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.
CITATION STYLE
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022). RNA velocity unraveled. PLoS Computational Biology, 18(9). https://doi.org/10.1371/journal.pcbi.1010492
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