{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Framework :: Jupyter", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Natural Language :: English", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Bio-Informatics" ], "description": "=======================================================\nMarkov Affinity-based Graph Imputation of Cells (MAGIC)\n=======================================================\n\n.. image:: https://img.shields.io/pypi/v/magic-impute.svg\n :target: https://pypi.org/project/magic-impute/\n :alt: Latest PyPi version\n.. image:: https://img.shields.io/cran/v/Rmagic.svg\n :target: https://cran.r-project.org/package=Rmagic\n :alt: Latest CRAN version\n.. image:: https://api.travis-ci.com/KrishnaswamyLab/MAGIC.svg?branch=master\n :target: https://travis-ci.com/KrishnaswamyLab/MAGIC\n :alt: Travis CI Build\n.. image:: https://img.shields.io/readthedocs/magic.svg\n :target: https://magic.readthedocs.io/\n :alt: Read the Docs\n.. image:: https://zenodo.org/badge/DOI/10.1016/j.cell.2018.05.061.svg\n :target: https://www.cell.com/cell/abstract/S0092-8674(18)30724-4\n :alt: Cell Publication DOI\n.. image:: https://img.shields.io/twitter/follow/KrishnaswamyLab.svg?style=social&label=Follow\n :target: https://twitter.com/KrishnaswamyLab\n :alt: Twitter\n.. image:: https://img.shields.io/github/stars/KrishnaswamyLab/MAGIC.svg?style=social&label=Stars\n :target: https://github.com/KrishnaswamyLab/MAGIC/\n :alt: GitHub stars\n\nMarkov 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.\n\nTo see how MAGIC can be applied to single-cell RNA-seq, elucidating the epithelial-to-mesenchymal transition, read our `publication in Cell`_.\n\n`David van Dijk, et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. 2018. Cell.`__\n\n.. _`publication in Cell`: https://www.cell.com/cell/abstract/S0092-8674(18)30724-4\n\n__ `publication in Cell`_\n\nFor R and MATLAB implementations of MAGIC, see\nhttps://github.com/KrishnaswamyLab/MAGIC.\n\n.. image:: https://raw.githubusercontent.com/KrishnaswamyLab/MAGIC/master/magic.gif\n :align: center\n :alt: Magic reveals the interaction between Vimentin (VIM), Cadherin-1 (CDH1), and Zinc finger E-box-binding homeobox 1 (ZEB1, encoded by colors).\n\n*Magic reveals the interaction between Vimentin (VIM), Cadherin-1\n(CDH1), and Zinc finger E-box-binding homeobox 1 (ZEB1, encoded by\ncolors).*\n\nInstallation\n~~~~~~~~~~~~\n\nInstallation with pip\n---------------------\n\nTo install with ``pip``, run the following from a terminal::\n\n pip install --user magic-impute\n\nInstallation from GitHub\n------------------------\n\nTo clone the repository and install manually, run the following from a\nterminal::\n\n git clone git://github.com/KrishnaswamyLab/MAGIC.git\n cd 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