{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T17:49:20Z","timestamp":1777916960319,"version":"3.51.4"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2047611"],"award-info":[{"award-number":["2047611"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32-GM136577"],"award-info":[{"award-number":["T32-GM136577"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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 analysis 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 considering the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code is available on GitHub (https:\/\/github.com\/JEFworks-Lab\/veloviz) and Bioconductor (https:\/\/bioconductor.org\/packages\/veloviz) with additional tutorials at https:\/\/JEF.works\/veloviz\/. Datasets used can be found on Zenodo (https:\/\/doi.org\/10.5281\/zenodo.4632471).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab653","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T07:46:10Z","timestamp":1631087170000},"page":"391-396","source":"Crossref","is-referenced-by-count":21,"title":["VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6113-0082","authenticated-orcid":false,"given":"Lyla","family":"Atta","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD 21218, USA"},{"name":"Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University , Baltimore, MD 21211, USA"},{"name":"Medical Scientist Training Program, Johns Hopkins University School of Medicine , Baltimore, MD 21205, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0325-2073","authenticated-orcid":false,"given":"Arpan","family":"Sahoo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD 21218, USA"},{"name":"Department of Computer Science, Johns Hopkins University , Baltimore, MD 21218, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0212-5451","authenticated-orcid":false,"given":"Jean","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD 21218, USA"},{"name":"Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University , Baltimore, MD 21211, USA"},{"name":"Department of Computer Science, Johns Hopkins University , Baltimore, MD 21218, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"2023020201173907200_btab653-B1","year":"2020"},{"key":"2023020201173907200_btab653-B2","doi-asserted-by":"crossref","first-page":"dev.173849","DOI":"10.1242\/dev.173849","article-title":"Comprehensive single cell mRNA profiling reveals a detailed roadmap for pancreatic endocrinogenesis","volume":"146","author":"Bastidas-Ponce","year":"2019","journal-title":"Development"},{"key":"2023020201173907200_btab653-B3","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1038\/s41587-020-0591-3","article-title":"Generalizing RNA velocity to transient cell states through dynamical modeling","volume":"38","author":"Bergen","year":"2020","journal-title":"Nat. Biotechnol"},{"key":"2023020201173907200_btab653-B4","author":"Boggust","year":"2019"},{"key":"2023020201173907200_btab653-B5","doi-asserted-by":"crossref","first-page":"7426","DOI":"10.1073\/pnas.0500334102","article-title":"Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps","volume":"102","author":"Coifman","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020201173907200_btab653-B6","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1038\/nmeth.3734","article-title":"Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis","volume":"13","author":"Fan","year":"2016","journal-title":"Nat. Methods"},{"key":"2023020201173907200_btab653-B7","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/s12276-020-0422-0","article-title":"Single-cell transcriptomics in cancer: computational challenges and opportunities","volume":"52","author":"Fan","year":"2020","journal-title":"Exp. Mol. Med"},{"key":"2023020201173907200_btab653-B8","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1002\/spe.4380211102","article-title":"Graph drawing by force-directed placement","volume":"21","author":"Fruchterman","year":"1991","journal-title":"Softw. Pract. Exp"},{"key":"2023020201173907200_btab653-B9","doi-asserted-by":"crossref","first-page":"107576","DOI":"10.1016\/j.celrep.2020.107576","article-title":"A quantitative framework for evaluating single-cell data structure preservation by dimensionality reduction techniques","volume":"31","author":"Heiser","year":"2020","journal-title":"Cell Rep"},{"key":"2023020201173907200_btab653-B10","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1016\/j.celrep.2018.10.026","article-title":"The mammalian spermatogenesis single-cell transcriptome, from spermatogonial stem cells to spermatids","volume":"25","author":"Hermann","year":"2018","journal-title":"Cell Rep"},{"key":"2023020201173907200_btab653-B11","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.stem.2018.04.014","article-title":"Single-cell transcriptomics meets lineage tracing","volume":"23","author":"Kester","year":"2018","journal-title":"Cell Stem Cell"},{"key":"2023020201173907200_btab653-B12","doi-asserted-by":"crossref","first-page":"5416","DOI":"10.1038\/s41467-019-13056-x","article-title":"The art of using t-SNE for single-cell transcriptomics","volume":"10","author":"Kobak","year":"2019","journal-title":"Nat. Commun"},{"key":"2023020201173907200_btab653-B13","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1038\/nprot.2016.015","article-title":"Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons","volume":"11","author":"Krishnaswami","year":"2016","journal-title":"Nat. Protoc"},{"key":"2023020201173907200_btab653-B14","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41586-018-0414-6","article-title":"RNA velocity of single cells","volume":"560","author":"La Manno","year":"2018","journal-title":"Nature"},{"key":"2023020201173907200_btab653-B15","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"L\u00e4hnemann","year":"2020","journal-title":"Genome Biol"},{"key":"2023020201173907200_btab653-B16","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res"},{"key":"2023020201173907200_btab653-B17","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.coisb.2018.02.009","article-title":"Exploring intermediate cell states through the lens of single cells","volume":"9","author":"MacLean","year":"2018","journal-title":"Curr. Opin. Syst. Biol"},{"key":"2023020201173907200_btab653-B18","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: Uniform Manifold Approximation and Projection","volume":"3","author":"McInnes","year":"2018","journal-title":"J. Open Source Softw"},{"key":"2023020201173907200_btab653-B19","doi-asserted-by":"crossref","first-page":"5324","DOI":"10.1126\/science.aau5324","article-title":"Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region","volume":"362","author":"Moffitt","year":"2018","journal-title":"Science"},{"key":"2023020201173907200_btab653-B20","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41587-019-0071-9","article-title":"A comparison of single-cell trajectory inference methods","volume":"37","author":"Saelens","year":"2019","journal-title":"Nat. Biotechnol"},{"key":"2023020201173907200_btab653-B21","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1038\/s41591-020-0844-1","article-title":"A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors","volume":"26","author":"Slyper","year":"2020","journal-title":"Nat. Med"},{"key":"2023020201173907200_btab653-B22","doi-asserted-by":"crossref","first-page":"dev170506","DOI":"10.1242\/dev.170506","article-title":"Concepts and limitations for learning developmental trajectories from single cell genomics","volume":"146","author":"Tritschler","year":"2019","journal-title":"Development"},{"key":"2023020201173907200_btab653-B23","doi-asserted-by":"crossref","first-page":"eaah4573","DOI":"10.1126\/science.aah4573","article-title":"Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors","volume":"356","author":"Villani","year":"2017","journal-title":"Science"},{"key":"2023020201173907200_btab653-B24","doi-asserted-by":"crossref","first-page":"E2467","DOI":"10.1073\/pnas.1714723115","article-title":"Fundamental limits on dynamic inference from single-cell snapshots","volume":"115","author":"Weinreb","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020201173907200_btab653-B25","doi-asserted-by":"crossref","first-page":"19490","DOI":"10.1073\/pnas.1912459116","article-title":"Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression","volume":"116","author":"Xia","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020201173907200_btab653-B26","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.cell.2019.10.003","article-title":"Landscape and dynamics of single immune cells in hepatocellular carcinoma","volume":"179","author":"Zhang","year":"2019","journal-title":"Cell"},{"key":"2023020201173907200_btab653-B27","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1016\/j.celrep.2018.11.003","article-title":"Single-cell transcriptomics characterizes cell-types in the subventricular zone and uncovers molecular defects impairing adult neurogenesis","volume":"25","author":"Zywitza","year":"2018","journal-title":"Cell Rep"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab653\/40442334\/btab653.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/2\/391\/49007145\/btab653.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/2\/391\/49007145\/btab653.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T22:37:30Z","timestamp":1675291050000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/2\/391\/6368063"}},"subtitle":[],"editor":[{"given":"Anthony","family":"Mathelier","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,9,10]]},"references-count":27,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab653","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.01.28.425293","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1,15]]},"published":{"date-parts":[[2021,9,10]]}}}