拓扑相关结构域（topologically associating domains，TADs）是哺乳动物基因组中重要的三维结构单元，对于调控基因组组织架构和基因表达发挥关键作用。然而，由于单细胞高通量染色质构象捕获（single-cell Hi-C）数据具有显著的超稀疏性，类TADs结构域特征发生退化，使得在单细胞Hi-C数据上识别类TADs结构域仍然面临挑战。本文提出一种基于Hi-C接触矩阵重构的单细胞类TADs结构域识别方法RecTAD，该方法首先采用图嵌入与谱传播增强相结合的方法重构单细胞Hi-C接触矩阵，增强其中社区信息，然后在重构的接触矩阵上基于多尺度Haar特征识别类TADs结构域。在下采样群体细胞Hi-C、模拟单细胞Hi-C、以及实验单细胞Hi-C数据上的实验结果表明，本文提出的RecTAD方法在识别准确性方面具有更好的表现，且展现出良好的生物学相关性，为单细胞尺度下三维基因组结构解析及其调控机制研究提供了一种有效的技术手段。RecTAD的源代码已公开发布于GitHub：https://github.com/doubletigerzju/RecTAD。

Topologically associating domains (TADs) are fundamental three-dimensional structural units of mammalian genomes and play critical roles in regulating genome organization and gene expression. However, the extreme sparsity of single-cell high-throughput chromosome conformation capture (single-cell Hi-C) data substantially degrades the features of TAD-like domains, posing significant challenges for their accurate identification. In this study, we proposed RecTAD, a single-cell TAD-like domain identification method based on Hi-C contact matrix reconstruction. Specifically, RecTAD reconstructed single-cell Hi-C contact matrices by integrating graph embedding with spectral propagation, thereby enhancing the underlying community information. Subsequently, TAD-like domains were identified from the reconstructed contact matrices using multi-scale Haar features. Extensive experimental results on downsampled bulk Hi-C data, simulated single-cell Hi-C data, and experimental single-cell Hi-C datasets demonstrate that RecTAD achieves superior identification accuracy and exhibits strong biological relevance. These results indicate that RecTAD provides an effective technical approach for analyzing three-dimensional genome architecture and investigating its regulatory mechanism at the single-cell level. The source code of RecTAD is publicly available at: https://github.com/doubletigerzju/RecTAD.