Many methods have been used to determine differential gene expression from single-cell RNA (scRNA)-seq data. We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed genes has important effects, particularly for some of the methods developed for bulk RNA-seq data analysis. However, we found that bulk RNA-seq analysis methods do not generally perform worse than those developed specifically for scRNA-seq. We also present conquer, a repository of consistently processed, analysis-ready public scRNA-seq data sets that is aimed at simplifying method evaluation and reanalysis of published results. Each data set provides abundance estimates for both genes and transcripts, as well as quality control and exploratory analysis reports.

已使用许多方法从单细胞RNA（scRNA）-seq数据确定差异基因表达。我们使用实验和合成数据评估了36种方法，发现被称为差异表达的基因的数量和特征存在相当大的差异。低表达基因的预过滤具有重要作用，特别是对于一些用于批量RNA-seq数据分析的方法。但是，我们发现大量的RNA-seq分析方法通常不会比专门针对scRNA-seq开发的方法表现更差。我们还介绍了conquer，这是一个经过持续处理的，可进行分析的公共scRNA-seq数据集的存储库，旨在简化方法评估和对已发表结果的重新分析。每个数据集都提供了基因和转录本的丰度估计，