{"id":31805727,"url":"https://github.com/theislab/trvae","last_synced_at":"2025-10-11T02:57:48.658Z","repository":{"id":41000258,"uuid":"186558676","full_name":"theislab/trVAE","owner":"theislab","description":"Conditional out-of-distribution prediction","archived":false,"fork":false,"pushed_at":"2024-08-02T16:21:37.000Z","size":43852,"stargazers_count":63,"open_issues_count":3,"forks_count":12,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-09-25T16:27:32.901Z","etag":null,"topics":["cvae","deep-learning","generative-model","mmd","single-cell"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/theislab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-05-14T06:26:59.000Z","updated_at":"2025-07-15T18:44:48.000Z","dependencies_parsed_at":"2022-09-07T00:00:25.713Z","dependency_job_id":null,"html_url":"https://github.com/theislab/trVAE","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"purl":"pkg:github/theislab/trVAE","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2FtrVAE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2FtrVAE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2FtrVAE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2FtrVAE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/theislab","download_url":"https://codeload.github.com/theislab/trVAE/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2FtrVAE/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279005953,"owners_count":26084009,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cvae","deep-learning","generative-model","mmd","single-cell"],"created_at":"2025-10-11T02:57:45.211Z","updated_at":"2025-10-11T02:57:48.651Z","avatar_url":"https://github.com/theislab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 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) [![Downloads](https://pepy.tech/badge/trvae)](https://pepy.tech/project/trvae)\n\n**Conditional out-of-distribution generation for unpaired data using transfer VAE [(Bioinformatics, 2020)](https://doi.org/10.1093/bioinformatics/btaa800).*\n\n**Note: We have upgraded trVAE to a faster and more efficient implementation. Please refer to [Here](https://github.com/theislab/scarches)**\n\n\u003cimg align=\"center\" src=\"./sketch/sketch.png?raw=true\"\u003e\n\n## Introduction\nA Keras (tensorflow \u003c 2.0) implementation of trVAE (transfer Variational Autoencoder) .\n\ntrVAE can be used for style transfer in images, predicting perturbations responses and batch-removal for single-cell RNA-seq.\n\n* For pytorch implementation check [Here](https://github.com/theislab/trvaep)\n## Getting Started\n\n## Installation\nBefore installing trVAE package, we suggest you to create a new Python 3.6 (or 3.7) \nvirtual env (or conda env) with the following steps:  \n\n### 1.  Installing virtualenv\n```bash\npip install virtualenv\n```\n\n### 2. Create a virtual with Python 3.6\n```bash\nvirtualenv trvae-env --python=python3.6 \n```\n\n### 3. trVAE package installation\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\ngit clone https://github.com/theislab/trVAE\ncd trVAE\npip install -r requirements\npython setup.py install \n```\n\n## Examples\n\n* 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.\n\n## Reproducing paper results:\nIn order to reproduce paper results visit [here](https://github.com/Naghipourfar/trVAE_reproducibility).\n\n## Reference\nIf you found trVAE useful please consider citing the published [manuscript.](https://academic.oup.com/bioinformatics/article/36/Supplement_2/i610/6055927) \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Ftrvae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheislab%2Ftrvae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Ftrvae/lists"}