Collaborative Research: Advanced statistical methods for single cell RNA sequencing studies

单细胞RNA测序已经成为基因组学中的有力工具，并已广泛用于 各种各样的应用，提供了前所未有的见解，许多基本的生物学问题， 以前很难解决。然而，分析scRNAseq数据面临重要的统计和 计算挑战，需要开发新的计算和统计方法。 主要挑战包括：（1）缺乏可靠的统计方法，可以控制隐藏的混杂因素 （2）缺乏准确的细胞亚群聚类方法， 定制scRNAseq研究;和（3）难以用scRNAseq鉴定功能性遗传变异 scRNAseq与其他遗传学研究整合的困难包括全基因组关联 问题研究我们提出的方法将解决这些挑战，并在以下方面是创新的：（1） 我们的方法控制隐藏的混杂效应桥梁之间的两个现有类别的统计 用于消除混淆效应的方法，因此预计在一系列 （2）我们的方法聚类细胞亚群提取聚类信息，从一个低维 因此，即使在使用scRNAseq数据的情况下， 原始高维基因表达矩阵是有噪声的;以及（3）我们用于识别等位基因的方法 单独使用scRNAseq数据的特异性/偏倚表达代表了第一次这样的尝试，我们的方法用于 将scRNAseq与GWAS整合也代表了首次此类尝试。我们提出的所有方法都是 针对scRNAseq数据量身定制，并将科普scRNAseq数据的复杂性和独特特征， 包括但不限于低覆盖率、计数性质和退出事件。我们将开发，分发， 并支持用户友好的开源软件实现我们的方法，以造福基因组学， 统计界。这里开发的统计方法将为开发类似的方法铺平道路 其他测序研究，包括亚硫酸氢盐测序和ATAC-seq研究。所提出的方法 对于理解组织组成的异质性和遗传结构是必不可少的， 复杂的性状和疾病-两者都是对人类健康至关重要的问题。

Single cell RNA sequencing has emerged as a powerful tool in genomics and has been used in a wide variety of applications, providing unprecedented insights into many basic biological questions that are previously difficult to address. However, analyzing scRNAseq data face important statistical and computational challenges that require the development of new computational and statistical methods. Key challenges include: (1) lack of robust statistical methods that can control for hidden confounding effects in a range of settings; (2) lack of accurate cell subpopulation clustering methods that are tailored to scRNAseq studies; and (3) difficulty in identifying functional genetic variations with scRNAseq alone and difficulty in integrating scRNAseq with other genetic studies include genome-wide association studies. Our proposed methods will address these challenges and are innovative in the following aspects: (1) our method for controlling for hidden confounding effects bridges between two existing classes of statistical methods for removing confounding effects and is thus expected to perform robustly across a range of scenarios; (2) our method for clustering cell subpopulations extracts clustering information from a lowdimensional representation of scRNAseq data and is thus expected to produce accurate results even when the original high-dimensional gene expression matrix is noisy; and (3) our method for identifying allele specific/biased expression using scRNAseq data alone represents the first such attempt and our method for integrating scRNAseq with GWASs also represents the first such attempt. All our proposed methods are tailored to scRNAseq data and will cope with the complexities and unique features of scRNAseq data, including, but not limited to, low-coverage, count nature, and drop-out events. We will develop, distribute, and support user-friendly open-source software implementing our methods to benefit the genomics and statistics community. The statistical methods developed here will pave ways for developing similar methods to other sequencing studies including bisulfite sequencing and ATAC-seq studies. The proposed methods are essential for understanding the heterogeneity of tissue compositions and the genetic architecture of complex traits and diseases - both are questions of central importance to human health.