Metadata-Version: 2.1
Name: trVAE
Version: 1.1.1
Summary: Condition out-of-sample prediction
Home-page: https://github.com/theislab/trvae
Author: Mohsen Naghipourfar, Mohammad Lotfollahi
Author-email: mohsen.naghipourfar@gmail.com, Mohammad.lotfollahi@helmholtz-muenchen.de
License: MIT
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)
        
        <img align="center" src="./sketch/sketch.png?raw=true">
        
        ## Introduction
        A Keras (tensorflow < 2.0) implementation of trVAE (transfer Variational Autoencoder) . trVAE is a deep generative model which learns mapping between multiple different styles (conditions). trVAE can be used for style transfer in images, predicting single-cell perturbations responses and batch removal.
        
        * For pytorch implementation check [Here](https://github.com/theislab/trvaep)
        ## Getting Started
        
        ## Installation
        Before installing trVAE package, we suggest you to create a new Python 3.6 (or 3.7) 
        virtual env (or conda env) with the following steps:  
        
        ### 1.  Installing virtualenv
        ```bash
        pip install virtualenv
        ```
        
        ### 2. Create a virtual with Python 3.6
        ```bash
        virtualenv trvae-env --python=python3.6 
        ```
        
        ### 3. trVAE package installation
        To install the latest version from PyPI, simply use the following bash script:
        ```bash
        pip install trvae
        ```
        or install the development version via pip: 
        ```bash
        pip install git+https://github.com/theislab/trvae.git
        ```
        
        or you can first install flit and clone this repository:
        ```bash
        git clone https://github.com/theislab/trVAE
        cd trVAE
        pip install -r requirements
        python setup.py install 
        ```
        
        ## Examples
        
        * For perturbation prediction check this [example](https://nbviewer.jupyter.org/github/theislab/trVAE/blob/master/examples/trVAE_Haber.ipynb) from Haber et al..
        
        ## Reproducing paper results:
        In order to reproduce paper results visit [here](https://github.com/Naghipourfar/trVAE_reproducibility).
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
