{"id":13869698,"url":"https://github.com/gao-lab/GLUE","last_synced_at":"2025-07-15T18:31:46.567Z","repository":{"id":37410990,"uuid":"398781299","full_name":"gao-lab/GLUE","owner":"gao-lab","description":"Graph-linked unified embedding for single-cell multi-omics data integration","archived":false,"fork":false,"pushed_at":"2025-06-16T17:59:33.000Z","size":6753,"stargazers_count":413,"open_issues_count":24,"forks_count":61,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-06-16T18:30:11.382Z","etag":null,"topics":["bioinformatics","deep-learning","single-cell","single-cell-multiomics"],"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/gao-lab.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-08-22T11:20:30.000Z","updated_at":"2025-06-16T17:59:37.000Z","dependencies_parsed_at":"2022-07-15T21:17:18.756Z","dependency_job_id":"b89fb32a-56a7-4954-af80-f1206b6d634c","html_url":"https://github.com/gao-lab/GLUE","commit_stats":null,"previous_names":[],"tags_count":8,"template":false,"template_full_name":null,"purl":"pkg:github/gao-lab/GLUE","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gao-lab%2FGLUE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gao-lab%2FGLUE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gao-lab%2FGLUE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gao-lab%2FGLUE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gao-lab","download_url":"https://codeload.github.com/gao-lab/GLUE/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gao-lab%2FGLUE/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265451443,"owners_count":23767768,"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":["bioinformatics","deep-learning","single-cell","single-cell-multiomics"],"created_at":"2024-08-05T20:01:12.432Z","updated_at":"2025-07-15T18:31:46.560Z","avatar_url":"https://github.com/gao-lab.png","language":"Python","funding_links":[],"categories":["Python","Software packages","Machine Learning Tasks and Models","Software packages and methods"],"sub_categories":["Multi-assay data integration","Foundation Models","Multi-omics autoencoders"],"readme":"# GLUE (Graph-Linked Unified Embedding)\n\n[![stars-badge](https://img.shields.io/github/stars/gao-lab/GLUE?logo=GitHub\u0026color=yellow)](https://github.com/gao-lab/GLUE/stargazers)\n[![pypi-badge](https://img.shields.io/pypi/v/scglue)](https://pypi.org/project/scglue)\n[![conda-badge](https://anaconda.org/bioconda/scglue/badges/version.svg)](https://anaconda.org/bioconda/scglue)\n[![docs-badge](https://readthedocs.org/projects/scglue/badge/?version=latest)](https://scglue.readthedocs.io/en/latest/?badge=latest)\n[![build-badge](https://github.com/gao-lab/GLUE/actions/workflows/build.yml/badge.svg)](https://github.com/gao-lab/GLUE/actions/workflows/build.yml)\n[![coverage-badge](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/Jeff1995/e704b2f886ff6a37477311b90fdf7efa/raw/coverage.json)](https://github.com/gao-lab/GLUE/actions/workflows/build.yml)\n[![style-badge](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)\n[![license-badge](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nGraph-linked unified embedding for single-cell multi-omics data integration\n\n![Model architecture](docs/_static/architecture.svg)\n\nFor more details, please check out our [publication](https://doi.org/10.1038/s41587-022-01284-4).\n\n## Directory structure\n\n```\n.\n├── scglue                  # Main Python package\n├── data                    # Data files\n├── evaluation              # Method evaluation pipelines\n├── experiments             # Experiments and case studies\n├── tests                   # Unit tests for the Python package\n├── docs                    # Documentation files\n├── custom                  # Customized third-party packages\n├── packrat                 # Reproducible R environment via packrat\n├── env.yaml                # Reproducible Python environment via conda\n├── pyproject.toml          # Python package metadata\n├── LICENSE\n└── README.md\n```\n\n## Installation\n\nThe `scglue` package can be installed via conda using one of the following commands:\n\n```sh\nconda install -c conda-forge -c bioconda scglue  # CPU only\nconda install -c conda-forge -c bioconda scglue pytorch-gpu  # With GPU support\n```\n\nOr, it can also be installed via pip:\n\n```sh\npip install scglue\n```\n\n\u003e Installing within a\n\u003e [conda environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)\n\u003e is recommended.\n\n## Usage\n\nPlease checkout the documentations and tutorials at\n[scglue.readthedocs.io](https://scglue.readthedocs.io).\n\nA Chinese version is also available [here](https://scglue.readthedocs.io/zh_CN/latest/).\n\n## Development\n\nInstall scglue in editable form via flit (first install flit via conda or pip\nif not installed already):\n\n```sh\nflit install -s\n```\n\nRun unit tests:\n\n```sh\npytest --cov=\"scglue\" --cov-report=\"term-missing\" tests [--cpu-only]\n```\n\nBuild documentation:\n\n```sh\nsphinx-build -b gettext docs docs/_build/gettext\nsphinx-intl update -p docs/_build/gettext -l zh_CN -d docs/locale\nsphinx-build -b html -D language=en docs docs/_build/html/en  # English version\nsphinx-build -b html -D language=zh_CN docs docs/_build/html/zh_CN  # Chinese version\n```\n\n## Reproduce results\n\n1. Checkout the repository to v0.2.0:\n\n   ```sh\n   git checkout tags/v0.2.0\n   ```\n\n2. Create a local conda environment using the `env.yaml` file,\nand then install scglue:\n\n   ```sh\n   conda env create -p conda -f env.yaml \u0026\u0026 conda activate ./conda\n   flit install -s\n   ```\n\n3. Set up a project-specific R environment:\n\n   ```R\n   packrat::restore()  # Packrat should be automatically installed if not available.\n   install.packages(\"data/download/Saunders-2018/DropSeq.util_2.0.tar.gz\", repos = NULL)\n   install.packages(\"custom/Seurat_4.0.2.tar.gz\", lib = \"packrat/custom\", repos = NULL)\n   ```\n\n   \u003e R 4.0.2 was used during the project, but any version above 4.0.0 should be compatible.\n\n4. Follow instructions in `data` to prepare the necessary data.\n5. Follow instructions in `evaluation` for method evaluation.\n6. Follow instructions in `experiments` for case studies.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgao-lab%2FGLUE","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgao-lab%2FGLUE","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgao-lab%2FGLUE/lists"}