RT Journal Article
SR Electronic
T1 reComBat: Batch effect removal in large-scale, multi-source omics data integration
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.22.469488
DO 10.1101/2021.11.22.469488
A1 Michael F. Adamer
A1 Sarah C. Brüningk
A1 Alejandro Tejada-Arranz
A1 Fabienne Estermann
A1 Marek Basler
A1 Karsten M. Borgwardt
YR 2021
UL http://biorxiv.org/content/early/2021/11/22/2021.11.22.469488.abstract
AB With the steadily increasing abundance of omics data produced all over the world, some-times decades apart and under vastly different experimental conditions residing in public databases, a crucial step in many data-driven bioinformatics applications is that of data integration. The challenge of batch effect removal for entire databases lies in the large number and coincide of both batches and desired, biological variation resulting in design matrix singularity. This problem currently cannot be solved by any common batch correction algorithm. In this study, we present reComBat, a regularised version of the empirical Bayes method to overcome this limitation. We demonstrate our approach for the harmonisation of public gene expression data of the human opportunistic pathogen Pseudomonas aeruginosa and study a several metrics to empirically demonstrate that batch effects are successfully mitigated while biologically meaningful gene expression variation is retained. reComBat fills the gap in batch correction approaches applicable to large scale, public omics databases and opens up new avenues for data driven analysis of complex biological processes beyond the scope of a single study.Competing Interest StatementThe authors have declared no competing interest.