Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data

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.