ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns
Abstract 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,...
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Wiley
2025-06-01
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| Online Access: | https://doi.org/10.1002/imt2.70041 |
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| _version_ | 1850156792337661952 |
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| author | Tao Wen Yong‐Xin Liu Lanlan Liu Guoqing Niu Zhexu Ding Xinyang Teng Jie Ma Ying Liu Shengdie Yang Penghao Xie Tianjiao Zhang Lei Wang Zhanyuan Lu Qirong Shen Jun Yuan |
| author_facet | Tao Wen Yong‐Xin Liu Lanlan Liu Guoqing Niu Zhexu Ding Xinyang Teng Jie Ma Ying Liu Shengdie Yang Penghao Xie Tianjiao Zhang Lei Wang Zhanyuan Lu Qirong Shen Jun Yuan |
| author_sort | Tao Wen |
| collection | DOAJ |
| description | Abstract 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 (https://github.com/taowenmicro/ggClusterNet). |
| format | Article |
| id | doaj-art-a10f1b3080c84184b409d70efee0af63 |
| institution | OA Journals |
| issn | 2770-596X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | iMeta |
| spelling | doaj-art-a10f1b3080c84184b409d70efee0af632025-08-20T02:24:23ZengWileyiMeta2770-596X2025-06-0143n/an/a10.1002/imt2.70041ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patternsTao Wen0Yong‐Xin Liu1Lanlan Liu2Guoqing Niu3Zhexu Ding4Xinyang Teng5Jie Ma6Ying Liu7Shengdie Yang8Penghao Xie9Tianjiao Zhang10Lei Wang11Zhanyuan Lu12Qirong Shen13Jun Yuan14Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaGenome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaInner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Key Laboratory of Black Soil Protection and Utilization (Hohhot), Ministry of Agriculture and Rural Affairs Inner Mongolia Key Laboratory of Degradation Farmland Ecological Restoration and Pollution Control Hohhot ChinaInner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Key Laboratory of Black Soil Protection and Utilization (Hohhot), Ministry of Agriculture and Rural Affairs Inner Mongolia Key Laboratory of Degradation Farmland Ecological Restoration and Pollution Control Hohhot ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaNational Agricultural Experimental Station for Agricultural Environment, Luhe Jiangsu Academy of Agricultural Sciences Nanjing ChinaInner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Key Laboratory of Black Soil Protection and Utilization (Hohhot), Ministry of Agriculture and Rural Affairs Inner Mongolia Key Laboratory of Degradation Farmland Ecological Restoration and Pollution Control Hohhot ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaJiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource‐saving Fertilizers, Key Laboratory of Green Intelligent Fertilizer Innovation Nanjing Agricultural University Nanjing ChinaAbstract 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 (https://github.com/taowenmicro/ggClusterNet).https://doi.org/10.1002/imt2.70041microbial co‐occurrence networksmodularitymulti‐omics networkmulti‐network comparisonnetwork visualizationtranskingdom networks |
| spellingShingle | Tao Wen Yong‐Xin Liu Lanlan Liu Guoqing Niu Zhexu Ding Xinyang Teng Jie Ma Ying Liu Shengdie Yang Penghao Xie Tianjiao Zhang Lei Wang Zhanyuan Lu Qirong Shen Jun Yuan ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns iMeta microbial co‐occurrence networks modularity multi‐omics network multi‐network comparison network visualization transkingdom networks |
| title | ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns |
| title_full | ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns |
| title_fullStr | ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns |
| title_full_unstemmed | ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns |
| title_short | ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns |
| title_sort | ggclusternet 2 an r package for microbial co occurrence networks and associated indicator correlation patterns |
| topic | microbial co‐occurrence networks modularity multi‐omics network multi‐network comparison network visualization transkingdom networks |
| url | https://doi.org/10.1002/imt2.70041 |
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