Statistical Methods for Bulk-Tissue and Single-Cell Multi-Omics Integration

项目总结/摘要 单细胞测序避免了与传统的批量群体数据相关的平均伪影， 在过去的几年里，技术发展迅速。这提供了新的学习机会 基因组，转录组和表观基因组异质性在细胞水平上没有细胞类型的混淆，但它 还需要新的分析方法。此类基因组研究的一个主要挑战是缺乏严格的 整合整体组织和单细胞测序数据以及比对多模式单细胞组学的方法 数据我实验室的研究项目围绕着开发统计/计算方法， 生物信息学工具，以更好地利用和分析不同类型的下一代测序数据， 专注于检测结构变异，破译基因组和转录组异质性，并评估 通过单细胞组学方法研究细胞异质性。我们的长远目标是将出现的问题 从新的生物医学数据到统计界，并提供数据驱动的统计方法和开放的 为生物医学研究人员提供更好的数据分析和实验设计工具。具体来说，在未来 五年来，我们提出的研究计划将集中在以下相互关联的目标：（一）批量组学 由单细胞测序辅助的去卷积，随后是与临床变量的关联测试;（ii）联合 对批量基因组测序和单细胞转录组测序数据进行建模， 单细胞水平的DNA和RNA变异;以及（iii）单细胞组学数据的多模式比对。期间 在此期间，我们将继续与实验室合作，应用我们开发的方法来询问 在生物学和临床环境下的细胞异质性。我们将免费提供我们的方法 和开源R包，其中将包括广泛的教程和工作流程，可访问和有用的 to the biomedical生物医学research研究community社区.

PROJECT SUMMARY/ABSTRACT Single-cell sequencing circumvents the averaging artifacts associated with traditional bulk population data and has seen rapid technological developments over the past few years. This offers new opportunities to study genomic, transcriptomic, and epigenomic heterogeneity at the cellular level without cell type confounding, but it also requires novel analytical approaches. One major challenge in such genomic studies is the lack of rigorous methods for integrating bulk-tissue and single-cell sequencing data and for aligning multi-modal single-cell omics data. The research program of my lab centers around developing statistical/computational methods and bioinformatics tools to better utilize and analyze different types of next-generation sequencing data, with a special focus on detecting structural variants, deciphering genomic and transcriptomic heterogeneity, and assessing cellular heterogeneity by single-cell omics approaches. Our long-term vision is to introduce problems arising from new biomedical data to the statistics community and to provide data-driven statistical methods and open- source tools to biomedical researchers for better data analysis and experimental design. Specifically, in the next five years, our proposed program of research will focus on the following interconnected objectives: (i) bulk omics deconvolution aided by single-cell sequencing, followed by association testing with clinical variables; (ii) joint modeling of bulk genomic sequencing and single-cell transcriptomic sequencing data to simultaneously infer DNA and RNA variation at the single-cell level; and (iii) multi-modal alignment of single-cell omics data. During this period, we will keep collaborating with experimental labs, applying our developed methods to interrogate cellular heterogeneity under both biological and clinical settings. We will provide our methods as freely available and open-source R packages, which will include extensive tutorials and workflows that are accessible and useful to the biomedical research community.