Metadata-Version: 2.1
Name: cellrank
Version: 1.3.1
Summary: CellRank for directed single-cell fate mapping
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| |CI| |Notebooks| |Docs| |Codecov|
        
        CellRank for directed single-cell fate mapping
        ==============================================
        
        .. 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 Markov state modeling of single-cell data. It contains
        two main modules: `kernels`_ compute cell-cell transition probabilities and `estimators`_ generate hypothesis based on
        these. Our kernels work with a variety of input data including `RNA velocity`_ (see `La Manno et al. (2018)`_ and
        `Bergen et al. (2020)`_), `cellular similarity`_ (both transcriptomic and spatial) and `pseudotime`_, among others.
        Our `VelocityKernel`_ takes into account **uncertainty in the velocities** and allows you to aggregate the short-range
        fate relations given by RNA velocity into longer trends along the phenotypic manifold. Our main estimator is
        *Generalized Perron Cluster Cluster Analysis* (G-PCCA) [GPCCA18]_ which coarse-grains the Markov chain
        into a set of macrostates which represent initial, terminal and intermediate states. For each transient cell,
        we compute its fate probability towards any terminal state. We show an example of such a fate map in the figure above,
        which has been computed using the data of `pancreatic endocrinogenesis`_. CellRank combines `kernels`_ and `estimators`_
        with a powerful `plotting API`_, enabling you to visualize e.g. smooth `gene expression trends`_ along lineages or
        fate-informed `circular embeddings`_, to name just a few.
        
        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`_.
        
        Getting started with CellRank
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        If you're new to CellRank, make sure to go though the `basic tutorial`_ which introduces you to CellRank's high-level
        API. Most biological systems require a bit more control, so be sure to check out the `kernels and estimators tutorial`_
        which allows to unlock the full power of CellRank. If you want to see individual functions in action, visit our
        `gallery`_.
        
        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 lineages while accounting for the continuous 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]'
            # or with external modules, see External API
            pip install 'cellrank[external]'
        
        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>`_, send us
        an `email <mailto:info@cellrank.org>`_ or `tweet`_ if you encounter a bug, need our help or just want to make a
        comment/suggestion.
        
        Contributing
        ^^^^^^^^^^^^
        We actively encourage any contribution! To get started, please check out both the `contribution guide`_ as well as the
        `external API`_. CellRank's modular structure makes it easy to contribute, be it a new method to compute cell-cell
        transition probabilities (`kernels`_), a new way to analyze a transition matrix (`estimators`_) or an addition to the
        `plotting API`_. If you're thinking of contributing a new kernel, we have a `kernel tutorial`_ that guides you trough
        the process.
        
        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
        
        .. |CI| image:: https://img.shields.io/github/workflow/status/theislab/cellrank/CI/master
            :target: https://github.com/theislab/cellrank/actions
            :alt: CI
        
        .. |Notebooks| image:: https://img.shields.io/github/workflow/status/theislab/cellrank_notebooks/CI/master?label=notebooks
            :target: https://github.com/theislab/cellrank_notebooks/actions
            :alt: CI-Notebooks
        
        .. |Docs|  image:: https://img.shields.io/readthedocs/cellrank
            :target: https://cellrank.readthedocs.io/en/stable
            :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
        
        
        .. _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
        .. _GPCCA18: https://doi.org/10.1021/acs.jctc.8b00079
        .. _pancreatic endocrinogenesis: https://doi.org/10.1242/dev.173849
        .. _cellrank.org: https://cellrank.org
        
        .. _kernels: https://cellrank.readthedocs.io/en/stable/classes.html#kernels
        .. _estimators: https://cellrank.readthedocs.io/en/stable/classes.html#estimators
        .. _plotting API: https://cellrank.readthedocs.io/en/stable/api.html#module-cellrank.pl
        .. _external API: https://cellrank.readthedocs.io/en/stable/external_api.html
        .. _contribution guide: https://github.com/theislab/cellrank/blob/master/CONTRIBUTING.rst
        
        .. _RNA velocity: https://cellrank.readthedocs.io/en/stable/classes.html#velocity-kernel
        .. _VelocityKernel: https://cellrank.readthedocs.io/en/stable/classes.html#velocity-kernel
        .. _cellular similarity: https://cellrank.readthedocs.io/en/stable/classes.html#connectivity-kernel
        .. _pseudotime: https://cellrank.readthedocs.io/en/stable/classes.html#pseudotime-kernel
        
        .. _gene expression trends: https://cellrank.readthedocs.io/en/stable/api/cellrank.pl.gene_trends.html#cellrank.pl.gene_trends
        .. _circular embeddings: https://cellrank.readthedocs.io/en/stable/api/cellrank.pl.circular_projection.html
        
        .. _basic tutorial: https://cellrank.readthedocs.io/en/stable/cellrank_basics.html
        .. _kernel tutorial: https://cellrank.readthedocs.io/en/stable/creating_new_kernel.html
        .. _kernels and estimators tutorial: https://cellrank.readthedocs.io/en/stable/kernels_and_estimators.html
        
        .. _scanpy: https://scanpy.readthedocs.io/en/stable/
        .. _scvelo: https://scvelo.readthedocs.io/
        
        .. _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
        .. _`tweet`: https://twitter.com/MariusLange8
        .. _preprint: https://www.biorxiv.org/content/10.1101/2020.10.19.345983v1
        .. _PageRank: https://en.wikipedia.org/wiki/PageRank#cite_note-1
        
        .. _gallery: https://cellrank.readthedocs.io/en/stable/auto_examples/index.html
        .. _macrostates: https://cellrank.readthedocs.io/en/stable/auto_examples/estimators/compute_macrostates.html
        .. _fate probabilities: https://cellrank.readthedocs.io/en/stable/auto_examples/estimators/compute_abs_probs.html
        .. _driver genes: https://cellrank.readthedocs.io/en/stable/auto_examples/estimators/compute_lineage_drivers.html
        
Keywords: GPCCA,Markov chain,RNA velocity,bio-informatics,single-cell,single-cell
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
Requires-Python: >=3.6
Provides-Extra: external
Provides-Extra: krylov
Provides-Extra: test
Provides-Extra: docs
Provides-Extra: dev
