B0753
Title: Mixture margin random-effects copula models for inferring microbial co-variation networks
Authors: Hongzhe Li - University of Pennsylvania (United States) [presenting]
Abstract: Longitudinal microbiome studies, in which data on a single subject are collected repeatedly over time, are becoming increasingly common in biomedical research. Such studies provide an opportunity to study the inherently dynamic nature of a microbiome in a way that cannot be done using cross-sectional studies. We develop random-effects copula models with mixed zero-beta margins to identify biologically meaningful temporally conserved co-variation between two bacterial taxa, while accounting for the excessive zeros seen in 16S rRNA and metagenomic sequencing data. The model assumes a random-effects model for the dependence parameter in the copulas, which captures the conserved microbial co-variation while allowing for a time-specific dependence parameters. Our analysis of the longitudinal pediatric DIABIMMUNE cohort identifies changes in both local and global patterns of microbial co-variation networks in infants treated with antibiotics. Our results show that the no-antibiotics network is less dependent on individual taxon, thus making it more stable than the antibiotics network and more robust to both targeted and random attacks.
