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pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
- Publication Year :
- 2020
- Publisher :
- Cold Spring Harbor Laboratory, 2020.
-
Abstract
- SummaryVariability 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 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 and https://pypi.org/project/combat/.Contactakpeli@epigenelabs.com
- Subjects :
- 0303 health sciences
Microarray
Computer science
Programming language
Python (programming language)
computer.software_genre
03 medical and health sciences
Bayes' theorem
0302 clinical medicine
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
030220 oncology & carcinogenesis
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
computer
030304 developmental biology
computer.programming_language
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....1326c6d011d453f113a8032b976a9463
- Full Text :
- https://doi.org/10.1101/2020.03.17.995431