ArticleBriefings in bioinformatics2024
HiCDiff: single-cell Hi-C data denoising with diffusion models.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- SpaDiff denoises sequence-based spatial transcriptomics via diffusion process.Cell reports methods · 2026Article
- Harmonizing single-cell 3D genome data with STARK and scNucleome.Genome biology · 2026Article
- Recent advances in single-cell bioinformatics for inferring higher-order chromatin contact maps.BMB reports · 2025Review
- SpaDiff: Denoising for Sequence-based Spatial Transcriptomics via Diffusion Process.bioRxiv : the preprint server for biology · 2025Article
- Unicorn: enhancing single-cell Hi-C data with blind super-resolution for 3D genome structure reconstruction.Bioinformatics (Oxford, England) · 2025Article
- Generative inpainting of incomplete Euclidean distance matrices of trajectories generated by a fractional Brownian motion.Scientific reports · 2025Article
- Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks.NAR genomics and bioinformatics · 2025Article
- ChromaFactor: Deconvolution of single-molecule chromatin organization with non-negative matrix factorization.PLoS computational biology · 2025Article
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Authors and funding
2 authors.
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Abstract
The genome-wide single-cell chromosome conformation capture technique, i.e. single-cell Hi-C (ScHi-C), was recently developed to interrogate the conformation of the genome of individual cells. However, single-cell Hi-C data are much sparser than bulk Hi-C data of a population of cells, and noise in single-cell Hi-C makes it difficult to apply and analyze them in biological research. Here, we developed the first generative diffusion models (HiCDiff) to denoise single-cell Hi-C data in the form of chromosomal contact matrices. HiCDiff uses a deep residual network to remove the noise in the reverse process of diffusion and can be trained in both unsupervised and supervised learning modes. Benchmarked on several single-cell Hi-C test datasets, the diffusion models substantially remove the noise in single-cell Hi-C data. The unsupervised HiCDiff outperforms most supervised non-diffusion deep learning methods and achieves the performance comparable to the state-of-the-art supervised deep learning method in terms of multiple metrics, demonstrating that diffusion models are a useful approach to denoising single-cell Hi-C data. Moreover, its good performance holds on denoising bulk Hi-C data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.