Differential expression analyses for single‐cell RNA‐Seq: old questions on new data

Background Single‐cell RNA sequencing (scRNA‐seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA‐seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA‐Seq data on single‐cell data, and some new methods for scRNA‐seq data have also been developed. Bulk and single‐cell RNA‐seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA‐seq data is needed. Results In this study, we conducted a series of experiments on scRNA‐seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA‐seq data and new methods specifically designed for scRNA‐seq data. We obtained observations and recommendations for the methods under different situations. Conclusions DE analysis methods should be chosen for scRNA‐seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA‐seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA‐seq data.