UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference

Mingze Gao, Chen Qiao and Yuanhua Huang ()
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Mingze Gao: University of Hong Kong
Chen Qiao: University of Hong Kong
Yuanhua Huang: University of Hong Kong

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract The recent breakthrough of single-cell RNA velocity methods brings attractive promises to reveal directed trajectory on cell differentiation, states transition and response to perturbations. However, the existing RNA velocity methods are often found to return erroneous results, partly due to model violation or lack of temporal regularization. Here, we present UniTVelo, a statistical framework of RNA velocity that models the dynamics of spliced and unspliced RNAs via flexible transcription activities. Uniquely, it also supports the inference of a unified latent time across the transcriptome. With ten datasets, we demonstrate that UniTVelo returns the expected trajectory in different biological systems, including hematopoietic differentiation and those even with weak kinetics or complex branches.

Date: 2022
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DOI: 10.1038/s41467-022-34188-7

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