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

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 条件如今，标记基因扩增和鸟枪宏基因组测序（联合，MGS）已被广泛应用。 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， nity。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款 去调查尽管MGS数据分析越来越可用，但仍然很困难。除了经典的 MGS数据固有的统计挑战，如组成性，稀疏性，过度分散性和 分类群之间的系统发育关系，大规模MGS研究的特点是额外的并发症，包括 实验偏差和隐藏的伪影（批次效应），如果不考虑，将使下游分析无效 为正确。目前的分析方法在很大程度上忽略或不足以处理这些困难。 该提案旨在为可重复的微生物组发现开发强大而强大的统计方法 其针对未知的批次效应进行调整并且抵抗测序偏差。在目标1中，我们将开发一种新的 通过新的替代变量分析和多分位数搜索不可测量伪影的方法 阈值化我们的方法改进了现有的替代变量分析方法， MGS数据的特征，包括变异性差异、稀疏性和零变异性。在 目标2和3，我们开发了偏倚抗性模型，用于评估微生物组-表型关联和群落 层次分析我们还将开发、分发和支持用户友好的软件，用于建议的方法， 贝内对整个研究界都有好处。所提出的方法将进行评估，对广泛的模拟 和分析真实的微生物组数据，包括来自我们的动机研究的数据，如VAPing Observational 研究（VAPORS）和新罕布什尔州出生队列研究。成功完成本提案 将填补微生物组研究兴趣日益增加与缺乏强大和抗偏见能力之间的差距 工具，并促进我们深入了解健康和疾病中的人类微生物组。

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.