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Welcome to InMoose documentation!

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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]_.

.. toctree::

   :maxdepth: 1

   :caption: Features

   pycombat

   data

   diffexp

.. toctree::

   :maxdepth: 1

   :caption: API

   deseq

   clustering

   edgepy

   limma

   sim

   utils

Contributing to InMoose

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Contribution guidelines are described in `CONTRIBUTING.md <https://github.com/epigenelabs/inmoose/blob/master/CONTRIBUTING.md>`_.

Authors

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Contact

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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

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The InMoose logo was designed by Léa Meunier.

Citing

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The :doc:`pycombat <pycombat>` module was previously `distributed independently <https://github.com/epigenelabs/pycombat>`_.

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

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* :ref:`genindex`

* :ref:`modindex`

* :ref:`search`