RNA Velocity generalized through dynamical modeling
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scVelo is a scalable toolkit for RNA velocity analysis in single cells, based on
Bergen et al. (Nature Biotech, 2020)_.
RNA velocity enables the recovery of directed dynamic information by leveraging splicing kinetics. scVelo generalizes the concept of RNA velocity (
La Manno et al., Nature, 2018_) by relaxing previously made assumptions with a stochastic and a dynamical model that solves the full transcriptional dynamics. It thereby adapts RNA velocity to widely varying specifications such as non-stationary populations.
scVelo is compatible with scanpy_ and hosts efficient implementations of all RNA velocity models.
scVelo's key applications ^^^^^^^^^^^^^^^^^^^^^^^^^ - estimate RNA velocity to study cellular dynamics. - identify putative driver genes and regimes of regulatory changes. - infer a latent time to reconstruct the temporal sequence of transcriptomic events. - estimate reaction rates of transcription, splicing and degradation. - use statistical tests, e.g., to detect different kinetics regimes.
scVelo has, for instance, recently been used to study immune response in COVID-19 patients and dynamic processes in human lung regeneration. Find out more in this list of
Latest news ^^^^^^^^^^^ - Feb/2021: scVelo goes multi-core - Dec/2020: Cover of
Nature Biotechnology_ - Nov/2020: Talk at
Single Cell Biology_ - Oct/2020:
Helmholtz Best Paper Award_ - Oct/2020: Map cell fates with
CellRank_ - Sep/2020: Talk at
Single Cell Omics_ - Aug/2020:
scVelo out in Nature Biotech_
Reference ^^^^^^^^^ Bergen et al. (2020), Generalizing RNA velocity to transient cell states through dynamical modeling,
Nature Biotech_. |dim|
Support ^^^^^^^ Found a bug or would like to see a feature implemented? Feel free to submit an
issue. Have a question or would like to start a new discussion? Head over to
GitHub discussions. In either case, you can also always send us an
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.. _scanpy: https://scanpy.readthedocs.io
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