Elastic Net Feature Selection

When to Use

• Use this skill for binary case-vs-control classification on bulk expression matrices.
• Use it when you need elastic net logistic regression feature selection, coefficient paths, and cv.glmnet-based lambda selection.
• Use custom labels such as Tumor and Normal only when the group file still contains exactly two outcome levels.

Out of Scope

• Survival or Cox modeling
• Multiclass outcomes
• Single-cell data
• Non-expression tables

Out-of-scope enforcement:

• If the group file contains any label outside the requested case_group and control_group, the command stops with SKILL_INVALID_DATA instead of silently dropping samples.
• If either requested class is missing after validation, the command stops with SKILL_INVALID_DATA.

When to Read External Files

Situation	File to Read	Purpose
Need to understand alpha, lambda choice, or feature-selection behavior	references/algorithm.md	Elastic net logistic regression, penalty mixing, cross-validation, and coefficient selection assumptions
Need the authoritative executable entrypoint	scripts/main.R	Run: Rscript scripts/main.R --input_file ... --group_file ... --output_dir ...
Need parameter examples, smoke-test commands, or recorded local runs	references/cli-guide.md	Verified CLI examples for normal runs, conservative runs, and test-data runs
Need bundled sample inputs for a first run or regression test	tests/data/	Sample expression matrix, group file, and feature list
Encounter errors, warnings, or timeout issues	references/troubleshooting.md	Common failures, console warning interpretation, and recovery steps

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Usage