Batch Effect Correction

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.

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When to Read External Files

| Situation | File to Read | Purpose | |-----------|--------------|---------| | Need algorithm details | references/algorithm.md | ComBat workflow, assumptions, and QC logic | | 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 and baseline run record | | Need test data | tests/data/ | Sample input files for testing |

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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

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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 | Sample metadata file (sample ID, group, and batch columns) | | -o | --output_dir | character | ./output/ | Output directory | | -b | --batch_column | character | batch | Batch column name in metadata | | -c | --group_column | character | group | Biological group column name in metadata | | -n | --sample_column | character | sample | Sample ID column name in metadata | | -l | --log_transform | character | auto | Log transform mode: auto, yes, no | | -t | --timeout_seconds | integer | 600 | Elapsed time limit in seconds; use 0 to disable | | -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 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

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Output Files

| File | Description | |------|-------------| | corrected_expression_matrix.csv | Batch-corrected expression matrix | | matched_sample_info.csv | Standardized metadata used in the analysis | | batch_before_boxplot.pdf | Sample distribution boxplot before correction | | batch_after_boxplot.pdf | Sample distribution boxplot after correction | | batch_before_pca.pdf | PCA scatter plot before correction with batch-colored points | | batch_after_pca.pdf | PCA scatter plot after correction with batch-colored points | | batch_before_clustering.pdf | Hierarchical clustering before correction | | batch_after_clustering.pdf | Hierarchical clustering after correction | | session_info.txt | R session and package version info |

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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

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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.

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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

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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

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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 | Input file exists but contains no data | Recreate or re-export the file | | SKILL_MISSING_COLUMNS | Metadata file is missing sample, group, or batch columns | Check header names or pass custom column names | | SKILL_SAMPLE_MISMATCH | Metadata sample IDs do not match expression matrix columns | Verify sample names between files | | SKILL_INVALID_DATA | Dataset fails minimum design checks ( "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."

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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

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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] Time-limit support through setTimeLimit()
• [x] File reading instructions in SKILL.md
• [x] Modular script structure in scripts/
• [x] Test data provided
• [x] Error handling with SKILL_* codes
• [x] QC plots generated before and after correction
• [x] References in references/ directory

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

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.