Metadata-Version: 1.1
Name: phate
Version: 0.2
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.tar.gz
Description: PHATE - Potential of Heat-diffusion for Affinity-based Trajectory Embedding
        ---------------------------------------------------------------------------
        
        PHATE has been implemented in Python (2.7 and >=3.5), R and Matlab.
        
        Python installation and dependencies
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        1. The Python version of PHATE can be installed using:
        
           ::
        
               $ git clone git://github.com/SmitaKrishnaswamy/PHATE.git
               $ cd Python
               $ python setup.py install --user
        
        2. PHATE depends on a number of ``python`` packages available on pypi
           and these dependencies are listed in ``setup.py`` All the
           dependencies will be automatically installed using the above commands
        
        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: 1.
        Make the test scripts executable
        
        ::
        
               $ cd PHATE/Python/test
               $ chmod +x phate_test_tree.py phate_test_mESC.py
               $ ./phate_test_tree.py #output saved in a png
        
        Jupyter Notebook
        ~~~~~~~~~~~~~~~~
        
        A demo on PHATE usage and visualization for single cell RNA-seq data can
        be found in this notebook:
        https://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb
        
Keywords: visualization,big data,dimensionality reduction,embedding,computational biology
Platform: UNKNOWN
