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

Abstract

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.

Feature extraction approach in single-cell gene expression profiling for cell-type marker identification

“…This leads 110 to reduced statistical power when attempting to identify differentially expressed marker genes. 111 Comparisons of various differential expression analysis methods for single-cell RNA-seq data have 112 shown that they yield different results both in terms of the number of detected DEG and consistency 113 of DEG's identity across the methods (Miao & Zhang, 2016). Performing pairwise differential 114 expression analysis to identify cell-type marker genes in heterogeneous single-cell RNA-seq data is 115 6 inefficient in terms of execution time and redundant use, especially when considering several cell-116 types in the heterogeneous cell population.…”

DEsingle for detecting three types of differential expression in single-cell RNA-seq data

“…Differential expression (DE) analysis is to detect genes whose expression levels are significantly different between the compared groups of samples (11)(12)(13)(14)(15). It has been a key task in transcriptome study since the early days of microarrays.…”

“…It has been a key task in transcriptome study since the early days of microarrays. Traditional DE analysis methods for RNA-seq data were designed for bulk RNA sequencing (11,16,17), which needs millions of cells in one sample (6,18), and most of those DE analysis methods focus on the detection of DE genes by their mean expression levels (11,12,16,17,19).…”

“…One important difference is that there are much more zero values in scRNA-seq data than in bulk RNA-seq data (12).…”

“…The methods we compared with include traditional DE detection methods edgeR (16) and DEGseq (17) that were developed for bulk RNA-seq data but have been also used on many scRNA-seq data (12), as well as new methods specifically developed for scRNA-seq data including BPSC (34), D3E (19), monocle (35), SCDE (25). We applied DEsingle on a real scRNA-seq dataset of human preimplantation embryonic cells of different days of embryo development (36) and found interesting and 10% represent the observed data obtained from the original data after mRNA capture procedure.…”