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: 10708908
• 负责人: Ni Zhao
• 金额: $ 36.43万
• 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/10708908
• 关键词: Acceleration; 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; beta diversity; data tools; data visualization; design; detection method; 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 状况。如今，Marker-Gene Amplicon和Shotgun Metagenomics测序（共同，MGS）已是 经常用于流行病学和临床研究，以研究微生物组交流的健康影响 恩。在公共领域，许多研究人员现在将MGS数据与其他研究人员一起存放 调查。尽管越来越多，MGS数据分析仍然很困难。除了经典 MGS数据固有的统计挑战，例如构成性，稀疏性，过度分散和 大规模MGS研究之间的系统发育关系具有其他并发症，包括 实验偏见和隐藏的伪影（批处理效应），如果未考虑的话，下游分析将无效 适当。当前的分析方法在很大程度上忽略或不足地处理了这些困难。 该建议旨在为可重复的微生物组开发强大而强大的统计方法 调整未知的批处理效应，并且对测序偏差有抵抗力。在AIM 1中，我们将开发一本小说 通过新型的替代变量分析和多个分位数来搜索未衡量的伪影的方法 阈值。我们的方法推进了现有的替代变量分析方法，以解决特定的cally解决 MGS数据的特征，包括变异性，稀疏性和零通气的差异。在 目标2和3，我们开发了抗偏见的建模，用于评估微生物组 - 表型关联和社区 水平分析。我们还将开发，分发和支持用户友好的软件，以供提出的方法 受益于整个研究界。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，包括我们激励研究的数据，如观测 研究研究（蒸气）和新罕布什尔州出生队列研究。成功完成此建议 将填补对微生物组的研究兴趣日益增长的差距与缺乏强大和偏见的差距 工具，并促进我们对健康和疾病中人类微生物组的深入了解。