removeBatchEffect

removeBatchEffect is a function in the limma package (Bioconductor) used to remove known batch effects from high-throughput expression data. It helps reduce non-biological variation introduced by differences between processing batches, enabling more accurate comparisons across samples.

The function operates by fitting a linear model to the expression data for each feature (such as a gene). It models the data as Y = Xβ + ε, where X includes the batch factor and optionally other covariates. The estimated batch effects are then subtracted from the data, producing an adjusted expression matrix with batch-related variability reduced. The input can be a numeric matrix (genes by samples) or an ExpressionSet-like object. A required argument is batch, a factor or vector describing batch membership for each sample. Optional covariates can be supplied as a matrix or via a formula to account for additional sources of variation. The output is a matrix of the same dimensions with batch effects removed.

Typical usage involves pre-normalized or log-transformed data. The function is suited for microarray or other array-based data; for RNA-seq data, it is common to use log2-transformed counts or voom-transformed values prior to applying removeBatchEffect. Users should be mindful that removing batch effects can potentially remove some biological signal if batch is confounded with the experimental groups, so careful study design and visualization (e.g., PCA) before and after correction are advised.

See also related batch correction methods such as ComBat, and broader limma workflows for differential expression analysis. Example usage: adjusted <- removeBatchEffect(expressionMatrix, batch=batchVector, covariates=covariateMatrix).