We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.
RNA velocity unraveled.
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作者:Gorin Gennady, Fang Meichen, Chari Tara, Pachter Lior
| 期刊: | PLoS Computational Biology | 影响因子: | 3.600 |
| 时间: | 2022 | 起止号: | 2022 Sep 12; 18(9):e1010492 |
| doi: | 10.1371/journal.pcbi.1010492 | ||
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