Elastic Net Feature Selection

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

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

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: aipoch
• Source: aipoch/medical-research-skills
• License: MIT
• Homepage: https://aipoch.com/agent-skills

Install and usage instructions live in the source repository linked above.