aibot88 LASSO Logistic Regression Analysis
When to Read External Files
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md |
LASSO objective function, cross-validation, and interpretation |
| Need to run analysis | scripts/main.R |
Execute: Rscript scripts/main.R --input_file ... --group_file ... |
| Encounter errors | references/troubleshooting.md |
Common errors and solutions |
| Need CLI examples | references/cli-guide.md |
Detailed CLI usage examples |
| Need test data | tests/data/ |
Sample input files for testing |
| Need workflow implementation details | scripts/run_analysis.R |
Inspect orchestration, outputs, and file-writing behavior |
| Need input-validation or error-handling details | scripts/utils.R, scripts/io.R |
Inspect validation, parsing, logging, and standardized safeguards |
Usage
Rscript scripts/main.R \
--input_file ./expression_matrix.csv \
--group_file ./groups.csv \
--case_group case \
--control_group control \
--output_dir ./output/ \
--nfolds 10 \
--timeout_seconds 1800 \
--seed 42
Arguments
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-i |
--input_file |
character | required | Expression matrix file (features as rows, samples as columns) |
-g |
--group_file |
character | required | Group file with sample and group columns |
-c |
--case_group |
character | required | Case class label encoded as 1 |
-t |
--control_group |
character | required | Control class label encoded as 0 |
-f |
--feature |
character | NULL |
Optional feature list file or comma-separated feature names |
-n |
--nfolds |
integer | 10 |
Cross-validation folds: 3, 5, 7, 10 |
--cv_title |
character | "" |
Optional title for the cross-validation plot | |
--path_title |
character | "" |
Optional title for the coefficient path plot | |
--timeout_seconds |
integer | 1800 |
Maximum elapsed runtime in seconds | |
-o |
--output_dir |
character | ./output/ |
Output directory |
-s |
--seed |
integer | 42 |
Random seed for reproducibility |
Input Format
Expression Matrix (input_file)
Features as rows, samples as columns, CSV or TSV format with feature IDs in the first column.
,Sample01,Sample02,Sample03
TSPAN6,1.8479,1.8318,3.8276
TNMD,0.0349,0.0533,1.3889
Group File (group_file)
CSV or TSV with sample IDs and binary-group labels.
sample,group
Sample01,case
Sample02,control
Sample03,case
Optional Feature File (feature)
One feature per line, or pass a comma-separated feature list directly on the CLI.
TNMD
DPM1
SCYL3
Output Files
| File | Description |
|---|---|
coefficient.csv |
All coefficients at lambda.min |
feature_matrix.csv |
Sample-level matrix with original group labels and binary event column |
selected_features.txt |
Non-zero features at lambda.min excluding the intercept, when available |
missing_features.txt |
Requested features not found in the matrix, when applicable |
lasso_lambda_binary_plot.pdf |
Cross-validation curve |
lasso_var_binary_plot.pdf |
Coefficient path plot |
session_info.txt |
R session and package version info |
Workflow
Step 1: Validate Input
WHEN checking validation rules or parsing behavior, READ: scripts/utils.R and scripts/io.R
- Check file existence
- Read expression matrix and group file
- Verify samples match between files
- Ensure both classes are present with at least 2 samples per class
Step 2: Prepare Modeling Matrix
WHEN checking class encoding or feature filtering behavior, READ: scripts/modeling.R
- Encode
case_groupas1andcontrol_groupas0 - Optionally restrict to a user-supplied feature panel
- Transpose expression data to sample-by-feature format
Step 3: Fit LASSO Logistic Regression
WHEN understanding the statistical method or lambda selection, READ: references/algorithm.md
- Train a binomial
glmnetmodel withalpha = 1 - Run
cv.glmnetto select the optimal lambda - Extract coefficients at
lambda.min
Step 4: Save Results and Visualizations
WHEN checking output generation or plot behavior, READ: scripts/run_analysis.R and scripts/plotting.R
- Save flat output files directly into
output_dir - Generate cross-validation and coefficient path PDF plots
- Leave plot titles empty by default unless the user provides custom titles
Methods
LASSO Logistic Regression
The model minimizes binomial deviance with an L1 penalty, shrinking weak coefficients to zero and performing embedded feature selection.
Cross-Validation
cv.glmnet evaluates candidate lambda values across nfolds folds and reports lambda.min and lambda.1se.
Examples
Basic Usage
Rscript scripts/main.R \
-i ./expression_matrix.csv \
-g ./groups.csv \
-c case \
-t control \
-o ./output
Use a Feature Panel
Rscript scripts/main.R \
-i ./expression_matrix.csv \
-g ./groups.csv \
-c case \
-t control \
-f ./genes.txt \
-o ./output
Custom Folds and Seed
Rscript scripts/main.R \
-i ./expression_matrix.csv \
-g ./groups.csv \
-c case \
-t control \
-n 5 \
--timeout_seconds 900 \
-s 123 \
-o ./output
Custom Plot Titles
Rscript scripts/main.R \
-i ./expression_matrix.csv \
-g ./groups.csv \
-c case \
-t control \
--cv_title "LASSO Cross-Validation" \
--path_title "LASSO Coefficient Paths" \
--timeout_seconds 1200 \
-o ./output
Error Handling
Common Errors
| Error | Cause | Solution |
|---|---|---|
SKILL_FILE_NOT_FOUND |
Input file does not exist | Check file path |
SKILL_EMPTY_FILE |
An input file exists but contains no data | Verify the file is not empty |
SKILL_PARSE_ERROR |
The input file cannot be parsed as CSV or TSV | Check delimiters, headers, and encoding |
SKILL_FILE_WRITE_ERROR |
The output directory cannot be created or written | Check output path and permissions |
SKILL_EMPTY_DATA |
The loaded table has no usable rows or columns | Verify that the input file contains valid data |
SKILL_MISSING_COLUMNS |
The group file does not provide the required columns | Provide sample and group columns |
SKILL_INVALID_TYPE |
A parameter or data field has the wrong type | Ensure numeric fields are numeric and strings are valid |
SKILL_SAMPLE_MISMATCH |
Sample IDs differ between matrix and group file | Make names match exactly |
SKILL_INVALID_GROUP |
Case/control labels not found in group file | Check --case_group and --control_group |
SKILL_INVALID_DATA |
Too few classes, samples, or valid features | Review input structure and feature list |
SKILL_INVALID_PARAMETER |
Unsupported nfolds or empty parameter |
Use documented argument values |
SKILL_DEPENDENCY_MISSING |
Required R package not installed | Install missing CRAN package |
SKILL_TIMEOUT |
Analysis exceeded the configured time limit | Reduce feature count or increase --timeout_seconds |
SKILL_MEMORY_ERROR |
The runtime environment cannot allocate enough memory | Reduce matrix size or available workload |
SKILL_RUNTIME_ERROR |
An unexpected runtime error occurred | Review the exact console error and retry |
IF error persists, READ: references/troubleshooting.md
Testing
Test with Sample Data
# Check help
Rscript scripts/main.R --help
# Run with sample data
Rscript scripts/main.R \
-i tests/data/expression_matrix.csv \
-g tests/data/groups.csv \
-c case \
-t control \
-f tests/data/genes.csv \
--timeout_seconds 1800 \
-o tests/output
Validation Commands
# Check coefficient output
ls -la tests/output/coefficient.csv
# Check plots exist
ls -la tests/output/lasso_lambda_binary_plot.pdf
ls -la tests/output/lasso_var_binary_plot.pdf
Implementation Checklist
- CLI parsing with
optparse -
set.seed()for reproducibility -
requireNamespace()dependency checks - Session info recording
- Timeout control with
--timeout_seconds - Temp file cleanup
- File reading instructions in SKILL.md
- Modular script structure (<150 lines per file)
- Test data provided
- Error handling with SKILL_* codes
- Scripts in
scripts/directory - References in
references/directory
Last updated: 2026-04-17 | Version: 1.0.0