ArticleBMC bioinformatics2023
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.
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110 citing papers in PubMed.
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- Ferroptosis-based therapeutic strategy targeting iron metabolism and SLC7A11 overcomes oxaliplatin resistance in patient-derived pancreatic cancer organoids.Journal of experimental & clinical cancer research : CR · 2026Article
- A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.bioRxiv : the preprint server for biology · 2026Article
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- Integrative analysis of tumor-educated platelets for stage-specific diagnosis, prognosis, and therapy in ovarian cancer.Discover oncology · 2026Article
- Single-Cell Proteomics Decodes the Cellular Response to Lysosomal Storage inInternational journal of molecular sciences · 2026Article
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- Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease.Annals of clinical and translational neurology · 2026Article
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50 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
backgroundVariability 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.
resultsIn 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 .
conclusionsWe 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.
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