ComBatseq

ComBat-seq is a statistical method for removing batch effects from RNA sequencing count data. It extends the ComBat framework, originally developed for Gaussian data, to handle count data by modeling gene expression counts with a negative binomial distribution and incorporating batch indicators into the mean structure. The goal is to separate technical variation attributable to batch from genuine biological signal, improving comparability across samples processed in different batches.

The method operates within an empirical Bayes framework that borrows strength across genes to stabilize batch effect estimates, which is helpful when sample sizes within batches are limited. By accounting for overdispersion inherent in count data, ComBat-seq seeks to adjust counts while preserving biologically relevant variation. The approach typically involves estimating gene- and batch-specific parameters and then generating adjusted counts that reflect a batch-corrected latent level.

ComBat-seq is implemented in the sva package as ComBat_seq and is designed for practical use with RNA-seq count matrices. Users input a count matrix, batch labels, and optional biological covariates or design information. The method can incorporate sequencing depth as an offset and produces batch-adjusted counts or transformed data suitable for downstream analyses such as differential expression testing, clustering, or visualization. The output is intended to reduce batch-driven artifacts while maintaining the biological structure of the data.

Limitations include the potential for residual batch effects if batch is confounded with biological conditions and the assumption that batch effects can be reasonably modeled within a negative binomial framework. Users should assess the effectiveness of correction and consider alternative or supplementary normalization strategies as needed.