Skip to main content

trVAE - Transfer Variational Autoencoders pytorch

Project description

trvaep PyPI version

Introduction

A pytorch 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 on images, predicting single-cell perturbations responses and batch removal.

Getting Started

Installation

Installation with pip

To install the latest version from PyPI, simply use the following bash script:

pip install trvaep

or install the development version via pip:

pip install git+https://github.com/theislab/trvaep.git

or you can first install flit and clone this repository:

pip install flit
git clone https://github.com/theislab/trvaep
cd trvaep
flit install

Examples

  • For perturbation prediction check this example for interferon (IFN)-β stimulation from Kang et al..

Reproducing paper results:

In order to reproduce paper results visit here.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

trvaep-0.0.2.tar.gz (480.4 kB view details)

Uploaded Source

Built Distribution

trvaep-0.0.2-py2.py3-none-any.whl (41.9 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file trvaep-0.0.2.tar.gz.

File metadata

  • Download URL: trvaep-0.0.2.tar.gz
  • Upload date:
  • Size: 480.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.21.0

File hashes

Hashes for trvaep-0.0.2.tar.gz
Algorithm Hash digest
SHA256 52b54ef8075821a5ae855cee8a40170ce4ffa917409c3dfe72807233ae104f90
MD5 8949a0077859ac9d877bf619dd2e0d43
BLAKE2b-256 39d2bd13381247d0dab950b2c1a70bb5ae24b02c7bf7c5df0e25f08c39e2f325

See more details on using hashes here.

File details

Details for the file trvaep-0.0.2-py2.py3-none-any.whl.

File metadata

  • Download URL: trvaep-0.0.2-py2.py3-none-any.whl
  • Upload date:
  • Size: 41.9 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.21.0

File hashes

Hashes for trvaep-0.0.2-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 b0d4e61300cca70984edda21c5784d1fac5fa783589493885509b3f59dccf92f
MD5 b907f0a97da71687326554406c6f298c
BLAKE2b-256 2e16156926cbba8c6172207433a47846856e4c36be53ebe54e61c4fe399e6150

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page