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

Rscript scripts/main.R \
  --input_file ./expression_matrix.csv \
  --group_file ./groups.csv \
  --feature_file ./genes.csv \
  --case_group case \
  --control_group control \
  --alpha auto \
  --alpha_grid 0,0.25,0.5,0.75,1 \
  --nfolds 5 \
  --lambda_choice lambda.min \
  --standardize TRUE \
  --timeout_seconds 600 \
  --output_dir ./output/ \
  --seed 42

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Arguments

Short	Long	Type	Default	Description
-i	--input_file	character	required	Expression matrix file (genes as rows, samples as columns)
-g	--group_file	character	required	Group information file with sample and group columns
-f	--feature_file	character	NULL	Optional feature list file; if omitted, all matrix rows are used
-c	--case_group	character	case	Positive class label in the group file
-d	--control_group	character	control	Negative class label in the group file
-a	--alpha	character	0.5	Elastic net mixing parameter: numeric 0-1, or auto for CV-based selection
	--alpha_grid	character	0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1	Comma-separated alpha candidates evaluated when alpha=auto
-n	--nfolds	integer	5	Cross-validation fold count; automatically reduced if a class has fewer samples
-l	--lambda_choice	character	lambda.min	Coefficient extraction rule: lambda.min or lambda.1se
-z	--standardize	logical	TRUE	Standardize features inside glmnet
-t	--timeout_seconds	integer	600	Elapsed timeout limit in seconds
-o	--output_dir	character	./output/	Output directory
-s	--seed	integer	42	Random seed for reproducibility

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Input Format

Expression Matrix (input_file)

Genes as rows, samples as columns, CSV format with gene IDs in the first column.

,Sample01,Sample02,Sample03
TNMD,0.0349,0.0533,1.3889
DPM1,4.8627,5.4208,5.6370

Group File (group_file)

CSV with sample IDs and binary group labels.

sample,group
Sample01,case
Sample02,control
Sample03,case

Feature File (feature_file)

Optional plain text or single-column CSV file with one feature per line.

TNMD
DPM1
SCYL3

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Output Files

File	Description
alpha_tuning.csv	Cross-validated performance summary for each alpha candidate
model_coefficients.csv	Coefficients at the selected lambda, including intercept
selected_features.csv	Sparse selected features sorted by absolute effect size; written empty when the chosen alpha is 0 (ridge)
feature_matrix.csv	Sample-by-feature analysis matrix used for model fitting
coefficient_path.pdf	Coefficient trajectory plot across lambda values
cv_curve.pdf	Cross-validation error curve with lambda.min and lambda.1se
session_info.txt	R session and package version info

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Workflow

Step 1: Validate Input

• WHEN preparing input files for a first run or regression test, READ: tests/data/
• Check file existence
• Reject empty input files
• Detect sample and group columns in the group file
• Reject group files that contain labels outside the requested binary comparison
• Validate sample matching between expression matrix and group file

Step 2: Prepare Modeling Matrix

• Restrict samples to the requested case and control groups
• Intersect the optional feature list with matrix row names
• Build a sample-by-feature numeric matrix for glmnet
• Drop zero-variance features before modeling

Step 3: Run Elastic Net

• WHEN deciding between alpha, lambda.min, and lambda.1se, READ: references/algorithm.md
• If alpha=auto, evaluate the candidate alpha_grid with the same cross-validation folds
• Fit the regularization path with glmnet
• Run cv.glmnet to estimate the optimal lambda
• Extract coefficients at lambda.min or lambda.1se
• Apply runtime timeout and capture non-fatal warnings

Step 4: Export Results

• WHEN you need exact invocation patterns or output inspection commands, READ: references/cli-guide.md
• Save tuning tables and selected features
• Generate coefficient path and cross-validation plots
• Record session information for reproducibility

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Methods

Elastic Net Logistic Regression

Elastic net combines lasso (L1) and ridge (L2) penalties through alpha, enabling sparse feature selection while stabilizing correlated predictors.

Cross-Validation

cv.glmnet evaluates the lambda path and reports both lambda.min and the more conservative lambda.1se.

Automatic Alpha Selection

When alpha=auto, the skill reuses the same cross-validation folds across all values in alpha_grid, compares the minimum cross-validated error for each candidate, and selects the best alpha before reporting coefficients and lambda-based outputs.

If the chosen alpha is 0, the model is ridge rather than sparse elastic net. In that case, selected_features.csv is written empty to avoid mislabeling dense ridge coefficients as selected features; use model_coefficients.csv for coefficient ranking instead.

Feature Selection Rule

Selected features are the coefficients whose absolute value exceeds a small numerical tolerance at the chosen lambda, excluding the intercept term.

If the chosen alpha is 0, the workflow writes an empty selected_features.csv because ridge coefficients are dense by design and should not be mislabeled as sparse selected features.

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Examples

Recommended First Run

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g groups.csv \
  -f genes.csv \
  -a auto \
  --alpha_grid 0,0.25,0.5,0.75,1 \
  -o output/first_run

Fixed-Alpha Baseline

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g groups.csv \
  -f genes.csv \
  -a 0.5 \
  -o output/fixed_alpha

More Conservative Selection

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g groups.csv \
  -l lambda.1se \
  -o output/lambda_1se

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Error Handling

Common Errors

Error	Cause	Solution	Read More
SKILL_FILE_NOT_FOUND	Input file does not exist	Check file path and permissions	references/troubleshooting.md#skill_file_not_found
SKILL_EMPTY_DATA	Input file exists but is empty	Re-export the input file with data rows	references/troubleshooting.md#skill_empty_data
SKILL_MISSING_COLUMNS	Group file lacks sample/group columns	Verify the group file structure	references/troubleshooting.md#skill_missing_columns
SKILL_SAMPLE_MISMATCH	Sample IDs do not overlap between files	Ensure matrix column names match the group file	references/troubleshooting.md#skill_sample_mismatch
SKILL_INVALID_PARAMETER	CLI parameter is invalid	Check allowed values and ranges	references/troubleshooting.md#skill_invalid_parameter
SKILL_INVALID_DATA	Too few samples or usable features remain	Review filtering choices and input data	references/troubleshooting.md#skill_invalid_data
SKILL_DEPENDENCY_MISSING	Required R package is not installed	Install missing packages before rerunning	references/troubleshooting.md#skill_dependency_missing
SKILL_PKG_VERSION	Installed package is too old	Upgrade the required package	references/troubleshooting.md#skill_pkg_version
SKILL_TIMEOUT	Run exceeded the configured time limit	Increase timeout_seconds or reduce data size	references/troubleshooting.md#skill_timeout
SKILL_RUNTIME_ERROR	An unexpected runtime or output-write failure occurred	Check output path permissions, free space, and the last console message	references/troubleshooting.md#skill_runtime_error

IF error persists, READ: references/troubleshooting.md

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Testing

Test with Sample Data

# Check help
Rscript scripts/main.R --help

# Run with bundled test data
Rscript scripts/main.R \
  -i tests/data/expression_matrix.csv \
  -g tests/data/groups.csv \
  -f tests/data/genes.csv \
  -a auto \
  --alpha_grid 0,0.5,1 \
  -o tests/output \
  -n 5 \
  -t 600

Validation Commands

# Inspect selected features (may be header-only if auto-alpha selects ridge)
cat tests/output/selected_features.csv

# Check plots exist
ls -la tests/output

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Implementation Checklist

• CLI parsing with optparse
• set.seed() for reproducibility
• requireNamespace() dependency checks
• Runtime package loading with library()
• Session info recording
• Timeout control with setTimeLimit()
• Console warning handling
• Out-of-scope label enforcement
• gc() snapshot reporting
• File reading instructions in SKILL.md
• Modular script structure
• Test data provided
• Error handling with SKILL_* codes
• Scripts in scripts/ directory
• References in references/ directory

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Last updated: 2026-04-20 | Version: 1.0.0