{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:54:39Z","timestamp":1785333279109,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010031","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000}}],"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Max Delbr\u00fcck Center for Molecular Medicine"},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"crossref","award":["01ZX1911B"],"award-info":[{"award-number":["01ZX1911B"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Dietmar Hopp Foundation"},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["SFB873"],"award-info":[{"award-number":["SFB873"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    A few years ago, it was proposed to use the simultaneous quantification of unspliced and spliced messenger RNA (mRNA) to add a temporal dimension to high-throughput snapshots of single cell RNA sequencing data. This concept can yield additional insight into the transcriptional dynamics of the biological systems under study. However, current methods for inferring cell state velocities from such data (known as RNA velocities) are afflicted by several theoretical and computational problems, hindering realistic and reliable velocity estimation. We discuss these issues and propose new solutions for addressing some of the current challenges in consistency of data processing, velocity inference and visualisation. We translate our computational conclusion in two velocity analysis tools: one detailed method\n                    <jats:italic>\u03ba<\/jats:italic>\n                    -velo and one heuristic method eco-velo, each of which uses a different set of assumptions about the data.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010031","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T14:35:22Z","timestamp":1664375722000},"page":"e1010031","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":30,"title":["Towards reliable quantification of cell state velocities"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8362-9634","authenticated-orcid":true,"given":"Val\u00e9rie","family":"Marot-Lassauzaie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1259-5040","authenticated-orcid":true,"given":"Brigitte Joanne","family":"Bouman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fearghal Declan","family":"Donaghy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasmin","family":"Demerdash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marieke Alida Gertruda","family":"Essers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9280-9170","authenticated-orcid":true,"given":"Laleh","family":"Haghverdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"issue":"7719","key":"pcbi.1010031.ref001","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41586-018-0414-6","article-title":"RNA velocity of single cells","volume":"560","author":"G La Manno","year":"2018","journal-title":"Nature"},{"issue":"12","key":"pcbi.1010031.ref002","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":"V Bergen","year":"2020","journal-title":"Nature Biotechnology"},{"issue":"1","key":"pcbi.1010031.ref003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-1945-3","article-title":"Protein velocity and acceleration from single-cell multiomics experiments","volume":"21","author":"G Gorin","year":"2020","journal-title":"Genome biology"},{"key":"pcbi.1010031.ref004","article-title":"Single-cell multi-omic velocity infers dynamic and decoupled gene regulation","author":"C Li","year":"2021","journal-title":"bioRxiv"},{"key":"pcbi.1010031.ref005","first-page":"1","article-title":"CellRank for directed single-cell fate mapping","author":"M Lange","year":"2022","journal-title":"Nature methods"},{"issue":"1","key":"pcbi.1010031.ref006","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1186\/s13059-021-02414-y","article-title":"Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation","volume":"22","author":"M Barile","year":"2021","journal-title":"Genome Biol"},{"issue":"8","key":"pcbi.1010031.ref007","doi-asserted-by":"crossref","first-page":"e10282","DOI":"10.15252\/msb.202110282","article-title":"RNA velocity\u2014current challenges and future perspectives","volume":"17","author":"V Bergen","year":"2021","journal-title":"Molecular systems biology"},{"issue":"1","key":"pcbi.1010031.ref008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4208\/csiam-am.SO-2020-0001","article-title":"On the Mathematics of RNA Velocity I: Theoretical Analysis","volume":"2","author":"T Li","year":"2021","journal-title":"CSIAM Transactions on Applied Mathematics"},{"key":"pcbi.1010031.ref009","article-title":"RNA velocity unraveled","author":"G Gorin","year":"2022","journal-title":"bioRxiv"},{"key":"pcbi.1010031.ref010","article-title":"Pumping the brakes on RNA velocity\u2014understanding and interpreting RNA velocity estimates","author":"SC Zheng","year":"2022","journal-title":"bioRxiv"},{"key":"pcbi.1010031.ref011","unstructured":"Gu Y, Blaauw DT, Welch J. Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics. In: International Conference on Machine Learning. 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