# scDIAGRAM: detecting chromatin compartments from individual single-cell Hi-C matrix without imputation or reference features

## TL;DR

This paper introduces scDIAGRAM, a new method to detect chromatin compartments in single-cell Hi-C data without needing imputation or reference features.

## Contribution

scDIAGRAM is the first method to detect A/B compartments directly from individual scHi-C matrices without imputation or external features.

## Key findings

- scDIAGRAM accurately detects chromatin compartments in simulated and real scHi-C datasets.
- The method captures compartmental shifts linked to transcriptional variation in mouse and human cells.
- It performs robustly across diverse biological contexts like brain development and leukemia.

## Abstract

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, 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.