Welcome to InMoose documentation!

InMoose is the INtegrated Multi Omic Open Source Environment.

InMoose is intended as a comprehensive state-of-the-art Python package for -omic data analysis. It comprises Python ports of popular and recognized R tools, namely ComBat [1] and ComBat-Seq [2].

Features

• Batch Effect Correction Tools

API

• edgepy
• utils

Contributing to InMoose

Contribution guidelines are described in CONTRIBUTING.md.

Authors

Contact

To report bugs (if any?), ask for support or request improvements and new features, please open a ticket on our Github repository. You may also directly contact:

Maximilien Colange at maximilien@epigenelabs.com

Logo

The InMoose logo was designed by Léa Meunier.

Citing

The pycombat module was previously distributed independently.

To cite InMoose, please use the following reference:

A. Behdenna, J. Haziza, C.-A. Azencott and A. Nordor. 2020. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. bioRxiv. doi:10.1101/2020.03.17.995431

References

[1]

W. E. Johnson, C. Li, A. Rabinovic. 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–12. doi:10.1093/biostatistics/kxj037

[2]

Y. Zhang, G. Parmigiani, W. E. Johnson. 2020. ComBat-Seq: batch effect adjustment for RNASeq count data. NAR Genomics and Bioinformatics, 2(3). doi:10.1093/nargab/lqaa078

Indices and tables

• Index

• Module Index

• Search Page