A0989
Title: Microbial interactions and community stability from longitudinal microbiome study
Authors: Huilin Li - New York University (United States) [presenting]
Abstract: Dynamic changes in microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods to model temporal dynamic patterns and quantify microbial interactions and community stability simultaneously. The aim is to propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlying dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, the estimate of community stability is further derived, and the core dynamic patterns are identified through network inference. ARZIMM is evaluated in comparison with the other methods through extensive simulation studies and real data analyses.
