower stability threshold, increase iterations
Too many features (>200)	Noisy data	Add pre-filtering, increase regularization
Low CV AUC (<0.6)	No signal, low power	Check data quality, add samples
High variance across folds	Small sample size	Use more folds, LOOCV
SHAP features differ from selected	Model using different signal	Review feature correlations

Export Results

import pandas as pd
import joblib

# Save biomarker panel
feature_names = X_train.columns[selected_idx].tolist()
pd.DataFrame({'feature': feature_names}).to_csv('biomarker_panel.csv', index=False)

# Save model and scaler for deployment
joblib.dump(clf, 'biomarker_classifier.joblib')
joblib.dump(scaler, 'feature_scaler.joblib')

Related Skills

• database-access/geo-data - Public expression cohorts for validation sets
• database-access/sra-data - Pull raw FASTQ for re-quantified validation cohorts
• database-access/uniprot-access - Protein-level features (sequence, GO terms, PTMs) for protein biomarkers
• machine-learning/biomarker-discovery - Detailed feature selection methods
• machine-learning/model-validation - Nested CV implementation details
• machine-learning/omics-classifiers - Classifier options and tuning
• machine-learning/prediction-explanation - SHAP and LIME interpretation
• differential-expression/de-results - Pre-filter with DE genes
• pathway-analysis/go-enrichment - Functional enrichment of biomarkers