Comparison of computational methods for 3D genome analysis at single-cell Hi-C level.

Abstract

Hi-C is a high-throughput chromosome conformation capture technology that is becoming routine in the literature. Although the price of sequencing has been dropping dramatically, high-resolution Hi-C data are not always an option for many studies, such as in single cells. However, the performance of current computational methods based on Hi-C at the ultra-sparse data condition has yet to be fully assessed. Therefore, in this paper, after briefly surveying the primary computational methods for Hi-C data analysis, we assess the performance of representative methods on data normalization, identification of compartments, Topologically Associating Domains (TADs) and chromatin loops under the condition of ultra-low resolution. We showed that most state-of-the-art methods do not work properly for that condition. Then, we applied the three best-performing methods on real single-cell Hi-C data, and their performance indicates that compartments may be a statistical feature emerging from the cell population, while TADs and chromatin loops may dynamically exist in single cells.