comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

 comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data 

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2599-6

In general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.

A few studies have compared differential expression analysis methods for scRNAseq data. Jaakkola et al. [40] compared five statistical analysis methods for scRNAseq data, three of which are for bulk RNAseq data analysis. Miao et al. [41] evaluated 14 differential expression analysis tools, three of which are newly developed for scRNAseq data and 11 of which are old methods for bulk RNAseq data. A recent comparison study [42] assessed six differential expression analysis tools, four of which were developed for scRNAseq and two of which were designed for bulk RNAseq. In this study, we consider all differential gene expression analysis tools that have been developed for scRNAseq data as of October 2018 (SCDE [21], MAST [29], scDD [39], D3E [33], Monocle2 [38], SINCERA [34], DEsingle [36], and SigEMD [37]). We also consider differential gene expression analysis tools that are designed for heterogeneous expression data (EMDomics [31]) and are commonly used for bulk RNAseq data (edgeR [4], DESeq2 [43]).

As of October 2018, we have identified eight software tools for differential expression analysis of scRNAseq data, which are designed specifically for such data [21, 29, 30, 33, 34, 36,37,38] (SCDE, MAST, scDD, D3E, Monocle2, SINCERA, DEsingle, and SigEMD).