scholarly journals Adjusting batch effects in microarray expression data using empirical Bayes methods

Biostatistics ◽  
2006 ◽  
Vol 8 (1) ◽  
pp. 118-127 ◽  
Author(s):  
W. Evan Johnson ◽  
Cheng Li ◽  
Ariel Rabinovic
Keyword(s):  
Empirical Bayes ◽  
Expression Data ◽  
Batch Effects ◽  
Bayes Methods ◽  
Microarray Expression Data ◽  
Empirical Bayes Methods ◽  
Microarray Expression

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

2020 ◽  
Author(s):  
Abdelkader Behdenna ◽  
Julien Haziza ◽  
Chloé-Agathe Azencott ◽  
Akpéli Nordor
Keyword(s):  
Empirical Bayes ◽  
Molecular Data ◽  
Technical Note ◽  
Mathematical Framework ◽  
Batch Effects ◽  
Microarray Expression Data ◽  
Link Type ◽  
Python Language ◽  
Empirical Bayes Methods ◽  
Microarray Expression

AbstractSummaryVariability 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 ImplementationpyComBat 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.

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