HiC2Self model scripts
Year: 2025
Type: article
Abstract: Hi-C is a chromosome conformation capture assay used to study 3D genome organization. The recent development of single-cell Hi-C technologies has further enabled the examination of 3D chromatin organization in individual cells, although these approaches often suffer from low-coverage libraries and data sparsity. Here we introduce HiC2Self, a self-supervised framework for denoising Hi-C contact maps that requires only low-coverage data as input. HiC2Self not only reconstructs key structures (such as TADs and significant loops) from bulk libraries, but its self-supervised training framework also allows it to easily reconstruct cell-type-specific Hi-C structures without the generalization challenges faced by supervised models. HiC2Self can also accurately reconstruct significant loops from Micro-C data at 1 kb resolution. Moreover, when applied to single-nucleus methyl-3C data, HiC2Self successfully reconstructs local TAD structures around specific genes at 10 kb resolution with as few as 50 cells. Finally, HiC2Self enables the examination of single-cell structures at 50 kb resolution in individual cells of the same cell type. HiC2Self thus provides a general tool for denoising bulk, pseudo-bulk, and single-cell 3D contact maps to enable downstream analyses.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6949370584
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