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+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}
+   }