{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:52:55Z","timestamp":1775746375667,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":15,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>scATAC-seq has enabled chromatin accessibility landscape profiling at the single-cell level, providing opportunities for determining cell-type-specific regulation codes. However, high dimension, extreme sparsity, and large scale of scATAC-seq data have posed great challenges to cell-type identification. Thus, there has been a growing interest in leveraging the well-annotated scRNA-seq data to help annotate scATAC-seq data. However, substantial computational obstacles remain to transfer information from scRNA-seq to scATAC-seq, especially for their heterogeneous features.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a new transfer learning method, scNCL, which utilizes prior knowledge and contrastive learning to tackle the problem of heterogeneous features. Briefly, scNCL transforms scATAC-seq features into gene activity matrix based on prior knowledge. Since feature transformation can cause information loss, scNCL introduces neighborhood contrastive learning to preserve the neighborhood structure of scATAC-seq cells in raw feature space. To learn transferable latent features, scNCL uses a feature projection loss and an alignment loss to harmonize embeddings between scRNA-seq and scATAC-seq. Experiments on various datasets demonstrated that scNCL not only realizes accurate and robust label transfer for common types, but also achieves reliable detection of novel types. scNCL is also computationally efficient and scalable to million-scale datasets. Moreover, we prove scNCL can help refine cell-type annotations in existing scATAC-seq atlases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and data used in this paper can be found in https:\/\/github.com\/CSUBioGroup\/scNCL-release.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad505","type":"journal-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T14:52:18Z","timestamp":1692197538000},"source":"Crossref","is-referenced-by-count":12,"title":["scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization"],"prefix":"10.1093","volume":"39","author":[{"given":"Xuhua","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6372-6798","authenticated-orcid":false,"given":"Ruiqing","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7547-6423","authenticated-orcid":false,"given":"Jinmiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR) , Singapore 138648, Singapore"},{"name":"Immunology Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore (NUS) , Singapore 117545, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]}],"member":"286","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"2023082607510876200_btad505-B1","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/s41587-021-00895-7","article-title":"Computational principles and challenges in single-cell data integration","volume":"39","author":"Argelaguet","year":"2021","journal-title":"Nat Biotechnol"},{"key":"2023082607510876200_btad505-B2","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1038\/s41592-020-00979-3","article-title":"MARS: discovering novel cell types across heterogeneous single-cell experiments","volume":"17","author":"Brbi\u0107","year":"2020","journal-title":"Nat Methods"},{"key":"2023082607510876200_btad505-B3","doi-asserted-by":"crossref","first-page":"eaba7721","DOI":"10.1126\/science.aba7721","article-title":"A human cell atlas of fetal gene expression","volume":"370","author":"Cao","year":"2020","journal-title":"Science"},{"key":"2023082607510876200_btad505-B4","author":"Cao","year":"2021"},{"key":"2023082607510876200_btad505-B5","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1038\/s41587-022-01284-4","article-title":"Multi-omics single-cell data integration and regulatory inference with graph-linked embedding","volume":"40","author":"Cao","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2023082607510876200_btad505-B6","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s13059-019-1854-5","article-title":"Assessment of computational methods for the analysis of single-cell ATAC-seq data","volume":"20","author":"Chen","year":"2019","journal-title":"Genome Biol"},{"key":"2023082607510876200_btad505-B7","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.cell.2018.06.052","article-title":"A single-cell atlas of in vivo mammalian chromatin accessibility","volume":"174","author":"Cusanovich","year":"2018","journal-title":"Cell"},{"key":"2023082607510876200_btad505-B8","volume-title":"Advances in Neural Information Processing Systems","author":"Dhamija"},{"key":"2023082607510876200_btad505-B9","doi-asserted-by":"crossref","first-page":"eaba7612","DOI":"10.1126\/science.aba7612","article-title":"A human cell atlas of fetal chromatin accessibility","volume":"370","author":"Domcke","year":"2020","journal-title":"Science"},{"key":"2023082607510876200_btad505-B10","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1016\/j.bbagen.2014.01.010","article-title":"Extracellular matrix: a dynamic microenvironment for stem cell niche","volume":"1840","author":"Gattazzo","year":"2014","journal-title":"Biochim Biophys Acta"},{"key":"2023082607510876200_btad505-B11","author":"granulocyte-sorted 10k, P","year":"2020"},{"key":"2023082607510876200_btad505-B12","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1038\/nrendo.2014.234","article-title":"Stromal cells and stem cells in clinical bone regeneration","volume":"11","author":"Grayson","year":"2015","journal-title":"Nat Rev Endocrinol"},{"key":"2023082607510876200_btad505-B13","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1038\/s41588-019-0424-9","article-title":"High-throughput single-cell chip-seq identifies heterogeneity of chromatin states in breast cancer","volume":"51","author":"Grosselin","year":"2019","journal-title":"Nat Genet"},{"key":"2023082607510876200_btad505-B14","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2023082607510876200_btad505-B15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10571-017-0504-2","article-title":"Microglia: housekeeper of the central nervous system","volume":"38","author":"Kabba","year":"2018","journal-title":"Cell Mol Neurobiol"},{"key":"2023082607510876200_btad505-B16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1002\/sctm.19-0123","article-title":"Stromal cells from perinatal and adult sources modulate the inflammatory immune response in vitro by decreasing Th1 cell proliferation and cytokine secretion","volume":"9","author":"Khoury","year":"2020","journal-title":"Stem Cells Transl Med"},{"key":"2023082607510876200_btad505-B17","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1101\/gr.268581.120","article-title":"Semisupervised adversarial neural networks for single-cell classification","volume":"31","author":"Kimmel","year":"2021","journal-title":"Genome Res"},{"key":"2023082607510876200_btad505-B18","doi-asserted-by":"crossref","first-page":"W90","DOI":"10.1093\/nar\/gkw377","article-title":"Enrichr: a comprehensive gene set enrichment analysis web server 2016 update","volume":"44","author":"Kuleshov","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023082607510876200_btad505-B19","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.gpb.2020.09.004","article-title":"SSRE: cell type detection based on sparse subspace representation and similarity enhancement","volume":"19","author":"Liang","year":"2021","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"2023082607510876200_btad505-B20","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41587-021-01161-6","article-title":"ScJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning","volume":"40","author":"Lin","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2023082607510876200_btad505-B21","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1007\/978-3-030-58548-8_26","volume-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IV 16","author":"Liu","year":"2020"},{"key":"2023082607510876200_btad505-B22","first-page":"10","article-title":"Jointly embedding multiple single-cell omics measurements","volume":"143","author":"Liu","year":"2019","journal-title":"Algorithms Bioinform"},{"key":"2023082607510876200_btad505-B23","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1038\/s41587-021-00927-2","article-title":"Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells","volume":"39","author":"Mimitou","year":"2021","journal-title":"Nat Biotechnol"},{"key":"2023082607510876200_btad505-B24","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1038\/nmeth.4402","article-title":"Reversed graph embedding resolves complex single-cell trajectories","volume":"14","author":"Qiu","year":"2017","journal-title":"Nat Methods"},{"key":"2023082607510876200_btad505-B25","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/550451a","article-title":"The human cell atlas: from vision to reality","volume":"550","author":"Rozenblatt-Rosen","year":"2017","journal-title":"Nature"},{"key":"2023082607510876200_btad505-B26","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s41586-018-0590-4","article-title":"Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris","volume":"562","author":"Schaum","year":"2018","journal-title":"Nature"},{"key":"2023082607510876200_btad505-B27","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1146\/annurev.neuro.24.1.1217","article-title":"Nerve growth factor signaling, neuroprotection, and neural repair","volume":"24","author":"Sofroniew","year":"2001","journal-title":"Annu Rev Neurosci"},{"key":"2023082607510876200_btad505-B28","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1038\/s41467-021-24172-y","article-title":"scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics","volume":"12","author":"Song","year":"2021","journal-title":"Nat Commun"},{"key":"2023082607510876200_btad505-B29","doi-asserted-by":"crossref","first-page":"i919","DOI":"10.1093\/bioinformatics\/btaa843","article-title":"SCIM: universal single-cell matching with unpaired feature sets","volume":"36","author":"Stark","year":"2020","journal-title":"Bioinformatics"},{"key":"2023082607510876200_btad505-B30","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1083\/jcb.63.3.949","article-title":"PINOCYTOSIS in FIBROBLASTS: quantitative studies in vitro","volume":"63","author":"Steinman","year":"1974","journal-title":"J Cell Biol"},{"key":"2023082607510876200_btad505-B31","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1038\/s41592-021-01282-5","article-title":"Single-cell chromatin state analysis with Signac","volume":"18","author":"Stuart","year":"2021","journal-title":"Nat Methods"},{"key":"2023082607510876200_btad505-B32","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1038\/nature13173","article-title":"Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq","volume":"509","author":"Treutlein","year":"2014","journal-title":"Nature"},{"key":"2023082607510876200_btad505-B33","author":"Vaze","year":"2021"},{"key":"2023082607510876200_btad505-B34","doi-asserted-by":"crossref","first-page":"e90","DOI":"10.1002\/cpz1.90","article-title":"Gene set knowledge discovery with Enrichr","volume":"1","author":"Xie","year":"2021","journal-title":"Curr Protoc"},{"key":"2023082607510876200_btad505-B35","first-page":"3964","author":"Xu","year":"2018"},{"key":"2023082607510876200_btad505-B36","doi-asserted-by":"crossref","first-page":"3505","DOI":"10.1038\/s41467-022-31104-x","article-title":"Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward","volume":"13","author":"Xu","year":"2022","journal-title":"Nat Commun"},{"key":"2023082607510876200_btad505-B37","doi-asserted-by":"crossref","first-page":"bbac311","DOI":"10.1093\/bib\/bbac311","article-title":"Globe: a contrastive learning-based framework for integrating single-cell transcriptome datasets","volume":"23","author":"Yan","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023082607510876200_btad505-B38","doi-asserted-by":"crossref","first-page":"btad099","DOI":"10.1093\/bioinformatics\/btad099","article-title":"CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity","volume":"39","author":"Yan","year":"2023","journal-title":"Bioinformatics"},{"key":"2023082607510876200_btad505-B39","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s42256-022-00518-z","article-title":"Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale","volume":"4","author":"Yang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2023082607510876200_btad505-B40","first-page":"2720","author":"You","year":"2019"},{"key":"2023082607510876200_btad505-B41","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13059-020-02008-0","article-title":"scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data","volume":"21","author":"Yu","year":"2020","journal-title":"Genome Biol"},{"key":"2023082607510876200_btad505-B42","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1186\/s13059-022-02706-x","article-title":"scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously","volume":"23","author":"Zhang","year":"2022","journal-title":"Genome Biol"},{"key":"2023082607510876200_btad505-B43","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s43588-022-00251-y","article-title":"Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets","volume":"2","author":"Zhao","year":"2022","journal-title":"Nat Comput Sci"},{"key":"2023082607510876200_btad505-B44","doi-asserted-by":"crossref","first-page":"3642","DOI":"10.1093\/bioinformatics\/btz139","article-title":"SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation","volume":"35","author":"Zheng","year":"2019","journal-title":"Bioinformatics"},{"key":"2023082607510876200_btad505-B45","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1126\/science.1229164","article-title":"Genome-wide detection of single-nucleotide and copy-number variations of a single human cell","volume":"338","author":"Zong","year":"2012","journal-title":"Science"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btad505\/51117245\/btad505.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/8\/btad505\/51269389\/btad505.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/8\/btad505\/51269389\/btad505.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T07:51:37Z","timestamp":1693036297000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btad505\/7243158"}},"subtitle":[],"editor":[{"given":"Christina","family":"Kendziorski","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":45,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad505","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,8,1]]},"published":{"date-parts":[[2023,8,1]]},"article-number":"btad505"}}