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Towards reliable quantification of cell state velocities

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 biolog...

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Hauptverfasser: Marot-Lassauzaie, Valérie (VerfasserIn) , Bouman, Brigitte J. (VerfasserIn) , Donaghy, Fearghal Declan (VerfasserIn) , Demerdash, Yasmin (VerfasserIn) , Essers, Marieke (VerfasserIn) , Haghverdi, Laleh (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: September 28, 2022
In: PLoS Computational Biology
Year: 2022, Jahrgang: 18, Heft: 9, Pages: 1-27
ISSN:1553-7358
DOI:10.1371/journal.pcbi.1010031
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1371/journal.pcbi.1010031
Verlag, lizenzpflichtig, Volltext: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010031
Volltext
Verfasserangaben:Valérie Marot-Lassauzaie, Brigitte Joanne Bouman, Fearghal Declan Donaghy, Yasmin Demerdash, Marieke Alida Gertruda Essers, Laleh Haghverdi
Beschreibung
Zusammenfassung: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 κ-velo and one heuristic method eco-velo, each of which uses a different set of assumptions about the data.
Beschreibung:Gesehen am 21.03.2023
Beschreibung:Online Resource
ISSN:1553-7358
DOI:10.1371/journal.pcbi.1010031

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