{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:52:40Z","timestamp":1769845960706,"version":"3.49.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1R35HG011939-01"],"award-info":[{"award-number":["1R35HG011939-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the complexity of the experimental protocol. We present a deep learning framework, scGrapHiC, that predicts pseudo-bulk scHi-C contact maps using pseudo-bulk scRNA-seq data. Specifically, scGrapHiC performs graph deconvolution to extract genome-wide single-cell interactions from a bulk Hi-C contact map using scRNA-seq as a guiding signal. Our evaluations show that scGrapHiC, trained on seven cell-type co-assay datasets, outperforms typical sequence encoder approaches. For example, scGrapHiC achieves a substantial improvement of 23.2% in recovering cell-type-specific Topologically Associating Domains over the baselines. It also generalizes to unseen embryo and brain tissue samples. scGrapHiC is a novel method to generate cell-type-specific scHi-C contact maps using widely available genomic signals that enables the study of cell-type-specific chromatin interactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The GitHub link: https:\/\/github.com\/rsinghlab\/scGrapHiC contains the source code of scGrapHiC and associated scripts to preprocess publicly available datasets to produce the results and visualizations we have discuss in this manuscript.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae223","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T09:34:03Z","timestamp":1719567243000},"page":"i490-i500","source":"Crossref","is-referenced-by-count":3,"title":["scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1803-1134","authenticated-orcid":false,"given":"Ghulam","family":"Murtaza","sequence":"first","affiliation":[{"name":"Department of Computer Science, Brown University , 115 Waterman Street , Providence, RI, 02912, United States"}]},{"given":"Byron","family":"Butaney","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brown University , 115 Waterman Street , Providence, RI, 02912, United States"}]},{"given":"Justin","family":"Wagner","sequence":"additional","affiliation":[{"name":"Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, 20899, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7523-160X","authenticated-orcid":false,"given":"Ritambhara","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brown University , 115 Waterman Street 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