Statistical methods for analyzing messy microbiome data: detection of hidden artifacts and robust modeling approaches

分析杂乱微生物组数据的统计方法：隐藏伪影的检测和稳健的建模方法

• 批准号: 10503637
• 负责人: Ni Zhao
• 金额: $ 38.02万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-23 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10503637
• 关键词: Address; Algorithms; Birth; Cations; Cells; Characteristics; Clinical Research; Cohort Studies; Collection; Communities; Complex; Computer software; DNA; Data; Data Analyses; Data Set; Deposition; Detection; Disease; Etiology; Evaluation; Failure; Foundations; Genes; Health; Human; Human Microbiome; Investigation; Methods; Modeling; Morphologic artifacts; New Hampshire; Observational Study; Odds Ratio; Performance; Personal Satisfaction; Phenotype; Phylogenetic Analysis; Play; Prevention strategy; Procedures; Process; Protocols documentation; Public Domains; Reproducibility; Research; Research Personnel; Resistance; Role; Sampling; Shotguns; Statistical Methods; Structure; Survival Analysis; Taxon; Taxonomy; Testing; Time; Work; analytical method; bacterial community; base; beta diversity; data tools; data visualization; design; disorder risk; epidemiology study; experimental study; high dimensionality; human microbiota; improved; interest; metagenomic sequencing; microbial; microbiome; microbiome analysis; microbiome research; microbiome sequencing; microorganism; novel; novel strategies; open source; research study; semiparametric; simulation; tool; transcriptome sequencing; treatment strategy; trustworthiness; user friendly software; vaping

Project Abstract: Recent research has highlighted the importance of human associated microbiota in many diseases and health conditions. Nowadays marker-gene amplicon and shotgun metagenomics sequencing (jointly, MGS) have been routinely used in epidemiological and clinical studies to investigate the health impact of the microbiome commu- nity. In the public domain, many researchers now deposit MGS data together with other data for other researchers to investigate. Despite being increasingly available, MGS data analysis remains difficult. In addition to the classic statistical challenges inherent to MGS data such as the compositionality, the sparsity, the over dispersion and the phylogenetic relationship between taxa, large scale MGS studies feature additional complications including the experimental bias and hidden artifacts (batch effects), which will invalidate downstream analysis if not accounted for properly. Current analytic approaches largely ignore or insufficiently handle these difficulties. This proposal aims to develop powerful and robust statistical methods for reproducible microbiome discoveries that adjust for unknown batch effects and are resistant to sequencing biases. In aim 1, we will develop a novel approach to search for unmeasured artifacts through a novel surrogate variable analysis and multiple quantile thresholding. Our approach advances the existing surrogate variable analysis approach to specifically address the characteristics of MGS data including the differences in variabilities, the sparsity and the zero inflation. In aims 2 & 3, we develop bias resistant modeling for assessing microbiome-phenotype association and community level analysis. We will also develop, distribute and support user-friendly software for the proposed methods to benefit the entire research community. The proposed methods will be evaluated against extensive simulations and analysis of real microbiome data including data from our motivating studies as in VAPing Observational Research Study (VAPORS) and the New Hampshire birth cohort study. Successful completion of this proposal will fill the gap between the increasing research interest in microbiome and the lack of robust and bias-resistant tools, and facilitate our in-depth understanding of human microbiome in health and disease.

项目摘要： 最近的研究强调了人类相关微生物群在许多疾病和健康中的重要性 如今，标记基因扩增子和鸟枪法宏基因组测序（联合称为 MGS）已成为主流。 常规用于流行病学和临床研究，以调查微生物群落对健康的影响 在公共领域，许多研究人员现在将 MGS 数据与其他研究人员的其他数据一起存放 尽管越来越可用，MGS 数据分析仍然很困难。 MGS 数据固有的统计挑战，例如组合性、稀疏性、过度分散性和 类群之间的系统发育关系，大规模 MG​​S 研究具有额外的并发症，包括 实验偏差和隐藏的人为因素（批次效应），如果不考虑这些因素将使下游分析无效 目前的分析方法很大程度上忽视或没有充分处理这些困难。 该提案旨在开发强大而稳健的统计方法来实现可重复的微生物组发现 调整未知批次效应并抵抗测序偏差 在目标 1 中，我们将开发一种新颖的方法。 通过新颖的替代变量分析和多分位数来搜索未测量的伪影的方法 我们的方法改进了现有的替代变量分析方法，以专门解决问题。 MGS数据的特征包括变量差异、稀疏性和零通胀。 目标 2 和 3，我们开发抗偏差模型来评估微生物组-表型关联和群落 我们还将为所提出的方法开发、分发和支持用户友好的软件。 所提出的方法将根据广泛的模拟进行评估。 以及对真实微生物组数据的分析，包括来自我们的 VAPing 观察性研究的数据 研究（VAPORS）和新罕布什尔州出生队列研究成功完成了该提案。 将填补对微生物组日益增长的研究兴趣与缺乏稳健和抗偏倚之间的空白 工具，并促进我们深入了解健康和疾病中的人类微生物组。