Methodology Development and Implementation for Microbiome Sequencing Data: Hierarchical Modeling on Clustered Taxa Counts with Repeated Measures

下一代测序的技术进步使研究人员能够揭示微生物群落的广泛变异性及其与不同疾病的关系。因此，了解影响微生物组组成的环境和宿主遗传因素变得至关重要。然而，由于微生物组测序数据的复杂性，该领域的强大和强大的方法还不发达，其中包括：a）微生物分类单元数据通常被分组为操作分类单元（OTU），并且这些计数通常是高度偏斜的、过度分散的和零膨胀的，B）分类层次聚类内的OTU计数通常是高度相关的，但是这种多变量性质通常被忽略，c）研究设计通常涉及从相关家庭成员中重复测量，从而引起时间和家庭相关性。在这个建议中，我将开发强大的生物信息学，统计和计算方法来克服这些挑战。具体而言，我建议使用潜变量（LV）的方法来共同建模多个分类群的纵向家庭研究框架内的层次分类集群。LV框架代表了集群的基本概念特征，并解释了不同类群之间的相关性。为了解决分类群计数的过度分散和零膨胀特征，我将对多元OTU结果应用零膨胀模型和障碍模型。

The technological advances in next generation sequencing have enabled researchers to unveil the wide variability in microbial communities and their relationships with different diseases. Therefore, it is becoming critical to understand both environmental and host genetic factors that impact the composition of the microbiome. However, robust and powerful methods in this area are underdeveloped due to the complexity of microbiome sequencing data, which includes: a) microbial taxa data are usually grouped into operational taxonomic units (OTUs) and these counts are often highly skewed, over-dispersed, and zero inflated, b) OTU counts within a taxonomic hierarchical cluster are often highly correlated, but this multivariate nature is usually ignored, c) the study designs often involve repeated measures taken from related family members, thus inducing temporal and familial correlations. In this proposal, I will develop powerful bioinformatics, statistical, and computational methods to overcome these challenges. Specifically, I propose to use the latent variable (LV) methodology to jointly model multiple taxa from hierarchical taxonomic clusters within a longitudinal family study framework. The LV framework represents the underlying conceptual traits of the cluster and explains the correlations among different taxa. To address the over-dispersed and zero inflated features of the taxa counts, I will apply both zero-inflated and hurdle models on the multivariate OTU outcomes.