The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain operational taxonomic units (OTUs) profiles for each subject. Due to the high-dimensionality and nonnormality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject-based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model beta-diversity as a response within a regression setting by utilizing the functional response models (FRMs), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.

中文翻译：

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主体间属性的半参数模型：在微生物组数据的 beta 多样性中的应用

人类微生物组在我们的健康中起着重要作用，识别与微生物组组成相关的因素有助于深入了解内在疾病机制。通过在高通量测序中对标记基因进行扩增和测序，将高度相似的序列组合在一起，我们获得了每个受试者的操作分类单元 (OTU) 配置文件。由于OTU的高维性和非正态性特征，引入多样性度量作为微生物群落水平的总结，包括基于距离的个体之间的β多样性。这种主体间属性的分析不适合基于主体内的主要统计范式，例如t-测试和线性回归。在本文中，我们提出了一种新方法，通过利用功能响应模型 (FRM)（一类用于主体间和主体内属性的半参数模型）将 beta 多样性建模为回归设置中的响应。新方法不仅解决了当前方法对横截面数据的 beta 多样性的局限性，而且为将来将该方法扩展到纵向和其他聚类数据提供了前提。所提出的方法用真实数据和模拟数据进行了说明。