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: 10468838
• 负责人: Li Ma
• 金额: $ 34.63万
• 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/10468838
• 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）与各种急性和慢性炎症有关。 慢性疾病和病症，如肥胖症、炎症性肠病、2型糖尿病、抑郁症， 和尿路感染（UTIs），以及宿主对各种治疗和健康的反应 这些疾病和条件的干预措施。由于微生物群所起的关键作用已经成为 越来越多的人认识到，微生物组测序数据集现在是在越来越多的 复杂的实验设计和调查策略。虽然这些数据具有许多共同特征， 现代大数据的挑战，如高维性和稀疏性，它们也具有特征 微生物群特有的，包括（i）细菌物种之间的显性和潜在的上下文关系， 例如它们的进化和功能关系;以及（ii）样本之间的实质性异质性， 样品间变异的复杂结构。现代微生物组研究的有效分析需要 新的统计方法，将这些重要特征纳入数据生成机制。 该项目的目标是开发一套统计模型、方法、算法和软件， 这一迫切需要。最初的目标是开发一个多尺度概率框架来建模微生物组 组合物，有效地表征高维，稀疏，和大量的交叉样本 微生物组测序数据的变化，并结合了各种常见的实验设计，如 协变量、批次效应和多个时间点，同时在灵活性、分析简约性和 计算易处理性另一个重点是开发微生物组组成的潜在变量模型 用于识别潜在结构的数据，如样本集群和物种亚群落。最终 目标是制作用户友好的开源软件，实现所有提出的方法， 微生物组测序数据的分析。所有开发的模型和方法都是由两个- PI Ma和他的团队的合作项目。一个是关于微生物群落的鉴定 另一项研究是关于老年妇女的微生物组的纵向变化， 接受造血干细胞移植的癌症患者。这些研究将作为所有 发展开发的模型、方法和软件不仅可以更好地预测健康状况， 这些和其他微生物组研究的结果，也有助于破译微生物组在各种 疾病和生物医学过程，最终目标是对微生物组进行个性化干预 患者的组合物，以改善健康状况。