Single-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data.

单细胞 Hi-C (scHi-C) 可以识别三维 (3D) 染色质组织的细胞间变异性，但测量的相互作用的稀疏性带来了分析挑战。在这里，我们报告了 Higashi，一种基于超图表示学习的算法，可以结合单个细胞之间的潜在相关性来增强接触图的整体插补。 Higashi 优于现有的 scHi-C 数据嵌入和插补方法，能够识别单细胞中的多尺度 3D 基因组特征，例如区室化和类似 TAD 的域边界，从而可以精确描绘细胞间的变异性。此外，与两种模式的单独分析相比，Higashi 可以将同一细胞中联合分析的表观基因组信号纳入超图表示学习框架，从而改善单核甲基 3C 数据的嵌入。在人类前额皮质的 scHi-C 数据集中，Higashi 确定了 3D 基因组特征与细胞类型特异性基因调控之间的联系。 Higashi 还可以扩展到分析单细胞多路染色质相互作用和其他多模式单细胞组学数据。