Novel Statistical Methods for Analyzing Complex Microbiome Data

分析复杂微生物组数据的新统计方法

基本信息

批准号：
10413176
负责人：
Yijuan Hu
金额：
$ 30.97万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
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/10413176
关键词：
Accounting Address Advocate Alzheimer&apos s Disease Basic Science Benchmarking Biological Biological Markers Categories Clinical Clinical Research Collaborations Complex Dancing 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 Methodology Methods Microbe Modeling Names Non-Insulin-Dependent Diabetes Mellitus Obesity Outcome Play Procedures Reproducibility Research Personnel Risk Role Running Sample Size Sampling Shotguns Statistical Methods Testing Universities Work base cost data complexity disease diagnosis disease prognosis flexibility high dimensionality improved interest longitudinal analysis metagenomic sequencing microbial community microbiome microbiome analysis microbiome research multiple datasets novel operational taxonomic units programs rRNA Genes semiparametric simulation trait user friendly software

项目摘要

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.

项目摘要/摘要必须阐明不同微生物在人类健康和疾病中发挥的作用。然而，来自（16S rRNA基因或shot弹枪元基因组学）测序研究的微生物组数据具有独特，并且复杂的特征，包括高维，稀疏性，过度分散性，组成性和实验性偏见。现有的假设检验的统计方法通常无法完全解释这些功能倾向于产生假阳性结果。该应用程序的目的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，可以测试各种假设（例如，差异丰度，动态变化，调解效应）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）。对于这些目的，我们提出以下具体目的：（目标1）开发一个新的框架，用于差异丰度的组成分析；（目标2）开发用于控制基于重新采样的多种刺激测试中蒙特卡洛错误率的方法；（目的 3）开发用于分析纵向数据的方法；（目标4）开发一个新的调解框架微生物组的分析；（目标5）开发和支持实施用户友好的软件程序目标1-4中开发的方法。我们将使用广泛的仿真研究评估这些方法，并我们正在积极参与的埃默里大学实际微生物组研究的多个数据集。