13 / 2026-02-01 16:08:25

scDIAGRAM: Detecting Chromatin Compartments from Individual Single-Cell Hi-C Matrix without Imputation or Reference Features

single-cell Hi-C,statistical modeling,cellular heterogeneity,A/B compartment

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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, and subsequently

assigns A/B labels using conventional genomic annotations. 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.