{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T07:34:10Z","timestamp":1772955250127,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1093\/bib\/bbae279","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T17:23:03Z","timestamp":1717003383000},"source":"Crossref","is-referenced-by-count":5,"title":["HiCDiff: single-cell Hi-C data denoising with diffusion models"],"prefix":"10.1093","volume":"25","author":[{"given":"Yanli","family":"Wang","sequence":"first","affiliation":[{"name":"University of Missouri Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, , Columbia, MO 65211, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-2853","authenticated-orcid":false,"given":"Jianlin","family":"Cheng","sequence":"additional","affiliation":[{"name":"University of Missouri Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, , Columbia, MO 65211, United States"}]}],"member":"286","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"2024061011300771000_ref1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s12551-018-0489-1","article-title":"Hi-C analysis: from data generation to integration","volume":"11","author":"Pal","year":"2019","journal-title":"Biophys Rev"},{"key":"2024061011300771000_ref2","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1038\/nature11082","article-title":"Topological domains in mammalian genomes identified by analysis of chromatin interactions","volume":"485","author":"Dixon","year":"2012","journal-title":"Nature"},{"key":"2024061011300771000_ref3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1126\/science.1181369","article-title":"Comprehensive mapping of long-range interactions reveals folding principles of the human genome","volume":"326","author":"Lieberman-Aiden","year":"2009","journal-title":"Science"},{"key":"2024061011300771000_ref4","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nature11049","article-title":"Spatial partitioning of the regulatory landscape of the X-inactivation Centre","volume":"485","author":"Nora","year":"2012","journal-title":"Nature"},{"key":"2024061011300771000_ref5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/nature12593","article-title":"Single-cell Hi-C reveals cell-to-cell variability in chromosome structure","volume":"502","author":"Nagano","year":"2013","journal-title":"Nature"},{"key":"2024061011300771000_ref6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature23001","article-title":"Cell-cycle dynamics of chromosomal organization at single-cell 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data","volume":"31","author":"Li","year":"2015","journal-title":"Bioinformatics"},{"key":"2024061011300771000_ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-015-0831-x","article-title":"HiC-pro: an optimized and flexible pipeline for Hi-C data processing","volume":"16","author":"Servant","year":"2015","journal-title":"Genome Biol"},{"key":"2024061011300771000_ref11","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1093\/bioinformatics\/btv754","article-title":"MOGEN: a tool for reconstructing 3D models of genomes from chromosomal conformation capturing data","volume":"32","author":"Trieu","year":"2016","journal-title":"Bioinformatics"},{"key":"2024061011300771000_ref12","volume-title":"HiCSR: a Hi-C super-resolution framework for producing highly realistic contact 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