Skip to main content

RNA velocity generalized through dynamical modeling

Project description

PyPI PyPIDownloads CI

scVelo - RNA velocity generalized through dynamical modeling

https://user-images.githubusercontent.com/31883718/67709134-a0989480-f9bd-11e9-8ae6-f6391f5d95a0.png

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

References

Manno et al. (2018), RNA velocity of single cells, Nature.

Bergen et al. (2020), Generalizing RNA velocity to transient cell states through dynamical modeling, Nature Biotech.

Bergen et al. (2021), RNA velocity - current challenges and future perspectives, Molecular Systems Biology.

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scvelo-0.2.4.tar.gz (198.1 kB view details)

Uploaded Source

Built Distribution

scvelo-0.2.4-py3-none-any.whl (196.3 kB view details)

Uploaded Python 3

File details

Details for the file scvelo-0.2.4.tar.gz.

File metadata

  • Download URL: scvelo-0.2.4.tar.gz
  • Upload date:
  • Size: 198.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/53.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.8.0

File hashes

Hashes for scvelo-0.2.4.tar.gz
Algorithm Hash digest
SHA256 ffacae961993df19034580ae748dc5bda12852e1da517b1f065ad2544850b040
MD5 810055136804463f19c7a6d74a9c79e3
BLAKE2b-256 89eea823eb9dc40db686c3354329dc548e364f87087c42dd72354d352f0b71c8

See more details on using hashes here.

File details

Details for the file scvelo-0.2.4-py3-none-any.whl.

File metadata

  • Download URL: scvelo-0.2.4-py3-none-any.whl
  • Upload date:
  • Size: 196.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/53.0.0 requests-toolbelt/0.9.1 tqdm/4.57.0 CPython/3.8.0

File hashes

Hashes for scvelo-0.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 80557f6266809d9199ab3a95bd36985af910f1b2cc36e21736c9e16756f82c5b
MD5 8c172076f72885ac22dfc273b19493af
BLAKE2b-256 4533cf283b7f39b941a6dfe60cd2db7cf172f5c1c0176d3382ebab91ac939576

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page