Metadata-Version: 2.1
Name: cellrank
Version: 1.1.0
Summary: CellRank - Probabilistic Fate Mapping using RNA Velocity
Home-page: https://github.com/theislab/cellrank
Author: Marius Lange, Michal Klein
Author-email: info@cellrank.org
Maintainer: Marius Lange, Michal Klein
Maintainer-email: info@cellrank.org
License: BSD
Download-URL: https://github.com/theislab/cellrank
Project-URL: Documentation, https://cellrank.readthedocs.io/en/latest
Project-URL: Source Code, https://github.com/theislab/cellrank
Description: |PyPI| |Bioconda| |Downloads| |Travis| |Notebooks| |Docs| |Codecov|
        
        
        CellRank - Probabilistic Fate Mapping using RNA Velocity
        ========================================================
        
        .. image:: https://raw.githubusercontent.com/theislab/cellrank/master/resources/images/cellrank_fate_map.png
           :width: 600px
           :align: center
        
        **CellRank** is a toolkit to uncover cellular dynamics based on scRNA-seq data with RNA velocity annotation,
        see `La Manno et al. (2018)`_ and `Bergen et al. (2020)`_. In short, CellRank models cellular dynamics as a Markov chain, where transition
        probabilities are computed based on **RNA velocity and transcriptomic similarity**, taking into account **uncertainty
        in the velocities** and the stochastic nature of cell fate decisions. The Markov chain is coarse-grained into a set of
        macrostates which represent initial & terminal states as well as transient intermediate states. For each transient cell,
        i.e. for each cell that's not assigned to a terminal state, we then compute its fate probability of it reaching any of the terminal states.
        We show an example of such a fate map in the figure above, which has been computed using the data of `pancreatic endocrinogenesis`_.
        
        CellRank scales to **large cell numbers**, is fully compatible with `scanpy`_ and `scvelo`_ and is **easy to use**.
        For **installation instructions**, **documentation** and **tutorials**, visit `cellrank.org`_.
        
        Manuscript
        ^^^^^^^^^^
        Please see our `preprint`_ on **bioRxiv** to learn more.
        
        CellRank's key applications
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
        - compute **initial & terminal** as well as **intermediate** macrostates of your biological system
        - infer **fate probabilities** towards the terminal states for each individual cell
        - visualize **gene expression trends** along specific linegeages while accounting for the continous nature of fate determination
        - identify **potential driver genes** for each identified cellular trajectory
        
        Installation
        ^^^^^^^^^^^^
        Install CellRank by running::
        
            conda install -c conda-forge -c bioconda cellrank
            # or with extra libraries, useful for large datasets
            conda install -c conda-forge -c bioconda cellrank-krylov
        
        or via PyPI::
        
            pip install cellrank
            # or with extra libraries, useful for large datasets
            pip install 'cellrank[krylov]'
        
        Why is it called "CellRank"?
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        CellRank **does not** rank cells, we gave the package this name because just like Google's original `PageRank`_
        algorithm, it works with Markov chains to aggregate relationships between individual objects (cells vs. websites)
        to learn about more global properties of the underlying dynamics (initial & terminal states and fate probabilities vs. website relevance).
        
        Support
        ^^^^^^^
        We welcome your feedback! Feel free to open an `issue <https://github.com/theislab/cellrank/issues/new/choose>`__
        or send us an `email <mailto:info@cellrank.org>`_ if you encounter a bug, need our help or just want to make a
        comment/suggestion.
        
        CellRank was developed in collaboration between the `Theislab`_ and the `Peerlab`_.
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/cellrank.svg
            :target: https://pypi.org/project/cellrank
            :alt: PyPI
        
        .. |Bioconda| image:: https://img.shields.io/conda/vn/bioconda/cellrank
            :target: https://bioconda.github.io/recipes/cellrank/README.html
            :alt: Bioconda
        
        .. |Travis| image:: https://img.shields.io/travis/com/theislab/cellrank/master
            :target: https://travis-ci.com/github/theislab/cellrank
            :alt: CI
        
        .. |Notebooks| image:: https://img.shields.io/travis/com/theislab/cellrank_notebooks?label=notebooks
            :target: https://travis-ci.com/github/theislab/cellrank_notebooks
            :alt: CI-Notebooks
        
        .. |Docs|  image:: https://img.shields.io/readthedocs/cellrank
            :target: https://cellrank.readthedocs.io/en/latest
            :alt: Documentation
        
        .. |Downloads| image:: https://pepy.tech/badge/cellrank
            :target: https://pepy.tech/project/cellrank
            :alt: Downloads
        
        .. |Codecov| image:: https://codecov.io/gh/theislab/cellrank/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/theislab/cellrank
            :alt: Coverage
        
        .. _preprint: https://doi.org/10.1101/2020.10.19.345983
        
        .. _PageRank: https://en.wikipedia.org/wiki/PageRank#cite_note-1
        
        .. _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
        
        .. _pancreatic endocrinogenesis: https://doi.org/10.1242/dev.173849
        
        .. _scanpy: https://scanpy.readthedocs.io/en/latest/
        
        .. _scvelo: https://scvelo.readthedocs.io/
        
        .. _cellrank.org: https://cellrank.org
        
        .. _Theislab: https://www.helmholtz-muenchen.de/icb/research/groups/theis-lab/overview/index.html
        
        .. _Peerlab: https://www.mskcc.org/research/ski/labs/dana-pe-er
        
Keywords: bio-informatics,single-cell,RNA velocity,Markov chain,GPCCA
Platform: Linux
Platform: MacOs
Platform: Windows
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Framework :: Jupyter
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Typing :: Typed
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Provides-Extra: krylov
Provides-Extra: test
Provides-Extra: docs
Provides-Extra: dev
