ggClusterNet

ggClusterNet enables microbial co-occurrence network analysis and visualization, including indicator correlation patterns, multi-factor analysis, transkingdom networks, and multi-omics integration.

Key Features:

• Microbial Co-occurrence Network Pipeline: Integrates network computation using Pearson, Spearman, and SparCC correlations with visualization, topological characterization of network and node properties, multi-network comparison with statistical testing, network stability and robustness analysis, and module identification and analysis.
• Network Mining Functions: Supports multi-factor, multi-treatment, and spatiotemporal-scale analysis through functions including Facet.Network() and module.compare.m.ts().
• Transkingdom Network Construction: Builds networks using microbiota, multi-omics, and other relevant data with diverse visualization layouts including MatCorPlot2() and cor_link3().
• Transkingdom and Multi-omics Network Analysis: Includes corBionetwork.st() and visualization algorithms for complex network exploration including model_maptree2(), model_Gephi.3(), and cir.squ().

Scientific Applications:

• Microbial Co-occurrence Network Analysis: Analyzes and visualizes microbial co-occurrence patterns and indicator correlations in microbiome research.
• Multi-factor Experimental Design Analysis: Addresses multi-factor experimental designs, multi-treatment conditions, and spatiotemporal-scale data in microbiome studies.
• Transkingdom Interaction Studies: Constructs and analyzes transkingdom networks integrating microbiota with multi-omics and other relevant data types.
• Multi-omics Data Integration: Explores complex network interactions across multiple omics layers in microbial community research.

Methodology:

Network computation uses Pearson, Spearman, and SparCC correlations; network stability is assessed through robustness analysis; modules are identified and analyzed; multi-network comparisons include statistical testing; topological properties are characterized at network and node levels.