Statistical methods for integrative analysis of multiple microbiome datasets

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。然而，在许多领域，由于样本量有限，研究结果往往不一致， 不同的研究人群(例如，不同的种族、性别、年龄)和技术可变性(例如，实验- TAL/分析管道)。例如，在艾滋病毒研究中，越来越多的证据表明肠道生物失调 有助于fl中的艾滋病毒相关。但是，对于它的特点还缺乏共识， 例如艾滋病毒感染是增加还是减少肠道中的微生物生物多样性，以及哪些分类群不同 艾滋病毒和艾滋病毒之间的关系。综合分析，它聚合了来自多项研究的信息，以增加 样本大小和提高功率，对于推动fi场向一致和可重现的方向发展是必要的。 有可能建议采取预防和治疗干预措施的报道。然而，这构成了严重的 由于研究之间的差异偏差和测量误差而带来的统计挑战。 这项建议目标是开发和验证统计方法，用于综合分析 可能使用不同的实验室和前处理程序生成的微生物组数据集。我们 将使用研究特定fic特性，例如研究群体、实验室和前处理管道， 并开发新的统计模型来表征微生物α(样本内)多样性、β多样性的变化 (样本间)多样性和丰富度(目标1)。我们将从微生物组质量的角度分析数据 控制项目，这是一项大型社区工作，通过多条管道对同一组样本进行排序， 旨在确定影响微生物组测序数据的技术变量，并以此为基础 确定如何最好地利用拟议方法中的信息(目标2)。 我们将把建议的方法应用于艾滋病毒微生物组重新分析项目，在该项目中，我们汇编了所有 可获得HIV肠道微生物组的16S rRNA基因测序数据，以进行全面评估。我们会 还将我们提出的方法应用于从环境研究所收集的多个队列的微生物组数据 在儿童健康结局(ECHO)中调查微生物群在影响儿童健康中的作用 儿童和青少年。我们预计，拟议的方法将对几乎所有领域产生广泛影响 微生物组研究，并为分析16S rRNA测序数据提供基础。

Project Abstract: Recent research has highlighted the importance of human associated microbiota in many diseases and health conditions. However, in many areas, results are often inconsistent across studies due limited sample sizes, heterogeneous study populations (e.g., different race, gender, age), and technical variability (e.g., experimen- tal/analysis pipelines). For example, in HIV studies there is increasing evidence suggesting that gut dysbiosis contributes to HIV-associated inflammation. However, there is still a lack of consensus on its characteristics, such as whether HIV infection increases or decreases the microbial biodiversity in the gut and which taxa differ between HIV+ and HIV-. Integrative analysis, which aggregates information from multiple studies to increase the sample sizes and boost power, is necessary to move the field forward toward consistent and reproducible dis- coveries with the potential of suggesting prophylactic and therapeutic intervention. This, however, poses serious statistical challenges due to the differential biases and measurement error between studies. The objective of this proposal is to develop and validate statistical methods for integrative analysis of multiple microbiome datasets that are potentially generated using different laboratory and pre-processing procedures. We will use the study-specific characteristics, such as study populations, laboratory and pre-processing pipelines, and develop novel statistical models for characterizing changes in microbial alpha (within-sample) diversity, beta (between-sample) diversity, and abundances (Aim 1). We will analyze the data from the microbiome quality control project, a large community effort that sequenced the same set of samples through multiple pipelines, designed to identify technical variables that impact the microbiome sequencing data, and use this as a basis to determine how to best use the information in the proposed methods (Aim 2). We will apply the proposed methods to the HIV microbiome re-analysis project, in which we have compiled all available 16s rRNA gene sequencing data for gut microbiome in HIV for a comprehensive evaluation. We will also apply our proposed methods to the microbiome data collected from multiple cohorts from the Environmental influences of child health outcomes (ECHO) to investigate the role of microbiome in impacting the health of children and adolescents. We expect that the proposed methods will have broad impact on almost all areas of microbiome research and provide a foundation for analyzing 16s rRNA sequencing data.