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Discrete Distributional Differential Expression (D 3 E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data
Open accessPreprint — published as 10.1186/s12859-016-0944-6 · Paper page
Abstract
The 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. Here, we 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.
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Liu, Y., Zhao, J., Adams, T. S., Wang, N., Schupp, J. C., Wu, W., McDonough, J. E., Chupp, G. L., Kaminski, N., Wang, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24013075
Li, H., Zhu, B., Xu, Z., Adams, T. S., Kaminski, N., & Zhao, H. (2021) · Figshare · doi:10.6084/m9.figshare.16880159
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Missarova, A., Dann, E., Rosen, L. U., Satija, R., & Marioni, J. (2024) · Figshare · doi:10.6084/m9.figshare.26750678
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Missarova, A., Dann, E., Rosen, L. U., Satija, R., & Marioni, J. (2024) · Figshare · doi:10.6084/m9.figshare.26750690
Missarova, A., Dann, E., Rosen, L. U., Satija, R., & Marioni, J. (2024) · Figshare · doi:10.6084/m9.figshare.26750684
Missarova, A., Dann, E., Rosen, L. U., Satija, R., & Marioni, J. (2024) · Figshare · doi:10.6084/m9.figshare.26750672
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Duan, B., Zhang, Q., & Hao, P. (2026) · Mathematics · doi:10.3390/math14203786
Metadata
- Authors
- Delmans, M., & Hemberg, M.
- Affiliations
- University of Cambridge (GB)
- Wellcome Sanger Institute (GB)
- Corresponding author
- Delmans, Mihails
- Hemberg, Martin
- Venue
- BMC Bioinformatics
- Publisher
- openRxiv
- Published
- 2015-06-10
- Volume
- 17
- Issue
- 1
- Pages
- 110-110
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4772470
- OpenAlex
- W2950583356
- Cited by
- 111
- References
- 48
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9 · 2023: 15 · 2022: 17 · 2021: 25 · 2020: 10 · 2019: 7 · 2018: 12 · 2017: 8 · 2016: 2
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- University of Cambridge
- Biotechnology and Biological Sciences Research Council (1497818)
- 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 › Gene Regulatory Network Analysis
- Keywords
- differential gene expression, single-cell RNA sequencing, stochastic gene expression models, gene expression heterogeneity, burst size, burst frequency, synthetic data evaluation
- MeSH terms
- Algorithms, Humans, RNA, Statistical Distributions, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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