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

[EN] 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. This work was funded by the Generalitat Valenciana through PROMETEO grants program for excellence research groups [PROMETEO 2016/093] and by the Spanish MICINN [PID2020-119537RB-I00]. Funding for open access charge: Universitat Politecnica de Valencia. Ugidos, M.; Nueda, MJ.; Prats-Montalbán, JM.; Ferrer, A.; Conesa, A.; Tarazona, S. (2022). MultiBaC: an R package to remove batch effects in multi-omic experiments. Bioinformatics. 38(9):2657-2658. https://doi.org/10.1093/bioinformatics/btac132