Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data.

• Main Authors: Hyeon-Jin Kim, Galip Gürkan Yardımcı, Giancarlo Bonora, Vijay Ramani, Jie Liu, Ruolan Qiu, Choli Lee, Jennifer Hesson, Carol B Ware, Jay Shendure, Zhijun Duan, William Stafford Noble
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-09-01
• Series: PLoS Computational Biology
• Online Access: https://doi.org/10.1371/journal.pcbi.1008173
• ISSN: 1553-734X; 1553-7358

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.