MultiBaC: an R package to remove batch effects in multi-omic experiments

Motivation: Batch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases. Results: In this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction.

Automated summary

This article introduces the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. It includes various graphical outputs for model validation and assessment of batch effect correction.