EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment.

ABSTRACT: Foundation models exhibit strong capabilities for downstream tasks by learning generalized representations through self-supervised pre-training on large datasets. While several foundation models have been developed for single-cell RNA-seq (scRNA-seq) data, there is still a lack of models specifically tailored for single-cell ATAC-seq (scATAC-seq), which measures epigenetic information in individual cells. The principal challenge in developing such a model lies in the vast number of scATAC peaks and the significant sparsity of the data, which complicates the formulation of peak-to-peak correlations. To address this challenge, we introduce EpiFoundation, a foundation model for learning cell representations from the high-dimensional and sparse space of peaks. EpiFoundation relies on an

SUBMITTER: Wu J 

PROVIDER: S-EPMC11839112 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature