pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

Author:

Behdenna Abdelkader,Colange Maximilien,Haziza Julien,Gema Aryo,Appé Guillaume,Azencott Chloé-Agathe,Nordor Akpéli

Abstract

Abstract Background Variability in datasets is not only the product of biological processes: they are also the product of technical biases. ComBat and ComBat-Seq are among the most widely used tools for correcting those technical biases, called batch effects, in, respectively, microarray and RNA-Seq expression data. Results In this technical note, we present a new Python implementation of ComBat and ComBat-Seq. While the mathematical framework is strictly the same, we show here that our implementations: (i) have similar results in terms of batch effects correction; (ii) are as fast or faster than the original implementations in R and; (iii) offer new tools for the bioinformatics community to participate in its development. pyComBat is implemented in the Python language and is distributed under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html) license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose and Python package at https://pypi.org/project/inmoose. Conclusions We present a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq for the correction of batch effects in microarray and RNA-Seq data. This new implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq, offers similar power for batch effect correction, at reduced computational cost.

Funder

European Union's Horizon 2020 research and innovation program

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

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