  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. We propose a computational approach for the
joint inference of microbiota systems from metagenomic data for a number of
body sites. The random graphical model (RGM) allows for heterogeneity across
the different environments while quantifying their relatedness at the
structural level. 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. Our results show how: the RGM
approach is able to capture varying levels of structural similarity across the
different body sites and how this is supported by their taxonomical
classification; the Bayesian implementation of the RGM fully quantifies
parameter uncertainty; the microbiome network posteriors show not only a stable
core, but also interesting individual differences between the various body
sites, as well as interpretable relationships between various classes of
microbes.
