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In-Nucleus Hi-C in Drosophila Cells
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DISHIC: An Effective Method for Identifying Differential Interaction in Single-Cell Hi-C.

Yichao Zhao, Ruiqing Zheng, Li Tang

    IEEE Transactions on Computational Biology and Bioinformatics
    |September 23, 2025
    PubMed
    Summary
    This summary is machine-generated.

    We developed DISHIC, a novel statistical method for analyzing single-cell 3D genome data. DISHIC accurately identifies differential chromatin interactions in sparse, noisy single-cell Hi-C data, improving our understanding of cellular regulation.

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    Area of Science:

    • Genomics
    • Computational Biology
    • Epigenetics

    Background:

    • Single-cell 3D genomics, particularly single-cell High-throughput Chromatin Conformation Capture (scHi-C), is crucial for understanding nuclear chromatin organization and its influence on cellular functions.
    • scHi-C data presents significant challenges due to inherent sparsity, noise, and heterogeneity, limiting downstream analysis of differential interactions.
    • Existing computational methods often fail to fully account for the statistical properties and covariates within scHi-C data.

    Purpose of the Study:

    • To develop a robust statistical method, DISHIC (Differential Interaction analysis in single-cell Hi-C), for accurate differential interaction analysis in scHi-C data.
    • To address the limitations of existing methods by explicitly modeling the statistical properties and incorporating covariates of sparse, noisy, and heterogeneous scHi-C data.
    • To provide a flexible and reliable tool for uncovering cell-type-specific regulatory mechanisms through multi-omics integration.

    Main Methods:

    • Developed DISHIC, a statistical method leveraging the Zero-Inflated Negative Binomial-based Wavelet (ZINB-WaVE) model, suitable for high-dimensional, zero-inflated count data.
    • Modeled bin pair interactions independently within each sample, incorporating both bin-pair-level and cell-level covariates to capture noise and heterogeneity.
    • Validated DISHIC's performance using real and simulated scHi-C datasets, comparing it against state-of-the-art methods.

    Main Results:

    • DISHIC demonstrated enhanced accuracy and reliability in detecting differential interactions compared to existing methods across various conditions.
    • The method effectively handled the sparsity, noise, and heterogeneity characteristic of scHi-C data.
    • A case study using multi-omics data from glial cells revealed intricate relationships between chromatin interactions, gene expression, and epigenetic modifications.

    Conclusions:

    • DISHIC provides a powerful and flexible statistical framework for analyzing differential interactions in single-cell 3D genomics data.
    • The method's ability to integrate covariates and model data properties improves the accuracy of identifying regulatory elements.
    • This work offers new insights into cell-type-specific regulatory mechanisms by integrating 3D chromatin structure with other omics data.