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Updated: Jun 24, 2025

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ggClusterNet: An R package for microbiome network analysis and modularity-based multiple network layouts.

Tao Wen1,2, Penghao Xie1, Shengdie Yang1

  • 1Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Key Laboratory of Green Intelligent Fertilizer Innovation, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers Nanjing Agricultural University Nanjing China.

Imeta
|June 13, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed ggClusterNet, an R package for ecological network analysis. This tool simplifies microbiome network visualization and mining with integrated functions and multiple layout algorithms.

Keywords:
R packagemicrobiomenetwork analysisvisualization

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Area of Science:

  • Ecology
  • Bioinformatics
  • Computational Biology

Background:

  • Network analysis is increasingly vital in ecological research.
  • Existing tools for ecological network analysis can be cumbersome.
  • There is a need for more user-friendly and powerful analytical software.

Purpose of the Study:

  • To introduce ggClusterNet, a novel R package designed for ecological network analysis.
  • To provide researchers with an accessible tool for constructing, visualizing, and mining microbial networks.
  • To enhance the ease and efficiency of network analysis in ecological studies.

Main Methods:

  • Development of an R package, ggClusterNet.
  • Integration of ten distinct network layout algorithms for enhanced visualization.
  • Inclusion of various functions for network mining, property calculation, and bipartite network analysis.
  • Implementation of pipeline functions for streamlined network analysis.

Main Results:

  • ggClusterNet offers ten specialized network layout algorithms for microbiome network visualization.
  • The package includes integrated functions for microbial network analysis, such as correlation, property calculation, and module identification.
  • Pipeline functions facilitate rapid network and bipartite network analysis.
  • The package is publicly available on GitHub and Gitee, with comprehensive documentation.

Conclusions:

  • ggClusterNet provides a convenient and powerful solution for ecological network analysis.
  • The package simplifies complex network mining and visualization tasks for researchers.
  • ggClusterNet is expected to facilitate advancements in ecological network research through improved accessibility and functionality.