Metadata-Version: 2.1
Name: magic-impute
Version: 2.0.4
Summary: MAGIC
Home-page: https://github.com/KrishnaswamyLab/MAGIC
Author: 
Author-email: 
License: GNU General Public License Version 2
Download-URL: https://github.com/KrishnaswamyLab/MAGIC/archive/v2.0.4.tar.gz
Description: =======================================================
        Markov Affinity-based Graph Imputation of Cells (MAGIC)
        =======================================================
        
        .. image:: https://img.shields.io/pypi/v/magic-impute.svg
            :target: https://pypi.org/project/magic-impute/
            :alt: Latest PyPi version
        .. image:: https://img.shields.io/cran/v/Rmagic.svg
            :target: https://cran.r-project.org/package=Rmagic
            :alt: Latest CRAN version
        .. image:: https://api.travis-ci.com/KrishnaswamyLab/MAGIC.svg?branch=master
            :target: https://travis-ci.com/KrishnaswamyLab/MAGIC
            :alt: Travis CI Build
        .. image:: https://img.shields.io/readthedocs/magic.svg
            :target: https://magic.readthedocs.io/
            :alt: Read the Docs
        .. image:: https://zenodo.org/badge/DOI/10.1016/j.cell.2018.05.061.svg
            :target: https://www.cell.com/cell/abstract/S0092-8674(18)30724-4
            :alt: Cell Publication DOI
        .. image:: https://img.shields.io/twitter/follow/KrishnaswamyLab.svg?style=social&label=Follow
            :target: https://twitter.com/KrishnaswamyLab
            :alt: Twitter
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            :target: https://github.com/KrishnaswamyLab/MAGIC/
            :alt: GitHub stars
        
        Markov Affinity-based Graph Imputation of Cells (MAGIC) is an algorithm for denoising high-dimensional data most commonly applied to single-cell RNA sequencing data. MAGIC learns the manifold data, using the resultant graph to smooth the features and restore the structure of the data.
        
        To see how MAGIC can be applied to single-cell RNA-seq, elucidating the epithelial-to-mesenchymal transition, read our `publication in Cell`_.
        
        `David van Dijk, et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. 2018. Cell.`__
        
        .. _`publication in Cell`: https://www.cell.com/cell/abstract/S0092-8674(18)30724-4
        
        __ `publication in Cell`_
        
        For R and MATLAB implementations of MAGIC, see
        https://github.com/KrishnaswamyLab/MAGIC.
        
        .. image:: https://raw.githubusercontent.com/KrishnaswamyLab/MAGIC/master/magic.gif
            :align: center
            :alt: Magic reveals the interaction between Vimentin (VIM), Cadherin-1 (CDH1), and Zinc finger E-box-binding homeobox 1 (ZEB1, encoded by colors).
        
        *Magic reveals the interaction between Vimentin (VIM), Cadherin-1
        (CDH1), and Zinc finger E-box-binding homeobox 1 (ZEB1, encoded by
        colors).*
        
        Installation
        ~~~~~~~~~~~~
        
        Installation with pip
        ---------------------
        
        To install with ``pip``, run the following from a terminal::
        
           pip install --user magic-impute
        
        Installation from GitHub
        ------------------------
        
        To clone the repository and install manually, run the following from a
        terminal::
        
           git clone git://github.com/KrishnaswamyLab/MAGIC.git
           cd MAGIC/python
           python setup.py install --user
        
        Usage
        ~~~~~
        
        Example data
        ------------
        
        The following code runs MAGIC on test data located in the MAGIC
        repository::
        
           import magic
           import pandas as pd
           import matplotlib.pyplot as plt
           X = pd.read_csv("MAGIC/data/test_data.csv")
           magic_operator = magic.MAGIC()
           X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
           plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
           plt.show()
           magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
        
        Interactive command line
        ------------------------
        
        We have included two tutorial notebooks on MAGIC usage and results
        visualization for single cell RNA-seq data.
        
        EMT data notebook:
        http://nbviewer.jupyter.org/github/KrishnaswamyLab/magic/blob/master/python/tutorial_notebooks/emt_tutorial.ipynb
        
        Bone Marrow data notebook:
        http://nbviewer.jupyter.org/github/KrishnaswamyLab/magic/blob/master/python/tutorial_notebooks/bonemarrow_tutorial.ipynb
        
        Help
        ~~~~
        
        If you have any questions or require assistance using MAGIC, 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 :: 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
Provides-Extra: test
Provides-Extra: doc
