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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

HetFCM: functional co-module discovery by heterogeneous network co-clustering.

Haojiang Tan^1,2, Maozu Guo³, Jian Chen⁴

¹School of Software, Shandong University, Jinan 250101, Shandong, China.

Nucleic Acids Research

|December 13, 2023

Summary

We developed HetFCM, a novel framework for detecting functional molecular co-modules from multi-omics data. This tool identifies key gene, miRNA, and lncRNA interactions linked to human diseases and plant traits.

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Functional molecular module analysis is crucial for understanding complex biological regulations.
Existing methods struggle with multi-omics data integration and cross-layer molecular interactions, leading to information loss.

Purpose of the Study:

To propose HetFCM, a heterogeneous network co-clustering framework for detecting functional co-modules.
To effectively model interplays and attributes of heterogeneous molecules across multiple layers.

Main Methods:

Constructing an attributed heterogeneous network to represent molecular interactions.
Employing multiple variational graph autoencoders for cross-layer association matrix generation.
Performing adaptive weighted co-clustering on association matrices and attribute data.

Main Results:

HetFCM successfully identified co-modules with denser topology and significant biological functions.
These modules are associated with human breast cancer subtypes and maize phenotypes like lipid storage and drought tolerance.
Empirical studies on Human and Maize datasets validated the framework's performance.

Conclusions:

HetFCM provides a robust tool for detecting functional co-modules in multi-omics data.
The framework offers novel insights into molecular mechanisms and can be applied to multi-layer functional modules.
A user-friendly tool is available for module detection and analysis.