Unicorn: enhancing single-cell Hi-C data with blind super-resolution for 3D genome structure reconstruction

TL;DR

This paper introduces ScUnicorn and 3DUnicorn, new tools that improve the quality of single-cell Hi-C data and enable accurate reconstruction of 3D genome structures.

Contribution

The novel blind super-resolution framework ScUnicorn and the maximum likelihood algorithm 3DUnicorn for high-resolution 3D genome structure reconstruction from scHi-C data.

Findings

ScUnicorn outperforms existing methods in enhancing scHi-C data quality with higher Peak Signal-to-Noise Ratio and Structural Similarity Index Measure.

3DUnicorn's reconstructed structures closely match experimental 3D-FISH data, confirming biological accuracy.

Abstract

Single-cell Hi-C (scHi-C) data provide critical insights into chromatin interactions at individual cell levels, uncovering unique genomic 3D structures. However, scHi-C datasets are characterized by sparsity and noise, complicating efforts to accurately reconstruct high-resolution chromosomal structures. In this study, we present ScUnicorn, a novel blind super-resolution framework for scHi-C data enhancement. ScUnicorn uses an iterative degradation kernel optimization process, unlike traditional super-resolution approaches, which rely on downsampling, predefined degradation ratios, or constant assumptions about the input data to reconstruct high-resolution interaction matrices. Hence, our approach more reliably preserves critical biological patterns and minimizes noise. Additionally, we propose 3DUnicorn, a maximum likelihood algorithm that leverages the enhanced scHi-C data to infer precise 3D chromosomal structures.

Our evaluation demonstrates that ScUnicorn achieves superior performance over the state-of-the-art methods in terms of Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and GenomeDisco scores. Moreover, 3DUnicorn’s reconstructed structures align closely with experimental 3D-FISH data, underscoring its biological relevance. Together, ScUnicorn and 3DUnicorn provide a robust framework for advancing genomic research by enhancing scHi-C data fidelity and enabling accurate 3D genome structure reconstruction.

Unicorn implementation is publicly accessible at https://github.com/OluwadareLab/Unicorn.

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