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Representation learning of RNA velocity reveals robust cell transitions.

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.

SUBMITTER: Qiao C

PROVIDER: S-EPMC8670433 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Representation learning of RNA velocity reveals robust cell transitions.

Qiao Chen C Huang Yuanhua Y

Proceedings of the National Academy of Sciences of the United States of America 20211201 49

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 re ...[more]

PMID: 34873054

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