pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
TL;DR: It is shown here that the new Python implementation of ComBat has similar results in terms of batch effects correction; is as fast or faster than the R implementation; and offers new tools for the bioinformatics community to participate in its development.
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Abstract: Summary Variability in datasets is 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 and https://pypi.org/project/combat/. Contact akpeli@epigenelabs.com
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