name: omics-ml-classifier description: Train and evaluate machine learning classifiers on omics data (gene expression, mutation scores, copy number, or any numeric feature matrix). Supports Random Forest, Logistic Regression, SVM, and XGBoost. Outputs cross-validation metrics, test set performance, feature importances, ROC curve, confusion matrix, and optional UMAP embedding. Works with any two-class or multi-class label. metadata: version: "1.0.0"

Omics ML Classifier

Purpose

Train a supervised machine learning classifier on omics data and evaluate its performance.

Typical use cases:

• Classify samples by cancer subtype from gene expression
• Predict treatment response from mutation or expression profiles
• Distinguish tumor from normal from multi-omics features
• Identify the most predictive genes or biomarkers for a phenotype

Supported models:

• random_forest — Random Forest (default; handles high-dimensional data, provides feature importance)
• logistic — Logistic Regression with L2 regularisation
• svm — Support Vector Machine (RBF kernel)
• xgboost — XGBoost gradient boosting (requires xgboost package)

Default model: random_forest

Reuse policy

This skill is designed for:

• Phenotype classification from any numeric omics matrix
• Biomarker discovery (via feature importance ranking)
• Model benchmarking across multiple classifier types
• Downstream validation of DE or pathway analysis results

This skill requires user-provided local files (feature matrix + label file).

Inputs

Required

• --features — feature matrix (TSV or CSV)
• Rows = samples
• Columns = features (gene names, protein IDs, etc.)
• Values = numeric (expression, mutation score, etc.)

• --labels — label file (TSV or CSV)
• Must have columns: sample, label
• sample must match row names in --features
• label is the class to predict (e.g. responder, non-responder)

• --outdir — output directory

Optional

• --model

Choices: random_forest, logistic, svm, xgboost Default: random_forest

• --select-k

Default: None (use all features) Select top-K features by ANOVA F-score before training. Recommended for very high-dimensional data (> 5000 features).

• --test-size

Default: 0.2 Fraction of samples held out for final test evaluation.

• --cv-folds

Default: 5 Number of stratified cross-validation folds.

• --random-state

Default: 42 Random seed for reproducibility.

• --prefix

Default: <model>_classifier Output file prefix.

• --top-n-features

Default: 30 Number of top features shown in the importance plot.

• --umap

Flag. Generate UMAP embedding coloured by class label. Requires umap-learn package.

Input file formats

Feature matrix (--features)

sample      GENE1    GENE2    GENE3
sample_001  12.4     0.3      8.7
sample_002  3.1      11.2     2.0
sample_003  14.8     0.1      9.1

Label file (--labels)

sample      label
sample_001  responder
sample_002  non-responder
sample_003  responder

Outputs

Always

• <prefix>.cv_results.tsv — per-fold and mean CV metrics (accuracy, ROC-AUC, F1)
• <prefix>.test_metrics.tsv — full classification report on held-out test set
• <prefix>.feature_importance.tsv — feature importance or coefficient magnitudes, ranked
• ml_summary.tsv — one-row summary of all key metrics
• summary.txt — human-readable summary

Plots

• <prefix>.roc_curve.png / .pdf — ROC curve with AUC annotation
• <prefix>.confusion_matrix.png / .pdf — normalised confusion matrix
• <prefix>.feature_importance.png / .pdf — top feature importance bar chart
• <prefix>.umap.png / .pdf — UMAP embedding (only when --umap is set)

Execution policy

Construct the command from required arguments and optional parameters.

Command template

python scripts/omics_ml_classifier.py \
  --features <FEATURES_FILE> \
  --labels <LABELS_FILE> \
  [--model <MODEL>] \
  [--select-k <K>] \
  [--test-size <FRAC>] \
  [--cv-folds <N>] \
  [--random-state <SEED>] \
  [--prefix <PREFIX>] \
  [--top-n-features <N>] \
  [--umap] \
  --outdir <OUTDIR>

Example commands

Default Random Forest

python scripts/omics_ml_classifier.py \
  --features expression_matrix.tsv \
  --labels sample_labels.tsv \
  --outdir results/

Logistic Regression with feature selection

python scripts/omics_ml_classifier.py \
  --features expr.tsv \
  --labels labels.tsv \
  --model logistic \
  --select-k 200 \
  --outdir results/

XGBoost with UMAP visualisation

python scripts/omics_ml_classifier.py \
  --features multi_omics.tsv \
  --labels subtype_labels.tsv \
  --model xgboost \
  --umap \
  --prefix xgb_subtype \
  --outdir results/

SVM with 10-fold CV

python scripts/omics_ml_classifier.py \
  --features expr.tsv \
  --labels labels.tsv \
  --model svm \
  --cv-folds 10 \
  --outdir results/

Downstream skill chaining

Parameter decision guide

Failure conditions

Fail clearly if:

• --features, --labels, or --outdir are missing
• No common samples between feature matrix and label file
• Label file is missing sample or label columns
• Feature matrix has no numeric columns after NaN filtering
• --model xgboost is requested but xgboost is not installed
• Fewer samples than --cv-folds (reduce --cv-folds)

Agent trigger examples

Trigger this skill when the user asks:

• "Build a classifier to predict cancer subtype from expression data"
• "Which genes best distinguish responders from non-responders?"
• "Train a Random Forest on my omics matrix"
• "Can you predict treatment response from this gene expression data?"
• "Run ML classification with cross-validation on my dataset"
• "What are the most important features for classifying tumor vs normal?"
• "Use XGBoost to classify my samples and show me a UMAP"

Do NOT trigger this skill when the user asks:

• "Cluster my samples" → use unsupervised clustering (not yet available)
• "Run differential expression" → use rnaseq-differential-expression
• "Train a deep learning model" → neural network skill (not yet available)
• "Predict drug sensitivity scores" → DepMap analysis, use depmap-analysis-for-gene

Notes

• All features are standardised (zero mean, unit variance) before training.
• Missing values (NaN) in the feature matrix are imputed with column medians.
• random_forest and xgboost provide native feature importances (Gini / gain).

logistic and svm use absolute coefficient values as proxy importances.

• ROC-AUC is reported as weighted OvR (one-vs-rest) for multi-class problems.
• For imbalanced classes, all models use class_weight="balanced" (except XGBoost).
• --select-k applies ANOVA F-score feature selection on the training fold only

to avoid data leakage.

• --umap requires umap-learn (pip install umap-learn); skipped gracefully if absent.