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Article Dans Une Revue BMC Bioinformatics Année : 2023

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

Abdelkader Behdenna
  • Fonction : Auteur
Maximilien Colange
  • Fonction : Auteur
Julien Haziza
  • Fonction : Auteur
Aryo Gema
  • Fonction : Auteur
Guillaume Appé
  • Fonction : Auteur
Chloé-Agathe Azencott
Akpéli Nordor
  • Fonction : Auteur

Résumé

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.

Dates et versions

hal-03114226 , version 1 (18-01-2021)

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Citer

Abdelkader Behdenna, Maximilien Colange, Julien Haziza, Aryo Gema, Guillaume Appé, et al.. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics, 2023, 24 (1), ⟨10.1186/s12859-023-05578-5⟩. ⟨hal-03114226⟩
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