Batch Corrector

Want to try the app with example data first?

Choose Count Data File (.csv or .xlsx)

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Choose Sample Metadata File (.csv or .xlsx)

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Select Feature Name Column:

Select Columns to Drop:

Select Batch Variable Column:

Select Sample Name Column:

My data has biological variation of interest

Select Biological Variable Column:

Note: Batch correction will be performed without preserving biological variation.

Choose batch correction method:

ComBat-Seq (recommended for RNA-seq other count-based data)

ComBat (recommended for metabolomics, intensity data)

ComBat-Seq: Uses negative binomial model, works directly with count data. Best for RNA-seq or other data with count-like properties and overdispersion.
ComBat: Uses log transformation + linear model. Best for metabolomics peak intensities, microarray data, or other continuous measurements with log-normal distributions.

Note: PCA plots will always use log-transformed data for better visualization, regardless of which batch correction method you choose.

The batch-corrected matrix should be used for visualization and clustering, but NOT for differential expression analysis
For differential expression (DESeq2, limma-voom, edgeR), use the original raw counts with the batch factor from the metadata
Include the batch correction factor as a covariate in your model design