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VeloViz: RNA-velocity informed 2D embeddings for visualizing cellular trajectories

View ORCID ProfileLyla Atta, View ORCID ProfileJean Fan
doi: https://doi.org/10.1101/2021.01.28.425293
Lyla Atta
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
3Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Jean Fan
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
4Department of Computer Science, Johns Hopkins University, Baltimore MD 21218, USA
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  • For correspondence: jeanfan@jhu.edu
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0 Abstract

RNA velocity analysis can predict cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with 2D embeddings derived from principal components, t-distributed stochastic neighbor embedding, among others. However, these 2D embeddings can yield different representations of the underlying trajectories, hindering the interpretation of cell state changes. To address this challenge, we developed VeloViz to create RNA-velocity-informed 2D embeddings. We show that by taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help ensure a more reliable representation of underlying cellular trajectories. VeloViz is available as an R package at https://github.com/JEFworks-Lab/veloviz.

Competing Interest Statement

The authors have declared no competing interest.

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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-NC 4.0 International license.
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Posted January 28, 2021.
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VeloViz: RNA-velocity informed 2D embeddings for visualizing cellular trajectories
Lyla Atta, Jean Fan
bioRxiv 2021.01.28.425293; doi: https://doi.org/10.1101/2021.01.28.425293
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VeloViz: RNA-velocity informed 2D embeddings for visualizing cellular trajectories
Lyla Atta, Jean Fan
bioRxiv 2021.01.28.425293; doi: https://doi.org/10.1101/2021.01.28.425293

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