ArticleBMC bioinformatics2016
Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
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Who cites it
55 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
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics.Cell reports methods · 2026Article
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.Genome research · 2024Article
- Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE.Genome biology · 2024Article
- Single-cell transcriptomics: background, technologies, applications, and challenges.Molecular biology reports · 2024Review
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- scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.BMC bioinformatics · 2023Article
- Multiplatform modeling of atrial fibrillation identifies phospholamban as a central regulator of cardiac rhythm.Disease models & mechanisms · 2023Article
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Sensitive cluster-free differential expression testing.bioRxiv : the preprint server for biology · 2023Article
- Two-sample test with g-modeling and its applications.Statistics in medicine · 2023Article
- Expression quantitative trait locus studies in the era of single-cell omics.Frontiers in genetics · 2023Review
- Bulk and Single-Cell RNA-Seq Analyses for Studies of Spermatogonia.Methods in molecular biology (Clifton, N.J.) · 2023Article
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2 authors.
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Abstract
backgroundThe advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.
resultsWe present a discrete, distributional method for differential gene expression (D(3)E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D(3)E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D(3)E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D(3)E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.
conclusionEvaluation using synthetic data shows that D(3)E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D(3)E makes it possible to gain additional biological insights.
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