Metadata-Version: 2.1
Name: scvelo
Version: 0.2.3
Summary: RNA velocity generalized through dynamical modeling
Home-page: https://github.com/theislab/scvelo
Author: Volker Bergen
Author-email: volker.bergen@helmholtz-muenchen.de
License: BSD
Download-URL: https://github.com/theislab/scvelo
Keywords: RNA,velocity,single cell,transcriptomics,stochastic,dynamical
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.6
Requires-Dist: anndata (>=0.7.5)
Requires-Dist: scanpy (>=1.5)
Requires-Dist: loompy (>=2.0.12)
Requires-Dist: umap-learn (<0.5,>=0.3.10)
Requires-Dist: numpy (>=1.17)
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Provides-Extra: dev
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|PyPI| |PyPIDownloads| |CI|

scVelo - RNA velocity generalized through dynamical modeling
============================================================

.. image:: https://user-images.githubusercontent.com/31883718/67709134-a0989480-f9bd-11e9-8ae6-f6391f5d95a0.png
   :width: 300px
   :align: left

**scVelo** is a scalable toolkit for RNA velocity analysis in single cells, based on
`Bergen et al. (Nature Biotech, 2020) <https://doi.org/10.1038/s41587-020-0591-3>`_.

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 <https://doi.org/10.1038/s41586-018-0414-6>`_)
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 <https://scholar.google.com/scholar?cites=18195185735875895912>`_.

Latest news
^^^^^^^^^^^
- Feb/2021: scVelo goes multi-core
- Dec/2020: Cover of `Nature Biotechnology <https://www.nature.com/nbt/volumes/38>`_
- Nov/2020: Talk at `Single Cell Biology <https://coursesandconferences.wellcomegenomecampus.org/our-events/single-cell-biology-2020/>`_
- Oct/2020: `Helmholtz Best Paper Award <https://twitter.com/ICBmunich/status/1318611467722199041>`_
- Oct/2020: Map cell fates with `CellRank <https://cellrank.org>`_
- Sep/2020: Talk at `Single Cell Omics <https://twitter.com/fabian_theis/status/1305621028056465412>`_
- Aug/2020: `scVelo out in Nature Biotech <https://www.helmholtz-muenchen.de/en/aktuelles/latest-news/press-information-news/article/48658/index.html>`_

Reference
^^^^^^^^^
Bergen *et al.* (2020), Generalizing RNA velocity to transient cell states through dynamical modeling,
`Nature Biotech <https://doi.org/10.1038/s41587-020-0591-3>`_.

Support
^^^^^^^
Found a bug or would like to see a feature implemented? Feel free to submit an
`issue <https://github.com/theislab/scvelo/issues/new/choose>`_.
Have a question or would like to start a new discussion? Head over to
`GitHub discussions <https://github.com/theislab/scvelo/discussions>`_.
In either case, you can also always send us an `email <mailto:mail@scvelo.org>`_.
Your help to improve scVelo is highly appreciated.
For further information visit `scvelo.org <https://scvelo.org>`_.


.. |PyPI| image:: https://img.shields.io/pypi/v/scvelo.svg
   :target: https://pypi.org/project/scvelo

.. |PyPIDownloads| image:: https://pepy.tech/badge/scvelo
   :target: https://pepy.tech/project/scvelo

.. |Docs| image:: https://readthedocs.org/projects/scvelo/badge/?version=latest
   :target: https://scvelo.readthedocs.io

.. |CI| image:: https://img.shields.io/github/workflow/status/theislab/scvelo/CI/master
   :target: https://github.com/theislab/scvelo/actions?query=workflow%3ACI

.. _scanpy: https://scanpy.readthedocs.io

