Abstract
Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we 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 underlining 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, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.
Highlights
The human microbiota, a diverse array of microbial organisms living in and on human bodies, form a dynamic ecosystem that plays a critical role in human health
We have conducted extensive simulation studies to evaluate the performance of autoregressive zero-inflated mixed-effects model (ARZIMM) in both model fitting and variable selection by comparing it with the competing methods: penalized Poisson auto-regression (Poisson), penalized log-normal multivariate auto-regression (MAR), and extended generalized Lotka-Volterra equations using Bayesian algorithm (MDSINE) (Bucci et al, 2016b)
We propose ARZIMM, an analytic platform which estimates the microbial interactions and community stability using longitudinal microbiome data
Summary
The human microbiota, a diverse array of microbial organisms living in and on human bodies, form a dynamic ecosystem that plays a critical role in human health. When a microbial community changes in response to an external perturbation, it undergoes a dynamic process and tends to evolve toward another stable state (Figure 1). This dynamic process is stochastic and varies according to the type and strength of perturbation, the community stability prior to the perturbation, and other subjectlevel relevant features. The recent rise in longitudinal studies, in which microbial samples are collected repeatedly over time, offers unique insights into the responses of such communities to perturbations and the associated dynamic patterns.
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