Proc Natl Acad Sci U S A. 2021 Dec 7;118(49):e2105859118. doi: 10.1073/pnas.2105859118.
ABSTRACT
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.
PMID:34873054 | DOI:10.1073/pnas.2105859118
