Abstract
The microbiome constitutes a complex microbial ecology of interacting components that regulates important pathways in the host. Measurements of microbial abundances are key to learning the intricate network of interactions amongst microbes. Microbial communities at various body sites tend to share some overall common structure, while also showing diversity related to the needs of the local environment. In this talk, I will describe a computational approach for the joint inference of microbiota systems from (count) metagenomic data for a number of body sites. The random graphical model (RGM) allows for heterogeneity across the different body sites via environment-specific copula graphical models, while quantifying their relatedness at the structural level via a joint generative model of the graphs. In addition, the model allows for the inclusion of external covariates at both the microbial and interaction levels, further adapting to the richness and complexity of microbiome data. In the last part of the talk, I will show how a similar methodology has been used to study cross-country cultural heterogeneity from (ordinal) survey data.
Reference: V. Vinciotti, E. Wit, F. Richter Random graphical model of microbiome interactions in related environments, https://arxiv.org/abs/2304.01956, 2023