Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Bioinformatics. 2018 Jul 1;34(13):i96-i104. doi: 10.1093/bioinformatics/bty285.
Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell-cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this type of data. In this work, we explore whether methods that have been developed previously for the analysis of bulk Hi-C data can be applied to scHi-C data. We apply methods designed for analysis of bulk Hi-C data to scHi-C data in conjunction with unsupervised embedding.
We find that one of these methods, HiCRep, when used in conjunction with multidimensional scaling (MDS), strongly outperforms three other methods, including a technique that has been used previously for scHi-C analysis. We also provide evidence that the HiCRep/MDS method is robust to extremely low per-cell sequencing depth, that this robustness is improved even further when high-coverage and low-coverage cells are projected together, and that the method can be used to jointly embed cells from multiple published datasets.
单细胞 Hi-C(scHi-C) 数据有望使科学家能够检测细胞核内 DNA 的 3D 结构，研究这种结构如何随机变化或沿发育或细胞周期轴变化。然而，Hi-C 数据分析需要考虑到这种类型数据的独特特征的方法。在这项工作中，我们探讨了以前为分析批量 Hi-C 数据而开发的方法是否可以应用于 scHi-C 数据。我们将专门为批量 Hi-C 数据分析而设计的方法与无监督嵌入结合起来应用于 scHi-C 数据。
我们发现，这些方法中的一种，HiCRep，与多维尺度分析（MDS）一起使用时，性能明显优于其他三种方法，包括以前用于 scHi-C 分析的一种技术。我们还提供了证据表明，HiCRep/MDS 方法对极低的每个细胞测序深度具有鲁棒性，当将高覆盖度和低覆盖度细胞一起投影时，这种鲁棒性进一步提高，并且该方法可用于联合嵌入来自多个已发表数据集的细胞。
