Representation learning of RNA velocity reveals robust cell transitions

Abstract

Significance The recently introduced RNA velocity methods, by leveraging the intrinsic RNA splicing process, have shown their unique capability of identifying the directionality of the cell differentiation trajectory. However, due to the minimal amount of unspliced RNA contents, the estimation of RNA velocity suffers from high noise and may result in less reliable trajectories. Here, we present Velocity Autoencoder (VeloAE), a tailored autoencoder to denoise RNA velocity for more accurate quantification of cell transitions. Through various biological systems, we demonstrate its effectiveness for correcting the inferred trajectory and its interpretability for linking the learned dimensions to underlying biological processes. RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. 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 Velocity Autoencoder (VeloAE), a tailored representation learning method, to learn a low-dimensional representation of RNA velocity on which cellular transitions can be robustly estimated. On various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture expected cellular differentiation in different biological systems. VeloAE, therefore, enhances the usefulness of RNA velocity for studying a wide range of biological processes.