Journal Article

MNetClass: a control-free microbial network clustering framework for identifying central subcommunities across ecological niches

Hou, Qingzhen, Feng, Qiang, Liu, Bingqiang, Wei, Fulan, Wang, Yihua

2025

Overview

MNetClass provides a valuable tool for microbiome network analysis, enabling the identification of key microbial subcommunities across diverse ecological niches. Implemented as an R package ( https://github.com/YihuaWWW/MNetClass ), it offers broad accessibility for researchers. Here, we systematically benchmarked MNetClass against existing microbial clustering methods on synthetic data using various performance metrics, demonstrating its superior efficacy. Notably, MNetClass operates without the need for control groups and effectively identifies central microbes, highlighting its potential as a robust framework for advancing microbiome research.

• Publisher: American Society for Microbiology,American Society for Microbiology (ASM)
• Subject: Algorithms / Analytical Methods / Autism Spectrum Disorder - microbiology / Cluster Analysis / clustering / Community Structure / Community Structure and Diversity / Host-Associated Microbiota / Human Microbiome / Humans / Methods and Protocols / Methods for Microbial Community Analysis / Microbial Co-Occurrence Networks / Microbial Community Profiling / Microbial Consortia and Networks / Microbial Ecology and Evolution / Microbial Interaction Networks / Microbial Interactions / microbiome / Microbiome Dynamics / Microbiome Research / Microbiota / Microbiota Diversity and Function / network analysis / random walk algorithm / rank-sum ratio–entropy weight evaluation model / Statistical and Computational Methods