Representation learning of RNA velocity reveals robust cell transitions

Abstract

AO_SCPLOWBSTRACTC_SCPLOWRNA velocity is a promising technique to reveal transient cellular dynamics among a heterogeneous cell population and quantify their transitions from single-cell transcriptome experiments. However, the cell transitions estimated from high dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present VeloAE, a tailored representation learning method to learn a low-dimensional representation of RNA velocity on which cell transitions can be robustly estimated. From various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture the expected cellular differentiation in different biological systems. VeloAE therefore enhances the usefulness of RNA velocity for studying a wide range of biological processes.