ArticleFrontiers in genetics2019
Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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50 citing papers in PubMed.
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- gSELECT: A novel pre-analysis machine-learning library enabling early hypothesis testing and predictive gene selection in single-cell data.Computational and structural biotechnology journal · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.Genome research · 2024Article
- Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression.Communications biology · 2024Article
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- Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression.bioRxiv : the preprint server for biology · 2024Article
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5 authors.
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Abstract
Detection of differentially expressed genes is a common task in single-cell RNA-seq (scRNA-seq) studies. Various methods based on both bulk-cell and single-cell approaches are in current use. Due to the unique distributional characteristics of single-cell data, it is important to compare these methods with rigorous statistical assessments. In this study, we assess the reproducibility of 9 tools for differential expression analysis in scRNA-seq data. These tools include four methods originally designed for scRNA-seq data, three popular methods originally developed for bulk-cell RNA-seq data but have been applied in scRNA-seq analysis, and two general statistical tests. Instead of comparing the performance across all genes, we compare the methods in terms of the rediscovery rates (RDRs) of top-ranked genes, separately for highly and lowly expressed genes. Three real and one simulated scRNA-seq data sets are used for the comparisons. The results indicate that some widely used methods, such as edgeR and monocle, have worse RDR performances compared to the other methods, especially for the top-ranked genes. For highly expressed genes, many bulk-cell-based methods can perform similarly to the methods designed for scRNA-seq data. But for the lowly expressed genes performance varies substantially; edgeR and monocle are too liberal and have poor control of false positives, while DESeq2 is too conservative and consequently loses sensitivity compared to the other methods. BPSC, Limma, DEsingle, MAST, t-test and Wilcoxon have similar performances in the real data sets. Overall, the scRNA-seq based method BPSC performs well against the other methods, particularly when there is a sufficient number of cells.
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