Statistical Methods for Network-based Integrative Analysis of Microbiome Data

基于网络的微生物组数据综合分析的统计方法

• 批准号: 10708748
• 负责人: Jing Ma
• 金额: $ 35.83万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708748
• 关键词: Address; Aging; Biological; Biological Markers; Canis familiaris; Collaborations; Communities; Computational algorithm; Computer software; Confidence Intervals; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Failure; Foundations; Goals; Health; Health Promotion; Human; Individual; Intervention; Joints; Knowledge; Measures; Metabolic Marker; Metabolite Interaction; Methodology; Methods; Microbe; Modeling; Multiomic Data; Network-based; Outcome; Pathway Analysis; Pathway interactions; Phylogenetic Analysis; Phylogeny; Play; Precision therapeutics; Procedures; Production; Public Health; Resources; Role; Sampling; Scientific Advances and Accomplishments; Software Tools; Solid; Statistical Methods; Structure; Study of Latinos; Systems Biology; Taxon; Taxonomy; Techniques; Testing; Trees; Uncertainty; Work; analytical tool; detection platform; drug metabolism; gut microbiome; high dimensionality; innovation; insight; metabolome; metabolomics; metagenome; microbial; microbiome; microbiome analysis; microbiome research; microbiota; multiple omics; novel; open source; outcome prediction; predictive modeling; structured data; therapeutic target; user friendly software

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.

项目摘要 在过去的十年中，在发现与hu相关的微生物组生物标志物方面取得了实质性进展， 人的健康和疾病。然而，尽管先前的工作令人兴奋，但我们目前仍然缺乏对 肠道微生物组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分析研究中产生，以阐明微生物组的多样性。 crobial功能pathways途径.不幸的是，现有的综合分析统计方法不足以 解决微生物组数据特有的分析挑战。微生物组数据是组成性的，零干扰的， 高维，高度结构化，其中样本与生态学定义的距离和类群相关， 与他们的血缘有关。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， 推断来应对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性- 我们的统计方法，产生可靠的和强大的推论有关微生物的功能途径与主机 健康状况。所提出的方法的动机是我们与拉丁美洲人的研究合作， 狗老化项目，并包括一个新的推理程序，联合分析微生物和代谢组学 网络（目标1），一种新的方法，联合降维，结合先验生物知识 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， 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）。我们将提高效率， 易于使用的软件工具，为拟议的方法（目标4）。这项工作是创新和重要的，因为它 将提供系统生物学对微生物组作用的见解，并有可能产生重大影响 新的微生物生物标志物的鉴定。成功完成本提案将产生重要的- 现有社区资源，包括综合分析的新方法及其用户友好型软件 工具.有了这些分析工具，该项目的长期目标是加速发现微生物组- 基于微生物组的治疗靶点，并有助于开发基于微生物组的精确疗法。