Lasso Logistics Analysis

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 |

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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

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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 |

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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

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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 |

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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_group as 1 and control_group as 0
• 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 glmnet model with alpha = 1
• Run cv.glmnet to 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

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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.

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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

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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

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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

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Implementation Checklist

• [x] CLI parsing with optparse
• [x] set.seed() for reproducibility
• [x] requireNamespace() dependency checks
• [x] Session info recording
• [x] Timeout control with --timeout_seconds
• [x] Temp file cleanup
• [x] File reading instructions in SKILL.md
• [x] Modular script structure (<150 lines per file)
• [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: 2026-04-17 | 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.