Abstract
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.
Highlights
Single-cell chromosome conformation capture methods, such as single-cell Hi-C, [1, 2, 3, 4, 5, 6] enable quantitative assessment of 3D conformation of chromosomes in individual cells
It has become possible to investigate the 3D conformations of the genomes of individual cells using a high throughput sequencing assay called single cell Hi-C
We further show that the 3D conformations of single cells are linked to the expression of cell type-specific genes and to cell cycle-associated conformational patterns
Summary
Single-cell chromosome conformation capture methods, such as single-cell Hi-C (scHi-C), [1, 2, 3, 4, 5, 6] enable quantitative assessment of 3D conformation of chromosomes in individual cells. We previously developed a similarity-based embedding method called HiCRep/MDS to project scHi-C data into low dimensional space and arrange cells according to their cell-cycle phases [10]. This approach leverages a similarity measure, stratum adjusted correlation coefficient, that was developed for comparing bulk Hi-C matrices [11], combined with multidimensional scaling (MDS) to preserve distances between individual scHi-C contact maps. HiCRep/MDS can very accurately recover cell cycle information from several Hi-C data sets, the method frequently fails to capture chromatin structural differences between cell types [12]
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