ArticleBMC bioinformatics2019
Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers, 2 of them syntheses that pooled it.
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Who cites it
155 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.Frontiers in genetics · 2022Pooled it
- Phase 2 trial (NCI-COTC030) of adjuvant inhaled recombinant human IL-15 combined with amputation and adjuvant chemotherapy in dogs with appendicular osteosarcoma.Frontiers in immunology · 2025Trial
- Induction of DNA-mediated immune responses by bacterial extracellular vesicles results in control of murine norovirus infection.Gut microbes · 2026Article
- Complementary ROC-Derived Indices for Screening Improper Expression Profiles in RNA-Seq Differential Expression Analysis.Balkan medical journal · 2026Article
- Benchmarking sketching methods on spatial transcriptomics data.Nucleic acids research · 2026Article
- BiGER: Bayesian rank aggregation in genomics with extended ranking schemes.Nature communications · 2026Article
- Structure-preserving multivariate hypothesis testing for mass spectrometry imaging and single-cell data.Bioinformatics (Oxford, England) · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.Nature machine intelligence · 2026Article
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- From bulk RNA sequencing to spatial transcriptomics: a comparative review of differential gene expression analysis methods.Human genomics · 2025Review
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- scMetaIntegrator: a meta-analysis approach to paired single-cell differential expression analysis.bioRxiv : the preprint server for biology · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- SciGeneX: enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics data.NAR genomics and bioinformatics · 2025Article
- Human endothelial cells promote a human neural stem cell type B phenotype via Notch signaling.Nature communications · 2025Article
- scMUSCL: multi-source transfer learning for clustering scRNA-seq data.Bioinformatics (Oxford, England) · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
95 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.
resultsIn this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.
conclusionsIn 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.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.