Awesome
trVAE
*Conditional out-of-distribution generation for unpaired data using transfer VAE (Bioinformatics, 2020).
Note: We have upgraded trVAE to a faster and more efficient implementation. Please refer to Here
<img align="center" src="./sketch/sketch.png?raw=true">Introduction
A Keras (tensorflow < 2.0) implementation of trVAE (transfer Variational Autoencoder) .
trVAE can be used for style transfer in images, predicting perturbations responses and batch-removal for single-cell RNA-seq.
- For pytorch implementation check Here
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
pip install virtualenv
2. Create a virtual with Python 3.6
virtualenv trvae-env --python=python3.6
3. trVAE package installation
To install the latest version from PyPI, simply use the following bash script:
pip install trvae
or install the development version via pip:
pip install git+https://github.com/theislab/trvae.git
or you can first install flit and clone this repository:
git clone https://github.com/theislab/trVAE
cd trVAE
pip install -r requirements
python setup.py install
Examples
- For perturbation prediction and batch-removal check this example from Haber et al.
Reproducing paper results:
In order to reproduce paper results visit here.
Reference
If you found trVAE useful please consider citing the published manuscript.