Differential exon usage in single cell RNA-seq

项目摘要 选择性剪接产生的转录本在组织和细胞类型之间存在差异， 有助于细胞分化和器官发育。尽管它在发展中的重要性， 细胞身份，选择性剪接通常在单细胞RNA测序（scRNA-seq）中被忽略 分析.这是由于在从scRNA中鉴定可靠的选择性剪接事件方面的挑战- seq数据，其读取深度有限，并且由于 分子捕获效率、扩增偏差和过多的零计数或丢失。我们提出 一种基于短读段全长scRNA-seq数据推断差异剪接的新方法， 利用映射到相邻外显子的读段计数。此外，我们计划系统地评估 现有的方法来回答有关变量，合并和 离散估计最后，我们将通过比较来评估基于短读取的方法的准确性。 针对使用新型协议生成的scRNA-seq。拟议的工作将增加我们的 了解短读scRNA-seq数据的实用性和局限性，并提供更好的 用于scRNA-seq中的可变剪接分析的管道。

Project Summary Alternative splicing generates transcripts that vary between tissues and cell types, and contributes to cell differentiation and organ development. Despite its importance in development and cell identity, alternative splicing is usually neglected in single cell RNA sequencing (scRNA-seq) analysis. This is due to the challenge in identifying confident alternative splicing events from scRNA- seq data, which are limited in read-depth, and comes with large amount of noise due to variation in molecule capture efficiency, amplification bias, and excessive zero-counts or dropouts. We propose a novel approach to infer differential splicing based on short-read full-length scRNA-seq data, utilizing read counts mapping to adjacent exons. In addition, we plan to systematically evaluate existing approaches to answer questions on the strategy regarding the variable, pooling and dispersion estimates. Finally, we will assess the accuracy of short read based methods by comparing against scRNA-seq generated with new types of protocols. The work proposed will increase our understanding on the utility and limitations of short read scRNA-seq data, and provide a better pipeline for alternative splicing analysis in scRNA-seq.