Statistical Methods for Network-based Integrative Analysis of Microbiome Data

项目摘要 在过去的十年里，在发现与HU-1相关的微生物组生物标志物方面取得了实质性进展。 人类的健康和疾病。然而，尽管之前的工作令人兴奋，但我们目前仍然缺乏对 肠道微生物群影响人类健康的机制。一个突出的挑战是如何整合 微生物组和其他组学数据类型在微生物组多组学项目fi研究中产生，以阐明微生物组和其他组学数据类型。 鳄鱼的功能通路。不幸的是，现有的用于综合分析的统计方法不足以 解决fic对微生物组数据的分析挑战。微生物组数据是组成的，零输入fl， 高维和高度结构化的，其中样本通过生态DefiNed距离和分类群相关 通过它们的系统发育关系。我们建议利用我们在网络分析和高维统计方面的专业知识 以解决微生物组数据分析所特有的这些挑战。我们的总体目标是发展严谨- 我们的统计方法，产生可靠而有力的推论，将微生物的功能途径与宿主联系起来 健康状况。建议的方法论是由我们与拉丁裔和 狗老化项目，并包括一种新的推论程序，用于微生物和代谢组的联合分析 网络(目标1)--一种结合先验生物知识的联合降维方法 关于样本之间和变量之间的关系(目标2)，以及一个强大的框架，用于联合 将微生物组和其他组学数据类型与健康结果相关联(目标3)。我们将开发EffiEncient和 拟议方法的易于使用的软件工具(目标4)。这项工作是创新的，标志着fi不能，因为它 将为系统生物学提供对微生物组作用的洞察，并有可能产生重大影响 新型微生物组生物标记物的同源fi阳离子研究。这项建议的成功完成将产生以下影响- 现有社区资源，包括综合分析的新方法及其用户友好的软件 工具。有了这些分析工具，这个项目的长期目标是加速发现微生物组-- 以治疗目标为基础，并有助于开发基于微生物组的精确疗法。

Project Summary The past decade has seen substantial progress in the discovery of microbiome biomarkers associated with hu- man health and diseases. However, despite the exciting prior work, we currently still lack an understanding of the mechanism by which the gut microbiome impacts human health. An outstanding challenge is how to integrate microbiome and other -omics data types generated in microbiome multi-omics profiling studies to elucidate mi- crobial functional pathways. Unfortunately, available statistical methods for integrative analysis do not adequately address the analytical challenges specific to microbiome data. Microbiome data are compositional, zero-inflated, high-dimensional, and highly structured where samples are related by ecologically defined distances and taxa are related by their phylogeny. We propose to use our expertise in network analysis and high-dimensional statistical inference to tackle these challenges unique to microbiome data analysis. Our overall objective is to develop rigor- ous statistical methods that yield reliable and powerful inferences relating microbial functional pathways with host health conditions. The proposed methodologies are motivated by our collaboration with the Study of Latinos and the Dog Aging Project, and include a novel inference procedure for joint analysis of microbial and metabolomic networks (Aim 1), a novel method for joint dimensionality reduction which incorporates prior biological knowledge about the relationships between samples and between variables (Aim 2), and a powerful framework for jointly associating microbiome and other -omics data types with health outcomes (Aim 3). We will develop efficient and easy-to-use software tools for the proposed methods (Aim 4). This work is innovative and significant, because it will provide systems biology insights into the role of the microbiome and has the potential to make a major impact on the identification of novel microbiome biomarkers. Successful completion of this proposal will generate impor- tant community resources, including new methodologies for integrative analysis and their user-friendly software tools. With these analytical tools, the longer-term goal of this project is to hasten the discovery of microbiome- based therapeutic targets and contribute to the development of microbiome-based precision therapies.