Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data

跨样本变异的统计建模和微生物组测序数据中潜在结构的学习

• 批准号: 10263932
• 负责人: Li Ma
• 金额: $ 34.69万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10263932
• 关键词: Acute Disease; Aging; Algorithms; Big Data; Cancer Patient; Characteristics; Chronic Disease; Complex; Computer software; Data; Data Set; Development; Disease; Effectiveness; Equilibrium; Experimental Designs; General Population; Goals; Health; Hematopoietic Stem Cell Transplantation; Heterogeneity; Human body; Immune response; Inflammatory Bowel Diseases; Intervention; Lead; Learning; Link; Longitudinal Studies; Malignant Neoplasms; Mental Depression; Methodology; Methods; Microbe; Modeling; Modernization; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Patients; Phylogenetic Analysis; Play; Process; Research; Role; Sampling; Statistical Data Interpretation; Statistical Models; Structure; Surveys; Testing; Time; Urinary tract infection; Variant; Woman; bacterial community; data sharing; design; flexibility; high dimensionality; human microbiota; improved; microbial; microbial community; microbiome; microbiome analysis; microbiome composition; microbiome research; microbiome sequencing; microbiota; open source; personalized intervention; software development; tool; user-friendly

PROJECT ABSTRACT The bacterial communities (microbiota) residing on the human body have been linked to a variety of acute and chronic diseases and conditions, such as obesity, inflammatory bowel disorders, Type 2 diabetes, depression, and urinary tract infections (UTIs), as well as to the host’s response to a variety of treatments and health interventions for these diseases and conditions. As the critical role played by the microbiota has become increasingly recognized, microbiome sequencing data sets are now routinely generated under ever more sophisticated experimental designs and survey strategies. While such data share many common features and challenges of modern big data, such as high-dimensionality and sparsity, they also possess characteristics peculiar to the microbiota, including (i) the explicit and latent contextual relationships among the bacterial species, such as their evolutionary and functional relationships; and (ii) the substantial heterogeneity across samples and complex structure in the sample-to-sample variation. Effective analysis of modern microbiome studies calls for new statistical methodology that incorporates these important characteristics in the data generative mechanism. This project’s objective is to develop a suite of statistical models, methods, algorithms, and software that meet this urgent need. An initial aim is to develop a multi-scale probabilistic framework for modeling microbiome compositions that effectively characterizes the high dimensionality, sparsity, and substantial cross-sample variation in microbiome sequencing data, and incorporates a variety of common experimental designs, such as covariates, batch effects, and multiple time points, while striking a balance in flexibility, analytical parsimony, and computational tractability. An additional focus is to develop latent variable models for microbiome compositional data for the purpose of identifying latent structures such as sample clusters and species subcommunities. A final aim is to produce user-friendly, open-source software that implements all of the proposed methods for the analysis of microbiome sequencing data. All of the models and methods developed are informed by two on- going collaborative projects of PI Ma and his team. One is on the identification of microbial communities associated with UTIs in aging women, and the other on the study of longitudinal changes in the microbiome of cancer patients undergoing hematopoietic stem cell transplantation. These studies will serve as testbeds for all development. The models, methods, and software developed will not only result in better prediction of the health outcomes in these and other microbiome studies but also help decipher the roles of microbiome in various diseases and biomedical processes, with the ultimate goal of personalized interventions on the microbiome compositions of patients to lead to improved health.

项目摘要 驻留在人体上的细菌群落（微生物群）与多种急性和 慢性疾病和疾病，例如肥胖，炎症性肠病，2型糖尿病，抑郁症， 和尿路感染（UTI），以及宿主对各种治疗和健康的反应 这些疾病和状况的干预措施。随着菌群的关键作用已成为 越来越认识的微生物组测序数据集现在常规生成更多 软化实验设计和调查策略。这些数据共享许多共同的功能，并且 现代大数据的挑战，例如高维和稀疏性，它们也具有特征 微生物群特有 例如他们的进化和功能关系； （ii）样品之间的实质异质性和 样品对样本变化中的复杂结构。现代微生物组研究的有效分析要求 新的统计方法将这些重要特征纳入数据通用机制。 该项目的目标是开发一套统计模型，方法，算法和软件 这种迫切的需求。最初的目的是开发一个多尺度的概率框架来建模微生物组 有效地表征高维度，稀疏性和实质跨样本的组成 微生物组测序数据的变化，并结合了各种常见的实验设计，例如 协变量，批处理效应和多个时间点，同时达到灵活性，分析简约和 计算障碍。另一个重点是开发微生物组组成的潜在变量模型 数据是为了识别潜在结构，例如样本簇和物种亚社区。决赛 目的是生产用户友好的开源软件，以实现所有提出的方法 分析微生物组测序数据。所有开发的模型和方法均由两个on- Pi Ma及其团队的合作项目。一个关于微生物群落的识别 与衰老妇女的UTI相关，另一个与研究的研究 接受造血干细胞移植的癌症患者。这些研究将作为所有人的测试床 发展。开发的模型，方法和软件不仅会更好地预测健康 这些和其他微生物组研究的结果，但也有助于破译微生物组在各种 疾病和生物医学过程，其最终目标是对微生物组的个性化干预措施 患者的组成可改善健康。