ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns

Since its initial release in 2022, <i>ggClusterNet</i> 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 <i>Facet.Network()</i> and <i>module.compare.m.ts()</i>; (3) Transkingdom network construction using microbiota, multi-omics, and other relevant data, with diverse visualization layouts such as <i>MatCorPlot2()</i> and <i>cor_link3()</i>; and (4) Transkingdom and multi-omics network analysis, including <i>corBionetwork.st()</i> and visualization algorithms tailored for complex network exploration, including <i>model_maptree2()</i>, <i>model_Gephi.3()</i>, and <i>cir.squ()</i>. The updates in <i>ggClusterNet 2</i> 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 <i>ggClusterNet 2</i>R package is open-source and available on GitHub (https://github.com/taowenmicro/ggClusterNet).