Advanced Statistical Methods for Microbiome Data Analysis

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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.