Metadata-Version: 2.4
Name: cellrank
Version: 2.0.7
Summary: CellRank: dynamics from multi-view single-cell data
Author: Marius Lange, Michal Klein, Philipp Weiler
Maintainer-email: Michal Klein <info@cellrank.org>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://github.com/theislab/cellrank
Project-URL: Download, https://cellrank.readthedocs.io/en/latest/installation.html
Project-URL: Bug Tracker, https://github.com/theislab/cellrank/issues
Project-URL: Documentation, https://cellrank.readthedocs.io
Project-URL: Source Code, https://github.com/theislab/cellrank
Keywords: single-cell,bio-informatics,RNA velocity,Markov chain,GPCCA
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Typing :: Typed
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
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License-File: LICENSE
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Dynamic: license-file

|PyPI| |Downloads| |CI| |Docs| |Codecov| |Discourse|

CellRank 2: Unified fate mapping in multiview single-cell data
==============================================================
.. image:: docs/_static/img/light_mode_overview.png#gh-light-mode-only
    :width: 600px
    :align: center
    :class: only-light

.. image:: docs/_static/img/dark_mode_overview.png#gh-dark-mode-only
    :width: 600px
    :align: center

**CellRank** is a modular framework to study cellular dynamics based on Markov state modeling of
multi-view single-cell data. See our `documentation`_, and the `CellRank 1`_ and `CellRank 2 manuscript`_ to learn more.

.. important::
    Please refer to `our citation guide <https://github.com/theislab/cellrank/blob/main/docs/about/cite.rst>`_ to cite our software correctly.

CellRank scales to large cell numbers, is fully compatible with the `scverse`_ ecosystem, and easy to use.
In the backend, it is powered by `pyGPCCA`_ (`Reuter et al. (2018)`_). Feel
free to open an `issue`_ if you encounter a bug, need our help or just want to make a comment/suggestion.

CellRank's key applications
---------------------------
- Estimate differentiation direction based on a varied number of biological priors, including RNA velocity
  (`La Manno et al. (2018)`_, `Bergen et al. (2020)`_), any pseudotime or developmental potential,
  experimental time points, metabolic labels, and more.
- Compute initial, terminal and intermediate macrostates.
- Infer fate probabilities and driver genes.
- Visualize and cluster gene expression trends.
- ... and much more, check out our `documentation`_.

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

.. |Downloads| image:: https://static.pepy.tech/badge/cellrank
    :target: https://pepy.tech/project/cellrank
    :alt: Downloads

.. |Discourse| image:: https://img.shields.io/discourse/posts?color=yellow&logo=discourse&server=https%3A%2F%2Fdiscourse.scverse.org
    :target: https://discourse.scverse.org/c/ecosystem/cellrank/
    :alt: Discourse

.. |CI| image:: https://img.shields.io/github/actions/workflow/status/theislab/cellrank/test.yml?branch=main
    :target: https://github.com/theislab/cellrank/actions
    :alt: CI

.. |Docs|  image:: https://img.shields.io/readthedocs/cellrank
    :target: https://cellrank.readthedocs.io/
    :alt: Documentation

.. |Codecov| image:: https://codecov.io/gh/theislab/cellrank/branch/main/graph/badge.svg
    :target: https://codecov.io/gh/theislab/cellrank
    :alt: Coverage


.. _La Manno et al. (2018): https://doi.org/10.1038/s41586-018-0414-6
.. _Bergen et al. (2020): https://doi.org/10.1038/s41587-020-0591-3
.. _Reuter et al. (2018): https://doi.org/10.1021/acs.jctc.8b00079

.. _scverse: https://scverse.org/
.. _pyGPCCA: https://github.com/msmdev/pyGPCCA

.. _CellRank 1: https://www.nature.com/articles/s41592-021-01346-6
.. _CellRank 2 manuscript: https://doi.org/10.1038/s41592-024-02303-9
.. _documentation: https://cellrank.org

.. _issue: https://github.com/theislab/cellrank/issues/new/choose
