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区系在许多疾病和健康中的重要性。 条件。如今，标记基因扩增子和鸟枪式元基因组测序(简称MGS)已经成为 通常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。 坚持不懈。在公共领域，许多研究人员现在将MGS数据与其他数据一起存储，以供其他研究人员使用 去调查。尽管mgs数据分析的可用性越来越高，但它仍然很难受到fi的追捧。除了经典之外 MGS数据固有的统计挑战，如组合性、稀疏性、过度离散性和 分类群之间的系统发育关系，大规模的MGS研究具有额外的复杂性，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 对于正确的。目前的分析方法在很大程度上忽略或不恰当地处理这些不同的fifi。 这项提议旨在为可复制的微生物组发现开发强大而可靠的统计方法 对未知的批次效应进行调整，并对测序偏差具有抵抗力。在目标1中，我们将开发一部小说 一种新的代理变量分析和多分位数搜索未测量伪影的方法 门槛设定。我们的方法改进了现有的代理变量分析方法来解决特定的fi问题 MGS数据的特征包括变量的差异、稀疏性和fl变换的零点。在……里面 目标2和3，我们开发了用于评估微生物组-表型关联和群落的抗偏倚模型 水平分析。我们还将为建议的方法开发、分发和支持用户友好的软件，以 BENEfit整个研究界。建议的方法将通过大量的模拟进行评估。 以及对真实微生物组数据的分析，包括来自我们的激励性研究的数据，如蒸发观测 研究研究(VAPORS)和新汉普郡出生队列研究。成功完成本建议书 fi是否会消除对微生物组日益增长的研究兴趣与缺乏健壮和耐偏倚之间的差距 工具，并有助于我们深入了解人类微生物群在健康和疾病方面的作用。

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.