Methods for microbiome compositional data

微生物组组成数据的方法

• 批准号: 10338342
• 负责人: Jun Chen
• 金额: $ 33.66万
• 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/10338342
• 关键词: Accounting; Address; Attention; Biological Markers; Biometry; Characteristics; Charge; Clinic; Collaborations; Communities; Complex; Computer software; Consensus; Data; Data Analyses; Data Analytics; Data Set; Development; Disease; Experimental Designs; Genome; Genomics; Goals; Grant; Health; Human; Human Microbiome; Lead; Linear Models; Manuscripts; Medical; Medicine; Metagenomics; Methodology; Methods; Modeling; Nature; Phylogenetic Analysis; Phylogeny; Procedures; Prognosis; Proteome; Regression Analysis; Research Design; Research Personnel; Role; Sample Size; Sampling; Science; Scientific Advances and Accomplishments; Scientist; Signal Transduction; Statistical Data Interpretation; Statistical Methods; Structure; Testing; Trees; analytical method; analytical tool; base; biomarker discovery; design; disease diagnosis; flexibility; improved; individualized medicine; inter-individual variation; interest; metabolome; methylome; microbial; microbiome; microbiome analysis; microbiome composition; microbiome research; multidimensional data; multiple omics; 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.

项目概要 该项目的广泛和长期目标涉及开发新颖的定量方法和 用于微生物组数据分析的生物统计工具，以帮助基于微生物组的发现科学。这 微生物组又称人类第二基因组，近年来受到广泛关注。 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 个体化医疗方法不可或缺的一部分，因为它不仅考虑了个体间的差异 疾病的各个方面，但也代表了一个潜在的可改变因素，可以通过 疗法。尽管微生物组研究取得了这些富有成效和有希望的发现，但目前尚未达成共识 当前领域如何正确地分析数据，更不用说优化和效率问题了 尚未得到解决。造成这种困境的几个挑战包括复杂的实验 微生物组研究的设计、微生物组和宿主之间未知的相互作用、极其稀疏和 数据的高维性、微生物类群的系统发育相关性以及微生物的组成结构 微生物组。因此，尽管已经开发了相当多的分析方法和工具 微生物组数据分析，方法学工具箱中存在一些具体的空白，阻碍了微生物组数据分析的进展 基于微生物组的生物医学科学。为了填补这些空白，该提案旨在开发强大而强大的 微生物组数据分析的定量方法和工具。具体来说，目标 1 侧重于开发稳健的 以及复杂研究设计中差异丰度分析的强大方法。将会开发出新的 解决微生物组数据中的零通货膨胀、成分效应和相关性的方法。目标 2 重点关注 提高微生物组多重测试能力的策略。它提出了两种新的多重测试 程序，分别解决混杂因素和系统发育相关性。目标 3 建议 开发将微生物组数据与其他组学整合的成分典型相关分析方法 数据。具体来说，它将开发一个高效、灵活的框架来集成异构组学数据 利用微生物组数据，解释组成效应和系统发育相关性。目标4将发展 用户友好且高效的软件包，使社区能够从方法和方法中最大程度地受益 该应用带来的科学进步。所提出的方法将通过模拟进行评估， 更重要的是，应用于个体化中心正在进行的几项微生物组研究 梅奥诊所的医学。所提出的定量方法和开源软件包将 有助于微生物组生物标志物的发现和基于微生物组的机制研究。所有方法和 根据这笔赠款开发的工具将免费提供给感兴趣的研究人员和公众。