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    "info": {
        "author": "Daniel Burkhardt, Krishnaswamy Lab, Yale University",
        "author_email": "daniel.burkhardt@yale.edu",
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            "Development Status :: 5 - Production/Stable",
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            "Natural Language :: English",
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            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Visualization"
        ],
        "description": "===========================================================================\nPHATE - Potential of Heat-diffusion for Affinity-based Trajectory Embedding\n===========================================================================\n\n.. image:: https://img.shields.io/pypi/v/phate.svg\n    :target: https://pypi.org/project/phate/\n    :alt: Latest PyPi version\n.. image:: https://img.shields.io/cran/v/phateR.svg\n    :target: https://cran.r-project.org/package=phateR\n    :alt: Latest CRAN version\n.. image:: https://api.travis-ci.com/KrishnaswamyLab/phate.svg?branch=master\n    :target: https://travis-ci.com/KrishnaswamyLab/PHATE\n    :alt: Travis CI Build\n.. image:: https://img.shields.io/readthedocs/phate.svg\n    :target: https://phate.readthedocs.io/\n    :alt: Read the Docs\n.. image:: https://zenodo.org/badge/DOI/10.1101/120378.svg\n    :target: https://doi.org/10.1101/120378\n    :alt: bioRxiv Preprint\n.. image:: https://img.shields.io/twitter/follow/KrishnaswamyLab.svg?style=social&label=Follow\n    :target: https://twitter.com/KrishnaswamyLab\n    :alt: Twitter\n.. image:: https://img.shields.io/github/stars/KrishnaswamyLab/PHATE.svg?style=social&label=Stars\n    :target: https://github.com/KrishnaswamyLab/PHATE/\n    :alt: GitHub stars\n\nPHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) is a tool for visualizing high dimensional data. PHATE uses a novel conceptual framework for learning and visualizing the manifold to preserve both local and global distances.\n\nTo see how PHATE can be applied to datasets such as facial images and single-cell data from human embryonic stem cells, check out our `preprint on BioRxiv`_.\n\n`Kevin R. Moon, David van Dijk, Zheng Wang, et al. Visualizing Transitions and Structure for Biological Data Exploration. 2018. BioRxiv.`__\n\n.. _`preprint on BioRxiv`: https://www.biorxiv.org/content/early/2017/03/24/120378\n\n__ `preprint on BioRxiv`_\n\nPHATE has been implemented in Python >=3.5, R_ and MATLAB_.\n\n.. _R: https://github.com/KrishnaswamyLab/phateR\n.. _MATLAB: https://github.com/KrishnaswamyLab/PHATE\n\nPython installation and dependencies\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nInstallation with ``pip``\n-------------------------\n\nThe Python version of PHATE can be installed using::\n\n       pip install --user phate\n\nInstallation from source\n------------------------\n\nThe Python version of PHATE can be installed from GitHub by running the following from a terminal::\n\n       git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git\n       cd Python\n       python setup.py install --user\n\nQuick Start\n~~~~~~~~~~~\n\nIf you have loaded a data matrix ``data`` in Python (cells on rows, genes on columns) you can run PHATE as follows::\n\n    import phate\n    phate_op = phate.PHATE()\n    data_phate = phate_op.fit_transform(data)\n\nPHATE accepts the following data types: ``numpy.array``, ``scipy.spmatrix``, ``pandas.DataFrame`` and ``anndata.AnnData``.\n\nUsage\n~~~~~\n\nPHATE has been implemented with an API that should be familiar to those\nwith experience using scikit-learn. The core of the PHATE package is the\n``PHATE`` class which is a subclass of ``sklearn.base.BaseEstimator``.\nTo get started, ``import phate`` and instantiate a ``phate.PHATE()``\nobject. Just like most ``sklearn`` estimators, ``PHATE()`` objects have\nboth ``fit()`` and ``fit_transform()`` methods. For more information,\ncheck out our notebook below.\n\nIf you want to try running our test script on a DLA fractal tree, run the following in a Python interpreter::\n\n        import phate\n        tree_data, tree_clusters = phate.tree.gen_dla()\n        phate_operator = phate.PHATE(k=15, t=100)\n        tree_phate = phate_operator.fit_transform(tree_data)\n        phate.plot.scatter2d(phate_operator, c=tree_clusters) # or phate.plot.scatter2d(tree_phate, c=tree_clusters)\n        phate.plot.rotate_scatter3d(phate_operator, c=tree_clusters)\n\nJupyter Notebooks\n~~~~~~~~~~~~~~~~~\n\nA demo on PHATE usage and visualization for single cell RNA-seq data can be found in this `Jupyter notebook <http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb>`_. A second tutorial is available `here <http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb>`_ which works with the artificial tree shown above in more detail. You can also access interactive versions of these tutorials on Google Colaboratory: `single cell RNA seq <https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb>`_, `artificial tree <https://colab.research.google.com/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb>`_.\n\nHelp\n^^^^\n\nIf you have any questions or require assistance using PHATE, please contact us at https://krishnaswamylab.org/get-help\n\n\n",
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