| name | batch-effect-correction |
| description | Use when correcting batch effects in merged bulk expression matrices with sample-level batch metadata while preserving biological group structure and generating before-and-after QC plots. NOT for: single-cell integration, raw FASTQ processing, differential expression without batch labels, or datasets without biological groups. |
| license | MIT |
| skill-author | AIPOCH |
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
| 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 |
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
| 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 |
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
| 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 |
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:
- Sample count retained after metadata matching and any subset filtering
- Batch count and group count used in the ComBat design matrix
- Log transformation applied (auto-detected, forced yes, or skipped)
- QC assessment: describe whether before/after PCA plots show reduced batch clustering
- 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
| 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 (< 2 batches, < 2 groups, < 2 samples per batch/group) | Review group counts, batch counts, and ID validity |
SKILL_INVALID_TYPE | Expression values are non-numeric or non-finite | Clean matrix values before running |
SKILL_TIMEOUT | Run exceeded the configured time limit | Increase --timeout_seconds or set it to 0 |
SKILL_DEPENDENCY_MISSING | Required R package is not installed | Install with: Rscript -e "BiocManager::install(c('sva','limma')); install.packages('ggplot2')" |
SKILL_RUNTIME_ERROR | Runtime I/O or filesystem error occurred | Check read/write permissions and environment |
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:
- A bulk RNA-seq or microarray expression matrix (CSV, genes as rows, samples as columns)
- 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
Rscript scripts/main.R --help
Rscript scripts/main.R \
-i tests/data/expression_matrix_merged.csv \
-g tests/data/sample_info.csv \
-o tests/output/
Validation Commands
ls -la tests/output/corrected_expression_matrix.csv
ls -la tests/output/matched_sample_info.csv
ls -la tests/output/batch_after_pca.pdf
Implementation Checklist
Last updated: 2026-04-27 | Version: 1.1.0
