zifalsnm: Zero-Inflated Bayesian factor analysis model with skew-normal priors for modeling microbiome data

MotivationAdvancements in next-generation sequencing have transformed our understanding of host-microbe interactions, revealing links between microbial composition and chronic conditions such as obesity, diabetes, IBD, and others. However, the analysis of microbiome data is complex due to its unique statistical characteristics. One primary objective is to achieve effective dimension reduction to manage high dimensionality while simultaneously accounting for the datas compositional nature and zero inflation. Although existing probabilistic models provide frameworks for composition estimation, they are often based on the assumption that log-ratio-transformed compositions are normally distributed. This assumption is problematic, as it often fails to capture the significant skewness inherent in these transformed compositions. ResultsWe propose a new model called the Zero-Inflated Factor Analysis Logistic Skew-Normal Multinomial (ZIFA-LSNM) model : a comprehensive Bayesian hierarchical framework designed to address the statistical challenges of microbiome data. ZIFA-LSNM integrates a zero-inflation component to handle excess zeros, employs factor analysis for dimensionality reduction, and, critically, utilizes skew-normal priors on the latent factors to explicitly model data asymmetry. Posterior inference is performed using a scalable and efficient variational inference algorithm. Through simulation studies and real data analysis, the ZIFA-LSNM model has shown to demonstrate superior performance in parameter recovery and composition estimation compared to its Gaussian-based counterparts. Availability and Implementationzifalsnm is implemented in a freely available R package: https://github.com/SaurabhP-MS/zifalsnm.git Supplementary InformationSupplementary material is available with this article.