{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T08:28:23Z","timestamp":1775377703737,"version":"3.50.1"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["R35GM133678"],"award-info":[{"award-number":["R35GM133678"]}]},{"name":"National Institute of Health","award":["R01NS125018"],"award-info":[{"award-number":["R01NS125018"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI-1751317"],"award-info":[{"award-number":["DBI-1751317"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["MRI-2215705"],"award-info":[{"award-number":["MRI-2215705"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["MRI-1429826"],"award-info":[{"award-number":["MRI-1429826"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute of Health","award":["1S10OD016290-01A1"],"award-info":[{"award-number":["1S10OD016290-01A1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single-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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>SHICEDO is publicly available at https:\/\/github.com\/wmalab\/SHICEDO.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf575","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T13:46:13Z","timestamp":1760449573000},"source":"Crossref","is-referenced-by-count":1,"title":["SHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1576-6148","authenticated-orcid":false,"given":"Jingong","family":"Huang","sequence":"first","affiliation":[{"name":"University of California Riverside Department of Computer Science and Engineering, , CA 92521,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Ma","sequence":"additional","affiliation":[{"name":"University of California Riverside Department of Statistics, , CA 92521,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Strobel","sequence":"additional","affiliation":[{"name":"University of California Riverside Department of Computer Science and Engineering, , CA 92521,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Hu","sequence":"additional","affiliation":[{"name":"University of California Riverside Department of Computer Science and Engineering, , CA 92521,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiantian","family":"Ye","sequence":"additional","affiliation":[{"name":"University of California Riverside Department of Statistics, , CA 92521,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3833-4498","authenticated-orcid":false,"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of California Riverside Department of Computer Science and Engineering, , CA 92521,","place":["United States"]},{"name":"University of California Riverside Institute of Integrative Genome Biology, , CA 92521,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-1621","authenticated-orcid":false,"given":"Wenxiu","family":"Ma","sequence":"additional","affiliation":[{"name":"University of California Riverside Department of Statistics, , CA 92521,","place":["United States"]},{"name":"University of California Riverside Institute of Integrative Genome Biology, , CA 92521,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"2025120118030267700_btaf575-B1","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":"2025120118030267700_btaf575-B2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/nature08973","article-title":"A three-dimensional model of the yeast genome","volume":"465","author":"Duan","year":"2010","journal-title":"Nature"},{"key":"2025120118030267700_btaf575-B3","doi-asserted-by":"crossref","first-page":"e1007287","DOI":"10.1371\/journal.pcbi.1007287","article-title":"DeepHiC: a generative adversarial network for enhancing Hi-C data resolution","volume":"16","author":"Hong","year":"2020","journal-title":"PLoS Comput Biol"},{"key":"2025120118030267700_btaf575-B4","first-page":"7132","author":"Hu","year":"2018"},{"key":"2025120118030267700_btaf575-B5","doi-asserted-by":"crossref","first-page":"i272","DOI":"10.1093\/bioinformatics\/btab272","article-title":"EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework","volume":"37","author":"Hu","year":"2021","journal-title":"Bioinformatics"},{"key":"2025120118030267700_btaf575-B6","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1038\/s41592-019-0547-z","article-title":"Simultaneous profiling of 3D genome structure and DNA methylation in single human cells","volume":"16","author":"Lee","year":"2019","journal-title":"Nat Methods"},{"key":"2025120118030267700_btaf575-B7","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":"2025120118030267700_btaf575-B8","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1093\/bioinformatics\/btab097","article-title":"HiCRep.py: fast comparison of Hi-C contact matrices in python","volume":"37","author":"Lin","year":"2021","journal-title":"Bioinformatics"},{"key":"2025120118030267700_btaf575-B9","doi-asserted-by":"crossref","first-page":"i99","DOI":"10.1093\/bioinformatics\/btz317","article-title":"hicGAN infers super resolution Hi-C data with generative adversarial networks","volume":"35","author":"Liu","year":"2019","journal-title":"Bioinformatics"},{"key":"2025120118030267700_btaf575-B10","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1126\/science.adg3797","article-title":"Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq","volume":"380","author":"Liu","year":"2023","journal-title":"Science"},{"key":"2025120118030267700_btaf575-B11","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/nmeth.3205","article-title":"Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincrna genes","volume":"12","author":"Ma","year":"2015","journal-title":"Nat Methods"},{"key":"2025120118030267700_btaf575-B12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.cell.2020.09.014","article-title":"The self-organizing genome: principles of genome architecture and function","volume":"183","author":"Misteli","year":"2020","journal-title":"Cell"},{"key":"2025120118030267700_btaf575-B13","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":"2025120118030267700_btaf575-B14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature23001","article-title":"Cell-cycle dynamics of chromosomal organization at single-cell resolution","volume":"547","author":"Nagano","year":"2017","journal-title":"Nature"},{"key":"2025120118030267700_btaf575-B15","doi-asserted-by":"crossref","first-page":"169155","DOI":"10.1016\/j.jmb.2025.169155","article-title":"scHiCcompare: an R package for differential analysis of single-cell Hi-C data","volume":"437","author":"Nguyen","year":"2025","journal-title":"J Mol Biol"},{"key":"2025120118030267700_btaf575-B16","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/nmeth.4155","article-title":"Massively multiplex single-cell Hi-C","volume":"14","author":"Ramani","year":"2017","journal-title":"Nat Methods"},{"key":"2025120118030267700_btaf575-B17","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.cell.2014.11.021","article-title":"A 3d map of the human genome at kilobase resolution reveals principles of chromatin looping","volume":"159","author":"Rao","year":"2014","journal-title":"Cell"},{"key":"2025120118030267700_btaf575-B18","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.cell.2020.12.032","article-title":"Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development","volume":"184","author":"Tan","year":"2021","journal-title":"Cell"},{"key":"2025120118030267700_btaf575-B19","doi-asserted-by":"crossref","first-page":"2701","DOI":"10.1093\/bioinformatics\/bty164","article-title":"Genomedisco: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs","volume":"34","author":"Ursu","year":"2018","journal-title":"Bioinformatics"},{"key":"2025120118030267700_btaf575-B20","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad458","article-title":"Single-cell Hi-C data enhancement with deep residual and generative adversarial networks","volume":"39","author":"Wang","year":"2023","journal-title":"Bioinformatics"},{"key":"2025120118030267700_btaf575-B21","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1101\/gr.220640.117","article-title":"HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient","volume":"27","author":"Yang","year":"2017","journal-title":"Genome Res"},{"key":"2025120118030267700_btaf575-B22","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1038\/s41592-021-01231-2","article-title":"SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data","volume":"18","author":"Yu","year":"2021","journal-title":"Nat Methods"},{"key":"2025120118030267700_btaf575-B23","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1038\/s41587-021-01034-y","article-title":"Multiscale and integrative single-cell Hi-C analysis with Higashi","volume":"40","author":"Zhang","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2025120118030267700_btaf575-B24","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1038\/s41467-018-03113-2","article-title":"Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus","volume":"9","author":"Zhang","year":"2018","journal-title":"Nat Commun"},{"key":"2025120118030267700_btaf575-B25","doi-asserted-by":"crossref","first-page":"14011","DOI":"10.1073\/pnas.1901423116","article-title":"Robust single-cell Hi-C clustering by convolution-and random-walk\u2013based imputation","volume":"116","author":"Zhou","year":"2019","journal-title":"Proc Natl Acad Sci USA"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf575\/64883354\/btaf575.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/12\/btaf575\/64883354\/btaf575.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/12\/btaf575\/64883354\/btaf575.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T23:03:11Z","timestamp":1764630191000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf575\/8300291"}},"subtitle":[],"editor":[{"given":"Anthony","family":"Mathelier","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,10,23]]},"references-count":25,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf575","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,12]]},"published":{"date-parts":[[2025,10,23]]},"article-number":"btaf575"}}