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Abstract:
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Single-cell RNA-seq enables the unprecedented interrogation of gene expression in single-cells. The stochastic nature of transcription is revealed in the bimodality of single-cell data, a feature shared across many single-cell platforms. I will present new methodology to analyze single-cell transcriptomic data that models this bimodality within a coherent generalized linear modeling framework. Our model permits direct inference on statistics formed by collections of genes, facilitating gene set enrichment analysis. The residuals defined by our model can be manipulated to interrogate cellular heterogeneity and gene-gene correlation across cells and conditions, providing insights into the temporal evolution of networks of co-expressed genes at the single-cell level. I will also discuss unwanted sources of variability in single-cell experiments and in particular the effect of the cellular detection rate defined as the fraction of genes turned on in a cell, and show how our model can account and adjust for such variability. Finally, I will illustrate this novel approach using several datasets that we have recently generated to characterize specific human immune cell subsets.
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