Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Cell Syst. 2022 Oct 19;13(10):798-807.e6. doi: 10.1016/j.cels.2022.09.004.
Single-cell Hi-C (scHi-C) technologies can probe three-dimensional (3D) genome structures in individual cells. However, existing scHi-C analysis methods are hindered by the data quality and complex 3D genome patterns. The lack of computational scalability and interpretability poses further challenges for large-scale analysis. Here, we introduce Fast-Higashi, an ultrafast and interpretable method based on tensor decomposition and partial random walk with restart, enabling joint identification of cell identities and chromatin meta-interactions from sparse scHi-C data. Extensive evaluations demonstrate the advantage of Fast-Higashi over existing methods, leading to improved delineation of rare cell types and continuous developmental trajectories. Fast-Higashi can directly identify 3D genome features that define distinct cell types and help elucidate cell-type-specific connections between genome structure and function. Moreover, Fast-Higashi can generalize to incorporate other single-cell omics data. Fast-Higashi provides a highly efficient and interpretable scHi-C analysis solution that is applicable to a broad range of biological contexts.
单细胞 Hi-C(scHi-C)技术可在单个细胞中探测三维(3D)基因组结构。然而,现有的 scHi-C 分析方法受到数据质量和复杂的 3D 基因组模式的限制。缺乏计算可扩展性和可解释性为大规模分析带来了进一步的挑战。在这里,我们介绍了 Fast-Higashi,这是一种基于张量分解和部分随机游走与重启的超快且可解释的方法,能够从稀疏 scHi-C 数据中联合识别细胞身份和染色质元相互作用。广泛的评估表明,Fast-Higashi 优于现有方法,从而改善了稀有细胞类型的划分和连续的发育轨迹。Fast-Higashi 可以直接识别定义不同细胞类型的 3D 基因组特征,并有助于阐明基因组结构和功能之间的细胞类型特异性连接。此外,Fast-Higashi 可以推广到纳入其他单细胞组学数据。Fast-Higashi 提供了一种高效且可解释的 scHi-C 分析解决方案,适用于广泛的生物学背景。