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 然而，在许多领域，由于样本量有限，研究结果往往不一致， 异质研究人群（例如，不同种族、性别、年龄）和技术变异性（例如，实验 例如，在艾滋病毒研究中，越来越多的证据表明肠道菌群失调。 然而，对其特征仍缺乏共识， 例如 HIV 感染是否会增加或减少肠道中的微生物多样性以及哪些分类群有所不同 HIV+ 和 HIV- 之间的综合分析，汇总多项研究的信息以提高 样本大小和增强功率，对于推动该领域朝着一致和可重复的方向发展是必要的 然而，这可能会提出预防性和治疗性干预。 由于研究之间的差异偏差和测量误差而导致统计挑战。 该提案的目的是开发和验证多种统计方法的综合分析 使用不同的实验室和预处理程序可能生成的微生物组数据集。 将使用研究特定的特征，例如研究人群、实验室和预处理管道， 并开发新的统计模型来表征微生物 α（样本内）多样性、β 的变化 （样本间）多样性和丰度（目标 1）。 控制项目，一个大型社区的努力，通过多个管道对同一组样本进行测序， 旨在识别影响微生物组测序数据的技术变量，并以此为基础 确定如何最好地使用所提出方法中的信息（目标 2）。 我们将把所提出的方法应用于艾滋病毒微生物组重新分析项目，在该项目中我们已经编制了所有 我们将利用 HIV 肠道微生物组的 16s rRNA 基因测序数据进行综合评估。 还将我们提出的方法应用于从环境研究的多个队列中收集的微生物组数据 儿童健康结果的影响 (ECHO)，以调查微生物组在影响儿童健康方面的作用 我们预计所提出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。