--- a/README.md
+++ b/README.md
@@ -1,79 +1,78 @@
-# Mowgli: Multi Omics Wasserstein inteGrative anaLysIs
-[](https://github.com/gjhuizing/Mowgli/actions/workflows/main.yml)
-[](https://codecov.io/gh/cantinilab/Mowgli)
-[](https://mowgli.readthedocs.io/en/latest/?badge=latest)
-[](https://img.shields.io/pypi/v/mowgli?color=blue)
-[](https://github.com/psf/black)
-[](https://zenodo.org/badge/latestdoi/391909874)
-
-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 paper!](https://www.nature.com/articles/s41467-023-43019-2)
-
-
-
-## Install the package
-
-Mowgli is implemented as a Python package seamlessly integrated within the scverse ecosystem, in particular Muon and Scanpy.
-
-### via PyPI (recommended)
-
-On all operating systems, the easiest way to install Mowgli is via PyPI. Installation should typically take a minute and is continuously tested with Python 3.10 on an Ubuntu virtual machine.
-
-```bash
-pip install mowgli
-```
-
-### via GitHub (development version)
-
-```bash
-git clone git@github.com:cantinilab/Mowgli.git
-pip install ./Mowgli/
-```
-
-### Test your installation (optional)
-
-```bash
-pytest .
-```
-
-## Getting started
-
-Mowgli takes as an input a Muon object and populates its `obsm` and `uns` fields with the embeddings and dictionaries, respectively. Visit [mowgli.rtfd.io](https://mowgli.rtfd.io/) for more documentation and tutorials.
-
-You may download a preprocessed 10X Multiome demo dataset [here](https://figshare.com/s/4c8e72cbb188d8e1cce8).
-
-A GPU is not required for small datasets, but is strongly recommended above 1,000 cells. On CPU, the [cell lines demo](https://mowgli.readthedocs.io/en/latest/vignettes/Liu%20cell%20lines.html) (206 cells) should run in under 5 minutes and the [PBMC demo](https://mowgli.readthedocs.io/en/latest/vignettes/PBMC.html) (500 cells) should run in under 10 minutes (tested on a Ubuntu 20.04 machine with an 11th gen i7 processor).
-
-```python
-import mowgli
-import mudata as md
-import scanpy as sc
-
-# Load data into a Muon object.
-mdata = md.read_h5mu("my_data.h5mu")
-
-# Initialize and train the model.
-model = mowgli.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)
-```
-
-## Publication
-
-```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}
-}
-```
-
-If you're looking for the repository with code to reproduce the experiments in our preprint, [here is is!](https://github.com/cantinilab/mowgli_reproducibility)
+# Mowgli: Multi Omics Wasserstein inteGrative anaLysIs
+[](https://github.com/gjhuizing/Mowgli/actions/workflows/main.yml)
+[](https://codecov.io/gh/cantinilab/Mowgli)
+[](https://mowgli.readthedocs.io/en/latest/?badge=latest)
+[](https://img.shields.io/pypi/v/mowgli?color=blue)
+[](https://github.com/psf/black)
+[](https://zenodo.org/badge/latestdoi/391909874)
+
+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 paper!](https://www.nature.com/articles/s41467-023-43019-2)
+
+
+## Install the package
+
+Mowgli is implemented as a Python package seamlessly integrated within the scverse ecosystem, in particular Muon and Scanpy.
+
+### via PyPI (recommended)
+
+On all operating systems, the easiest way to install Mowgli is via PyPI. Installation should typically take a minute and is continuously tested with Python 3.10 on an Ubuntu virtual machine.
+
+```bash
+pip install mowgli
+```
+
+### via GitHub (development version)
+
+```bash
+git clone git@github.com:cantinilab/Mowgli.git
+pip install ./Mowgli/
+```
+
+### Test your installation (optional)
+
+```bash
+pytest .
+```
+
+## Getting started
+
+Mowgli takes as an input a Muon object and populates its `obsm` and `uns` fields with the embeddings and dictionaries, respectively. Visit [mowgli.rtfd.io](https://mowgli.rtfd.io/) for more documentation and tutorials.
+
+You may download a preprocessed 10X Multiome demo dataset [here](https://figshare.com/s/4c8e72cbb188d8e1cce8).
+
+A GPU is not required for small datasets, but is strongly recommended above 1,000 cells. On CPU, the [cell lines demo](https://mowgli.readthedocs.io/en/latest/vignettes/Liu%20cell%20lines.html) (206 cells) should run in under 5 minutes and the [PBMC demo](https://mowgli.readthedocs.io/en/latest/vignettes/PBMC.html) (500 cells) should run in under 10 minutes (tested on a Ubuntu 20.04 machine with an 11th gen i7 processor).
+
+```python
+import mowgli
+import mudata as md
+import scanpy as sc
+
+# Load data into a Muon object.
+mdata = md.read_h5mu("my_data.h5mu")
+
+# Initialize and train the model.
+model = mowgli.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)
+```
+
+## Publication
+
+```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}
+}
+```
+
+If you're looking for the repository with code to reproduce the experiments in our preprint, [here is is!](https://github.com/cantinilab/mowgli_reproducibility)
Datasets
Models
