mowgli 0.4.0

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.

Mowgli: Multi Omics Wasserstein inteGrative anaLysIs

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!

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.

pip install mowgli

via GitHub (development version)

git clone [e-mail]:cantinilab/Mowgli.git
pip install ./Mowgli/

Test your installation (optional)

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 for more documentation and tutorials.

You may download a preprocessed 10X Multiome demo dataset here.

A GPU is not required for small datasets, but is strongly recommended above 1,000 cells. On CPU, the cell lines demo (206 cells) should run in under 5 minutes and the PBMC demo (500 cells) should run in under 10 minutes (tested on a Ubuntu 20.04 machine with an 11th gen i7 processor).

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

@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!