Enhancing the expressivity of single-cell chromatin accessibility representations through adaptive region embeddings

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) enables the characterization of epigenomic regulatory landscapes and cellular heterogeneity at single-cell resolution. However, learning effective representations from scATAC-seq data remains challenging because of its extreme sparsity, high dimensionality, and variability in genomic regions across datasets. Here, we introduce scChromHEARTs, a representation learning framework for single-cell Chromatin accessibility based on High-Expressivity Adaptive Region-aware embedding Transformations and a tailored self-supervised learning strategy. Rather than assigning fixed embeddings to predefined genomic regions, scChromHEARTs generates adaptive embeddings for arbitrary genomic intervals from their chromosomal identities and genomic coordinates. Combined with subset-based contrastive learning, this design enables efficient representation learning across the full genomic region space. scChromHEARTs achieved strong performance across diverse downstream applications, including cell-type annotation, perturbed cell generation, batch correction, and cross-dataset transferability. Ablation studies further demonstrate that input embeddings designed to finely encode genomic structure substantially improve representation quality. These results highlight the importance of designing input embeddings for effective representation learning of epigenomic data. Code is available at https://github.com/anonymous/ChromHEARTs.