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开发新的定量方法， 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。的 微生物组，也被称为人类的第二基因组，在过去的几年里受到了广泛的关注。 由于其在人类健康和疾病中的关键作用，人类微生物组现在已被认为是一种重要的生物学机制。 这是个体化医疗方法的一个组成部分，因为它不仅考虑了个体间的差异， 在疾病的各个方面，而且也代表了一个潜在的可改变的因素， 治疗学尽管微生物组研究取得了这些富有成效和有希望的发现，但在 目前的领域，如如何适当地分析数据，更不用说优化和效率问题， 还有待解决。几个挑战构成了这一困境，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未知的相互作用， 数据的高维性，微生物类群的系统发育相关性， 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，方法工具箱中存在一些具体的差距，阻碍了微生物组数据分析的进展。 微生物生物医学科学为了填补这些空白，该提案旨在开发强大的 微生物组数据分析的定量方法和工具。具体而言，目标1侧重于开发强大的 以及复杂研究设计中差异丰度分析的强大方法。它将开发新的 解决零通货膨胀、组成效应和微生物组数据相关性的方法。目标2侧重于 提高微生物群范围内多重检测能力的策略。提出了两种新的多重检验方法 程序，分别解决混淆因素和系统发育相关性。目标3建议， 开发组合典型相关分析方法，用于将微生物组数据与其他组学数据整合 数据具体来说，它将开发一个高效且灵活的框架来集成异构组学数据 与微生物组数据，占成分效应和系统发育相关性。Aim 4将开发 用户友好和高效的软件包，使社区可以最大限度地受益于方法和 这一应用所带来的科学进步。所提出的方法将使用模拟进行评估， 更重要的是，应用于个性化中心正在进行的几项微生物组研究， 马约诊所的医学。拟议的定量方法和开放源码软件包将 有助于微生物生物标志物的发现和基于微生物的机制研究。所有方法和 利用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。