{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T05:03:02Z","timestamp":1784955782909,"version":"3.55.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T00:00:00Z","timestamp":1714694400000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["# 12271522"],"award-info":[{"award-number":["# 12271522"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["# 11901575"],"award-info":[{"award-number":["# 11901575"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council","award":["# 17301519"],"award-info":[{"award-number":["# 17301519"]}]},{"name":"Hong Kong Research Grants Council","award":["# 17309522"],"award-info":[{"award-number":["# 17309522"]}]},{"name":"Hung Hing Ying Physical Sciences Research Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the emergence of large amount of single-cell RNA sequencing (scRNA-seq) data, the exploration of computational methods has become critical in revealing biological mechanisms. Clustering is a representative for deciphering cellular heterogeneity embedded in scRNA-seq data. However, due to the diversity of datasets, none of the existing single-cell clustering methods shows overwhelming performance on all datasets. Weighted ensemble methods are proposed to integrate multiple results to improve heterogeneity analysis performance. These methods are usually weighted by considering the reliability of the base clustering results, ignoring the performance difference of the same base clustering on different cells. In this paper, we propose a high-order element-wise weighting strategy based self-representative ensemble learning framework: scEWE. By assigning different base clustering weights to individual cells, we construct and optimize the consensus matrix in a careful and exquisite way. In addition, we extracted the high-order information between cells, which enhanced the ability to represent the similarity relationship between cells. scEWE is experimentally shown to significantly outperform the state-of-the-art methods, which strongly demonstrates the effectiveness of the method and supports the potential applications in complex single-cell data analytical problems.<\/jats:p>","DOI":"10.1093\/bib\/bbae203","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T10:31:09Z","timestamp":1714732269000},"source":"Crossref","is-referenced-by-count":5,"title":["scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data"],"prefix":"10.1093","volume":"25","author":[{"given":"Yixiang","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mathematics, Renmin University of China , No. 59 Zhong guancun Street, 100872, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mathematics, Renmin University of China , No. 59 Zhong guancun Street, 100872, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wai-Ki","family":"Ching","sequence":"additional","affiliation":[{"name":"Department of Mathematics, The University of Hong Kong , Pokfulam Road , Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,5,2]]},"reference":[{"issue":"4","key":"2024050310305446000_ref1","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1093\/bib\/bbz063","article-title":"Machine learning and statistical methods for clustering single-cell rna-sequencing data","volume":"21","author":"Petegrosso","year":"2020","journal-title":"Brief Bioinform"},{"issue":"21","key":"2024050310305446000_ref2","first-page":"3684","article-title":"Single cell clustering based on cell-pair differentiability correlation and variance analysis","volume":"34","author":"Hao","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"1","key":"2024050310305446000_ref3","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1038\/s41467-021-22008-3","article-title":"Model-based deep embedding for constrained clustering analysis of single cell rna-seq data","volume":"12","author":"Tian","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"2024050310305446000_ref4","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1016\/j.apm.2020.08.065","article-title":"A kernel non-negative matrix factorization framework for single cell clustering","volume":"90","author":"Jiang","year":"2021","journal-title":"App Math Model"},{"key":"2024050310305446000_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-0984-y","article-title":"Pcareduce: hierarchical clustering of single cell transcriptional profiles","volume":"17","author":"\u017eurauskien\u0117","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"2024050310305446000_ref6","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":"7568","key":"2024050310305446000_ref7","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/nature14966","article-title":"Single-cell messenger rna sequencing reveals rare intestinal cell types","volume":"525","author":"Gr\u00fcn","year":"2015","journal-title":"Nature"},{"issue":"1","key":"2024050310305446000_ref8","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"},{"key":"2024050310305446000_ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-1175-6","article-title":"Celltree: an r\/bioconductor package to infer the hierarchical structure of cell populations from single-cell rna-seq data","volume":"17","author":"duVerle","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"11","key":"2024050310305446000_ref10","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"Scenic: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"issue":"2","key":"2024050310305446000_ref11","doi-asserted-by":"crossref","first-page":"1700232","DOI":"10.1002\/pmic.201700232","article-title":"Simlr: a tool for large-scale genomic analyses by multi-kernel learning","volume":"18","author":"Wang","year":"2018","journal-title":"Proteomics"},{"issue":"12","key":"2024050310305446000_ref12","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1093\/bioinformatics\/btv088","article-title":"Identification of cell types from single-cell transcriptomes using a novel clustering method","volume":"31","author":"Chen","year":"2015","journal-title":"Bioinformatics"},{"issue":"4","key":"2024050310305446000_ref13","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":"1","key":"2024050310305446000_ref14","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":"22","key":"2024050310305446000_ref15","doi-asserted-by":"crossref","first-page":"5042","DOI":"10.1093\/bioinformatics\/btac652","article-title":"scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network","volume":"38","author":"Zhongyuan","year":"2022","journal-title":"Bioinformatics"},{"issue":"8","key":"2024050310305446000_ref16","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1093\/bioinformatics\/bty793","article-title":"Safe-clustering: single-cell aggregated (from ensemble) clustering for single-cell rna-seq data","volume":"35","author":"Yang","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2024050310305446000_ref17","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1093\/nar\/gkz959","article-title":"Same-clustering: S ingle-cell a ggregated clustering via m ixture model e nsemble","volume":"48","author":"Huh","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2024050310305446000_ref18","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2020.604790","article-title":"Sc-gpe: a graph partitioning-based cluster ensemble method for single-cell","volume":"11","author":"Zhu","year":"2020","journal-title":"Front Genet"},{"key":"2024050310305446000_ref19","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1145\/2983323.2983745","article-title":"Robust spectral ensemble clustering","volume-title":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","author":"Tao","year":"2016"},{"issue":"2","key":"2024050310305446000_ref20","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1101\/gr.254557.119","article-title":"Sharp: hyperfast and accurate processing of single-cell rna-seq data via ensemble random projection","volume":"30","author":"Wan","year":"2020","journal-title":"Genome Res"},{"key":"2024050310305446000_ref21","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":"5","key":"2024050310305446000_ref22","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":"11","key":"2024050310305446000_ref23","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":"2024050310305446000_ref24","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":"6167","key":"2024050310305446000_ref25","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1126\/science.1245316","article-title":"Single-cell rna-seq reveals dynamic, random monoallelic gene expression in mammalian cells","volume":"343","author":"Deng","year":"2014","journal-title":"Science"},{"issue":"1","key":"2024050310305446000_ref26","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.cell.2016.01.047","article-title":"Heterogeneity in oct4 and sox2 targets biases cell fate in 4-cell mouse embryos","volume":"165","author":"Goolam","year":"2016","journal-title":"Cell"},{"issue":"7500","key":"2024050310305446000_ref27","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"},{"issue":"1","key":"2024050310305446000_ref28","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1038\/nn.3881","article-title":"Unbiased classification of sensory neuron types by large-scale single-cell rna sequencing","volume":"18","author":"Usoskin","year":"2015","journal-title":"Nat Neurosci"},{"issue":"5","key":"2024050310305446000_ref29","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"issue":"4","key":"2024050310305446000_ref30","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Syst"},{"issue":"5","key":"2024050310305446000_ref31","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TCYB.2017.2702343","article-title":"Locally weighted ensemble clustering","volume":"48","author":"Huang","year":"2017","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"2024050310305446000_ref32","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TSMC.2018.2876202","article-title":"Enhanced ensemble clustering via fast propagation of cluster-wise similarities","volume":"51","author":"Huang","year":"2018","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"2024050310305446000_ref33","first-page":"7970","article-title":"Clustering ensemble meets low-rank tensor approximation","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Jia","year":"2021"},{"key":"2024050310305446000_ref34","first-page":"1400","article-title":"Structural deep clustering network","volume-title":"Proceedings of the web conference 2020","author":"Bo"},{"key":"2024050310305446000_ref35","first-page":"9978","article-title":"Deep fusion clustering network","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"W","year":"2021"},{"key":"2024050310305446000_ref36","first-page":"1","article-title":"Adamtsl4, a secreted glycoprotein, is a novel immune-related biomarker for primary glioblastoma multiforme","volume":"2019","author":"Zhao","year":"2019","journal-title":"Dis Markers"},{"key":"2024050310305446000_ref37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroscience.2017.11.001","article-title":"Quantitative analysis of kynurenine aminotransferase ii in the adult rat brain reveals high expression in proliferative zones and corpus callosum","volume":"369","author":"Song","year":"2018","journal-title":"Neuroscience"},{"key":"2024050310305446000_ref38","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1038\/nsmb.2660","article-title":"Single-cell rna-seq profiling of human preimplantation embryos and embryonic stem cells","volume":"20","author":"Yang","year":"2013","journal-title":"Nat Struct Mol Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae203\/57390388\/bbae203.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae203\/57390388\/bbae203.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T10:31:49Z","timestamp":1714732309000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae203\/7663427"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,27]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae203","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,5,1]]},"published":{"date-parts":[[2024,3,27]]},"article-number":"bbae203"}}