ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911232767819776 |
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| author | Jiao, Yifeng Liu, Yuchen Zhang, Yu Guo, Xin Wu, Yushuai Jiang, Chen Li, Jiyang Zhang, Hongwei Han, Limei Gao, Xin Qi, Yuan Cheng, Yuan |
| author_facet | Jiao, Yifeng Liu, Yuchen Zhang, Yu Guo, Xin Wu, Yushuai Jiang, Chen Li, Jiyang Zhang, Hongwei Han, Limei Gao, Xin Qi, Yuan Cheng, Yuan |
| contents | The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneously. Key challenges lie in the high dimensionality and sparsity of scATAC-seq data, as well as the lack of a standardized schema for representing open chromatin regions (OCRs). Here, we present ChromFound, a foundation model tailored for scATAC-seq. ChromFound utilizes a hybrid architecture and genome-aware tokenization to effectively capture genome-wide long contexts and regulatory signals from dynamic chromatin landscapes. Pretrained on 1.97 million cells from 30 tissues and 6 disease conditions, ChromFound demonstrates broad applicability across 6 diverse tasks. Notably, it achieves robust zero-shot performance in generating universal cell representations and exhibits excellent transferability in cell type annotation and cross-omics prediction. By uncovering enhancer-gene links undetected by existing computational methods, ChromFound offers a promising framework for understanding disease risk variants in the noncoding genome. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data Jiao, Yifeng Liu, Yuchen Zhang, Yu Guo, Xin Wu, Yushuai Jiang, Chen Li, Jiyang Zhang, Hongwei Han, Limei Gao, Xin Qi, Yuan Cheng, Yuan Genomics Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneously. Key challenges lie in the high dimensionality and sparsity of scATAC-seq data, as well as the lack of a standardized schema for representing open chromatin regions (OCRs). Here, we present ChromFound, a foundation model tailored for scATAC-seq. ChromFound utilizes a hybrid architecture and genome-aware tokenization to effectively capture genome-wide long contexts and regulatory signals from dynamic chromatin landscapes. Pretrained on 1.97 million cells from 30 tissues and 6 disease conditions, ChromFound demonstrates broad applicability across 6 diverse tasks. Notably, it achieves robust zero-shot performance in generating universal cell representations and exhibits excellent transferability in cell type annotation and cross-omics prediction. By uncovering enhancer-gene links undetected by existing computational methods, ChromFound offers a promising framework for understanding disease risk variants in the noncoding genome. |
| title | ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data |
| topic | Genomics Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2505.12638 |