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Modeling and visualizing RNA velocity of single cells

Talk by Jean Fan (Johns Hopkins): Modeling and visualizing RNA velocity of single cells Single-cell transcriptomics provide a static snapshot of transcriptional states for individual cells. The continuum of transcriptional states for cells along dynamic processes can be used to infer how cell states may change over time. In this talk, I will discuss how RNA velocity analysis can be applied to infer dynamics of gene expression and predict the future transcriptional state of a cell from single cell RNA-sequencing and imaging data. Still, to interpret cell state changes from RNA velocity analysis, current approaches project the observed current and predicted future transcriptional states onto 2-dimensional embeddings to visualize the putative directed cellular trajectory. However, these approaches can yield different representations of the underlying trajectory. Furthermore, when intermediate cell states are not well represented, current embeddings may not capture global relationships between cell subpopulations, thereby further hindering the interpretation of cell state changes. I will further discuss alternative approaches my lab has developed called VeloViz to visualize cellular trajectories by directly taking into consideration cells’ predicted future transcriptional states inferred from RNA velocity analysis.