machine-learning-omics-classifiers

name	machine-learning-omics-classifiers
description	Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers. Use when building diagnostic or prognostic classifiers from expression or variant data.
tool_type	python
primary_tool	sklearn

Version Compatibility

Reference examples tested with: matplotlib 3.8+, pandas 2.2+, 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.

Classification Models for Omics Data

"Build a classifier from my gene expression data" -> Train RandomForest, XGBoost, or logistic regression models on omics features with proper preprocessing and evaluation metrics.

• Python: sklearn.ensemble.RandomForestClassifier(), xgboost.XGBClassifier()

Core Workflow

Goal: Train a classification model on omics data and evaluate its predictive performance.

Approach: Build a scaled pipeline with a Random Forest classifier, fit on training data, and assess with ROC-AUC on held-out test data.

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score, roc_curve
import matplotlib.pyplot as plt

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1))
])
pipe.fit(X_train, y_train)

y_pred = pipe.predict(X_test)
y_prob = pipe.predict_proba(X_test)[:, 1]
print(classification_report(y_test, y_pred))
print(f'ROC-AUC: {roc_auc_score(y_test, y_prob):.3f}')

XGBoost Classifier

Goal: Train a gradient-boosted tree classifier using the sklearn-compatible XGBoost API.

Approach: Configure XGBClassifier with proper parameter names (avoiding deprecated aliases) and wrap in a scaling pipeline.

from xgboost import XGBClassifier

# Use sklearn-compatible API with proper parameters (avoid deprecated seed, nthread)
xgb = XGBClassifier(
    n_estimators=100,
    max_depth=6,
    learning_rate=0.1,
    random_state=42,  # NOT seed
    n_jobs=-1,        # NOT nthread
    eval_metric='logloss'
)

pipe = Pipeline([('scaler', StandardScaler()), ('clf', xgb)])
pipe.fit(X_train, y_train)

Logistic Regression with Regularization

Goal: Build an interpretable linear classifier that simultaneously selects sparse biomarker features.

Approach: Use L1-regularized logistic regression with built-in cross-validation for penalty selection, then extract nonzero coefficients as selected features.

from sklearn.linear_model import LogisticRegressionCV

# L1 for sparse biomarkers, L2 for correlated features, elasticnet for mixed
logit = LogisticRegressionCV(
    Cs=10,
    cv=5,
    penalty='l1',
    solver='saga',
    max_iter=1000,
    random_state=42
)
pipe = Pipeline([('scaler', StandardScaler()), ('clf', logit)])
pipe.fit(X_train, y_train)

# Get selected features (nonzero coefficients)
feature_mask = logit.coef_[0] != 0
selected = X.columns[feature_mask]

ROC Curve Visualization

Goal: Generate a publication-quality ROC curve showing classifier discrimination ability.

Approach: Compute false/true positive rates from predicted probabilities and plot with AUC annotation.

fpr, tpr, _ = roc_curve(y_test, y_prob)
auc = roc_auc_score(y_test, y_prob)

plt.figure(figsize=(6, 6))
plt.plot(fpr, tpr, label=f'ROC (AUC = {auc:.3f})')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.savefig('roc_curve.png', dpi=150)

Multi-class Classification

Goal: Handle classification tasks with more than two classes while addressing class imbalance.

Approach: Encode labels numerically and use balanced class weights to upweight underrepresented classes during training.

from sklearn.metrics import classification_report
from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
y_encoded = le.fit_transform(y)

# Use class_weight for imbalanced data
rf = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=42)

Feature Importance from Trees

Goal: Rank features by their contribution to tree-based classifier predictions.

Approach: Extract Gini importances from a fitted Random Forest and sort to identify top contributing features.

import pandas as pd

importances = pipe.named_steps['clf'].feature_importances_
feature_imp = pd.DataFrame({'feature': X.columns, 'importance': importances})
feature_imp = feature_imp.sort_values('importance', ascending=False).head(20)

Preprocessing Guidelines

Data Type	Scaler	Notes
Log-counts (RNA-seq)	StandardScaler	Assumes ~normal after log
TPM/FPKM	StandardScaler	Gene-wise centering
Raw counts	None	Tree models handle counts
Mixed features	ColumnTransformer	Different scalers per type

Related Skills

• machine-learning/model-validation - Proper model evaluation
• machine-learning/prediction-explanation - Explain predictions with SHAP
• machine-learning/biomarker-discovery - Reduce features before modeling