文献类型： 外文期刊

作者： Wen, Tao ¹ ; Liu, Yong-Xin ² ; Liu, Lanlan ¹ ; Niu, Guoqing ¹ ; Ding, Zhexu ¹ ; Teng, Xinyang ¹ ; Ma, Jie ³ ; Liu, Ying ³ ; Yang, Shengdie ¹ ; Xie, Penghao ¹ ; Zhang, Tianjiao ¹ ; Wang, Lei ⁴ ; Lu, Zhanyuan ³ ; Shen, Qirong ¹ ; Yuan, Jun ¹ ;

作者机构： 1.Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Solid Organ Wastes, Educ Minist Engn Ctr Resource saving Fertilizers, Jiangsu Prov Key Lab Organ Solid Waste Utilizat,Ke, Nanjing 210095, Peoples R China

2.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Genome Anal Lab Minist Agr & Rural Affairs, Shenzhen, Peoples R China

3.Inner Mongolia Acad Agr & Anim Husb Sci, Key Lab Black Soil Protect & Utilizat Hohhot, Inner Mongolia Key Lab Degradat Farmland Ecol Rest, Minist Agr & Rural Affairs, Hohhot 010031, Peoples R China

4.Jiangsu Acad Agr Sci, Natl Agr Expt Stn Agr Environm, Nanjing, Peoples R China

关键词： microbial co-occurrence networks; modularity; multi-omics network; multi-network comparison; network visualization; transkingdom networks

期刊名称：IMETA （ 影响因子：33.2； 五年影响因子：33.2 ）

ISSN： 2770-5986

年卷期： 2025 年 4 卷 3 期

页码：

收录情况： SCI

摘要： Since its initial release in 2022, ggClusterNet has become a vital tool for microbiome research, enabling microbial co-occurrence network analysis and visualization in over 300 studies. To address emerging challenges, including multi-factor experimental designs, multi-treatment conditions, and multi-omics data, we present a comprehensive upgrade with four key components: (1) A microbial co-occurrence network pipeline integrating network computation (Pearson/Spearman/SparCC correlations), visualization, topological characterization of network and node properties, multi-network comparison with statistical testing, network stability (robustness) analysis, and module identification and analysis; (2) Network mining functions for multi-factor, multi-treatment, and spatiotemporal-scale analysis, including Facet.Network() and module.compare.m.ts(); (3) Transkingdom network construction using microbiota, multi-omics, and other relevant data, with diverse visualization layouts such as MatCorPlot2() and cor_link3(); and (4) Transkingdom and multi-omics network analysis, including corBionetwork.st() and visualization algorithms tailored for complex network exploration, including model_maptree2(), model_Gephi.3(), and cir.squ(). The updates in ggClusterNet 2 enable researchers to explore complex network interactions, offering a robust, efficient, user-friendly, reproducible, and visually versatile tool for microbial co-occurrence networks and indicator correlation patterns. The ggClusterNet 2R package is open-source and available on GitHub ().