Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United States of America.
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.
PLoS Comput Biol. 2022 Sep 12;18(9):e1010492. doi: 10.1371/journal.pcbi.1010492. eCollection 2022 Sep.
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.
我们对 RNA 速度方法进行了全面分析,旨在了解流行实现所基于的各种假设的适用性。除了提供基础数学的完整阐述外,我们还对生物数据集进行了模拟和控制实验,以评估工作流程对参数选择和基础生物学的敏感性。最后,我们主张对 RNA 速度采用更严格的方法,并提出了一种马尔可夫分析框架,为改进和缓解当前问题指明了方向。