DiffDomain-Spectrum identifies structurally reorganized TADs from sparse aggregated single-cell Hi-C contact maps

Structurally reorganized topologically associating domains (TADs) capture condition- or cell-type-specific remodeling of chromatin contacts and are important for understanding genome organization in health and disease. Emerging single-cell Hi-C (scHi-C) technologies enable such comparisons across heterogeneous cell populations, but aggregated scHi-C contact maps remain sparse at biologically meaningful 25 kb resolution, limiting reliable TAD reorganization detection. Here we present DiffDomain-Spectrum, a spectral statistical framework for identifying reorganized TADs between conditions or cell types from aggregated raw scHi-C contact maps. It tests normalized TAD-level difference matrices without separately normalizing sparse maps or enhancing individual scHi-C contact maps. Comparison with a semicircle-law null integrates evidence across the full eigenvalue spectrum. Across multiple scHi-C platforms, DiffDomain-Spectrum balances false positive control and detection sensitivity relative to alternative bulk callers, and detects a substantially higher proportion of reference TADs as reorganized than the boundary-focused single-cell method scHiCluster. Detected TADs show coherent aggregate contact patterns and CTCF binding changes and are enriched for differentially expressed genes, supporting biological relevance. Together, these results establish DiffDomain-Spectrum as a statistically principled framework for comparative domain-level analysis of sparse aggregated scHi-C contact maps without single-cell map enhancement.