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000181904 1001_ $$00000-0002-8362-9634$$aMarot-Lassauzaie, Valérie$$b0
000181904 245__ $$aTowards reliable quantification of cell state velocities.
000181904 260__ $$aSan Francisco, Calif.$$bPublic Library of Science$$c2022
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000181904 520__ $$aA 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.
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000181904 7001_ $$00000-0003-1259-5040$$aBouman, Brigitte Joanne$$b1
000181904 7001_ $$aDonaghy, Fearghal Declan$$b2
000181904 7001_ $$0P:(DE-He78)5dd8fe881be96755fdff6576c9f8c11f$$aDemerdash, Yasmin$$b3$$udkfz
000181904 7001_ $$0P:(DE-He78)ba3fae49054b6bfaaa289b05ecd936d6$$aEssers, Marieke$$b4$$udkfz
000181904 7001_ $$00000-0001-9280-9170$$aHaghverdi, Laleh$$b5
000181904 773__ $$0PERI:(DE-600)2193340-6$$a10.1371/journal.pcbi.1010031$$gVol. 18, no. 9, p. e1010031 -$$n9$$pe1010031 -$$tPLoS Computational Biology$$v18$$x1553-734X$$y2022
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