reComBat-seq: Regularized negative binomial regression for batch-effect correction in underdetermined transcriptomics datasets

MotivationBatch effect correction is essential for the integration of large-scale transcriptomics datasets such as single-cell RNA-seq or multi-study bulk RNA-seq datasets for reducing technical noise that may mask biological signal. Existing correction methods, either do not produce count data output which is crucial for state-of-the-art downstream analyses such as differential expression analysis or fail to converge in underdetermined study designs. ResultsWe present reComBat-seq, a method that extends the Negative Binomial regression framework of ComBat-seq by incorporating Elastic Net regularization. This approach resolves problems with rank-deficient design matrices while also preserving the integer nature of count data. Benchmarking on simulated and real datasets such as single-cell RNA-seq data demonstrates that reComBat-seq successfully removes batch effects in complex study designs while maintaining compatibility with downstream differential expression tools. Availability and ImplementationreComBat-seq source code can be found at https://github.com/menchelab/reComBat-seq. All code to reproduce the presented analyses can be found at https://github.com/menchelab/reComBatseq_Studies. Data produced in this study is available at https://doi.org/10.5281/zenodo.19736515. Used single-cell RNA-seq data can be found at https://doi.org/10.5281/zenodo.14234956. Supplementary InformationProofs and volcano plots of differential expression analysis