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Related Experiment Video

Updated: Jul 29, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

HONMF: integration analysis of multi-omics microbiome data via matrix factorization and hypergraph.

Yuanyuan Ma^1,2, Lifang Liu³, Yingjun Ma⁴

¹School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, Hubei, China.

Bioinformatics (Oxford, England)

|May 22, 2023

Summary

This summary is machine-generated.

We developed HONMF, a novel method for analyzing multi-omics microbiome data. This tool integrates bacterial, fungal, and viral data to reveal microbial community interactions and improve disease understanding.

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Area of Science:

Microbiome research
Computational biology
Systems biology

Background:

Multi-omics microbiome data offer insights into microbial diversity (bacterial, fungal, viral).
Microbial community composition changes are linked to environments and critical illnesses.
Analyzing microbiome heterogeneity and cross-kingdom interactions presents significant challenges.

Purpose of the Study:

To introduce HONMF, a method for integrative analysis of multi-modal microbiome data.
To enable identification, visualization, and downstream analysis of microbial samples.
To facilitate feature selection and cross-kingdom association analysis.

Main Methods:

HONMF utilizes hypergraph induced orthogonal non-negative matrix factorization.
It integrates distinct latent variables specific to each composition profile via graph fusion.
The method is unsupervised and designed to handle bacterial, fungal, and viral microbiome data.

Main Results:

HONMF demonstrates superior performance in data visualization and clustering across diverse datasets.
The method successfully identifies discriminative microbial features.
It enables bacterium-fungus-virus association analysis, yielding biological insights.

Conclusions:

HONMF effectively integrates multi-modal microbiome data for comprehensive analysis.
The method enhances understanding of ecological interactions and microbial pathogenesis.
HONMF provides valuable tools for microbiome research and clinical applications.