A Dirichlet Autoregressive Model for the Analysis of Microbiota Time‐Series Data

Abstract

Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.

Multivariate statistical monitoring system for microbial population dynamics

“…In microbiome analysis, the principal coordinate analysis is often used with the distance matrix among samples [49,62]. Furthermore, several previous studies have performed the PCA based on the composition of microbial species [63][64][65]. The purpose of the dimensionality reduction in these previous studies was to visualize the transition of individual samples into different states.…”