{"id":31805770,"url":"https://github.com/theislab/sc_mae","last_synced_at":"2025-10-11T02:58:22.213Z","repository":{"id":236945227,"uuid":"784251668","full_name":"theislab/sc_mae","owner":"theislab","description":null,"archived":false,"fork":false,"pushed_at":"2024-05-29T16:57:27.000Z","size":421,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-22T09:28:43.863Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/theislab.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-04-09T13:39:31.000Z","updated_at":"2025-01-16T06:41:20.000Z","dependencies_parsed_at":"2024-04-29T11:04:36.838Z","dependency_job_id":null,"html_url":"https://github.com/theislab/sc_mae","commit_stats":null,"previous_names":["theislab/sc_mae"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/theislab/sc_mae","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2Fsc_mae","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2Fsc_mae/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2Fsc_mae/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2Fsc_mae/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/theislab","download_url":"https://codeload.github.com/theislab/sc_mae/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theislab%2Fsc_mae/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279005949,"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":[],"created_at":"2025-10-11T02:58:16.946Z","updated_at":"2025-10-11T02:58:22.206Z","avatar_url":"https://github.com/theislab.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Masked Autoencoder in Single-Cell Genomics\n==========================================\n\nThis repository provides a simple setting to train a Masked Autoencoder (MAE) on single-cell genomics data with a random masking strategy. The provided code is designed to work with a smaller scale `adata` object that fits into memory.\n\nContents\n--------\n\n- ``data.py``: Module for loading and preprocessing single-cell genomics data.\n- ``Masking.ipynb``: Jupyter notebook demonstrating the random masking strategy.\n- ``models.py``: Contains the implementation of the Masked Autoencoder.\n- ``train.py``: Script for training the Masked Autoencoder model.\n- ``train.sh``: Bash script for executing the training process.\n\nSystem Requirements\n-------------------\n\n- Python 3.10\n- Dependencies listed in `requirements.txt`\n\nUsage\n-----\n\n1. Clone the repository:\n\n.. code-block:: bash\n\n    git clone https://github.com/theislab/sc_mae.git\n\n2. Install the required dependencies:\n\n.. code-block:: bash\n\n    pip install -r requirements.txt\n\n3. Prepare the data:\n\n- Download the sample data from the publication mentioned in the citation section or use your own processed adata object.\n\n4. Execute the training script:\n\n.. code-block:: bash\n\n    bash train.sh\n\nDemo\n----\n\nTo apply this code, follow these steps:\n\n1. **Download Sample Data**: You can download the `adata` object from the publication mentioned in the citation section or use your own processed h5ad object.\n\n2. **Prepare Data**: If you are using your own data, make sure it is preprocessed and compatible with the provided code. Otherwise, follow the data loading and preprocessing steps in `data.py`.\n\n3. **Train the Model**: Execute the training script `train.py` by running `bash train.sh`. Adjust the hyperparameters and configurations as needed in the script.\n\nCitation\n--------\n\nThis repository is a part of a larger project and serves as a simplified demo. If you use this code in your research, please cite the following paper:\n\n**Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics**\n\n`Link to the paper \u003chttps://doi.org/10.1101/2024.02.16.580624\u003e`_\n\n`Link to the repository \u003cgithub.com/theislab/ssl_in_scg\u003e`_\n\nIf you use the sample data in your research, please cite the following paper:\n\n**COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas**\n\n`Link to the paper \u003chttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825482/\u003e`_\n\nAcknowledgments\n---------------\n\n- The sample data used in this project is sourced from the COMBATdb.\n\nContribution\n------------\n\nContributions to improve this codebase are welcome. Please fork the repository and submit a pull request with your changes.\n\nLicense\n-------\n\nThis project is licensed under the MIT License - see `MIT License \u003chttps://opensource.org/licenses/MIT\u003e`_.\n\nPlease refer to the main repository for more detailed information and a more elaborate analysis.\n\nAuthors\n-------\n\nsc_mae was written by `Till Richter \u003ctill.richter@helmholtz-munich.de\u003e`_.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fsc_mae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheislab%2Fsc_mae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fsc_mae/lists"}