Single-cell Hi-C (scHi-C) provides unprecedented insight into 3D genome organization, but its sparse and noisy data
pose challenges in accurately detecting A/B compartments, which are crucial for understanding chromatin structure
and gene regulation. We presented scDIAGRAM, a data-driven method for annotating A/B compartments in single
cells using direct statistical modeling and graph community detection. Unlike existing approaches, scDIAGRAM
infers chromatin compartments directly from individual scHi-C matrix without imputation or external reference
features. Accuracy and robustness of scDIAGRAM were illustrated through simulated scHi-C datasets and a human
cell line. We applied scDIAGRAM to real scHi-C datasets from the mouse brain cortex, mouse embryonic development,
and human acute myeloid leukemia (AML), demonstrating its ability to capture compartmental shifts associated
with transcriptional variation. This robust framework offers new insights into the functional roles of chromatin compartments
at single-cell resolution across various biological contexts.

single-cell Hi-C;A/B compartments;Statistical Analysis;heterogeneity