Bayesian latent ising model for joint microbial and metabolomic network inference

Understanding the complex microbial interactions and their implications for host health is a critical endeavor in biomedical research. In this paper, we propose a transformation-free Bayesian inference approach for estimating microbial and metabolomic association networks based on a latent Ising model. Our method addresses the challenges posed by the compositionality and zero-inflation of microbiome data, offering computational efficiency and versatility for mixed data types. By integrating two-component mixture models tailored to microbiome and metabolome data, along with spike-and-slab priors for sparse graph estimation and a pseudolikelihood approximation for efficient Bayesian computation, we provide a unified framework for joint microbial and metabolomic network inference. Simulation studies demonstrate the superior performance of our method compared to existing approaches, and an application to a real bacterial vaginosis microbiome-metabolome dataset reveals intriguing interaction patterns. Our proposed approach offers a promising avenue for uncovering biological insights from complex microbiome data and holds potential for advancing our understanding of microbiome-associated diseases and therapeutic interventions.