Advanced Statistical Methods for Microbiome Data Analysis

由于高通量测序技术的进步，微生物组的重要性 人类的健康与疾病已日益得到人们的认识。发展稳健、实力雄厚 适应微生物组数据特点的方法已严重落后于技术水平 进展，特别是在复杂疾病研究中的应用。受到持续不断的激励 微生物组项目，我们建议开发一种荟萃分析方法来合成多个微生物组 协会研究；一种分析纵向微生物组数据的方法；以及微生物的鉴定方法 共变簇。我们将开发实现这些方法的软件程序并将其应用于 正在进行的微生物组研究。该项目产生的方法和工具将促进更好的 了解微生物组对人类健康和疾病的作用。 相关性（参见说明）： 微生物组研究是了解复杂人类机制的一个有前景的领域 疾病。微生物组数据独特的结构和特征使得许多标准的分析 方法不足。在此应用中，我们建议开发先进的方法和计算 微生物组数据分析工具，并将这些方法应用于正在进行的项目，以揭示微生物组的作用 复杂人类疾病的微生物组。

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.