ReComBat: Batch-effect Removal in Large-scale Multi-source Gene-expression Data Integration

Overview

Journal Bioinform Adv

Publisher Oxford University Press

Specialty Biology

Date 2023 Jan 26

PMID 36699372

Authors

Michael F Adamer

Sarah C Bruningk

Alejandro Tejada-Arranz

Fabienne Estermann

Marek Basler

Karsten Borgwardt

Affiliations

Soon will be listed here.

Abstract

Motivation: With the steadily increasing abundance of omics data produced all over the world 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 of batches and biological variation, which can result in design matrix singularity. This problem can currently not be solved satisfactorily by any common batch-correction algorithm.

Results: We present , a regularized version of the empirical Bayes method to overcome this limitation and benchmark it against popular approaches for the harmonization of public gene-expression data (both microarray and bulkRNAsq) of the human opportunistic pathogen . Batch-effects are successfully mitigated while biologically meaningful gene-expression variation is retained. 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.

Availability And Implementation: The code is available at https://github.com/BorgwardtLab/reComBat, all data and evaluation code can be found at https://github.com/BorgwardtLab/batchCorrectionPublicData.

Supplementary Information: Supplementary data are available at online.

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