Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

Abstract

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.

Leveraging gene correlations in single cell transcriptomic data

“…Single cell RNA-sequencing (scRNAseq), along with the related method of single nucleus RNA-sequencing, now offers researchers unparalleled opportunities to interrogate cells as individuals. Methods have been developed to classify cell types; identify gene expression markers; infer lineages; learn gene regulatory relationships, and examine the effects of experimental manipulations on both levels of gene expression and cell type abundances (Das et al, 2022; Junttila et al, 2022; Nguyen et al, 2021; Simmons, 2022; Tam and Ho, 2020; Tritschler et al, 2019; Xie et al, 2021). Because scRNAseq data are noisy, reliable inference requires leveraging information across many cells, trading off sensitivity for statistical power.…”

A Framework for Comparison and Assessment of Synthetic RNA-Seq Data

“…In the case of scRNA-seq, one has to manage a large amount of expression data—the measurements can be for several thousands of genes for many individual cells, generated in a single experiment. Here, the analysis of DEGs is the main tool for the identification of gene markers for cell type detection and for inputs to secondary analyses, such as gene set analysis, gene network and pathways analysis [ 25 ]. An operational framework for the analysis of DEGs for scRNA-seq data is presented in [ 25 ], along with a classification and summary of the available methods for scRNA-seq data.…”

“…Here, the analysis of DEGs is the main tool for the identification of gene markers for cell type detection and for inputs to secondary analyses, such as gene set analysis, gene network and pathways analysis [ 25 ]. An operational framework for the analysis of DEGs for scRNA-seq data is presented in [ 25 ], along with a classification and summary of the available methods for scRNA-seq data.…”

A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data

“…scRNA-seq protocols can be classified into two categories, full-length transcript sequencing approaches and 3 -end or 5 -end transcript sequencing [ 2 ]. In addition, 3 -end or 5 -end transcript sequencing are known as unique molecular identifier (UMI) tag-based protocols, and full-length transcript sequencing is known as a non-UMI-based protocol [ 3 ]. UMI tag-based scRNA-seq protocols uses UMI tags for different transcript molecules.…”

“…Compared with UMI tag-based sequencing, the Non-UMI-based scRNA-seq protocols sequence whole transcripts [ 2 ]. Non-UMI-based protocols include Smart-seq2, MATQ-seq, and Fluidigm C1 [ 3 ].…”