SsJSD: A Fusion of Sparsity and Spatial Information for Hi-C Single-Cell Clustering

Thomas M. Menino Convention & Exhibition Center 

Single-cell high-throughput chromatin conformation capture (scHi-C) profiles 3D genome architecture at cellular resolution. While recent frameworks use spatial patterns for dissimilarity measures in clustering, the inherent sparsity and high dimensionality of scHi-C matrices pose challenges. Crucially, existing measures often fail to distinguish biologically meaningful structural zeros (SZs) from technical dropouts.
We introduce ssJSD (spatial and structural-zero-aware Jensen-Shannon Divergence), a framework explicitly accounting for scHi-C sparsity. By integrating band-wise contact profiles with SZ-induced sparsity matrices, ssJSD leverages both spatial patterns and biological absence of contacts. We adopted two integration strategies: early fusion, concatenating information into a single representation, and late fusion, integrating JSD-based dissimilarities via diverse averaging. Through simulations and applications to human cell lines and prefrontal cortex data, we demonstrate that ssJSD improves clustering accuracy and effectively distinguishes cell types. Our findings highlight that integrating SZ patterns is important for accurately quantifying cell-to-cell variability.

Single cell Hi-C

Single-cell clustering

Contact distance profile

Structural zeros

Jensen-Shannon divergence

Data integration 

Section on Statistics in Genomics and Genetics