A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies
Open accessAbstract
Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.
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Methods designed for bulk RNA-seq data perform favourably for differential gene expression analysis.
“but methods designed for bulk RNA-seq data perform favourably84–86.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [84]
The consensus and robustness between differential gene expression tools is low.
“The consensus and robustness between DGE tools is low83,84”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [84]
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Self-citationA Framework for Comparison and Assessment of Synthetic RNA-Seq Data
Shakola, F., Palejev, D., & Ivanov, I. V. (2022) · Genes · doi:10.3390/genes13122362 · Cited by 5 · see citing works
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Das, S., Pradhan, U. K., & Rai, S. N. (2022) · Current Bioinformatics · doi:10.2174/1574893617666220823114108 · Cited by 5 · see citing works
Self-citationBayesian-frequentist hybrid inference framework for single cell RNA-seq analyses
Han, G., Yan, D., Sun, Z., Fang, J., Chang, X., Wilson, L., & Liu, Y. (2024) · Human Genomics · doi:10.1186/s40246-024-00638-0 · Cited by 4 · see citing works
Immunopipe: a comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline
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A primer on single‐cell RNA ‐seq analysis using dendritic cells as a case study
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DeepExDC interprets genomic compartmentalization changes in single-cell Hi-C data
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Parallelizing RNA-Seq Analysis with BioSkel: A FastFlow Based Prototype
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The curses of performing differential expression analysis using single-cell dataPreprint
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Single-Cell RNA Sequencing Data Analysis
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GEVIS: A Workflow‐Driven Visual Analytics Approach to Differential Gene Expression Analysis
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- Authors
- Das, S., Rai, A., Merchant, M. L., Cave, M. C., & Rai, S. N.
- Affiliations
- Indian Agricultural Statistics Research Institute (IN)
- University of Louisville (US)
- Corresponding author
- Rai, Shesh N.
- Venue
- Genes
- Publisher
- MDPI AG
- Published
- 2021-12-02
- Volume
- 12
- Issue
- 12
- Pages
- 1947
- ISSN
- 2073-4425 (ISSN-L)
- PMC
- PMC8701051
- OpenAlex
- W3217753204
- Cited by
- 35
- References
- 83
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 10 · 2023: 7 · 2022: 6 · 2021: 1
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genes (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Indian Council of Agricultural Research (OM No. 18(02)/2016-EQR/Edn.)
- National Institutes of Health (5P20GM113226, PI: McClain; 1P42ES023716, PI: Srivastava; 5P30GM127607-02, PI: Jones; 1P20GM125504-01, PI: Lamont; 2U54HL120163, PI: Bhatnagar/Robertson; 1P20GM135004, PI: Yan; 1R35ES0238373-01, PI: Cave; 1R01ES029846, PI: Bhatnagar; 1R01ES027778-01A1, PI:)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- differential expression analysis, single-cell RNA sequencing, comparison of statistical methods, method performance evaluation, method selection, performance indicators
- MeSH terms
- RNA-Seq, Algorithms, Animals, Humans, Software, Sequence Analysis, RNA, Gene Expression Profiling, Databases, Nucleic Acid, Mice, Single-Cell Analysis
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