Lyla Atta1,2,3, Arpan Sahoo1,4, Jean Fan1,2,4. 1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA. 2. Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, USA. 3. Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA. 4. Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
Abstract
MOTIVATION: Single cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. RESULTS: We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. AVAILABILITY: Source code is available on GitHub (http://github.com/JEFworks-Lab/veloviz) and Bioconductor (http://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Single cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. RESULTS: We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. AVAILABILITY: Source code is available on GitHub (http://github.com/JEFworks-Lab/veloviz) and Bioconductor (http://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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