Center for Quantitative Biology: A focus on "omics", from organisms to single cells

单细胞分析的最新进展使得能够对数千至数十万个个体细胞的DNA序列、RNA表达、甲基化和蛋白质丰度进行全基因组测量。捕获或推断空间或时间信息的方法提供了额外的上下文信息，以创建基因活性和功能的详细细胞水平图片。虽然全基因组单细胞分析为复杂组织的生物学提供了前所未有的见解，但由于大量的测试假设以及随之而来的低统计功效和难以解释，因此在逐个基因的基础上分析这些数据是非常具有挑战性的。虽然基因水平分析对于大量组织数据呈现出类似的问题，但是由于增加的噪声、膨胀的零计数和多模态分布，这些问题对于单细胞数据被放大。解决这些挑战的一种有希望的方法是基因集测试或途径分析。基因集测试最初是为批量组织数据开发的，是一种假设聚合方法，其利用关于基因之间的功能关系的先验知识来测试较少数量的假设，从而提高解释，复制和统计功效。通过结合一组或途径中所有基因的单细胞测量，基因组测试还可以降低方差并减轻稀缺性和多模型分布的影响。不幸的是，单细胞和批量基因组数据之间的统计学和生物学差异使其成为针对单细胞数据的批量组织开发的测试方法。尽管有定制技术的动机，但在开发专门用于单细胞数据的基因集测试方法或集合方面做的工作很少，对于一些重要的应用，如动态过程的分析，相关的方法还不存在。我们将通过开发一套针对单细胞基因表达数据特征优化的基因集测试方法，解决目前支持单细胞基因集分析的局限性。为了验证这些方法，我们将使用组织驻留记忆CD8 T细胞的发育和功能。

Recent advances in single cell analysis have enabled the genome-wide measurement of DNA sequence, RNA expression, methylation and protein abundance for thousands to hundreds-of-thousands of individual cells. Methods that capture or infer spatial or temporal information provide additional contextual information to create a detailed, cell-level picture of gene activity and function. Although genome-wide single cell profiling provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by- gene basis is extraordinarily challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. While a gene-level analysis presents similar problems for bulk tissue data, the issues are magnified for single cell date due to increased noise, inflated zero counts and multi-modal distributions. One promising approach or addressing these challenges is gene set testing, or pathway analysis. Originally developed for bulk tissue data, gene set testing is a hypothesis aggregation method that leverages prior knowledge regarding the functional relationships between genes to test a smaller number hypotheses and thereby improve interpretation, replication and statistical power. By combining the single cell measurements for all genes in a set or pathway, gene set testing can also decrease variance and mitigate the impact of scarcity and multi-model distributions. Unfortunately, statistical and biological differences between single cell and bulk genomic data make it testing methods developed for bulk tissue on single cell data. Despite the motivation for customized techniques, little work has been done to develop gene set testing methods or collections that are specialized for single cell data and , for some important applications like the analysis of dynamic processes, relevant methods do not yet exist. We will address the limitations of current support for gene set analysis of single celldata by developing a suite of gene set testing methods optimized for the characteristics of single cellgene expression data. To validate these methods, we will us with the development and function of tissue-resident memory CD8 T cells.