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Abstract:
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Recent technological breakthroughs have made it possible to measure gene expression at the single-cell level. These advances coupled with new computational algorithms allow us to better describe the types of single cells, characterize the stochasticity of gene expression across cells, and improve our understanding of cellular function in health and disease. A fundamental theme in RNA-seq is to detect genes with differential expression. However, current single-cell RNA-seq protocols are complex and introduce technical biases that vary across cells. Differential expression analysis in the context of single cells must not only account for technical noise, but also characterize changes in gene expression beyond a shift in mean. There is now ample evidence for gene transcription being a bursty process, with the two-state on/off bursting model having both empirical support and mathematical tractability. Here we present a method for differential expression analysis that can account for cell-specific technical bias, and detect and characterize changes specific to the transcriptional bursting process. We will show results from both simulation as well as real data analysis.
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