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    "info": {
        "author": "Mohsen Naghipourfar",
        "author_email": "mohsen.naghipourfar@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)"
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        "description": "# trVAE [![PyPI version](https://badge.fury.io/py/trvae.svg)](https://badge.fury.io/py/trvae) [![Build Status](https://travis-ci.org/theislab/trVAE.svg?branch=master)](https://travis-ci.org/theislab/trVAE)\n\n<img align=\"center\" src=\"./sketch/sketch.png?raw=true\">\n\n## Introduction\nA Keras (with tensorflow backend) implementation of trVAE. trVAE is a deep generative model which learns mapping between multiple different styles (conditions). trVAE can be used for style transfer in images, single-cell perturbations response across celltypes, times and etc.  \n<div float=\"left\">\n  <img src=\"https://www.tensorflow.org/images/tf_logo_transp.png\" height=\"80\" >\n  <img src=\"https://s3.amazonaws.com/keras.io/img/keras-logo-2018-large-1200.png\" height=\"80\">\n</div>\n<div float=\"right\">\n</div>\n\n## Getting Started\n\n## Installation\n\n### Installation with pip\nTo install the latest version from PyPI, simply use the following bash script:\n```bash\npip install trvae\n```\nor install the development version via pip: \n```bash\npip install git+https://github.com/theislab/trvae.git\n```\n\nor you can first install flit and clone this repository:\n```bash\npip install flit\ngit clone https://github.com/theislab/trVAE\ncd trVAE\nflit install\n```\n\n## Examples\n\n## Reproducing paper results:\nIn order to reproduce paper results visit [here](https://github.com/Naghipourfar/trVAE_reproducibility).\n\n\n## References\nLotfollahi, Mohammad and Wolf, F. Alexander and Theis, Fabian J.\n**\"scGen predicts single-cell perturbation responses.\"**\nNature Methods, 2019. [pdf](https://rdcu.be/bMlbD)\n",
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