Bayesian Graphical Compositional Regression for Microbiome Data
Bayesian Graphical Compositional Regression for Microbiome Data
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DOI：
10.1080/01621459.2019.1647212
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发表时间：
2019-08-26
影响因子：
3.7
通讯作者：
Ma, Li
中科院分区：
文献类型：
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作者：
Mao, Jialiang;Chen, Yuhan;Ma, Li
微生物组研究中的一项重要任务是测试微生物组组成的存在，并对不同样本组之间的差异进行表征。这个问题的重要挑战包括样本之间巨大的组内异质性，以及潜在混杂变量的存在，如果忽略这些变量，就会增加错误发现的机会，并降低识别真实差异的能力。我们提出了一个概率框架，通过结合三个想法来克服这些问题：(I)基于系统发育树的跨组比较问题分解为一系列局部测试，(Ii)连接局部测试的图形模型，以允许跨类群共享信息，以及(Iii)结合协变量并整合组内差异的贝叶斯测试策略，避免潜在的不稳定点估计。使用所提出的方法，我们分析了美国人的肠道数据，以比较不同饮食习惯的参与者的肠道微生物群组成。我们的分析表明：(I)食用水果、海鲜、蔬菜和全谷物的频率与肠道微生物组组成密切相关；(Ii)当调整不同的相关协变量时，分析的结论可能会发生巨大变化，这表明在比较微生物组组成与观察性研究的数据时，有必要仔细选择和包括可能的混杂因素。对于这篇文章，包括可用于复制该作品的材料的标准化描述，可作为在线补充提供。
An important task in microbiome studies is to test the existence of and give characterization to differences in the microbiome composition across groups of samples. Important challenges of this problem include the large within-group heterogeneities among samples and the existence of potential confounding variables that, when ignored, increase the chance of false discoveries and reduce the power for identifying true differences. We propose a probabilistic framework to overcome these issues by combining three ideas: (i) a phylogenetic tree-based decomposition of the cross-group comparison problem into a series of local tests, (ii) a graphical model that links the local tests to allow information sharing across taxa, and (iii) a Bayesian testing strategy that incorporates covariates and integrates out the within-group variation, avoiding potentially unstable point estimates. With the proposed method, we analyze the American Gut data to compare the gut microbiome composition of groups of participants with different dietary habits. Our analysis shows that (i) the frequency of consuming fruit, seafood, vegetable, and whole grain are closely related to the gut microbiome composition and (ii) the conclusion of the analysis can change drastically when different sets of relevant covariates are adjusted, indicating the necessity of carefully selecting and including possible confounders in the analysis when comparing microbiome compositions with data from observational studies. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.