{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T02:22:41Z","timestamp":1784254961706,"version":"3.55.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T00:00:00Z","timestamp":1686009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020AAA0107100"],"award-info":[{"award-number":["2020AAA0107100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Clustering methods have been widely used in single-cell RNA-seq data for investigating tumor heterogeneity. Since traditional clustering methods fail to capture the high-dimension methods, deep clustering methods have drawn increasing attention these years due to their promising strengths on the task. However, existing methods consider either the attribute information of each cell or the structure information between different cells. In other words, they cannot sufficiently make use of all of this information simultaneously. To this end, we propose a novel single-cell deep fusion clustering model, which contains two modules, i.e. an attributed feature clustering module and a structure-attention feature clustering module. More concretely, two elegantly designed autoencoders are built to handle both features regardless of their data types. Experiments have demonstrated the validity of the proposed approach, showing that it is efficient to fuse attributes, structure, and attention information on single-cell RNA-seq data. This work will be further beneficial for investigating cell subpopulations and tumor microenvironment. The Python implementation of our work is now freely available at https:\/\/github.com\/DayuHuu\/scDFC.<\/jats:p>","DOI":"10.1093\/bib\/bbad216","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T13:54:56Z","timestamp":1686318896000},"source":"Crossref","is-referenced-by-count":74,"title":["scDFC: A deep fusion clustering method for single-cell RNA-seq data"],"prefix":"10.1093","volume":"24","author":[{"given":"Dayu","family":"Hu","sequence":"first","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxuan","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology , No. 109 Deya Road, 410073 Changsha, Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,6,6]]},"reference":[{"issue":"4","key":"2023072020144643400_ref1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.trecan.2018.02.003","article-title":"Single-cell transcriptomic analysis of tumor heterogeneity","volume":"4","author":"Levitin","year":"2018","journal-title":"Trends in cancer"},{"issue":"4","key":"2023072020144643400_ref2","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.cell.2020.01.015","article-title":"Single-cell transcriptome atlas of murine endothelial cells","volume":"180","author":"Kalucka","year":"2020","journal-title":"Cell"},{"issue":"9","key":"2023072020144643400_ref3","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/s12276-020-0422-0","article-title":"Single-cell transcriptomics in cancer: computational challenges and opportunities","volume":"52","author":"Fan","year":"2020","journal-title":"Exp Mol Med"},{"issue":"9","key":"2023072020144643400_ref4","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1038\/s41590-020-0736-z","article-title":"Single-cell transcriptome profiling reveals neutrophil heterogeneity in homeostasis and infection","volume":"21","author":"Xie","year":"2020","journal-title":"Nat Immunol"},{"issue":"3","key":"2023072020144643400_ref5","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/j.celrep.2016.12.060","article-title":"Single-cell transcriptomic analysis defines heterogeneity and transcriptional dynamics in the adult neural stem cell lineage","volume":"18","author":"Dulken","year":"2017","journal-title":"Cell Rep"},{"key":"2023072020144643400_ref6","first-page":"7603","article-title":"Deep graph clustering via dual correlation reduction","volume":"36","author":"Liu","year":"2022","journal-title":"In: Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"3","key":"2023072020144643400_ref7","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1038\/s41592-020-01050-x","article-title":"Joint probabilistic modeling of single-cell multi-omic data with totalvi","volume":"18","author":"Gayoso","year":"2021","journal-title":"Nat Methods"},{"issue":"1","key":"2023072020144643400_ref8","doi-asserted-by":"crossref","first-page":"e46","DOI":"10.18547\/gcb.2017.vol3.iss1.e46","article-title":"Bayesian inference for single-cell clustering and imputing","volume":"3","author":"Azizi","year":"2017","journal-title":"Genomics and Computational Biology"},{"issue":"1","key":"2023072020144643400_ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-017-1188-0","article-title":"Cidr: ultrafast and accurate clustering through imputation for single-cell rna-seq data","volume":"18","author":"Lin","year":"2017","journal-title":"Genome Biol"},{"issue":"10","key":"2023072020144643400_ref10","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.1093\/bioinformatics\/btac168","article-title":"Edclust: an em\u2013mm hybrid method for cell clustering in multiple-subject single-cell rna sequencing","volume":"38","author":"Wei","year":"2022","journal-title":"Bioinformatics"},{"issue":"5","key":"2023072020144643400_ref11","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/nmeth.4236","article-title":"Sc3: consensus clustering of single-cell rna-seq data","volume":"14","author":"Kiselev","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2023072020144643400_ref12","first-page":"1","article-title":"Deepcpg: accurate prediction of single-cell dna methylation states using deep learning","volume":"18","author":"Angermueller","year":"2017","journal-title":"Genome Biol"},{"key":"2023072020144643400_ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-2769-6","article-title":"Combining gene ontology with deep neural networks to enhance the clustering of single cell rna-seq data","volume":"20","author":"Peng","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"10","key":"2023072020144643400_ref14","first-page":"1906","article-title":"A model-based constrained deep learning clustering approach for spatially resolved single-cell data","volume":"32","author":"Lin","year":"2022","journal-title":"Genome Res"},{"issue":"1","key":"2023072020144643400_ref15","doi-asserted-by":"crossref","first-page":"bbab321","DOI":"10.1093\/bib\/bbab321","article-title":"Sccaes: deep clustering of single-cell rna-seq via convolutional autoencoder embedding and soft k-means","volume":"23","author":"Hang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023072020144643400_ref16","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/BIBM52615.2021.9669638","article-title":"Deepci: a deep learning based clustering method for single cell rna-seq data","volume-title":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Liang","year":"2021"},{"issue":"1","key":"2023072020144643400_ref17","doi-asserted-by":"crossref","first-page":"2338","DOI":"10.1038\/s41467-020-15851-3","article-title":"Deep learning enables accurate clustering with batch effect removal in single-cell rna-seq analysis","volume":"11","author":"Li","year":"2020","journal-title":"Nat Commun"},{"issue":"4","key":"2023072020144643400_ref18","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/s42256-019-0037-0","article-title":"Clustering single-cell rna-seq data with a model-based deep learning approach","volume":"1","author":"Tian","year":"2019","journal-title":"Nat Mach Intell"},{"issue":"5","key":"2023072020144643400_ref19","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"Satija","year":"2015","journal-title":"Nat Biotechnol"},{"issue":"1","key":"2023072020144643400_ref20","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1038\/s41467-021-22197-x","article-title":"Scgnn is a novel graph neural network framework for single-cell rna-seq analyses","volume":"12","author":"Wang","year":"2021","journal-title":"Nat Commun"},{"issue":"8","key":"2023072020144643400_ref21","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1093\/bioinformatics\/btac099","article-title":"Scgac: a graph attentional architecture for clustering single-cell rna-seq data","volume":"38","author":"Cheng","year":"2022","journal-title":"Bioinformatics"},{"issue":"11","key":"2023072020144643400_ref22","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1101\/gr.177725.114","article-title":"Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell rna sequencing","volume":"24","author":"Biase","year":"2014","journal-title":"Genome Res"},{"issue":"23","key":"2023072020144643400_ref23","doi-asserted-by":"crossref","first-page":"7285","DOI":"10.1073\/pnas.1507125112","article-title":"A survey of human brain transcriptome diversity at the single cell level","volume":"112","author":"Darmanis","year":"2015","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"2023072020144643400_ref24","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.cell.2017.09.004","article-title":"Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns","volume":"171","author":"Martin Enge","year":"2017","journal-title":"Cell"},{"issue":"4","key":"2023072020144643400_ref25","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/ni.3368","article-title":"The heterogeneity of human cd127+ innate lymphoid cells revealed by single-cell rna sequencing","volume":"17","author":"Bj\u00f6rklund","year":"2016","journal-title":"Nat Immunol"},{"issue":"1","key":"2023072020144643400_ref26","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1038\/s41467-019-09639-3","article-title":"A bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies","volume":"10","author":"Sun","year":"2019","journal-title":"Nat Commun"},{"issue":"15","key":"2023072020144643400_ref27","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1016\/j.devcel.2022.07.004","article-title":"Single-cell and spatial mapping identify cell types and signaling networks in the human ureter","volume":"57","author":"Fink","year":"2022","journal-title":"Dev Cell"},{"issue":"4","key":"2023072020144643400_ref28","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1016\/j.cell.2019.09.035","article-title":"Transcriptional basis of mouse and human dendritic cell heterogeneity","volume":"179","author":"Brown","year":"2019","journal-title":"Cell"},{"issue":"6568","key":"2023072020144643400_ref29","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1126\/science.abj2949","article-title":"Profiling cellular diversity in sponges informs animal cell type and nervous system evolution","volume":"374","author":"Musser","year":"2021","journal-title":"Science"},{"key":"2023072020144643400_ref30","article-title":"Graph attention networks","volume":"1050","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv preprint arXiv:171010903"},{"key":"2023072020144643400_ref31","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: a graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J Comput Applied Math"},{"key":"2023072020144643400_ref32","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2022.3220948","article-title":"Rethinking graph auto-encoder models for attributed graph clustering","author":"Mrabah","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2023072020144643400_ref33","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J Classification"},{"issue":"Dec","key":"2023072020144643400_ref34","first-page":"583","article-title":"Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions","volume":"3","author":"Strehl","year":"2002","journal-title":"J Mach Learn Res"},{"key":"2023072020144643400_ref35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-021-02544-3","article-title":"Feature selection revisited in the single-cell era","volume":"22","author":"Yang","year":"2021","journal-title":"Genome Biol"},{"issue":"6","key":"2023072020144643400_ref36","doi-asserted-by":"crossref","first-page":"bbab147","DOI":"10.1093\/bib\/bbab147","article-title":"Improving single-cell rna-seq clustering by integrating pathways","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"2023072020144643400_ref37","doi-asserted-by":"crossref","first-page":"bbab379","DOI":"10.1093\/bib\/bbab379","article-title":"Consensus-based clustering of single cells by reconstructing cell-to-cell dissimilarity","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/4\/bbad216\/50916655\/bbad216.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/4\/bbad216\/50916655\/bbad216.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T20:15:48Z","timestamp":1689884148000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad216\/7190935"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,6]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad216","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,7]]},"published":{"date-parts":[[2023,6,6]]},"article-number":"bbad216"}}