Metadata-Version: 2.1
Name: phate
Version: 0.2.2
Summary: PHATE
Home-page: https://github.com/KrishnaswamyLab/PHATE
Author: Daniel Burkhardt, Krishnaswamy Lab, Yale University
Author-email: daniel.burkhardt@yale.edu
License: GNU General Public License Version 2
Download-URL: https://github.com/KrishnaswamyLab/PHATE/archive/v0.2.2.tar.gz
Description: ===========================================================================
        PHATE - Potential of Heat-diffusion for Affinity-based Trajectory Embedding
        ===========================================================================
        
        .. image:: https://img.shields.io/pypi/v/phate.svg
            :target: https://pypi.org/project/phate/
            :alt: Latest PyPi version
        .. image:: https://img.shields.io/readthedocs/phate.svg
            :target: https://phate.readthedocs.io/
            :alt: Read the Docs
        .. image:: https://zenodo.org/badge/DOI/10.1101/120378.svg
            :target: https://www.biorxiv.org/content/early/2017/12/01/120378
            :alt: bioRxiv Preprint
        .. image:: https://img.shields.io/twitter/follow/KrishnaswamyLab.svg?style=social&label=Follow
            :target: https://twitter.com/KrishnaswamyLab
            :alt: Twitter
        .. image:: https://img.shields.io/github/stars/KrishnaswamyLab/PHATE.svg?style=social&label=Stars
            :target: https://github.com/KrishnaswamyLab/PHATE/
            :alt: GitHub stars
        
        PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another. To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic stem cells, human embryonic stem cells, and bone marrow samples, check out our preprint on BioRxiv.
        
        PHATE has been implemented in Python (2.7 and >=3.5), R_ and MATLAB_.
        
        .. _R: https://github.com/KrishnaswamyLab/phater
        .. _MATLAB: https://github.com/KrishnaswamyLab/PHATE
        
        Python installation and dependencies
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Installation with ``pip``
        -------------------------
        
        The Python version of PHATE can be installed using::
        
               pip install --user phate
        
        Installation from source
        ------------------------
        
        The Python version of PHATE can be installed from GitHub by running the following from a terminal::
        
               git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git
               cd Python
               python setup.py install --user
        
        Usage
        ~~~~~
        
        PHATE has been implemented with an API that should be familiar to those
        with experience using scikit-learn. The core of the PHATE package is the
        ``PHATE`` class which is a subclass of ``sklearn.base.BaseEstimator``.
        To get started, ``import phate`` and instantiate a ``phate.PHATE()``
        object. Just like most ``sklearn`` estimators, ``PHATE()`` objects have
        both ``fit()`` and ``fit_transform()`` methods. For more information,
        check out our notebook below.
        
        If you want to try running our test script on a DLA fractal tree, run the following in a Python interpreter::
        
                import phate
                import matplotlib.pyplot as plt
                tree_data, tree_clusters = phate.tree.gen_dla()
                phate_operator = phate.PHATE(k=15, t=100)
                tree_phate = phate_operator.fit_transform(tree_data)
                plt.scatter(tree_phate[:,0], tree_phate[:,1], c=tree_clusters)
                plt.show()
        
        Jupyter Notebooks
        ~~~~~~~~~~~~~~~~~
        
        A demo on PHATE usage and visualization for single cell RNA-seq data can be found in this notebook_: http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb
        
        .. _notebook: http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb
        
        A second tutorial is available here_ which works with the artificial tree shown above in more detail: http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb
        
        .. _here: http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb
        
Keywords: visualization,big-data,dimensionality-reduction,embedding,computational-biology
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Provides-Extra: docs
Provides-Extra: tests
