Methods for microbiome compositional data

微生物组组成数据的方法

• 批准号: 10580718
• 负责人: Jun Chen
• 金额: $ 32.19万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580718
• 关键词: Accounting; Address; Attention; Biological Markers; Biometry; Characteristics; Charge; Clinic; Collaborations; Communities; Complex; Computer software; Consensus; Data; Data Analyses; Data Analytics; Data Correlations; Data Set; Development; Disease; Experimental Designs; Genome; Genomics; Goals; Grant; Health; Human; Human Microbiome; Lead; Linear Models; Manuscripts; Medical; Medicine; Metagenomics; Methodology; Methods; Modeling; Multiomic Data; Nature; Phylogenetic Analysis; Phylogeny; Procedures; Prognosis; Proteome; Regression Analysis; Research Design; Research Personnel; Role; Sample Size; Sampling; Scheme; Science; Scientific Advances and Accomplishments; Scientist; Signal Transduction; Statistical Data Interpretation; Statistical Methods; Structure; Testing; Trees; analytical method; analytical tool; biomarker discovery; design; disease diagnosis; flexibility; improved; individualized medicine; inter-individual variation; interest; metabolome; methylome; microbial; microbiome; microbiome analysis; microbiome research; multidimensional data; novel; open source; programs; simulation; statistics; targeted treatment; theories; tool; transcriptome; two-dimensional; user friendly software; user-friendly

Project Summary The broad and long-term objective of this project concerns the development of novel quantitative methods and biostatistical tools for microbiome data analytics to aid in microbiome-based discovery sciences. The microbiome, also called the second genome of the human, has received much attention in the past few years. Due to its critical roles in human health and disease, the human microbiome has now been recognized as an integral part of the individualized medicine approach because it not only accounts for inter-individual variability in all aspects of a disease but also represents a potentially modifiable factor that is amenable to targeting by therapeutics. Despite those fruitful and promising findings from microbiome studies, there is no consensus in the current field as how to appropriately analyze the data, let alone the optimality and efficiency issues that have yet to be addressed. Several challenges amount to this predicament, including complex experimental designs of microbiome studies, an unknown interplay between microbiome and host, extremely sparsity and high dimensionality of the data, phylogenetic relatedness of the microbial taxa, and compositional structure of microbiome. As a result, although quite a few analytical methods and tools have been developed for microbiome data analysis, several specific gaps exist in the methodological toolbox, hindering the advance of microbiome-based biomedical sciences. To fill these gaps, this proposal aims to develop robust and powerful quantitative methods and tools for microbiome data analysis. Specifically, Aim 1 focuses on developing robust and powerful methods for differential abundance analysis in complex study designs. It will develop new methods to address zero-inflation, compositional effects and correlations in microbiome data. Aim 2 focuses on strategies to increase the power of microbiome-wide multiple testing. It proposes two new multiple testing procedures, which address confounders and phylogenetic relatedness, respectively. Aim 3 proposes to develop compositional canonical correlation analysis methods for integrating microbiome data with other omics data. Specifically, it will develop an efficient and flexible framework for integrating heterogeneous omics data with microbiome data, accounting for compositional effects and phylogenetic relatedness. Aim 4 will develop user-friendly and efficient software packages so the community can benefit maximally from methodological and scientific advances resulting from this application. The proposed methods will be evaluated using simulations, and more importantly, applications to several ongoing microbiome studies in the Center of Individualized Medicine at Mayo Clinic. The proposed quantitative methods and open-source software packages will contribute to microbiome biomarker discovery and microbiome-based mechanistic studies. All methods and tools developed under this grant will be made available free of charge to interested researchers and the public.

项目摘要 该项目的广泛和长期目标涉及新的定量方法的发展和 微生物组数据分析的生物统计工具有助于基于微生物组的发现科学。这 微生物组，也称为人类的第二个基因组，在过去的几年中受到了很多关注。 由于其在人类健康和疾病中的关键作用，人类微生物组现已被认为是 个性化医学方法的整体部分，因为它不仅说明了个体间的变异性 在疾病的各个方面，但也代表了一个潜在的可修改因素 疗法。尽管从微生物组研究中有这些富有成果且有前途的发现，但仍未达成共识 当前的领域是如何适当分析数据，更不用说最佳和效率问题 尚未解决。几个挑战等于这一困境，包括复杂的实验 微生物组研究的设计，微生物组和宿主之间的未知相互作用，极度稀疏性和 数据的高维度，微生物类群的系统发育相关性以及组成结构 微生物组。结果，尽管已经开发了许多分析方法和工具 微生物组数据分析，方法学工具箱中存在一些特定差距，阻碍了 基于微生物组的生物医学科学。为了填补这些空白，该建议旨在发展强大而有力的 微生物组数据分析的定量方法和工具。具体而言，AIM 1专注于发展强大 以及复杂研究设计中差异丰度分析的强大方法。它将发展新的 微生物组数据中解决零通气，组成效应和相关性的方法。 AIM 2专注于 提高微生物组多重测试功能的策略。它提出了两个新的多重测试 分别解决混杂因素和系统发育相关性的程序。目标3提出 开发组成规范相关分析方法，用于将微生物组数据与其他Omics整合在一起 数据。具体而言，它将开发一个有效且灵活的框架，用于集成异构的OMICS数据 使用微生物组数据，考虑了组成效应和系统发育相关性。目标4将发展 用户友好和高效的软件包，因此社区可以从方法论和 该应用程序导致的科学进步。提出的方法将使用模拟评估， 更重要的是，在个性化的中心进行了一些正在进行的微生物组研究的应用 梅奥诊所的医学。建议的定量方法和开源软件包将 有助于微生物组生物标志物发现和基于微生物组的机理研究。所有方法和 根据该赠款开发的工具将免费提供给感兴趣的研究人员和公众。