Metadata-Version: 2.1
Name: phate
Version: 1.0.6
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/v1.0.6.tar.gz
Description: PHATE - Visualizing Transitions and Structure for Biological Data Exploration
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        [![Latest PyPI version](https://img.shields.io/pypi/v/phate.svg)](https://pypi.org/project/phate/)
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        ### Quick Start
        If you would like to get started using PHATE, check out our [**guided tutorial in Python**](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb).
        
        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`.
        
        ### Introduction
        
        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 publication in Nature Biotechnology.
        
        [Moon, van Dijk, Wang, Gigante et al. **Visualizing Transitions and Structure for Biological Data Exploration**. 2019. *Nature Biotechnology*.](https://doi.org/10.1038/s41587-019-0336-3)
        
        PHATE has been implemented in [Python >=3.5](#python), [MATLAB](https://github.com/KrishnaswamyLab/PHATE/#matlab) and [R](https://github.com/KrishnaswamyLab/phateR/).
        
        ### Table of Contents
        
        * [System Requirements](#system-requirements)
        * [Installation with pip](#installation-with-pip)
        * [Installation from source](#installation-from-source)
        * [Quick Start](#quick-start)
        * [Tutorial and Reference](#tutorial-and-reference)
        * [Help](#help)
        
        ### System Requirements
        
        * Windows (>= 7), Mac OS X (>= 10.8) or Linux
        * [Python >= 3.5](https://www.python.org/downloads/)
        
        All other software dependencies are installed automatically when installing PHATE.
        
        ### Installation with `pip`
        
        The Python version of PHATE can be installed by running the following from a terminal:
        
            pip install --user phate
        
        Installation of PHATE and all dependencies should take no more than five minutes.
        
        ### 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 PHATE/Python
            python setup.py install --user
        
        ### Tutorial and Reference
        
        For more information, read the [documentation on ReadTheDocs](http://phate.readthedocs.io/) or view our tutorials on GitHub: [single-cell RNA-seq](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/EmbryoidBody.ipynb), [artificial tree](http://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb). 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
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: doc
