ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns DOI Creative Commons
Tao Wen, Yongxin Liu, Lanlan Liu

и другие.

iMeta, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

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

Язык: Английский

ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns DOI Creative Commons
Tao Wen, Yongxin Liu, Lanlan Liu

и другие.

iMeta, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

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

Язык: Английский

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