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. Its current focus is on analysis of bulk transcriptomic data (microarray and RNA-Seq). It comprises Python ports of popular and recognized R tools, name ComBat [Johnson2007], ComBat-Seq [Zhang2020], DESeq2 [Love2014], edgeR [Chen2016], limma [Ritchie2015] and splatter [Zappia2017].

Check out our tutorial notebook!

Features

API

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 one of the following references:

M. Colange, G. Appé, L. Meunier, S. Weill, W.E. Johnson, A. Nordor, A. Behdenna. 2025. Bridging the gap between R and Python in bulk transcriptomic data analysis with InMoose. Nature Scientific Reports 15:18104. doi:10.1038/s41598-025-03376-y.

A. Behdenna, M. Colange, J. Haziza, A. Gema, G. Appé, C.-A. Azencott and A. Nordor. 2023. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics 24:459. doi:10.1186/s12859-023-05578-5

M. Colange, G. Appé, L. Meunier, S. Weill, A. Nordor, A. Behdenna. 2025. Differential Expression Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python. BMC Bioinformatics 26:160. doi:10.1186/s12859-025-06180-7