Statistical methods for integrative analysis of multiple microbiome datasets

多个微生物组数据集综合分析的统计方法

• 批准号: 10380772
• 负责人: Ni Zhao
• 金额: $ 20.31万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10380772
• 关键词: 16S ribosomal RNA sequencing; Address; Adolescent; Age; Area; Binomial Distribution; Biodiversity; Bioinformatics; Characteristics; Child Health; Communities; Consensus; DNA; Data; Data Set; Disease; Evaluation; Foundations; Future; Gender; HIV; HIV Infections; Health; Heterogeneity; Human; Individual; Inflammation; Laboratories; Literature; Measurement; Medical Research; Methodology; Methods; Modeling; Outcome; Performance; Phenotype; Phylogenetic Analysis; Prevention; Prevention strategy; Procedures; Protocols documentation; Public Health; Quality Control; Race; Recommendation; Reporting; Reproducibility; Research; Role; Sample Size; Sampling; Side; Statistical Methods; Statistical Models; Structure; Taxon; Testing; Therapeutic Intervention; Work; analysis pipeline; base; beta diversity; cohort; design; experimental analysis; gut dysbiosis; gut microbiome; human microbiota; improved; interest; microbial; microbiome; microbiome analysis; microbiome research; microbiome sequencing; multilevel analysis; novel; prophylactic; simulation; study population; tool; treatment strategy

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.

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 但是，在许多领域，由于样本量有限，结果通常是不一致的 异构研究人群（例如不同的种族，性别，年龄）和技术变异性（例如，实验 - TAL/分析管道），例如，在HIV研究中，有越来越多的证据表明 有助于艾滋病毒相关的炎症。 例如，艾滋病毒是增加还是减少肠道中的微生物生物多样性以及哪个分类单元不同 在HIV+和HIV - 综合分析之间 样本量和增强功率是将领域向前迈向一致且可重复的不良功能所必需的 覆盖物具有暗示预防性和治疗性干预的潜力。 研究之间的统计挑战和研究之间的测量误差引起的。 该提案的目的是开发和验证统计方法以集成多个 潜在使用不同的实验室和处理程序生成的微生物组数据集 将使用特殊特征的特征，例如研究人群，实验室和预处理管道， 并开发新的统计模型，以表征微生物α（样本内）多样性的变化，beta （样本之间）多样性和丰度（AIM 1）。 控制项目是一项巨大的社区努力。 旨在确定影响微生物组测序数据的技术变量，并用作 确定如何最好地使用支撑方法中的信息（AIM 2）。 我们将将支撑方法应用于Hib微生物组重新分析项目，其中我们已编译所有分析 可用的16S rRNA基因测序数据用于HIV中的肠道微生物组，以进行全面评估 还将我们的支撑方法应用于从环境中从多个队列中收集的微生物组数据 对儿童健康结果的影响（ECHO）调查微生物组在影响健康中的作用 儿童和青少年，我们期望拟议的方法几乎对 微生物组研究并为分析16S rRNA测序数据提供了基础。