| name | elastic-net-feature-selection |
| description | Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification, lambda.min, lambda.1se. NOT for: survival/Cox modeling, multiclass outcomes, single-cell data, or non-expression tables. |
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 |
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
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 |
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
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 |
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
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.
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
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
Testing
Test with Sample Data
Rscript scripts/main.R --help
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
cat tests/output/selected_features.csv
ls -la tests/output
Implementation Checklist
Last updated: 2026-04-20 | Version: 1.0.0
