Nguyen, M.; Wall, B. P. G.; Harrell, J. C.; Dozmorov, M. G.

Changes in the three-dimensional (3D) structure of the human genome are key indicators of cancer and developmental disorders. Techniques like chromatin conformation capture (Hi-C) have been developed to study these global 3D structures, typically requiring millions of cells and an extremely high sequencing depth (around 1 billion reads per sample) for bulk Hi-C. In contrast, single-cell Hi-C (scHi-C) captures 3D structures at the individual cell level but faces significant data sparsity, marked by a high proportion of zeros. While differential analysis methods exist for bulk Hi-C data, they are limited for scHi-C data. To address this, we developed a method for differential scHi-C analysis, building on existing techniques in the HiCcompare R package. Our approach imputes sparse scHi-C data by considering genomic distances and creates pseudo-bulk Hi-C matrices by summing condition-specific data. The data are normalized using LOESS regression, and differential chromatin interactions are detected via Gaussian Mixture Model (GMM) clustering. Our workflow outperforms existing methods in identifying differential chromatin interactions across various genomic distances, fold changes, resolutions, and sample sizes in both simulated and experimental contexts. This allows for effective detection of cell type-specific differences in chromatin structure, which has meaningful associations with biological and epigenetic features. Our method is implemented in the scHiCcompare R package, available at https://github.com/dozmorovlab/scHiCcompare.