Metadata-Version: 2.1
Name: phate
Version: 1.0.4
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/v1.0.4.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
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            :alt: Latest Conda version
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            :alt: Latest CRAN version
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            :target: https://travis-ci.com/KrishnaswamyLab/PHATE
            :alt: Travis CI Build
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            :target: https://phate.readthedocs.io/
            :alt: Read the Docs
        .. image:: https://zenodo.org/badge/DOI/10.1038/s41587-019-0336-3.svg
            :target: https://www.nature.com/articles/s41587-019-0336-3
            :alt: Nature Biotechnology Publication
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        PHATE (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.
        
        To see how PHATE can be applied to datasets such as facial images and single-cell data from human embryonic stem cells, check out our `Nature Biotechnology publication`_.
        
        `Moon, van Dijk, Wang, Gigante et al. **Visualizing Transitions and Structure for Biological Data Exploration**. 2019. *Nature Biotechnology*.`__
        
        .. _`Nature Biotechnology publication`: https://doi.org/10.1038/s41587-019-0336-3
        
        __ `Nature Biotechnology publication`_
        
        PHATE has been implemented in Python >=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
        
        Quick Start
        ~~~~~~~~~~~
        
        If you have loaded a data matrix ``data`` in Python (cells on rows, genes on columns) you can run PHATE as follows::
        
            import phate
            phate_op = phate.PHATE()
            data_phate = phate_op.fit_transform(data)
        
        PHATE accepts the following data types: ``numpy.array``, ``scipy.spmatrix``, ``pandas.DataFrame`` and ``anndata.AnnData``.
        
        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 scprep
            tree_data, tree_clusters = phate.tree.gen_dla()
            phate_operator = phate.PHATE(k=15, t=100)
            tree_phate = phate_operator.fit_transform(tree_data)
            scprep.plot.scatter2d(tree_phate, c=tree_clusters)
            phate_operator.set_params(n_components=3)
            tree_phate = phate_operator.transform()
            scprep.plot.rotate_scatter3d(tree_phate, c=tree_clusters)
        
        Jupyter Notebooks
        ~~~~~~~~~~~~~~~~~
        
        A 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>`_.
        
        Help
        ^^^^
        
        If you have any questions or require assistance using PHATE, please contact us at https://krishnaswamylab.org/get-help
        
Keywords: visualization,big-data,dimensionality-reduction,embedding,manifold-learning,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 :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Provides-Extra: test
Provides-Extra: doc
