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

Yu Zhang (HKUST) · Yuchen Liu (Fudan University) · Chen Jiang (Shanghai Academy of Artificial Intelligence for Science) · Xin Guo (Shanghai Academy of Artificial Intelligence for Science) · LIMEI HAN (Fudan University) · Yuan Cheng (Fudan University) · Xin Gao (KAUST) · Yifeng Jiao (Shanghai Academy of Artificial Intelligence for Science) · Yushuai Wu (Shanghai Academy of Artificial Intelligence for Science) · Jiyang Li (Shanghai Academy of Artificial Intelligence for Science) · Hongwei Zhang (Fudan University) · Yuan Qi (Fudan University)
chromatin accessibilitycross-omics predictionenhancer-gene linksfoundation modelsgenome-aware tokenizationhigh dimensionalityhybrid architecturelong contextsmulti-omics analysisnoncoding genomeopen chromatin regionsregulatory signalsscatac-seqsparsityuniversal cell representationszero-shot 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. The implementation of ChromFound is available via https://github.com/JohnsonKlose/ChromFound.