ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data

• URL: http://arxiv.org/abs/2505.12638v2
• Date: Tue, 20 May 2025 02:40:30 GMT
• Title: ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
• Authors: Yifeng Jiao, Yuchen Liu, Yu Zhang, Xin Guo, Yushuai Wu, Chen Jiang, Jiyang Li, Hongwei Zhang, Limei Han, Xin Gao, Yuan Qi, Yuan Cheng,
• Abstract summary: Single-cell Assay for Transposase- Chromatin using sequencing (scATAC-eq)<n>ChromFound is a foundation model tailored for scATAC-eq.
• Score: 24.68667275455985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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