reComBat: Batch effect removal in large-scale, multi-source omics data integration

Adamer, Michael R. and Brüningk, Sarah C. and Tejada-Arranz, Alejandro and Estermann, Fabienne and Basler, Marek and Borgwardt, Karsten. (2021) reComBat: Batch effect removal in large-scale, multi-source omics data integration.

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Official URL: https://edoc.unibas.ch/87154/

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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.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Infection Biology > Infection Biology (Basler)
UniBasel Contributors:Basler, Marek
Item Type:Preprint
Number of Pages:14
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
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Last Modified:26 Apr 2022 10:20
Deposited On:08 Mar 2022 10:09

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