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.

翻译：微生物组构成了一个由相互作用组分组成的复杂微生物生态系统，调控着宿主的重要通路。微生物丰度测量是解析微生物间复杂相互作用网络的关键。不同身体部位的微生物群落既有共通的整体结构，又展现出与局部环境需求相关的多样性。我们提出了一种计算方法，用于从多个身体部位的宏基因组数据中联合推断微生物系统。随机图模型（RGM）允许在不同环境间存在异质性的同时，在结构层面量化其相关性。此外，该模型可在微生物和相互作用两个层面纳入外部协变量，进一步适应微生物组数据的丰富性和复杂性。我们的研究结果表明：RGM方法能够捕获不同身体部位间不同层次的结构相似性，且此相似性得到其分类学分类的支持；RGM的贝叶斯实现完全量化了参数不确定性；微生物组网络后验不仅显示出稳定的核心，还揭示了不同身体部位间有趣的个体差异，以及各类微生物间可解释的关系。