Novel Statistical Methods for Analyzing Complex Microbiome Data

分析复杂微生物组数据的新统计方法

• 批准号: 10595015
• 负责人: Yijuan Hu
• 金额: $ 30.94万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595015
• 关键词: Acceleration; Address; Advocate; Alzheimer' s Disease; Basic Science; Benchmarking; Biological; Biological Markers; Clinical; Clinical Research; Collaborations; Complex; Data; Data Set; Disease; Disease Outcome; Equation; Failure; Genes; Goals; Health; Human; Human Microbiome; Individual; Inflammatory Bowel Diseases; Intervention; Longitudinal Studies; Malignant Neoplasms; Mediating; Mediation; Metagenomics; Methodology; Methods; Microbe; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Paper; Play; Procedures; Reproducibility; Research Personnel; Risk; Role; Running; Sample Size; Sampling; Shotguns; Statistical Methods; Testing; Universities; Work; cost; data complexity; disease diagnosis; disease prognosis; flexibility; high dimensionality; improved; interest; longitudinal analysis; microbial community; microbiome; microbiome analysis; microbiome research; multiple datasets; novel; operational taxonomic units; programs; rRNA Genes; semiparametric; simulation; trait; user friendly software

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.

项目总结/摘要 阐明不同微生物在人类健康和疾病中所起的作用至关重要。然而，在这方面， 来自（16 S rRNA基因或鸟枪宏基因组）测序研究的微生物组数据具有独特的 复杂的特征，包括高维性、稀疏性、过度分散性、组合性和实验性 bias.现有的假设检验统计方法往往不能充分考虑这些特征， 往往会产生假阳性结果此应用程序的目标是开发强大而灵活的统计 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），并适应大范围的数据集（例如， 连续或离散的感兴趣特征、纵向数据）。为此，我们提出以下具体建议： 目的：（目的1）开发一个新的差异丰度成分分析框架;（目的2） 在基于重采样的多假设检验中，开发控制蒙特-卡罗误差率的方法;（目的 3)制定分析纵向数据的方法;（目标4）制定新的调解框架 微生物组分析;以及（目标5）开发和支持用户友好的软件程序， 目标1-4中制定的方法。我们将使用广泛的模拟研究来评估这些方法， 来自埃默里大学的真实的微生物组研究的多个数据集，我们积极参与其中。