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

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 459 - 9
• Main Authors: Behdenna, Abdelkader, Colange, Maximilien, Haziza, Julien, Gema, Aryo, Appé, Guillaume, Azencott, Chloé-Agathe, Nordor, Akpéli
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.12.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Batch effects; Bayes factors; Bayes Theorem; Bayesian statistics; Bias; Binomial distribution; Bioinformatics; Biological activity; Biomedical and Life Sciences; Breast cancer; Colorectal cancer; Community involvement; Community participation; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Science; Datasets; Evaluation; Gene expression; Libraries; Life Sciences; Mathematical analysis; Methods; Microarrays; Molecular dynamics; Normal distribution; Open source; Open source software; Optimization; Ovarian cancer; Programming languages; RNA; RNA sequencing; RNA-Seq; Software; Source code; Transcriptomics; France; Open source; Transcriptomics; Bayesian statistics; Batch effects
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05578-5

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.