CMiNet: An R package and user‐friendly Shiny App for constructing consensus microbiome networks

Abstract Microbial 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 inferred networks and the absence of a gold standard (a universally accepted reference for benchmarking) for validation. We developed CMiNet , an R package and interactive Shiny App (https://cminet.wid.wisc.edu ) that enables consensus microbiome network construction by integrating up to 10 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. CMiNet integrates results from multiple inference methods through a voting strategy that retains edges supported by a user‐defined number of methods. To assess robustness, we complement this with a bootstrap analysis that quantifies edge stability under resampling. By jointly reporting method support and bootstrap confidence, CMiNet provides a reproducible framework that explicitly communicates both agreement across methods and stability under perturbation. We applied CMiNet to gut and soil microbiome datasets, constructing consensus networks that retained edges supported by multiple methods and confirmed by bootstrap reproducibility values. To identify disease‐associated taxa, we developed an integrative strategy that compared results across machine learning, differential abundance and network‐based approaches, ensuring that selected taxa were consistently recovered across methods. In the soil dataset, this analysis highlighted key taxa such as Ktedonobacteria, Acidobacteriae, Vicinamibacteria, MB‐A2‐108, Ignavibacteria and Anaerolineae , all of which were confirmed by multiple independent strategies.