scVelo is a scalable toolkit for RNA velocity analysis in single cells, based on Bergen et al. (Nature Biotech, 2020).
RNA velocity enables directed dynamic information retrieval by taking advantage of splicing kinetics. scVelo generalizes the concept of RNA velocity (La Manno et al., Nature, 2018) by relaxing assumptions previously made with a stochastic and dynamic model that solves the complete transcriptional dynamics. Therefore, it adapts the speed of RNA to very diverse specifications, such as non-stationary populations.
scVelo supports scanpy and hosts efficient implementations of all RNA velocity models.
ScVelo Key Applications
estimate RNA velocity to study cell dynamics.
identify putative driver genes and regulatory change regimes.
infer a latent time to reconstruct the temporal sequence of transcriptomic events.
estimate the transcription, splicing and degradation reaction rates.
use statistical tests, for example, to detect different kinetic regimes.
scVelo, for example, has recently been used to study the immune response in COVID-19 patients and the dynamic processes in human lung regeneration. Learn more in this list of application examples.
Aug / 2021: publication of perspectives on MSB
February / 2021: scVelo goes multi-core
Dec / 2020: Cover of Nature Biotechnology
Nov / 2020: Talk at Single Cell Biology
Oct / 2020: Helmholtz Award for Best Article
Oct / 2020: Map of cell destinations with CellRank
Sep / 2020: Talk at Single Cell Omics
Aug / 2020: scVelo at Nature Biotech
La Manno et al. (2018), Single cell RNA velocity, Nature.
Bergen et al. (2020), Generalization of RNA velocity to transient cell states by dynamic modeling, Nature Biotech.
Bergen et al. (2021), RNA Velocity: Current Challenges and Future Prospects, Molecular Systems Biology.
Found an error or would you like to see a function implemented? Feel free to submit a problem. Do you have any questions or would you like to start a new discussion? Head over to the GitHub discussions. In any case, you can always email us. Your help in improving scVelo is greatly appreciated. For more information, visit scvelo.org.