PhyMapNet: A Phylogeny-Guided Bayesian Framework for Reliable Microbiome Network Inference

Abstract

Understanding microbial interactions is essential for revealing the ecological structure and functional organization of microbiomes. However, network inference remains challenging due to the compositional, sparse, and high-dimensional nature of microbiome data, as well as the lack of gold-standard interaction benchmarks. Here, we introduce PhyMapNet, a Bayesian Gaussian Graphical Model framework that explicitly integrates phylogenetic information to infer microbiome networks. We introduce PhyMapNet, a phylogeny-aware method for reconstructing microbial association networks. PhyMapNet infers conditional dependencies among taxa by estimating a sparse precision matrix, while incorporating evolutionary relatedness through a kernel defined on phylogenetic distances. This integration of biological prior information improves the robustness and interpretability of the inferred networks. We evaluated PhyMapNet on two real-world microbiome datasets (Smoking and Caffeine), performing extensive robustness analyses under bootstrap and noisy perturbations, which demonstrated the reproducibility of the algorithm. The results also revealed that network structure can vary with parameter selection, highlighting the algorithms sensitivity to hyperparameters. Because PhyMapNet is computationally efficient and can explore thousands of parameter configurations within an hour, we developed a tuning-free framework that constructs reliable consensus networks by aggregating results across a broad hyperparameter space. This strategy yields consensus microbiome networks with controllable sparsity, offering a practical way to adjust network density while retaining highly stable edges. We then compared these consensus networks with those inferred by nine established methods and observed statistically significant overlap. To support reproducibility and practical use, we provide an open-source R package implementation of PhyMapNet.