ScHiCAtt: Enhancing single-cell Hi-C data resolution using attention-based models.

The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains one of the leading methods for unraveling 3D genome structures; however, limited resolution, data sparsity, and incomplete coverage in single-cell Hi-C data pose significant challenges for comprehensive analysis. Traditional convolutional neural network-based models often suffer from blurring and loss of fine details, while generative adversarial network based methods encounter difficulties in maintaining diversity and generalization. Moreover, existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance high-resolution data in another cell type. To address these limitations, we propose ScHiCAtt (Single-cell Hi-C Attention-Based Model), which leverages attention mechanisms to capture both long-range and local dependencies in Hi-C data, significantly enhancing resolution while preserving biologically meaningful interactions. By dynamically focusing on regions of interest, attention mechanisms effectively mitigate data sparsity and enhance model performance in low-resolution contexts. Extensive experiments on Human and Drosophila single-cell Hi-C data demonstrate that ScHiCAtt consistently outperforms existing methods in terms of computational and biological reproducibility metrics across various downsampling ratios. Our results also show superior generalization across different chromosomes of the same cell type, as well as across cell types, species, and from single-cell to bulk Hi-C data, highlighting the robustness and adaptability of our approach. ScHiCAtt source code is publicly available at https://github.com/OluwadareLab/ScHiCAtt.