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bioRxiv · 10.1101/2025.05.14.654019

Hi-Compass resolves cell-type chromatin interactions by single-cell and spatial ATAC-seq data across biological scales

Sun, Y.-C.·Jiang, W.-J.·Cai, K.-W.·Wu, H.-J.
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Abstract

Computational prediction of three-dimensional (3D) genome organization provides an alternative approach to overcome the cost and technical limitations of Hi-C experiments. However, current Hi-C prediction models are constrained by their narrow applicability to studying the impact of genetic variation on genome folding in specific cell lines, significantly restricting their biological utility. We present Hi-Compass, a generalizable deep learning model that accurately predicts chromatin organization across diverse biological contexts, from bulk to single-cell samples. Hi-Compass outperforms existing methods in prediction accuracy and is generalizable to unseen cell types through chromatin accessibility data, enabling broad applications in single cell omics. Hi-Compass successfully resolves cell-type-specific 3D genome architectures in complex biological scenarios, including immune cell states, organ heterogeneity, and tissue spatial organization. Furthermore, Hi-Compass enables integrative analysis of single-cell multiome data, linking chromatin interaction dynamics to gene expression changes across cell clusters, and mapping disease variants to pathogenic genes. Hi-Compass also extends to spatial multi-omics data, generating spatially resolved Hi-C maps that reveal domain-specific chromatin interactions linked to spatial gene expression patterns.

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Sun, Y.-C., Jiang, W.-J., Cai, K.-W., Wu, H.-J.. 2025-05-17. Hi-Compass resolves cell-type chromatin interactions by single-cell and spatial ATAC-seq data across biological scales. https://doi.org/10.1101/2025.05.14.654019

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