Advanced Statistical Methods for Microbiome Data Analysis

微生物组数据分析的高级统计方法

• 批准号: 10689210
• 负责人: Zhengzheng Tang
• 金额: $ 25.62万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10689210
• 关键词: Affect; Antibiotics; Area; Asthma; Behavior; Biological; Biological Markers; Biological Models; Birth; Characteristics; Childhood Asthma; Clinical; Collaborations; Complex; Data; Data Analyses; Development; Developmental Process; Diagnostic; Diet; Diffuse; Disease; Disease Outcome; Disease Progression; Drug usage; Environment; Environmental Exposure; Epidemiology; Equation; Exhibits; Future; Health; High-Throughput Nucleotide Sequencing; Human; Human Microbiome; Human body; Immunologics; Individual; Infant; Instruction; Life; Link; Longitudinal Studies; Measures; Meta-Analysis; Metadata; Methods; Microbe; Multiple Birth Offspring; Participant; Pathway Analysis; Pattern; Pharmaceutical Preparations; Privacy; Probiotics; Reporting; Research; Role; Signal Transduction; Statistical Methods; Structure; Taxon; Technology; Time; Universities; Variant; Virus Diseases; Visualization; Wheezing; analytical method; cohort; computerized tools; design; drug efficacy; falls; human disease; longitudinal analysis; microbial; microbial community; microbial composition; microbiome; microbiome research; microbiota transplantation; nasal microbiome; novel; programs; response; software development; statistics; tool

Thanks to advances in high-throughput sequencing technologies, the importance of the microbiome in human health and disease has been increasingly recognized. The development of robust and powerful methods that adapt to the features of the microbiome data has seriously fallen behind the technological advances, especially for the application to the study of complex diseases. Motivated by ongoing microbiome projects, we propose to develop a meta-analysis method to synthesize multiple microbiome association studies; a method to analyze longitudinal microbiome data; and a method to identify microbial co-variation clusters. We will develop software programs implementing these methods and apply them in ongoing microbiome studies. The methods and tools resulting from this project will promote a better understanding of the role of the microbiome for human health and disease. RELEVANCE (See instructions): Microbiome research is a promising area to understand mechanisms underpinning complex human diseases. The unique structure and characteristics of microbiome data render many standard analytic approaches inadequate. In this application, we propose to develop advanced methods and computational tools for microbiome data analyses and apply the methods to ongoing projects to uncover the roles of microbiome for complex human diseases.

由于高通量测序技术的进步，微生物组在 人类健康与疾病的关系日益受到重视。扎实有力的发展 适应微生物组数据特点的方法严重落后于技术 进展，特别是在复杂疾病研究中的应用。受持续不断的激励 微生物组计划，我们建议开发一种荟萃分析方法来合成多个微生物组 关联研究；分析纵向微生物组数据的方法；以及识别微生物的方法 协变星系团。我们将开发实现这些方法的软件程序，并将它们应用于 正在进行的微生物组研究。这个项目产生的方法和工具将促进更好的 了解微生物群对人类健康和疾病的作用。 相关性(请参阅说明)： 微生物组研究是理解复杂人类基础机制的一个很有前途的领域 疾病。微生物组数据的独特结构和特点提供了许多标准分析 方法不足。在这一应用中，我们建议开发先进的方法和计算 用于微生物组数据分析的工具，并将这些方法应用于正在进行的项目，以揭示 用于复杂人类疾病的微生物组。