Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website (https://scrnaseq-course.cog.sanger.ac.uk/website/index.html), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.

单细胞 RNA 测序 (scRNA-seq) 是一种流行且强大的技术，可让您分析大量单个细胞的整个转录组。然而，对这些实验产生的大量数据的分析需要专门的统计和计算方法。在这里，我们概述了处理 scRNA-seq 数据所涉及的计算工作流程。我们讨论了一些最常见的任务和可用于解决核心生物学问题的工具。在本文和我们的配套网站 (https://scrnaseq-course.cog.sanger.ac.uk/website/index.html) 中，我们提供了有关执行计算分析的最佳实践的指南。