Metadata-Version: 2.1
Name: scFates
Version: 0.2.2
Summary: scanpy compatible python suite for fast tree inference and advanced pseudotime downstream analysis
Home-page: https://github.com/LouisFaure/scFates
Author: Louis Faure
Author-email: 
License: UNKNOWN
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        license| |Code style: black|
        
        Description
        ===========
        
        This package provides a scalable Python suite for fast tree inference
        and advanced pseudotime downstream analysis, with a focus on fate
        biasing. This package is compatible with anndata object format used in
        scanpy or scvelo pipelines. A complete documentation of this package is
        available `here <https://scfates.readthedocs.io/en/latest>`__.
        
        Tree inference algorithms
        =========================
        
        The user have the choice between two algorithm for tree inference:
        
        ElPiGraph
        ---------
        
        For scTree, the `python <https://github.com/j-bac/elpigraph-python/>`__
        implementation of the ElPiGraph algorithm is used, which include GPU
        accelerated principal tree inference. A self-contained description of
        the algorithm is available
        `here <https://github.com/auranic/Elastic-principal-graphs/blob/master/ElPiGraph_Methods.pdf>`__
        or in the related `paper <https://www.mdpi.com/1099-4300/22/3/296>`__
        
        A `R implementation <https://github.com/Albluca/ElPiGraph.R>`__ of this
        algorithm is also available, coded by `Luca
        Albergante <https://github.com/Albluca>`__
        
        A native MATLAB implementation of the algorithm (coded by `Andrei
        Zinovyev <https://github.com/auranic/>`__ and `Evgeny
        Mirkes <https://github.com/Mirkes>`__) is also
        `available <https://github.com/auranic/Elastic-principal-graphs>`__
        
        Simple PPT
        ----------
        
        A `simple
        PPT <https://www.acsu.buffalo.edu/~yijunsun/lab/Paper/simplePPT.pdf>`__
        inspired approach, translated from the `crestree R
        package <https://github.com/hms-dbmi/crestree>`__, code has been also
        adapted to run on GPU for accelerated tree inference.
        
        Citations
        =========
        
        Code for PPT inference and most of downstream pseudotime analysis was
        initially written in a `R
        package <https://github.com/hms-dbmi/crestree>`__ by Ruslan Soldatov for
        the following paper:
        
        Soldatov, R., Kaucka, M., Kastriti, M. E., Petersen, J., Chontorotzea,
        T., Englmaier, L., … Adameyko, I. (2019). Spatiotemporal structure of
        cell fate decisions in murine neural crest. Science, 364(6444).
        
        if you are using ElPiGraph, please cite :
        
        Albergante, L., Mirkes, E. M., Chen, H., Martin, A., Faure, L.,
        Barillot, E., … Zinovyev, A. (2020). Robust And Scalable Learning Of
        Complex Dataset Topologies Via Elpigraph. Entropy, 22(3), 296.
        
        Code for preprocessing has been translated from R package pagoda2, if
        you use any of these functions (``scf.pp.batch_correct`` &
        ``scf.pp.find_overdispersed``), please cite:
        
        Nikolas Barkas, Viktor Petukhov, Peter Kharchenko and Evan Biederstedt
        (2021). pagoda2: Single Cell Analysis and Differential Expression. R
        package version 1.0.2.
        
        Palantir python tool provides a great dimensionality reduction method,
        which usually lead to consitent trees with scFates, if use
        ``scf.pp.diffusion``, please cite:
        
        Manu Setty and Vaidotas Kiseliovas and Jacob Levine and Adam Gayoso and
        Linas Mazutis and Dana Pe’er (2019) Characterization of cell fate
        probabilities in single-cell data with Palantir. Nature Biotechnology
        
        Installation
        ============
        
        scFates 0.2.1 is now available on pypi, you can install it using:
        
        ::
        
           pip install -U scFates
        
        or the latest development version can be installed from GitHub:
        
        ::
        
           pip install git+https://github.com/LouisFaure/scFates
        
        Python dependencies
        -------------------
        
        scFates gives the choice of between SimplePPT and ElPiGraph for learning
        a principal graph from the data. Elpigraph needs to be installed from
        its github repository with the following command:
        
        ::
        
           pip install git+https://github.com/j-bac/elpigraph-python.git
        
        R dependencies
        --------------
        
        scFates rely on the R package *mgcv* to perform testing and fitting of
        the features on the peudotime tree. Package is installed in an R session
        with the following command:
        
        ::
        
           install.packages('mgcv')
        
        GPU dependencies (optional)
        ---------------------------
        
        If you have a nvidia GPU, scFates can leverage CUDA computations for
        speedups in some functions, for that you will need `Rapids
        0.17 <https://rapids.ai/>`__ installed.
        
        Docker container
        ----------------
        
        scFates can be run on a `Docker
        container <https://hub.docker.com/repository/docker/louisfaure/scfates>`__
        based on Rapids 0.17 container, which provide a gpu enabled environment
        with Jupyter Lab. Use the following command:
        
        ::
        
           docker run --rm -it --gpus all -p 8888:8888 -p 8787:8787 -p 8786:8786 \
               louisfaure/scfates:version-0.2.1
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/scFates.svg
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