Mowgli: Multi Omics Wasserstein inteGrative anaLysIs

====================================================

.. toctree::

   :hidden:

   :maxdepth: 1

   :glob:

   :caption: Getting started

   vignettes/*

.. toctree::

   :hidden:

   :maxdepth: 3

   :caption: API

   models

   pl

   tl

   utils

   score

Mowgli is a novel method for the integration of paired multi-omics data with any type and number of omics, combining integrative Nonnegative Matrix Factorization and Optimal Transport. `Read the preprint here <https://www.biorxiv.org/content/10.1101/2023.02.02.526825v2>`_ and `fork the code here <https://github.com/cantinilab/Mowgli>`_!

.. image:: ../../figure.png

   :alt: Explanatory figure

Install the package

-------------------

Mowgli is implemented as a Python package seamlessly integrated within the scverse ecosystem, in particular Muon and Scanpy.

via PyPI (recommended)

^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: bash

   pip install mowgli

via GitHub (development version)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: bash

   git clone git@github.com:cantinilab/Mowgli.git

   pip install ./Mowgli/

Getting started

---------------

Mowgli takes as an input a Muon object and populates its `obsm` and `uns` fiels with the embeddings and dictionaries, respectively. Visit the **Getting started** and **API** sections for more documentation and tutorials.

You may download a 10X Multiome demo dataset at https://figshare.com/s/4c8e72cbb188d8e1cce8.

.. code-block:: python

   from mowgli import models

   import muon as mu

   import scanpy as sc

   # Load data into a Muon object.

   mdata = mu.load_h5mu("my_data.h5mu")

   # Initialize and train the model.

   model = models.MowgliModel(latent_dim=15)

   model.train(mdata)

   # Visualize the embedding with UMAP.

   sc.pp.neighbors(mdata, use_rep="W_OT")

   sc.tl.umap(mdata)

   sc.pl.umap(mdata)

Citation

--------

.. code-block:: bibtex

  @article{huizing2023paired,

     title={Paired single-cell multi-omics data integration with Mowgli},

     author={Huizing, Geert-Jan and Deutschmann, Ina Maria and Peyr{\'e}, Gabriel and Cantini, Laura},

     journal={Nature Communications},

     volume={14},

     number={1},

     pages={7711},

     year={2023},

     publisher={Nature Publishing Group UK London}

   }