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--- |
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license: apache-2.0 |
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--- |
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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. |
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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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``` |
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@article {Wu2025.02.05.636688, |
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author = {Wu, Juncheng and Wan, Changxin and Ji, Zhicheng and Zhou, Yuyin and Hou, Wenpin}, |
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title = {EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment}, |
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elocation-id = {2025.02.05.636688}, |
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year = {2025}, |
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doi = {10.1101/2025.02.05.636688}, |
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URL = {https://www.biorxiv.org/content/early/2025/02/08/2025.02.05.636688}, |
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eprint = {https://www.biorxiv.org/content/early/2025/02/08/2025.02.05.636688.full.pdf}, |
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journal = {bioRxiv} |
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} |
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``` |
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