PT  - JOURNAL ARTICLE
AU  - Abdelkader Behdenna
AU  - Julien Haziza
AU  - Chloé-Agathe Azencott
AU  - Akpéli Nordor
TI  - pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
AID  - 10.1101/2020.03.17.995431
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.17.995431
4099  - http://biorxiv.org/content/early/2020/03/18/2020.03.17.995431.short
4100  - http://biorxiv.org/content/early/2020/03/18/2020.03.17.995431.full
AB  - Summary Variability in datasets are not only the product of biological processes: they are also the product of technical biases. ComBat is one of the most widely used tool for correcting those technical biases, called batch effects, in microarray expression data.In this technical note, we present a new Python implementation of ComBat. While the mathematical framework is strictly the same, we show here that our implementation: (i) has similar results in terms of batch effects correction; (ii) is as fast or faster than the R implementation of ComBat and; (iii) offers new tools for the bioinformatics community to participate in its development.Availability and Implementation pyComBat is implemented in the Python language and is available under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html) license at https://github.com/epigenelabs/pyComBat.