Journal
BMC BIOINFORMATICS
Volume 17, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12859-016-0944-6
Keywords
Single-cell RNA-seq; Differential gene expression; Stochastic gene expression; Software; Transcriptional bursting model
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Funding
- University of Cambridge BBSRC DTP
- Wellcome Trust
- Biotechnology and Biological Sciences Research Council [1497818] Funding Source: researchfish
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Background: 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. Results: We present a discrete, distributional method for differential gene expression ((DE)-E-3), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate (DE)-E-3, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since (DE)-E-3 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 (DE)-E-3 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. Conclusion: Evaluation using synthetic data shows that (DE)-E-3 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 (DE)-E-3 makes it possible to gain additional biological insights.
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