Metadata-Version: 2.1
Name: scvelo
Version: 0.2.2
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)
Requires-Dist: scanpy (>=1.5)
Requires-Dist: loompy (>=2.0.12)
Requires-Dist: umap-learn (>=0.3.10)
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|PyPI| |PyPIDownloads| |travis|

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.
The methods are based on our preprint
`Bergen et al. (2019) <https://doi.org/10.1101/820936>`_.

RNA velocity enables the recovery of directed dynamic information by leveraging splicing information.
scVelo generalizes the concept of RNA velocity (`La Manno et al., 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.

See `<https://scvelo.org>`_ for documentation and tutorials.

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.

Reference
---------
Bergen et al. (2019), *Generalizing RNA velocity to transient cell states through dynamical modeling*,
`biorxiv <https://doi.org/10.1101/820936>`_.

Support
-------
Feel free to submit an `issue <https://github.com/theislab/scvelo/issues/new/choose>`_
or send us an `email <mailto:mail@scvelo.org>`_.
Your help to improve scVelo is highly appreciated.


.. |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

.. |travis| image:: https://travis-ci.org/theislab/scvelo.svg?branch=master
   :target: https://travis-ci.org/theislab/scvelo

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

