Alessandrì Luca, Arigoni Maddalena, Calogero Raffaele
Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.
Methods Mol Biol. 2019;1979:425-432. doi: 10.1007/978-1-4939-9240-9_25.
Differential expression analysis is an important aspect of bulk RNA sequencing (RNAseq). A lot of tools are available, and among them DESeq2 and edgeR are widely used. Since single-cell RNA sequencing (scRNAseq) expression data are zero inflated, single-cell data are quite different from those generated by conventional bulk RNA sequencing. Comparative analysis of tools used to detect differentially expressed genes between two groups of single cells showed that edgeR with quasi-likelihood F-test (QLF) outperforms other methods.In bulk RNAseq, differential expression is mainly used to compare limited number of replicates of two or more biological conditions. However, scRNAseq differential expression analysis might be also instrumental to identify the main players of cells subpopulation organization, thus requiring the use of multiple comparisons tools. Nowadays, edgeR is one of the few tools that are able to handle both zero inflated matrices and multiple comparisons. Here, we provide a guide to the use of edgeR as a tool to detect differential expression in single-cell data.
差异表达分析是 bulk RNA 测序（RNAseq）的一个重要方面。有许多工具可供使用，其中 DESeq2 和 edgeR 被广泛应用。由于单细胞 RNA 测序（scRNAseq）表达数据存在零膨胀现象，单细胞数据与传统 bulk RNA 测序产生的数据有很大不同。对用于检测两组单细胞之间差异表达基因的工具进行比较分析表明，采用拟似然 F 检验（QLF）的 edgeR 优于其他方法。在 bulk RNAseq 中，差异表达主要用于比较两种或更多生物学条件下数量有限的重复样本。然而，scRNAseq 差异表达分析对于识别细胞亚群组织的主要参与者也可能有帮助，因此需要使用多重比较工具。如今，edgeR 是少数能够处理零膨胀矩阵和多重比较的工具之一。在此，我们提供一份关于使用 edgeR 作为检测单细胞数据中差异表达工具的指南。
