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Single-cell Hi-C (scHi-C) technology enables probing of higher-order chromatin structures in individual cells. It provides an opportunity to get a deeper insight into genomic compartmentalization changes of single cells across different conditions, paving the way to a common understanding of the interplay among compartmental organization, genome functions, and cellular phenotypes. Unfortunately, there are only a few methods currently available for the differential analysis of A/B compartments on Hi-C data at the bulk level; the computational analysis of compartmentalization changes at the single-cell level is a field in its infancy. Herein, we propose DeepExDC, an interpretable 1D convolutional neural network for differential analysis of A/B compartments in scHi-C data on a genome-wide scale. It accepts Hi-C contact matrices at the single-cell level, runs without any distribution assumption and differential pattern limitation, and interprets genomic compartmentalization changes across multiple conditions. The results on simulated and experimental scHi-C data show that our DeepExDC has higher accuracies in detecting different types of compartmentalization changes, and the interpretation values are demonstrated to be able to reflect compartment changes across cell types. It is also observed that the differential compartments given by DeepExDC agree well with those by state-of-the-art methods at the bulk level, help to characterize heterogeneity of single cells, and exhibit a reasonable biological relevance in multiple regards. In addition, considering that DeepExDC is free of distribution assumptions and differential patterns, we attempted to transfer it onto scRNA-seq and scATAC-seq data; it is interesting that our method also presents considerable power compared with the competing methods.

DeepExDC interprets genomic compartmentalization changes in single-cell Hi-C data