标题： 相关环境中的微生物组相互作用的随机图模型 显示英文标题

标题： Random graphical model of microbiome interactions in related environments

摘要： 微生物组构成了一个复杂的微生物生态，其中相互作用的组分调节着宿主中的重要通路。 微生物丰度的测量是了解微生物之间复杂相互作用网络的关键。 不同身体部位的微生物群落往往共享一些总体的共同结构，同时也会表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）允许在不同环境中存在异质性，同时在结构层面上量化它们的相关性。 此外，该模型允许在微生物和相互作用层面包含外部协变量，进一步适应微生物组数据的丰富性和复杂性。 我们的结果表明：RGM方法能够捕捉不同身体部位之间结构相似性的不同层次，并且这种相似性得到了它们的分类学鉴定的支持；RGM的贝叶斯实现能够完全量化参数不确定性；微生物组网络后验不仅显示了一个稳定的核心，还显示了不同身体部位之间的有趣个体差异，以及不同类别的微生物之间的可解释关系。 显示英文摘要

摘要： 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.