{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T02:22:38Z","timestamp":1784254958317,"version":"3.55.0"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61922020"],"award-info":[{"award-number":["61922020"]}],"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":["61771331"],"award-info":[{"award-number":["61771331"]}],"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":["61902259"],"award-info":[{"award-number":["61902259"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2018A0303130084"],"award-info":[{"award-number":["2018A0303130084"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Single-cell RNA-sequencing (scRNA-seq) data widely exist in bioinformatics. It is crucial to devise a distance metric for scRNA-seq data. Almost all existing clustering methods based on spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretization of the learned labels by k-means clustering. However, this common practice has potential flaws that may lead to severe information loss and degradation of performance. Furthermore, the performance of a kernel method is largely determined by the selected kernel; a self-weighted multiple kernel learning model can help choose the most suitable kernel for scRNA-seq data. To this end, we propose to automatically learn similarity information from data. We present a new clustering method in the form of a multiple kernel combination that can directly discover groupings in scRNA-seq data. The main proposition is that automatically learned similarity information from scRNA-seq data is used to transform the candidate solution into a new solution that better approximates the discrete one. The proposed model can be efficiently solved by the standard support vector machine (SVM) solvers. Experiments on benchmark scRNA-Seq data validate the superior performance of the proposed model. Spectral clustering with multiple kernels is implemented in Matlab, licensed under Massachusetts Institute of Technology (MIT) and freely available from the Github website, https:\/\/github.com\/Cuteu\/SMSC\/.<\/jats:p>","DOI":"10.1093\/bib\/bbaa216","type":"journal-article","created":{"date-parts":[[2020,8,15]],"date-time":"2020-08-15T11:10:55Z","timestamp":1597489855000},"source":"Crossref","is-referenced-by-count":52,"title":["A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data"],"prefix":"10.1093","volume":"22","author":[{"given":"Ren","family":"Qi","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Management, Shenzhen Polytechnic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronic and Communication Engineering, Shenzhen Polytechnic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"2021072112100338900_ref1","first-page":"236","article-title":"Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells","volume-title":"Nature","author":"Shalek","year":"2013"},{"key":"2021072112100338900_ref2","first-page":"1209","article-title":"Machine learning and statistical methods for clustering single-cell RNA-sequencing data","volume-title":"Briefings in bioinformatics","author":"Petegrosso","year":"2020"},{"key":"2021072112100338900_ref3","first-page":"1","article-title":"An accurate and robust imputation method scImpute for single-cell RNA-seq data","volume-title":"Nature communications","author":"Li","year":"2018"},{"key":"2021072112100338900_ref4","first-page":"63","article-title":"Design and computational analysis of single-cell RNA-sequencing experiments","volume-title":"Genome biology","author":"Bacher","year":"2016"},{"key":"2021072112100338900_ref5","first-page":"133","article-title":"Computational and analytical challenges in single-cell transcriptomics","volume-title":"Nature Reviews Genetics","author":"Stegle","year":"2015"},{"key":"2021072112100338900_ref6","first-page":"e166","article-title":"Network embedding-based representation learning for single cell RNA-seq data","volume-title":"Nucleic acids research","author":"Li","year":"2017"},{"key":"2021072112100338900_ref7","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/978-1-4939-7717-8_19","article-title":"Applications of single-cell sequencing for Multiomics","volume":"1754","author":"Xu","year":"2018","journal-title":"Methods Mol Biol"},{"issue":"5","key":"2021072112100338900_ref8","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1093\/molbev\/mst040","article-title":"Quartet-net: a quartet-based method to reconstruct phylogenetic networks","volume":"30","author":"Yang","year":"2013","journal-title":"Mol Biol Evol"},{"issue":"1","key":"2021072112100338900_ref9","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1752-0509-8-21","article-title":"Quartet-based methods to reconstruct phylogenetic networks","volume":"8","author":"Yang","year":"2014","journal-title":"BMC Syst Biol"},{"key":"2021072112100338900_ref10","first-page":"1","article-title":"Systems biology intertwines with single cell and AI","author":"Wang","year":"2019","journal-title":"BioMed Central"},{"key":"2021072112100338900_ref11","first-page":"495","article-title":"Spatial reconstruction of single-cell gene expression data","volume-title":"Nature biotechnology","author":"Satija","year":"2015"},{"key":"2021072112100338900_ref12","first-page":"411","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume-title":"Nature biotechnology","author":"Butler","year":"2018"},{"key":"2021072112100338900_ref13","first-page":"1974","article-title":"Identification of cell types from single-cell transcriptomes using a novel clustering method","volume-title":"Bioinformatics","author":"Xu","year":"2015"},{"key":"2021072112100338900_ref14","first-page":"e1004575","article-title":"SINCERA: a pipeline for single-cell RNA-Seq profiling analysis","volume-title":"PLoS computational biology","author":"Guo","year":"2015"},{"key":"2021072112100338900_ref15","first-page":"8934","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Sarfraz","year":"2019"},{"key":"2021072112100338900_ref16","volume-title":"Seventeenth International Conference on Machine Learning","author":"Ishioka","year":"2000"},{"issue":"1","key":"2021072112100338900_ref17","first-page":"100","article-title":"Algorithm AS 136: a K-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J R Stat Soc"},{"issue":"1","key":"2021072112100338900_ref18","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s12859-016-0984-y","article-title":"pcaReduce: hierarchical clustering of single cell transcriptional profiles","volume":"17","author":"Yau","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2021072112100338900_ref19","article-title":"On spectral clustering: analysis and an algorithm","author":"Ng","year":"2002","journal-title":"Adv Neural Information Processing Sys"},{"key":"2021072112100338900_ref20","article-title":"Towards k-means-friendly spaces: simultaneous deep learning and clustering","volume-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70","author":"Yang","year":"2017"},{"key":"2021072112100338900_ref21","first-page":"e101","article-title":"QUBIC: a qualitative biclustering algorithm for analyses of gene expression data","volume-title":"Nucleic acids research","author":"Li","year":"2009"},{"key":"2021072112100338900_ref22","first-page":"4474","article-title":"MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome","volume-title":"Bioinformatics","author":"Ma","year":"2019"},{"key":"2021072112100338900_ref23","first-page":"1143","article-title":"QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data","volume-title":"Bioinformatics","author":"Xie","year":"2020"},{"issue":"21","key":"2021072112100338900_ref24","doi-asserted-by":"crossref","first-page":"3684","DOI":"10.1093\/bioinformatics\/bty390","article-title":"Single cell clustering based on cell-pair differentiability correlation and variance analysis","volume":"34","author":"Jiang","year":"2018","journal-title":"Bioinformatics"},{"key":"2021072112100338900_ref25","first-page":"483","article-title":"SC3: consensus clustering of single-cell RNA-seq data","volume-title":"Nature methods","author":"Kiselev","year":"2017"},{"issue":"9","key":"2021072112100338900_ref26","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":"Yan","year":"2013","journal-title":"Nat Struct Mol Biol"},{"issue":"1","key":"2021072112100338900_ref27","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":"6167","key":"2021072112100338900_ref28","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":"10","key":"2021072112100338900_ref29","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/nbt.2967","article-title":"Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex","volume":"32","author":"Pollen","year":"2014","journal-title":"Nat Biotechnol"},{"issue":"7500","key":"2021072112100338900_ref30","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":"6","key":"2021072112100338900_ref31","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1016\/j.celrep.2014.08.029","article-title":"Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells","volume":"8","author":"Ting","year":"2014","journal-title":"Cell Rep"},{"issue":"6190","key":"2021072112100338900_ref32","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1126\/science.1254257","article-title":"Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma","volume":"344","author":"Patel","year":"2014","journal-title":"Science"},{"issue":"1","key":"2021072112100338900_ref33","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"},{"key":"2021072112100338900_ref34","first-page":"1138","article-title":"Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq","volume-title":"Science","author":"Zeisel","year":"2015"},{"key":"2021072112100338900_ref35","first-page":"1187","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","volume-title":"Cell","author":"Klein","year":"2015"},{"key":"2021072112100338900_ref36","doi-asserted-by":"crossref","first-page":"858","DOI":"10.5772\/122","article-title":"Learning with $\\ell^1 $-graph for image analysis","volume-title":"IEEE transactions on image processing","author":"Cheng","year":"2009"},{"key":"2021072112100338900_ref37","article-title":"A new simplex sparse learning model to measure data similarity for clusterin. In: Twenty-Fourth International Joint Conference ong","author":"Huang","year":"2015","journal-title":"Artificial Intelligence"},{"key":"2021072112100338900_ref38","first-page":"210","article-title":"Kernel-driven similarity learning","volume-title":"Neurocomputing","author":"Kang","year":"2017"},{"key":"2021072112100338900_ref39","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1109\/CVPR.2009.5206547","volume-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","author":"Elhamifar","year":"2009"},{"issue":"4\u20136","key":"2021072112100338900_ref40","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.neucom.2009.08.014","article-title":"A general kernelization framework for learning algorithms based on kernel PCA","volume":"74","author":"Zhang","year":"2010","journal-title":"Neurocomputation"},{"key":"2021072112100338900_ref41","volume-title":"Multiclass Spectral Clustering. In null","author":"Stella","year":"2003"},{"key":"2021072112100338900_ref42","first-page":"12","article-title":"The Laplacian spectrum of graphs","volume-title":"Graph theory, combinatorics, and applications","author":"Mohar","year":"1991"},{"key":"2021072112100338900_ref43","first-page":"652","article-title":"On a theorem of Weyl concerning eigenvalues of linear transformations I","volume-title":"Proceedings of the National Academy of Sciences of the United States of America","author":"Fan","year":"1949"},{"key":"2021072112100338900_ref44","first-page":"397","article-title":"A feasible method for optimization with orthogonality constraints","volume-title":"Mathematical Programming","author":"Wen","year":"2013"},{"key":"2021072112100338900_ref45","first-page":"1","article-title":"A generalized solution of the orthogonal procrustes problem","volume-title":"Psychometrika","author":"Sch\u00f6nemann","year":"1966"},{"key":"2021072112100338900_ref46","first-page":"1532","article-title":"Feature selection and kernel learning for local learning-based clustering","volume-title":"IEEE transactions on pattern analysis and machine intelligence","author":"Zeng","year":"2010"},{"key":"2021072112100338900_ref47","first-page":"1737","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Cai","year":"2013"},{"issue":"10","key":"2021072112100338900_ref48","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of community hierarchies in large networks","author":"Blondel","year":"2008","journal-title":"J Stat Mech"},{"key":"2021072112100338900_ref49","first-page":"8934","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Sarfraz","year":"2019"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/4\/bbaa216\/39135913\/bbaa216.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/4\/bbaa216\/39135913\/bbaa216.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T20:20:52Z","timestamp":1696537252000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaa216\/5916937"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,1]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,7,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaa216","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,7]]},"published":{"date-parts":[[2020,10,1]]},"article-number":"bbaa216"}}