Collaborative Research: New Statistical Methods for Microbiome Data Analysis

合作研究：微生物组数据分析的新统计方法

• 批准号: 2113360
• 负责人: Jun Chen
• 金额: $ 9.2万
• 依托单位: Mayo Clinic Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113360&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Statistical; Methods

The human microbiome, the collection of micro-organisms associated with the human body, has been increasingly recognized as an important player in human health and disease. Human microbiome research focuses on deciphering the intricate relationship between the microbiome and the host and identifying microbial biomarkers for disease prevention, diagnosis, and treatment. Current technologies to study the human microbiome involve sequencing the microbial DNA in the sample, upon which the identity and the abundance of the micro-organisms can be determined. Analysis of such microbiome sequencing data raises many statistical challenges. First, the data are zero-inflated. A typical microbiome dataset contains more than 75% zeros. Second, the data are compositional. The abundance change in one microbe will automatically lead to changes in the relative abundance of others, making identification of the "driver" microbe difficult. Third, the microbes are phylogenetically related. Closely related microbes usually share similar biological traits. Finally, the human microbiome is subject to many environmental confounders. Controlling these confounders is essential to make valid statistical inferences. The project will develop novel statistical methods for analyzing microbiome data addressing these challenges. The research results will be disseminated through scientific publications as well as seminar and conference presentations. The PIs will develop, distribute, document, and maintain R software packages via GitHub and CRAN for developed methods, and provide tutorials with example datasets. The PIs will test the software in real-world settings thoroughly. Given the popularity of the multi-omics approach to study the human microbiome, the delivered software packages will be of particular interest to microbiome investigators. The PIs will train students at the intersection of high-dimensional statistics, optimization, and genomics.The project has two research thrusts. In the first thrust, the PIs will develop a new statistical learning framework for microbiome data to simultaneously tackle the high-dimensionality, compositional effect, zero-inflation, and phylogenetic information. In particular, the new framework includes a novel zero imputation method based on a new Dirichlet mixture model, a general approach for handling compositional effect in supervised/unsupervised statistical learning, and a robust structure adaptive method to incorporate external information encoded in the phylogenetic tree. In the second thrust, the PIs will develop a two-dimensional false discovery rate (FDR) control procedure for powerful confounder adjustment in microbiome association analysis. The procedure uses the test statistics from the unadjusted analysis as auxiliary statistics to filter out a large number of irrelevant features, and false discovery rate control is then performed based on the test statistics from the adjusted analysis on the reduced set. The PIs will investigate both model-based and model-free approaches, and prove the asymptotic FDR control.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人体微生物组是与人体相关的微生物的集合，已越来越被认为是人类健康和疾病的重要参与者。 人类微生物组研究的重点是破译微生物组与宿主之间的复杂关系，并确定用于疾病预防，诊断和治疗的微生物生物标志物。 目前研究人类微生物组的技术涉及对样品中的微生物DNA进行测序，据此可以确定微生物的身份和丰度。 对这种微生物组测序数据的分析提出了许多统计挑战。首先，数据是零膨胀的。一个典型的微生物组数据集包含超过75%的零。第二，数据是组成性的。一种微生物的丰度变化会自动导致其他微生物相对丰度的变化，使得识别“驱动”微生物变得困难。第三，微生物是遗传相关的。密切相关的微生物通常具有相似的生物学特征。最后，人类微生物组受到许多环境因素的影响。控制这些混杂因素对于做出有效的统计推断至关重要。 该项目将开发新的统计方法来分析微生物组数据，以应对这些挑战。研究结果将通过科学出版物以及研讨会和会议介绍进行传播。PI将通过GitHub和CRAN开发，分发，记录和维护R软件包，并提供示例数据集的教程。PI将在真实环境中彻底测试软件。鉴于多组学方法研究人类微生物组的流行，所提供的软件包将对微生物组研究人员特别感兴趣。PI将培养学生在高维统计，优化和基因组学的交叉点。在第一个目标中，PI将为微生物组数据开发一个新的统计学习框架，以同时解决高维、组成效应、零通胀和系统发育信息。特别是，新的框架包括一个新的零插补方法的基础上，一个新的Dirichlet混合模型，一个一般的方法来处理监督/无监督统计学习中的成分效应，和一个强大的结构自适应方法，将外部信息编码的系统发育树。在第二个目标中，PI将开发二维错误发现率（FDR）控制程序，用于微生物组关联分析中的强大混杂因素调整。该过程使用来自未调整分析的测试统计量作为辅助统计量来过滤掉大量不相关的特征，然后基于来自缩减集上的调整分析的测试统计量来执行错误发现率控制。PI将研究基于模型和无模型的方法，并证明渐近FDR控制。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Benchmarking differential abundance analysis methods for correlated microbiome sequencing data

相关微生物组测序数据差异丰度分析方法的基准测试

• DOI: 10.1093/bib/bbac607
• 发表时间: 2023
• 期刊: Briefings in Bioinformatics
• 影响因子: 9.5
• 作者: Yang, Lu;Chen, Jun
• 通讯作者: Chen, Jun

dICC: distance-based intraclass correlation coefficient for metagenomic reproducibility studies

dICC：用于宏基因组重现性研究的基于距离的组内相关系数

• DOI: 10.1093/bioinformatics/btac618
• 发表时间: 2022
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Chen, Jun;Zhang, Xianyang;Schwartz, ed., Russell
• 通讯作者: Schwartz, ed., Russell

D-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies

• DOI: 10.1093/bioinformatics/btab498
• 发表时间: 2021-07-13
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Chen, Jun;Zhang, Xianyang
• 通讯作者: Zhang, Xianyang