Novel Statistical Methods for Analyzing Complex Microbiome Data

项目摘要/摘要 阐明不同微生物在人类健康和疾病中的作用是当务之急。然而， 来自(16S rRNA基因或鸟枪式元基因组)测序研究的微生物组数据具有独特的和 复杂特征，包括高维性、稀疏性、过度分散性、组合性和实验性 偏见。现有的假设检验统计方法往往不能完全解释这些特征，因此 容易产生假阳性结果。该应用程序的目标是开发健壮且灵活的统计 在存在所有数据复杂性的情况下表现良好的方法允许测试各种假设(例如， 差异丰度、动态变化、中介效应)，并适应广泛的数据集(例如， 感兴趣的连续或离散特征、纵向数据)。为此，我们提出以下具体建议 目标：(目标1)开发一个新的差异丰度成分分析框架；(目标2) 开发在基于重抽样的多假设检验中控制蒙特卡罗错误率的方法 3)开发分析纵向数据的方法；(目标4)开发新的调解框架 分析微生物组；和(目标5)开发和支持用户友好的软件程序执行 目标1-4中发展的方法。我们将使用广泛的模拟研究来评估这些方法 我们积极参与的埃默里大学真实微生物组研究的多个数据集。

Project Summary/Abstract It is imperative to elucidate the roles that different microbes play in human health and diseases. However, microbiome data from (either 16S rRNA gene or shotgun metagenomic) sequencing studies have unique and complex features, including high-dimensionality, sparsity, overdispersion, compositionality, and experimental bias. Existing statistical methods for hypothesis testing often fail to account for these features in full and thus tend to yield false-positive results. The goal of this application is to develop robust and flexible statistical methods that perform well in the presence of all data complexities, allow testing of various hypotheses (e.g., differential abundance, dynamic changes, mediation effects), and accommodate a wide range of datasets (e.g., continuous or discrete traits of interest, longitudinal data). To these ends, we propose the following specific aims: (Aim 1) to develop a new framework for compositional analysis of differential abundance; (Aim 2) to develop methods for controlling Monte-Carlo error rate in resampling-based multiple-hypotheses testing; (Aim 3) to develop methods for analyzing longitudinal data; (Aim 4) to develop a new framework for mediation analysis of the microbiome; and (Aim 5) to develop and support a user-friendly software program implementing the methods developed in Aims 1-4. We will evaluate these methods using extensive simulation studies and multiple datasets from real microbiome studies at Emory University that we are actively involved in.