{"id":22731060,"url":"https://github.com/genentech/svae","last_synced_at":"2025-04-14T00:31:15.048Z","repository":{"id":65472685,"uuid":"562713629","full_name":"Genentech/sVAE","owner":"Genentech","description":null,"archived":false,"fork":false,"pushed_at":"2023-07-21T14:07:07.000Z","size":1612,"stargazers_count":55,"open_issues_count":4,"forks_count":5,"subscribers_count":6,"default_branch":"main","last_synced_at":"2024-09-26T02:01:33.042Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Genentech.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-11-07T05:04:17.000Z","updated_at":"2024-07-03T00:27:38.000Z","dependencies_parsed_at":"2024-09-26T02:01:02.866Z","dependency_job_id":null,"html_url":"https://github.com/Genentech/sVAE","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2FsVAE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2FsVAE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2FsVAE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Genentech%2FsVAE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Genentech","download_url":"https://codeload.github.com/Genentech/sVAE/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":229117070,"owners_count":18022819,"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","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":[],"created_at":"2024-12-10T19:19:18.066Z","updated_at":"2024-12-10T19:19:18.698Z","avatar_url":"https://github.com/Genentech.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling\n\nThis repository contains an implementation of the sparse VAE framework applied to single-cell perturbation data, as descibed in [\"Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling\"](https://arxiv.org/abs/2211.03553). \n\n\n[![Stars](https://img.shields.io/github/stars/Genentech/sVAE?logo=GitHub\u0026color=yellow)](https://github.com/Genentech/sVAE/stargazers)\n[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)\n\n\u003ccenter\u003e\n    \u003cimg src=\"svae+.png?raw=true\" width=\"750\"\u003e\n\u003c/center\u003e\nOverview of the sparse VAE framework applied to single-cell perturbation data. (A) Input data are gene expression profiles of cells under different genetic or chemical perturbations (colors), as well as the intervention label. (B) A schematic of the generative model, and the causal semantics of the sparse VAE (C) Three method outputs. (i) identification of target latent variables, encoded as a causal graph between the interventions and latent variables; (ii) a disentangled latent model for which individual latent variables are more likely to be interpreted as the activity of a relevant biological process; and (iii) the generalization of the generative model to unseen interventions (e.g., for latent target identification).\n\n## User guide\n\n\n### Installation\nDownload or clone this repository. Then from inside the folder simply run:\n```\npip install -e . \n```\n\n### Example\nAn example script for the sandbox can be found in ``` entry_points/demo.py```.\nThe code for reproducing the real data analysis can be found in ``` entry_points/run_real_data_replogle_wandb.py```.\n\n## References\n\n```\n@article{svae+,\n  title={Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling},\n  author={Lopez, Romain and Tagasovska, Natasa and Ra, Stephen and Cho, Kyunghyun and Pritchard, Jonathan K. and Regev, Aviv },\n  journal={Conference on Causal Learning and Reasoning},\n  year={2023},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgenentech%2Fsvae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgenentech%2Fsvae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgenentech%2Fsvae/lists"}