Batch Effect Correction

Prerequisites

Run the following before the first analysis to install all required R packages:

Rscript -e "if (!require('BiocManager', quietly=TRUE)) install.packages('BiocManager'); BiocManager::install(c('sva', 'limma')); install.packages('ggplot2', repos='https://cloud.r-project.org')"

Note: sva and limma are Bioconductor packages and require BiocManager for installation. ggplot2 is a standard CRAN package.

The skill cannot run until these packages are installed. In new or bare R environments, always run the prerequisite step first.

When to Read External Files

Usage

Rscript scripts/main.R \
  --input_file ./expression_matrix.csv \
  --group_file ./sample_info.csv \
  --output_dir ./output/ \
  --batch_column batch \
  --group_column group \
  --sample_column sample \
  --log_transform auto \
  --timeout_seconds 600 \
  --seed 42

Arguments

Input Format

Expression Matrix (input_file)

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

"","Sample01","Sample02","Sample03"
"GeneA",5.12,4.87,6.03
"GeneB",8.44,8.11,7.95

Requirements:

• Gene IDs must be unique and non-empty
• Sample column names must be unique and non-empty
• Expression values must be numeric and finite
• Extra expression-matrix sample columns not present in metadata are allowed and will be ignored with a warning

Sample Metadata (group_file)

CSV with sample ID, biological group, and batch columns.

"sample","group","batch"
"Sample01","Control","Batch1"
"Sample02","Case","Batch1"
"Sample03","Case","Batch2"

Requirements:

• Sample IDs must be unique and non-empty
• At least 2 biological groups are required
• At least 2 batches are required
• Each group and each batch must contain at least 2 samples
• Metadata may describe a subset of expression-matrix samples; the analysis will keep only metadata-matched samples and warn about ignored expression columns

Output Files

Workflow

Step 1: Validate Input

• Check file existence and non-empty input files
• Validate metadata column presence
• Verify expression values are numeric and finite
• Confirm at least 2 groups, 2 batches, and at least 2 samples per group/batch

Step 2: Align and Prepare Matrix

• Reorder expression columns to match metadata sample order
• Keep only metadata-matched samples; warn if the expression matrix contains extra samples absent from metadata
• Decide whether log transformation is needed (auto, yes, or no)
• Apply log2(x + 1) only when required

Step 3: Run Batch Correction

• Build the design matrix with biological group information
• Run sva::ComBat() to remove batch-driven variation
• Preserve modeled biological group structure during correction

Step 4: Normalize and Export Results

• Apply limma::normalizeBetweenArrays() after ComBat
• Write the corrected matrix and matched metadata
• Save before/after QC plots and session information

Methods

ComBat

Empirical Bayes batch-effect correction using sva::ComBat(). Recommended when merged bulk expression datasets contain known batch labels and at least two biological groups.

Log Transformation

Supports auto, yes, and no. The auto mode applies log2(x + 1) only when the matrix appears to be on a raw-like scale.

normalizeBetweenArrays

Post-correction normalization with limma::normalizeBetweenArrays() to reduce remaining cross-sample distribution differences.

QC Visualization

Generates paired boxplots, PCA scatter plots with conditional batch ellipses, and hierarchical clustering plots before and after correction to assess whether batch-driven structure is reduced.

Agent Response Contract

After a successful run, report:

1. Sample count retained after metadata matching and any subset filtering
2. Batch count and group count used in the ComBat design matrix
3. Log transformation applied (auto-detected, forced yes, or skipped)
4. QC assessment: describe whether before/after PCA plots show reduced batch clustering
5. Artifact paths: corrected_expression_matrix.csv, batch_after_pca.pdf, batch_after_clustering.pdf

Examples

Basic Usage

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g sample_info.csv \
  -o ./output

With Custom Metadata Columns

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g metadata.csv \
  -o ./output \
  -n sample_id \
  -c condition \
  -b platform_batch

Disable Log Transform and Timeout

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g sample_info.csv \
  -o ./output \
  -l no \
  -t 0 \
  -s 42

Error Handling

Common Errors

IF error persists, READ: references/troubleshooting.md

Troubleshooting note: In environments where packages are not yet installed, SKILL_DEPENDENCY_MISSING will fire before file-validation or --help. Install dependencies first, then re-run to expose file-related errors or access --help.

Input Validation

This skill accepts:

1. A bulk RNA-seq or microarray expression matrix (CSV, genes as rows, samples as columns)
2. A sample metadata file (CSV) with sample ID, biological group, and batch columns; at least 2 batches and 2 biological groups are required

If the user's request does not involve batch effect correction on merged bulk expression matrices — for example, asking to integrate single-cell RNA-seq data, process raw FASTQ files, run differential expression without batch labels, or analyze datasets with only one batch — do not proceed with the workflow. Instead respond:

"Batch Effect Correction is designed to remove batch-driven variation from merged bulk expression matrices using ComBat, while preserving biological group structure. Your request appears to be outside this scope. Please provide a multi-batch expression matrix with sample-level batch metadata, or use a more appropriate tool for single-cell integration, differential expression, or raw sequencing processing."

Testing

Test with Sample Data

# Check help (requires packages installed)
Rscript scripts/main.R --help

# Run with bundled test data
Rscript scripts/main.R \
  -i tests/data/expression_matrix_merged.csv \
  -g tests/data/sample_info.csv \
  -o tests/output/

Validation Commands

# Check corrected matrix exists
ls -la tests/output/corrected_expression_matrix.csv

# Check matched metadata exists
ls -la tests/output/matched_sample_info.csv

# Check PCA output exists
ls -la tests/output/batch_after_pca.pdf

Implementation Checklist

• CLI parsing with optparse
• set.seed() for reproducibility
• requireNamespace() dependency checks
• Session info recording
• Time-limit support through setTimeLimit()
• File reading instructions in SKILL.md
• Modular script structure in scripts/
• Test data provided
• Error handling with SKILL_* codes
• QC plots generated before and after correction
• References in references/ directory

Last updated: 2026-04-27 | Version: 1.1.0