Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America.
PLoS Comput Biol. 2020 Sep 18;16(9):e1008173. doi: 10.1371/journal.pcbi.1008173. eCollection 2020 Sep.
Single-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate nine different single-cell combinatorial indexed Hi-C (sci-Hi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 19,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sci-Hi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.
单细胞 Hi-C（scHi-C）技术可在单个细胞中检测全基因组染色质相互作用，使我们能够深入了解 3D 基因组结构。然而，scHi-C 数据的极度稀疏性给分析带来了重大障碍，限制了我们从隐藏的生物学信息中提取信息的能力。在这项工作中，我们通过将主题建模应用于 scHi-C 数据来解决这个问题。主题建模非常适合在离散数据集中发现潜在主题。在我们的分析中，我们从五个人类细胞系（GM12878、H1Esc、HFF、IMR90 和 HAP1）生成了九个不同的单细胞组合索引 Hi-C（sci-Hi-C）文库，其中包含超过 19000 个细胞。我们证明主题建模能够成功地以“染色质主题”的形式从 sci-Hi-C 数据中捕获细胞类型差异。我们还展示了与这些主题中基因对相关的特定隔室结构的富集。
