The microbiome plays a critical role in human health and disease, and there is a strong scientific interest in linking specific features of the microbiome to clinical outcomes. There are key aspects of microbiome data, however, that limit the applicability of standard variable selection methods. In particular, the observed data are compositional, as the counts within each sample have a fixed-sum constraint. In addition, microbiome features, typically quantified as operational taxonomic units, often reflect microorganisms that are similar in function, and may therefore have a similar influence on the response variable. To address the challenges posed by these aspects of the data structure, we propose a variable selection technique with the following novel features: a generalized transformation and z-prior to handle the compositional constraint, and an Ising prior that encourages the joint selection of microbiome features that are closely related in terms of their genetic sequence similarity. We demonstrate that our proposed method outperforms existing penalized approaches for microbiome variable selection in both simulation and the analysis of real data exploring the relationship of the gut microbiome to body mass index.

中文翻译：

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用于微生物组特征选择的具有结构化先验的贝叶斯组合回归

微生物组在人类健康和疾病中起着至关重要的作用，将微生物组的特定特征与临床结果联系起来具有强烈的科学兴趣。然而，微生物组数据的一些关键方面限制了标准变量选择方法的适用性。特别是，观察到的数据是组合的，因为每个样本中的计数都有一个固定的总和约束。此外，微生物组特征，通常量化为操作分类单元，通常反映功能相似的微生物，因此可能对响应变量有类似的影响。为了解决数据结构的这些方面带来的挑战，我们提出了一种具有以下新颖特征的变量选择技术：广义变换和z- 在处理组成约束之前，以及鼓励联合选择在基因序列相似性方面密切相关的微生物组特征的 Ising 先验。我们证明，我们提出的方法在模拟和真实数据分析中都优于现有的微生物组变量选择惩罚方法，以探索肠道微生物组与体重指数的关系。