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Updated: Jul 10, 2025

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Published on: November 3, 2023

Paired single-cell multi-omics data integration with Mowgli.

Geert-Jan Huizing^1,2, Ina Maria Deutschmann³, Gabriel Peyré⁴

¹Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015, Paris, France. geert-jan.huizing@pasteur.fr.

Nature Communications

|November 24, 2023

Summary

This summary is machine-generated.

We developed Mowgli, a new multi-omics integration tool. It improves cell clustering and biological interpretation for complex single-cell data analysis.

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Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-Cell Multi-Omics Experiments

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Published on: October 4, 2021

Area of Science:

Computational Biology
Bioinformatics
Multi-omics Data Integration

Background:

Simultaneous profiling of multiple molecular layers from single cells is a recent advancement.
There is a growing need for computational methods to jointly analyze these multi-view datasets.
Existing methods may face limitations in integrating diverse omics data types and numbers.

Purpose of the Study:

To introduce Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), a novel method for integrating paired multi-omics data.
To enhance the clustering performance and biological interpretability of integrative Nonnegative Matrix Factorization (iNMF).
To provide a flexible tool for analyzing any type and number of omics data.

Main Methods:

Mowgli combines integrative Nonnegative Matrix Factorization (iNMF) with Optimal Transport.
The method is designed for paired multi-omics data integration.
It is implemented as a Python package within the scverse ecosystem.

Main Results:

Mowgli was applied to paired single-cell multi-omics data from 10X Multiome, CITE-seq, and TEA-seq.
Performance benchmarks show Mowgli is competitive with state-of-the-art methods for cell clustering.
Mowgli demonstrates superior performance in biological interpretability compared to existing methods.

Conclusions:

Mowgli offers a powerful and interpretable approach for integrating diverse single-cell multi-omics data.
The tool enhances the analysis of complex biological systems by improving both clustering and interpretability.
Mowgli is readily available for the research community through its Python package and GitHub repository.