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1. SHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing

   https://doi.org/10.1093/bioinformatics/btaf575  

   Huang, Jingong; Ma, Rui; Strobel, Michael; Hu, Yangyang; Ye, Tiantian; Jiang, Tao; Ma, Wenxiu (December 2025, Bioinformatics)

   Mathelier, Anthony (Ed.)

   Abstract MotivationSingle-cell Hi-C (scHi-C) technologies have significantly advanced our understanding of the 3D genome organization. However, scHi-C data are often sparse and noisy, leading to substantial computational challenges in downstream analyses. ResultsIn this study, we introduce SHICEDO, a novel deep-learning model specifically designed to enhance scHi-C contact matrices by imputing missing or sparsely captured chromatin contacts through a generative adversarial framework. SHICEDO leverages the unique structural characteristics of scHi-C matrices to derive customized features that enable effective data enhancement. Additionally, the model incorporates a channel-wise attention mechanism to mitigate the over-smoothing issue commonly associated with scHi-C enhancement methods. Through simulations and real-data applications, we demonstrate that SHICEDO outperforms the state-of-the-art methods, achieving superior quantitative and qualitative results. Moreover, SHICEDO enhances key structural features in scHi-C data, thus enabling more precise delineation of chromatin structures such as A/B compartments, TAD-like domains, and chromatin loops. Availability and implementationSHICEDO is publicly available at https://github.com/wmalab/SHICEDO. 

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

   https://doi.org/10.1093/bioinformatics/btaf177  

   Chandrashekar, Mohan_Kumar B; Menon, Rohit; Olowofila, Samuel; Oluwadare, Oluwatosin (July 2025, Bioinformatics)

   Motivation: 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. Result: 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. Availability and implementation: Unicorn implementation is publicly accessible at https://github.com/OluwadareLab/Unicorn. 

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3. Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks

   https://doi.org/10.1093/nargab/lqaf027  

   Wang, Yanli; Cheng, Jianlin (January 2025, NAR Genomics and Bioinformatics)

   Abstract The spatial conformation of chromosomes and genomes of single cells is relevant to cellular function and useful for elucidating the mechanism underlying gene expression and genome methylation. The chromosomal contacts (i.e. chromosomal regions in spatial proximity) entailing the three-dimensional (3D) structure of the genome of a single cell can be obtained by single-cell chromosome conformation capture techniques, such as single-cell Hi-C (ScHi-C). However, due to the sparsity of chromosomal contacts in ScHi-C data, it is still challenging for traditional 3D conformation optimization methods to reconstruct the 3D chromosome structures from ScHi-C data. Here, we present a machine learning-based method based on a novel SO(3)-equivariant graph neural network (HiCEGNN) to reconstruct 3D structures of chromosomes of single cells from ScHi-C data. HiCEGNN consistently outperforms both the traditional optimization methods and the only other deep learning method across diverse cells, different structural resolutions, and different noise levels of the data. Moreover, HiCEGNN is robust against the noise in the ScHi-C data. 

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4. Leveraging Nearest Neighbors for Faithful scHi-C Data Imputation

   https://doi.org/10.1007/978-3-031-94950-0_24  

   Zhao, Bin; Liu, Lu (January 2025, Springer Nature Switzerland)

   Single-cell Hi-C (scHi-C) is a powerful technique for probing the three-dimensional architecture of genomes of individual cells. However, scHi-C data is very sparse, posing computational and analytical challenges for downstream analyses. Existing imputation methods tend to over-impute the data and result in inflated contact frequencies, which may obscure important biological signals. In this paper, we propose a novel imputation method, scHi-C-INN, which leverages the information from the nearest neighbors’ contact matrices. scHi-C-INN preserves the inherent characteristics of the raw data and enhances the quality of scHi-C data. We compare scHi-C-INN with several existing imputation methods on two publicly available scHi-C datasets, and demonstrate its superior performance in terms of cell type clustering and sparseness, and its comparability on TAD-like boundary detection and improved similarities between single cells of the same cell type. The scHi-C-INN will be available at https://github.com/sdontsay/scHi-C-INN. 

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5. ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures

   https://doi.org/10.1093/nar/gkaa872  

   Ye, Tiantian; Ma, Wenxiu (October 2020, Nucleic Acids Research)

   null (Ed.)

   Abstract The recently developed Hi-C technique has been widely applied to map genome-wide chromatin interactions. However, current methods for analyzing diploid Hi-C data cannot fully distinguish between homologous chromosomes. Consequently, the existing diploid Hi-C analyses are based on sparse and inaccurate allele-specific contact matrices, which might lead to incorrect modeling of diploid genome architecture. Here we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model. The proposed ASHIC methods impute allele-specific contact maps from diploid Hi-C data and simultaneously infer allelic 3D structures. Through simulation studies, we demonstrated that ASHIC methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, in the analyses of diploid Hi-C datasets in mouse and human, our ASHIC-ZIPM method produced fine-resolution diploid chromatin maps and 3D structures and provided insights into the allelic chromatin organizations and functions. To summarize, our work provides a statistically rigorous framework for investigating fine-scale allele-specific chromatin conformations. The ASHIC software is publicly available at https://github.com/wmalab/ASHIC. 

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