Xiaoqing Wu, Zian Wang, Rui Jiang, Xiaoyang Chen
Single-cell Hi-C enables the characterization of three-dimensional chromatin organization in individual cells but remains challenging to analyze due to extreme sparsity and uneven contact distributions across genomic distances. These properties result in strong near-diagonal signals and complex multiscale interaction patterns that hinder effective modeling. Here, we present Hi-Cformer, a transformer-based method that simultaneously models multiscale blocks of single-cell chromatin contact maps through a specialized attention mechanism designed to capture dependencies across genomic regions and scales. Hi-Cformer learns robust low-dimensional cell representations from sparse single-cell Hi-C data, leading to improved separation of cell types compared to existing methods. In addition, Hi-Cformer accurately imputes chromatin interaction signals associated with cellular heterogeneity, including topologically associating domain-like boundaries and A/B compartments. Leveraging the learned embeddings, Hi-Cformer further enables accurate and robust cell type annotation across both intra- and inter-dataset scenarios.