Novel statistical methods for controlled variable selection of microbiome data

项目摘要/摘要 科学界越来越认识到微生物群落的重要作用 在许多疾病和健康状况下发挥作用。微生物群落的结构(例如，相对的 不同分类群和微生物网络/相互作用的丰度)会因许多 环境和寄主因素。对微生物群如何相互作用的科学研究，以及他们的 环境和寄主之间的关系可以帮助我们更好地理解 与微生物组有关的疾病和健康状况。尽管研究兴趣之大令人难以置信 通过创新使用尖端技术(16S rRNA基因)获得海量数据 测序、鸟枪式元基因组测序和代谢组学)，统计工具仍然不足 可以完全处理微生物组数据的复杂性，包括高维、系统发育 相关性、相对较小的样本量、成分限制等。这项提议的主要目标是 开发统计功能强大且计算效率高的方法来解决分析中的这些挑战 微生物组数据。特别是，这项研究将应用于高通量微生物组数据，并导致 新的统计控制变量选择方法，1)选择真正 在预先指定的错误发现率(FDR)下与疾病相关结果相关联，其中 结果可以是感兴趣的单一疾病结果，也可以是多个变量，如多个继发性疾病 与疾病相关的表型；以及b)确定相关的类群和类群-代谢物相互作用 疾病转归低于某一FDR门槛。我们提出的方法是创新的，因为它可以 两者都选择重要的分类群特征或分类群-代谢物相互作用，并控制FDR，这 极大地提高了微生物组关联研究中发现结果的重复性和可靠性。 增强的分类群选择将进一步促进下游基于实验室的功能研究， 最终导致在预防、检测、治疗和监测许多健康方面的潜在改进 以及从微生物组的角度来看的疾病状况。这项提议的完成也将有助于弥合 在微生物组研究方面迅速增长的研究兴趣与缺乏分析工具之间的差距。在……里面 除了在同行评议的期刊上发表外，我们还将通过会议传播我们的成果 以及向更广泛的科学界免费提供的开放源码软件。建议的方法是 对于更好地理解微生物组机制及其与宿主基因组或 某些疾病的病理过程中的代谢体，这些疾病对人类健康至关重要。

Project Summary/Abstract The scientific community is increasingly appreciative of the important role that the microbiome community plays in many diseases and health conditions. The structure of the microbiome community (e.g., relative abundances of different taxa and microbial network/interactions) is subject to change in response to many environment and host factors. Scientific investigation of how microbiome interact with each other, with their environment and with their host can shed light on our understanding of the underlying biological mechanism of microbiome-related disease and health conditions. Despite the incredible amount of research interest and availability of massive data through the innovative use of cutting-edge techniques (16S rRNA gene sequencing, shotgun metagenomics sequencing and metabolomics), there are still insufficient statistical tools that can fully handle the complexity of microbiome data, including the high-dimensionality, phylogenetic relatedness, relatively small sample size, compositional constraint and others. The main goal of this proposal is to develop statistically powerful and computationally efficient methods to address these challenges in analyzing microbiome data. In particular, this research will be applied to high-throughput microbiome data and lead to new statistical controlled variable selection methods that 1) select a subgroup of taxa that are genuinely associated with disease-related outcomes under a pre-specified false discovery rate (FDR), where the outcomes can be either a single disease outcome of interest or multivariate such as multiple secondary phenotypes related to the disease; and b) identify taxa and taxa-metabolite interactions that are associated with a disease outcome under a certain FDR threshold. Our proposed methods are innovative in that it can both select important taxa features or taxa-metabolites interactions and have the FDR being controlled, which largely enhances the reproducibility and reliability of the discovery results in microbiome association studies. The enhanced taxa selection would further facilitate downstream laboratory-based functional studies, eventually leading to potential improvements in prevention, detection, treatment and monitoring of many health and disease conditions from a microbiome's perspective. Completion of this proposal will also help bridging the gap between the burgeoning research interest in microbiome studies and the lack of analytical tools. In addition to publication in peer-reviewed journals, we will make our results disseminated through conferences and open-source software that is freely available to the wider scientific community. The proposed methods are essential for improved understanding of microbiome mechanism along with its interaction with host genome or metabolome in the pathology of certain diseases, which are of central importance to human health.