CMiNet: An R Package and User-Friendly Shiny App for Constructing Consensus Microbiome Networks

Abstract

O_LIMicrobial networks offer critical insights into community structure, ecological interactions, and host-microbe dynamics. However, constructing reliable microbiome networks remains challenging due to variability among existing inference methods, limited overlap between networks, and the absence of a gold standard for validation. C_LIO_LIWe developed CMiNet (https://cminet.wid.wisc.edu), an interactive Shiny application and R package that enables consensus microbiome network construction by integrating up to ten widely used inference algorithms. CMiNet supports both correlation-based and conditional dependence-based methods and provides users with flexible options to construct individual or consensus networks across different approaches. C_LIO_LICMiNet employs a consensus-based filtering strategy that retains only edges supported by multiple methods, leading to more robust network structures that better reflect underlying biological interactions. This approach enhances reproducibility, minimizes method-specific biases, and improves biological interpretability. Its user-friendly interface allows researchers to upload data, customize network construction parameters, visualize networks interactively, and export results--without requiring programming expertise. C_LIO_LIWe demonstrated CMiNet using both gut and soil microbiome datasets. In the soil microbiome application, robust network construction enabled the consistent identification of disease-associated taxa. Specifically, we identified a set of key taxa, including Ktedonobacteria, Acidobacteriae, Vicinamibacteria, MB-A2-108, Planctomycetes, and Anaerolineae, selected by multiple independent methods. C_LI