标题： 用团对数线性模型建模微生物群落中的关联 显示英文标题

标题： Modeling association in microbial communities with clique loglinear models

摘要： 微生物群落在复杂生物过程中发挥的重要作用日益受到关注。现代研究这些群落通常使用宏基因组样本的二代测序来确定群落组成。我们提出了一种基于团簇对数线性模型和贝叶斯模型平均的统计技术，以识别在不同分类水平上与显著关联的宏基因组样本中的微生物成分。我们描述了模型类别、模型选择的随机搜索技术以及感兴趣后验概率估计的计算方法。我们通过人类微生物组计划的数据以及慢性伤口愈合皮肤微生物组的研究数据展示了我们的方法。我们的技术还识别了微生物成分之间的显著依赖关系，作为可能的微生物共生的证据。关键词：列联表，图形模型，模型选择，微生物组，下一代测序 显示英文摘要

摘要： There is a growing awareness of the important roles that microbial communities play in complex biological processes. Modern investigation of these often uses next generation sequencing of metagenomic samples to determine community composition. We propose a statistical technique based on clique loglinear models and Bayes model averaging to identify microbial components in a metagenomic sample at various taxonomic levels that have significant associations. We describe the model class, a stochastic search technique for model selection, and the calculation of estimates of posterior probabilities of interest. We demonstrate our approach using data from the Human Microbiome Project and from a study of the skin microbiome in chronic wound healing. Our technique also identifies significant dependencies among microbial components as evidence of possible microbial syntrophy. KEYWORDS: contingency tables, graphical models, model selection, microbiome, next generation sequencing