用状态空间模型提升Hi-C数据分辨率，更准识别染色质三维结构。
HiCMamba: Enhancing Hi-C Resolution and Identifying 3D Genome Structures with State Space Modeling
- 基于UNet和全览扫描块的架构，融合多尺度全局与局部感知。
- 在低覆盖数据上仍能精准恢复染色质互作频率，优于现有方法。
- 适合基因组结构研究者，尤其关注TADs与环结构分析的团队。
Hi-C技术通过测量全基因组范围内的染色质相互作用频率，为研究细胞核内三维基因组结构提供了强大工具。然而，高测序成本和技术挑战常导致Hi-C数据覆盖度有限，进而影响染色质互作频率估计的准确性。为此，我们提出一种基于深度学习的新方法HiCMamba，采用状态空间模型提升Hi-C接触图分辨率。该方法利用基于UNet的自编码器架构，堆叠提出的全览扫描块，实现多尺度下对全局与局部感受野的联合感知。实验表明，HiCMamba在性能上超越当前最优方法，同时显著降低计算资源消耗。此外，由HiCMamba重建的接触图中识别出的三维基因组结构（包括拓扑关联域TADs和染色质环）经相关表观基因组特征验证，具有生物学可信性。本工作展示了状态空间模型作为基础框架在提升Hi-C分辨率方面的潜力。
原文摘要 · Abstract (English)
Hi-C technology measures genome-wide interaction frequencies, providing a powerful tool for studying the 3D genomic structure within the nucleus. However, high sequencing costs and technical challenges often result in Hi-C data with limited coverage, leading to imprecise estimates of chromatin interaction frequencies. To address this issue, we present a novel deep learning-based method HiCMamba to enhance the resolution of Hi-C contact maps using a state space model. We adopt the UNet-based auto-encoder architecture to stack the proposed holistic scan block, enabling the perception of both global and local receptive fields at multiple scales. Experimental results demonstrate that HiCMamba outperforms state-of-the-art methods while significantly reducing computational resources. Furthermore, the 3D genome structures, including topologically associating domains (TADs) and loops, identified in the contact maps recovered by HiCMamba are validated through associated epigenomic features. Our work demonstrates the potential of a state space model as foundational frameworks in the field of Hi-C resolution enhancement.
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