Integrative clustering of cells and samples using multi-modal single-cell data

单细胞基因组技术，如单细胞rna-seq，已经成为一种强有力的量化技术。 单个细胞的分子状态，并可用于阐明复杂组织的细胞构建块 和疾病。鉴于最近单细胞技术的快速发展，新的统计和计算 需要使用多种方法来高效地分析具有多种数据类型的大规模单单元数据集，例如 基因和蛋白质的表达。离散贝叶斯分层模型已被广泛应用于无监督 对自然语言处理(NLP)等领域的离散数据类型进行建模。我们已经开发出一种 一种用于基因双聚类的贝叶斯分层模型--细胞潜狄里克莱特分配 变成模块，把细胞变成亚群。我们将开发能够执行细胞集群的新模型 使用多模式基因组数据将患者分成亚群，或使用单一- 细胞数据和患者级别的特征。这些新方法将以可伸缩和 计算性和非计算性用户均可访问的可解释的基于云的框架。目标 本研究的目的是(1)开发新的模型来执行多模式和多层次的综合聚类 单细胞数据，(2)开发R包和基于云的平台，具有Web界面，用于快速推理和 大规模数据集的可视化，以及(3)将CELDA模型应用于来自各种 生物学环境，包括癌症、肺发育和免疫学。总的来说，这些目标将是 由一个在计算生物学和生物信息学方面拥有丰富专业知识的跨学科团队完成， 生物统计学、计算机科学、分子和细胞生物学。

Single-cell genomic technologies such as single-cell RNA-seq have emerged as powerful techniques to quantify molecular states of individual cells and can be used to elucidate the cellular building blocks of complex tissues and diseases. Given recent rapid advances in single-cell technologies, novel statistical and computational approaches are needed to efficiently analyze large-scale single-cell datasets with multiple data types such as gene and protein expression. Discrete Bayesian hierarchical models have been widely used for unsupervised modeling of discrete data types in fields such as Nature Language Processing (NLP). We have developed a Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform bi-clustering of genes into modules and cells into subpopulations. We will develop novel models that can perform clustering of cells into subpopulations using multi-modal genomic data or clustering of patients into subgroups using both single- cell data and patient-level characteristics. These novel methods will be made available in a scalable and interpretable cloud-based framework accessible to both computational and non-computational users. The aims of this study are to (1) develop novel models to perform integrative multi-modal and multi-level clustering with single-cell data, (2) develop an R package and cloud-based platform with a web interface for rapid inference and visualization of large-scale datasets, and (3) apply Celda models to single-cell datasets from a variety of biological settings including cancer, lung development, and immunology. Overall, these aims will be accomplished by an interdisciplinary team with strong expertise in computational biology and bioinformatics, biostatistics, computer science, and molecular and cellular biology.