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. 2022 Nov 3;13:6586. doi: 10.1038/s41467-022-34188-7

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — a UMAP coordinates of the velocity field shown in streamlines (left) and predicted latent time (right) from UniTVelo. b Estimated RNA velocity field in streamlines by scVelo. c Cd44, Celf2, and Taok3 are selected as examples, illustrating UniTVelo could accurately capture cell states and directionality whilst scVelo failed to capture the relative cell states using almond shape phase portraits. Upper panel: Regression result by scVelo. Lower panel: Inferred cell state by UniTVelo, gray and black line demonstrates part of induction and repression phase of RBF kernel respectively and the black part is used by the model. Red arrows show the correct directionality of cellular dynamics. d Heatmaps of predicted induction and repression gene expressions are resolved along the inferred cell time, showing a clear separation in temporal space. Entities are smoothed spliced counts.