A1550
Title: Mixtures of logistic matrix-variate normal multinomial models for efficient clustering longitudinal microbiome data
Authors: Yuan Fang - Old Dominion University (United States) [presenting]
Abstract: Microbiome taxa count data, derived from next-generation sequencing, are inherently high-dimensional, over-dispersed, and reveal only relative abundance, making them compositional and constrained to a simplex. To model such data, the logistic normal multinomial (LNM) approach transforms relative abundances from a simplex to real Euclidean space using the additive log-ratio transformation. Mixtures of LNM models have been developed for clustering microbiome data, adopting an efficient framework for parameter estimation using variational approximations to reduce the computational overhead. In this talk, it will be illustrated that the LNM mixture models provide a flexible framework, which can be easily adopted by assuming different data structures and distributions at the hidden layer latent space. Specifically, a matrix-LNM model is presented that introduces a matrix variate normal distribution at the latent layer, designed for time-coursed microbiome data. This approach captures both temporal dependencies and inter-sample correlations, offering a structured approach to longitudinal microbiome analysis. In addition, a family of models is also proposed by incorporating the modified Cholesky decomposition and imposing constraints on the components of the covariance matrix. Through simulation studies and real data analysis, the model's effectiveness in identifying dynamic patterns and clustering temporal microbiome profiles is demonstrated.
