Metadata-Version: 2.1
Name: magic-impute
Version: 1.4.0
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/v1.4.0.tar.gz
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: doc
Provides-Extra: test
Requires-Dist: numpy (>=1.14.0)
Requires-Dist: pandas (>=0.21.0)
Requires-Dist: scipy (>=1.1.0)
Requires-Dist: matplotlib
Requires-Dist: scikit-learn (>=0.19.1)
Requires-Dist: tasklogger (>=0.2.1)
Requires-Dist: graphtools (>=0.1.9)
Requires-Dist: scprep (>=0.7.1)
Provides-Extra: doc
Requires-Dist: sphinx; extra == 'doc'
Requires-Dist: sphinxcontrib-napoleon; extra == 'doc'
Provides-Extra: test
Requires-Dist: nose2; extra == 'test'
Requires-Dist: anndata; extra == 'test'

=======================================================
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
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    :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 and imputation of single cells applied to single-cell RNA
sequencing data, as described in Van Dijk D *et al.* (2018), *Recovering
Gene Interactions from Single-Cell Data Using Data Diffusion*, Cell
https://www.cell.com/cell/abstract/S0092-8674(18)30724-4.

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 git+git://github.com/KrishnaswamyLab/MAGIC.git#subdirectory=python

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.


