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Pumping the brakes on RNA velocity – understanding and interpreting RNA velocity estimates

View ORCID ProfileShijie C. Zheng, View ORCID ProfileGenevieve Stein-O’Brien, View ORCID ProfileLeandros Boukas, View ORCID ProfileLoyal A. Goff, View ORCID ProfileKasper D. Hansen
doi: https://doi.org/10.1101/2022.06.19.494717
Shijie C. Zheng
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
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Genevieve Stein-O’Brien
2Department of Genetic Medicine, Johns Hopkins School of Medicine
3Department of Neuroscience, Johns Hopkins School of Medicine
4Kavli Neurodiscovery Institute, Johns Hopkins University
5Quantitative Sciences Division, Department of Oncology, Johns Hopkins School of Medicine
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Leandros Boukas
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Department of Genetic Medicine, Johns Hopkins School of Medicine
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Loyal A. Goff
2Department of Genetic Medicine, Johns Hopkins School of Medicine
3Department of Neuroscience, Johns Hopkins School of Medicine
4Kavli Neurodiscovery Institute, Johns Hopkins University
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  • For correspondence: loyalgoff@jhmi.edu khansen@jhsph.edu
Kasper D. Hansen
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Department of Genetic Medicine, Johns Hopkins School of Medicine
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  • For correspondence: loyalgoff@jhmi.edu khansen@jhsph.edu
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ABSTRACT

RNA velocity analysis of single cells promises to predict temporal dynamics from gene expression. Indeed, in many systems, it has been observed that RNA velocity produces a vector field that qualitatively reflects known features of the system. Despite this observation, the limitations of RNA velocity estimates are poorly understood. Using real data and simulations, we dissect the impact of different steps in the RNA velocity workflow on the estimated vector field. We find that the process of mapping RNA velocity estimates into a low-dimensional representation, such as those produced by UMAP, has a large impact on the result. The RNA velocity vector field strongly depends on the k-NN graph of the data. This dependence leads to significant estimator errors when the k-NN graph is not a faithful representation of the true data structure, a feature that cannot be known for most real datasets. Finally, we establish that RNA velocity estimates expression speed neither at the gene nor cellular level. We propose that RNA velocity is best considered a smoothed interpolation of the observed k-NN structure, as opposed to an extrapolation of future cellular states, and that the use of RNA velocity as a validation of latent space embedding structures is circular.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Grammar errors have been addressed. Otherwise no changes.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 25, 2022.
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Pumping the brakes on RNA velocity – understanding and interpreting RNA velocity estimates
Shijie C. Zheng, Genevieve Stein-O’Brien, Leandros Boukas, Loyal A. Goff, Kasper D. Hansen
bioRxiv 2022.06.19.494717; doi: https://doi.org/10.1101/2022.06.19.494717
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Pumping the brakes on RNA velocity – understanding and interpreting RNA velocity estimates
Shijie C. Zheng, Genevieve Stein-O’Brien, Leandros Boukas, Loyal A. Goff, Kasper D. Hansen
bioRxiv 2022.06.19.494717; doi: https://doi.org/10.1101/2022.06.19.494717

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