https://github.com/theislab/trvae
Conditional out-of-distribution prediction
https://github.com/theislab/trvae
cvae deep-learning generative-model mmd single-cell
Last synced: 8 months ago
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Conditional out-of-distribution prediction
- Host: GitHub
- URL: https://github.com/theislab/trvae
- Owner: theislab
- License: mit
- Created: 2019-05-14T06:26:59.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-08-02T16:21:37.000Z (almost 2 years ago)
- Last Synced: 2025-09-25T16:27:32.901Z (9 months ago)
- Topics: cvae, deep-learning, generative-model, mmd, single-cell
- Language: Python
- Homepage:
- Size: 41.8 MB
- Stars: 63
- Watchers: 5
- Forks: 12
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# trVAE [](https://badge.fury.io/py/trVAE) [](https://travis-ci.org/theislab/trVAE) [](https://pepy.tech/project/trvae)
**Conditional out-of-distribution generation for unpaired data using transfer VAE [(Bioinformatics, 2020)](https://doi.org/10.1093/bioinformatics/btaa800).*
**Note: We have upgraded trVAE to a faster and more efficient implementation. Please refer to [Here](https://github.com/theislab/scarches)**
## 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](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 and batch-removal 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).
## Reference
If you found trVAE useful please consider citing the published [manuscript.](https://academic.oup.com/bioinformatics/article/36/Supplement_2/i610/6055927)