dcHiC: differential compartment analysis of Hi-C datasets

The compartmental organization of chromatin and its changes play important roles in distinct biological processes carried out by mammalian genomes. However, differential compartment analyses have been mostly limited to pairwise comparisons and with a main focus on only the compartment flips (e.g., A-to-B). Here, we introduce dcHiC, which utilizes quantile normalized compartment scores and a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on three collections of Hi-C contact maps from mouse neural differentiation (n=3), mouse hematopoiesis (n=10) and human LCL cell lines (n=20), we show its effectiveness and sensitivity in detecting biologically relevant differences, including those validated by orthogonal experiments. Across these experiments, dcHiC reported regions with dynamically regulated genes associated with cell identity, along with correlated changes in chromatin states, replication timing and lamin B1 association. With its efficient implementation, dcHiC not only enables high-resolution compartment analysis but also includes a suite of additional features, including standalone browser visualization, differential interaction identification, and time-series clustering. As such, it is an essential addition to the Hi-C analysis toolbox for the ever-growing number of contact maps being generated. dcHiC is freely available at https://github.com/ay-lab/dcHiC, and examples from this paper can be seen at https://ay-lab.github.io/dcHiC.