DISHIC: An Effective Method for Identifying Differential Interaction in Single-Cell Hi-C

Single-cell three-dimensional (3D) genomics plays a vital role in understanding the spatial organization of chromatin within the cell nucleus. It provides insights into how chromatin structures influence regulatory mechanisms and cellular functions that are specific to different cell types and states. Analyzing differential interactions-the fundamental units of the 3D genome-at the single-cell level is crucial for extracting this valuable information. However, single-cell 3D genome data such as single-cell High-throughput Chromatin Conformation Capture (scHi-C) data are inherently sparse, noisy and heterogeneous, posing significant challenges that limit the downstream analyses. Although existing computational methods offer helpful solutions for identifying differential interactions at the single-cell level, most of them do not explicitly account for intrinsic statistical properties of the data, nor do they comprehensively incorporate both within-sample and between-sample covariates. In this study, we developed a statistical method named DISHIC (Differential Interaction analysis in single-cell Hi-C) to perform differential interaction analysis in scHi-C data. DISHIC leverages the Zero-Inflated Negative Binomial-based Wavelet (ZINB-WaVE) model, making it well-suited for high-dimensional zero-inflated count data with high dispersion. It models each bin pair in each sample independently while incorporating both bin-pair-level and cell-level covariates, allowing it to effectively capture noise and heterogeneity in sparse scHi-C data. This approach can detect differential interactions with greater accuracy and reliability while also allowing users to flexibly define covariates as input based on their specific needs. We evaluated the performance of DISHIC using both real and simulated datasets, demonstrating its enhanced effectiveness compared to existing state-of-the-art methods under various conditions. Furthermore, we conducted a comprehensive case study analyzing multi-omics datasets from two types of glial cells. This study explored the intricate relationships among chromatin interactions, gene expressions, and epigenetic modifications, providing new insights into cell-type-specific regulatory mechanisms.