The microbiome plays an important role in human health by mediating the path from environmental exposures to health outcomes. The relative abundances of the high‐dimensional microbiome data have an unit‐sum restriction, rendering standard statistical methods in the Euclidean space invalid. To address this problem, we use the isometric log‐ratio transformations of the relative abundances as the mediator variables. To select significant mediators, we consider a closed testing‐based selection procedure with desirable confidence. Simulations are provided to verify the effectiveness of our method. As an illustrative example, we apply the proposed method to study the mediation effects of murine gut microbiome between subtherapeutic antibiotic treatment and body weight gain, and identify Coprobacillus and Adlercreutzia as two significant mediators.

中文翻译：

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高维和组成微生物组数据中的中介效应选择


微生物组通过调节从环境暴露到健康结果的路径，在人类健康中发挥着重要作用。高维微生物组数据的相对丰度具有单位和限制，使得欧几里得空间中的标准统计方法无效。为了解决这个问题，我们使用相对丰度的等距对数比变换作为中介变量。为了选择重要的中介变量，我们考虑采用基于封闭测试的选择程序，并具有理想的置信度。提供模拟来验证我们方法的有效性。作为一个说明性的例子，我们应用所提出的方法来研究小鼠肠道微生物组在亚治疗抗生素治疗和体重增加之间的中介作用，并确定粪杆菌和Adlercreutzia是两个重要的中介体。