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

项目摘要 驻留在人体上的细菌群落(微生物区系)与各种急性和非典型肺炎有关 慢性疾病和状况，如肥胖、炎症性肠道疾病、2型糖尿病、抑郁症、 和尿路感染，以及宿主对各种治疗和健康的反应 针对这些疾病和状况的干预措施。随着微生物区系扮演的关键角色已经成为 越来越多的人认识到，微生物组测序数据集现在经常在越来越多的情况下生成 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，并且 现代大数据面临的挑战，如高维和稀疏，也具有特点 微生物区系所特有的，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)样本和 样本之间的差异中的复杂结构。现代微生物组研究的有效分析需要 将这些重要特征纳入数据生成机制的新统计方法。 该项目的目标是开发一套符合以下条件的统计模型、方法、算法和软件 这种迫切的需要。最初的目标是开发用于模拟微生物组的多尺度概率框架 有效表征高维性、稀疏性和大量交叉样本的成分 微生物组测序数据的变异，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，同时在灵活性、分析简洁性和 计算可操纵性。另一个重点是开发微生物组组成的潜变量模型。 数据，以确定潜在结构，如样本群和物种亚群落。决赛 目标是生产用户友好的开放源码软件，实现所有建议的方法 微生物组测序数据分析。所有开发的模型和方法都由两个On-On-On提供信息- 正在进行皮马和他的团队的合作项目。一个是关于微生物群落的鉴定 与老年妇女尿路感染有关，另一篇是关于老年人微生物群纵向变化的研究 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试验床 发展。开发的模型、方法和软件不仅将导致对健康的更好预测 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不同的 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 患者的成分有助于改善健康状况。

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.