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| license: apache-2.0 |
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| ### Introduction |
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| We propose the **MiniAtlas** dataset, containing more than 100,000 scATAC-seq with paired scRNA-seq as training data, across 19 tissues and 56 cell types, facilitating the training of foundation models. This dataset can be used to training single-cell multiomics fundation model. |
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| ### Subsets |
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| This dataset is divided into four subsets to accommodate different research needs and access limitations: |
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| 1. `full_atlas_atac.h5ad` and `full_atlas_rna.h5ad` (~120k samples): full data of MiniAtlas, containing all tissues and cell types. |
| 2. Evaluation set for different tissues: containing three tissues (Kidney, PBMC, BMMC), can be used to cell-type annotation or RNA-prediction fine-tuning and evaluation. |
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| ### Citation |
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| If you find MiniAtlas useful for your research and applications, please cite using this BibTeX: |
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| ``` |
| @article {Wu2025.02.05.636688, |
| author = {Wu, Juncheng and Wan, Changxin and Ji, Zhicheng and Zhou, Yuyin and Hou, Wenpin}, |
| title = {EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment}, |
| elocation-id = {2025.02.05.636688}, |
| year = {2025}, |
| doi = {10.1101/2025.02.05.636688}, |
| URL = {https://www.biorxiv.org/content/early/2025/02/08/2025.02.05.636688}, |
| eprint = {https://www.biorxiv.org/content/early/2025/02/08/2025.02.05.636688.full.pdf}, |
| journal = {bioRxiv} |
| } |
| ``` |
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