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RNA Velocity generalized through dynamical modeling

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scVelo - 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

application examples 

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
_ - 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 
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Support ^^^^^^^ Found a bug or would like to see a feature implemented? Feel free to submit an

. 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
. Your help to improve scVelo is highly appreciated. For further information visit 

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