{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:30:58Z","timestamp":1791995458836,"version":"4.4.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":50,"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":["62372303"],"award-info":[{"award-number":["62372303"]}],"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":["62002234"],"award-info":[{"award-number":["62002234"]}],"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":["62131004"],"award-info":[{"award-number":["62131004"]}],"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":["81971015"],"award-info":[{"award-number":["81971015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The technology for analyzing single-cell multi-omics data has advanced rapidly and has provided comprehensive and accurate cellular information by exploring cell heterogeneity in genomics, transcriptomics, epigenomics, metabolomics and proteomics data. However, because of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover related omics variables from different blocks. Here, we present a novel algorithm that performs joint dimensionality reduction learning and cell clustering analysis on single-cell multi-omics data using non-negative matrix factorization that we named scMNMF. We formulate the objective function of joint learning as a constrained optimization problem and derive the corresponding iterative formulas through alternating iterative algorithms. The major advantage of the scMNMF algorithm remains its capability to explore hidden related features among omics data. Additionally, the feature selection for dimensionality reduction and cell clustering mutually influence each other iteratively, leading to a more effective discovery of cell types. We validated the performance of the scMNMF algorithm using two simulated and five real datasets. The results show that scMNMF outperformed seven other state-of-the-art algorithms in various measurements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>scMNMF code can be found at https:\/\/github.com\/yushanqiu\/scMNMF.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bib\/bbae228","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:02:24Z","timestamp":1714089744000},"source":"Crossref","is-referenced-by-count":32,"title":["scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization"],"prefix":"10.1093","volume":"25","author":[{"given":"Yushan","family":"Qiu","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Shenzhen University , 518000, Guangdong , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Shenzhen University , 518000, Guangdong , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Life and Health Sciences, Northeastern University , Shenyang, 110169 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu, 610056 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"issue":"9","key":"2024051614212520700_ref1","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/nrn.2017.85","article-title":"Neuronal cell-type classification: challenges, opportunities and the path forward","volume":"18","author":"Zeng","year":"2017","journal-title":"Nat Rev Neurosci"},{"issue":"5","key":"2024051614212520700_ref2","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/nmeth.1315","article-title":"Mrna-seq whole-transcriptome analysis of a single cell","volume":"6","author":"Tang","year":"2009","journal-title":"Nat Methods"},{"issue":"11","key":"2024051614212520700_ref3","doi-asserted-by":"crossref","first-page":"e1004575","DOI":"10.1371\/journal.pcbi.1004575","article-title":"Sincera: a pipeline for single-cell rna-seq profiling analysis","volume":"11","author":"Guo","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"7568","key":"2024051614212520700_ref4","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"},{"key":"2024051614212520700_ref5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1186\/s12859-015-0857-9","article-title":"Methods for the integration of multi-omics data: mathematical aspects","volume":"17","author":"Bersanelli","year":"2016","journal-title":"BMC bioinformatics"},{"issue":"5","key":"2024051614212520700_ref6","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","article-title":"Challenges in unsupervised clustering of single-cell rna-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat Rev Genet"},{"key":"2024051614212520700_ref7","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.inffus.2018.09.012","article-title":"Machine learning for integrating data in biology and medicine: principles, practice, and opportunities","volume":"50","author":"Zitnik","year":"2019","journal-title":"Inf Fusion"},{"issue":"12","key":"2024051614212520700_ref8","article-title":"Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J Mach Learn Res"},{"issue":"4","key":"2024051614212520700_ref9","doi-asserted-by":"crossref","first-page":"e35236","DOI":"10.1371\/journal.pone.0035236","article-title":"Integrative subtype discovery in glioblastoma using icluster","volume":"7","author":"Shen","year":"2012","journal-title":"PLoS One"},{"issue":"1","key":"2024051614212520700_ref10","first-page":"1","article-title":"Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification","volume":"16","author":"Dingming","year":"2015","journal-title":"BMC Genomics"},{"issue":"1","key":"2024051614212520700_ref11","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":"2024051614212520700_ref12","doi-asserted-by":"crossref","first-page":"572242","DOI":"10.3389\/fgene.2020.572242","article-title":"Ec-pgmgr: ensemble clustering based on probability graphical model with graph regularization for single-cell rna-seq data","volume":"11","author":"Zhu","year":"2020","journal-title":"Front Genet"},{"issue":"18","key":"2024051614212520700_ref13","doi-asserted-by":"crossref","first-page":"4789","DOI":"10.1093\/bioinformatics\/btaa593","article-title":"Multiple kernel learning for integrative consensus clustering of omic datasets","volume":"36","author":"Cabassi","year":"2020","journal-title":"Bioinformatics"},{"issue":"14","key":"2024051614212520700_ref14","doi-asserted-by":"crossref","first-page":"4137","DOI":"10.1093\/bioinformatics\/btaa282","article-title":"Citefuse enables multi-modal analysis of cite-seq data","volume":"36","author":"Kim","year":"2020","journal-title":"Bioinformatics"},{"issue":"5","key":"2024051614212520700_ref15","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat Biotechnol"},{"issue":"30","key":"2024051614212520700_ref16","doi-asserted-by":"crossref","first-page":"7723","DOI":"10.1073\/pnas.1805681115","article-title":"Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations","volume":"115","author":"Duren","year":"2018","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"2024051614212520700_ref17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-02015-1","article-title":"Mofa+: a statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol"},{"key":"2024051614212520700_ref18","first-page":"2020","article-title":"Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells","volume":"18","author":"Gayoso","year":"2020","journal-title":"Biorxiv"},{"issue":"11","key":"2024051614212520700_ref19","doi-asserted-by":"crossref","first-page":"5814","DOI":"10.1093\/nar\/gkaa314","article-title":"Brem-sc: a bayesian random effects mixture model for joint clustering single cell multi-omics data","volume":"48","author":"Wang","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2024051614212520700_ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1645-z","article-title":"Clonealign: statistical integration of independent single-cell rna and dna sequencing data from human cancers","volume":"20","author":"Campbell","year":"2019","journal-title":"Genome Biol"},{"key":"2024051614212520700_ref21","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.neucom.2013.03.034","article-title":"Locally discriminative spectral clustering with composite manifold","volume":"119","author":"Li","year":"2013","journal-title":"Neurocomputing"},{"issue":"8","key":"2024051614212520700_ref22","first-page":"1548","article-title":"Graph regularized nonnegative matrix factorization for data representation","volume":"33","author":"Cai","year":"2010","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2024051614212520700_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-1932-8","article-title":"Scai: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles","volume":"21","author":"Jin","year":"2020","journal-title":"Genome Biol"},{"issue":"4","key":"2024051614212520700_ref24","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1101\/gr.267906.120","article-title":"Linear-time cluster ensembles of large-scale single-cell rna-seq and multimodal data","volume":"31","author":"Ringeling","year":"2021","journal-title":"Genome Res"},{"issue":"1","key":"2024051614212520700_ref25","doi-asserted-by":"crossref","first-page":"7705","DOI":"10.1038\/s41467-022-35031-9","article-title":"Clustering of single-cell multi-omics data with a multimodal deep learning method","volume":"13","author":"Lin","year":"2022","journal-title":"Nat Commun"},{"issue":"1","key":"2024051614212520700_ref26","doi-asserted-by":"crossref","first-page":"btac736","DOI":"10.1093\/bioinformatics\/btac736","article-title":"Clustering single-cell multi-omics data with moclust","volume":"39","author":"Yuan","year":"2023","journal-title":"Bioinformatics"},{"issue":"13","key":"2024051614212520700_ref27","doi-asserted-by":"crossref","first-page":"e117","DOI":"10.1093\/nar\/gkw430","article-title":"Tscan: pseudo-time reconstruction and evaluation in single-cell rna-seq analysis","volume":"44","author":"Ji","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2024051614212520700_ref28","article-title":"Umap: uniform manifold approximation and projection for dimension reduction","volume":"3","author":"McInnes","year":"2018","journal-title":"arXiv preprint arXiv:180203426"},{"issue":"1","key":"2024051614212520700_ref29","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat Genet"},{"issue":"3","key":"2024051614212520700_ref30","doi-asserted-by":"crossref","first-page":"bbad149","DOI":"10.1093\/bib\/bbad149","article-title":"Ssnmdi: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell rna-seq data","volume":"24","author":"Qiu","year":"2023","journal-title":"Brief Bioinform"},{"issue":"D1","key":"2024051614212520700_ref31","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"String v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"suppl_1","key":"2024051614212520700_ref32","doi-asserted-by":"crossref","first-page":"D535","DOI":"10.1093\/nar\/gkj109","article-title":"Biogrid: a general repository for interaction datasets","volume":"34","author":"Stark","year":"2006","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2024051614212520700_ref33","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nmeth.4083","article-title":"A scored human protein\u2013protein interaction network to catalyze genomic interpretation","volume":"14","author":"Li","year":"2017","journal-title":"Nat Methods"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae228\/57701947\/bbae228.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae228\/57701947\/bbae228.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T10:22:16Z","timestamp":1715854936000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae228\/7675151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,27]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae228","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]]},"published":{"date-parts":[[2024,3,27]]},"article-number":"bbae228"}}