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]_.

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 :doc:`pycombat <pycombat>` module was previously distributed independently.

To cite InMoose, please use one of the following references:

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 7;24(1):459. :doi:`10.1186/s12859-023-05578-5`

M. Colange, G. Appé, L. Meunier, S. Weill, A. Nordor, A. Behdenna. 2024. Differential Expression Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python. Bioarxiv. :doi:`10.1101/2024.11.14.623578`

Indices and tables

:ref:`genindex`
:ref:`modindex`
:ref:`search`