{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:28:39Z","timestamp":1784820519839,"version":"3.55.0"},"reference-count":87,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T00:00:00Z","timestamp":1562198400000},"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\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["#1R01GM131399-01"],"award-info":[{"award-number":["#1R01GM131399-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"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"}]},{"name":"National Key R&D Program of China","award":["2018YFC0910405"],"award-info":[{"award-number":["2018YFC0910405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.<\/jats:p>","DOI":"10.1093\/bib\/bbz062","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T11:38:20Z","timestamp":1556537900000},"page":"1196-1208","source":"Crossref","is-referenced-by-count":190,"title":["Clustering and classification methods for single-cell RNA-sequencing data"],"prefix":"10.1093","volume":"21","author":[{"given":"Ren","family":"Qi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjun","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,7,4]]},"reference":[{"key":"2020082408070154700_ref1","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":"2020082408070154700_ref2","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"},{"key":"2020082408070154700_ref3","doi-asserted-by":"crossref","first-page":"12","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"},{"issue":"6","key":"2020082408070154700_ref4","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1093\/bioinformatics\/bty708","article-title":"A cluster robustness score for identifying cell subpopulations in single cell gene expression datasets from heterogeneous tissues and tumors","volume":"35","author":"Kanter","year":"2019","journal-title":"Bioinformatics"},{"key":"2020082408070154700_ref5","doi-asserted-by":"crossref","DOI":"10.1101\/409961","article-title":"QUBIC2: a novel biclustering algorithm for large-scale bulk RNA-sequencing and single-cell RNA-sequencing data analysis","author":"Xie","year":"2018"},{"issue":"3","key":"2020082408070154700_ref6","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1101\/gr.161034.113","article-title":"From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing","volume":"24","author":"Marinov","year":"2014","journal-title":"Genome Res"},{"issue":"13","key":"2020082408070154700_ref7","first-page":"2","article-title":"A survey of best practices for RNA-seq data analysis","volume":"17","author":"Conesa","year":"2016","journal-title":"Genome Biol"},{"key":"2020082408070154700_ref8","doi-asserted-by":"crossref","first-page":"43597","DOI":"10.1038\/srep43597","article-title":"Analysis of co-associated transcription factors via ordered adjacency differences on motif distribution","volume":"7","author":"Pan","year":"2017","journal-title":"Sci Rep"},{"key":"2020082408070154700_ref9","doi-asserted-by":"crossref","first-page":"15145","DOI":"10.1038\/srep15145","article-title":"Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases","volume":"5","author":"Yang","year":"2015","journal-title":"Sci Rep"},{"issue":"6","key":"2020082408070154700_ref10","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1093\/bioinformatics\/bty718","article-title":"A Bayesian model for single cell transcript expression analysis on MERFISH data","volume":"35","author":"Johannes","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2020082408070154700_ref11","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemometr Intell Lab Syst"},{"key":"2020082408070154700_ref12","doi-asserted-by":"crossref","DOI":"10.1101\/437020","article-title":"Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-seq data","author":"Peng","year":"2018"},{"issue":"1","key":"2020082408070154700_ref13","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s13059-015-0805-z","article-title":"ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis","volume":"16","author":"Pierson","year":"2015","journal-title":"Genome Biol"},{"key":"2020082408070154700_ref14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2018.04.082","article-title":"Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites","volume":"324","author":"Wei","year":"2019","journal-title":"Neurocomputing"},{"key":"2020082408070154700_ref15","article-title":"Developing a multi-dose computational model for drug-induced hepatotoxicity prediction based on toxicogenomics data","author":"Su","year":"2018","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2020082408070154700_ref16","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.artmed.2017.03.001","article-title":"Improved prediction of protein\u2013protein interactions using novel negative samples, features, and an ensemble classifier","volume":"83","author":"Wei","year":"2017","journal-title":"Artif Intell Med"},{"issue":"10","key":"2020082408070154700_ref17","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1089\/cmb.2007.0209","article-title":"Run probabilities of seed-like patterns and identifying good transition seeds","volume":"15","author":"Yang","year":"2008","journal-title":"J Comput Biol"},{"key":"2020082408070154700_ref18","doi-asserted-by":"crossref","DOI":"10.1145\/1273496.1273523","article-title":"Information-theoretic metric learning","volume-title":"Icml 07: International Conference on Machine Learning","author":"Davis","year":"2007"},{"key":"2020082408070154700_ref19","first-page":"1473","article-title":"Distance metric learning for large margin nearest neighbor classification","volume-title":"NIPS","author":"Weinberger","year":"2005"},{"key":"2020082408070154700_ref20","first-page":"2464","article-title":"Geometric mean metric learning","volume-title":"ICML","author":"Zadeh","year":"2016"},{"issue":"1","key":"2020082408070154700_ref21","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 Ser C Appl Stat"},{"issue":"1","key":"2020082408070154700_ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J R Stat Soc Series B Stat Methodol"},{"key":"2020082408070154700_ref23","article-title":"On spectral clustering: analysis and an algorithm","volume-title":"Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic","author":"Ng","year":"2001"},{"issue":"7568","key":"2020082408070154700_ref24","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":"Grun","year":"2015","journal-title":"Nature"},{"issue":"1","key":"2020082408070154700_ref25","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":"\u017durauskien\u0117","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"21","key":"2020082408070154700_ref26","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"},{"issue":"2","key":"2020082408070154700_ref27","doi-asserted-by":"crossref","first-page":"e1006792","DOI":"10.1371\/journal.pcbi.1006792","article-title":"IRIS-EDA: an integrated RNA-Seq interpretation system for gene expression data analysis","volume":"15","author":"Monier","year":"2019","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"2020082408070154700_ref28","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.celrep.2014.01.035","article-title":"Tumor evolution in response to chemotherapy: phenotype versus genotype","volume":"6","author":"Navin","year":"2014","journal-title":"Cell Rep"},{"key":"2020082408070154700_ref29","doi-asserted-by":"crossref","first-page":"22811","DOI":"10.1038\/srep22811","article-title":"A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia","volume":"6","author":"Liu","year":"2016","journal-title":"Sci Rep"},{"issue":"3","key":"2020082408070154700_ref30","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.celrep.2013.12.041","article-title":"Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity","volume":"6","author":"Almendro","year":"2014","journal-title":"Cell Rep"},{"issue":"6114","key":"2020082408070154700_ref31","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":"Chenghang","year":"2012","journal-title":"Science"},{"issue":"2","key":"2020082408070154700_ref32","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.cell.2012.06.030","article-title":"Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm","volume":"150","author":"Wang","year":"2012","journal-title":"Cell"},{"issue":"7513","key":"2020082408070154700_ref33","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1038\/nature13600","article-title":"Clonal evolution in breast cancer revealed by single nucleus genome sequencing","volume":"512","author":"Wang","year":"2014","journal-title":"Nature"},{"issue":"2","key":"2020082408070154700_ref34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1038\/icb.1994.26","article-title":"Transcription of individual genes in eukaryotic cells occurs randomly and infrequently","volume":"72","author":"Ross","year":"1994","journal-title":"Immunol Cell Biol"},{"issue":"1","key":"2020082408070154700_ref35","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1038\/ng869","article-title":"Regulation of noise in the expression of a single gene","volume":"31","author":"Ozbudak","year":"2002","journal-title":"Nat Genet"},{"issue":"10","key":"2020082408070154700_ref36","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1038\/nmeth.1253","article-title":"Imaging individual mRNA molecules using multiple singly labeled probes","volume":"5","author":"Raj","year":"2008","journal-title":"Nat Methods"},{"issue":"2","key":"2020082408070154700_ref37","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/nmeth.2772","article-title":"Quantitative single-cell RNA-seq with unique molecular identifiers","volume":"11","author":"Islam","year":"2014","journal-title":"Nat Methods"},{"issue":"4","key":"2020082408070154700_ref38","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nmeth.4220","article-title":"Power analysis of single-cell RNA-sequencing experiments","volume":"14","author":"Svensson","year":"2016","journal-title":"Nat Methods"},{"issue":"3","key":"2020082408070154700_ref39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2010-11-3-r25","article-title":"A scaling normalization method for differential expression analysis of RNA-seq data","volume":"11","author":"Robinson","year":"2010","journal-title":"Genome Biol"},{"key":"2020082408070154700_ref40","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"issue":"3","key":"2020082408070154700_ref41","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1093\/biostatistics\/kxr031","article-title":"Normalization, testing, and false discovery rate estimation for RNA-sequencing data","volume":"13","author":"Li","year":"2012","journal-title":"Biostatistics"},{"issue":"7","key":"2020082408070154700_ref42","doi-asserted-by":"crossref","first-page":"3010","DOI":"10.1073\/pnas.89.7.3010","article-title":"Analysis of gene expression in single live neurons","volume":"89","author":"Eberwine","year":"1992","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"5","key":"2020082408070154700_ref43","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":"6","key":"2020082408070154700_ref44","doi-asserted-by":"crossref","first-page":"e1005420","DOI":"10.1371\/journal.pcbi.1005420","article-title":"A comprehensive overview and evaluation of circular RNA detection tools","volume":"13","author":"Zeng","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"2020082408070154700_ref45","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"issue":"17","key":"2020082408070154700_ref46","doi-asserted-by":"crossref","first-page":"e156","DOI":"10.1093\/nar\/gkx681","article-title":"Using neural networks for reducing the dimensions of single-cell RNA-Seq data","volume":"45","author":"Lin","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"19","key":"2020082408070154700_ref47","doi-asserted-by":"crossref","first-page":"e166","DOI":"10.1093\/nar\/gkx750","article-title":"Network embedding-based representation learning for single cell RNA-seq data","volume":"45","author":"Li","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"2020082408070154700_ref48","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1093\/bib\/bbv033","article-title":"Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks","volume":"17","author":"Zeng","year":"2016","journal-title":"Brief Bioinform"},{"issue":"1","key":"2020082408070154700_ref49","first-page":"55","article-title":"Similarity computation strategies in the microRNA-disease network: a survey","volume":"15","author":"Zou","year":"2016","journal-title":"Brief Funct Genomics"},{"key":"2020082408070154700_ref50","article-title":"An introduction to dimensionality reduction using Matlab","author":"Maaten","year":"2007"},{"key":"2020082408070154700_ref51","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-3184-9","volume-title":"Introduction to Multivariate Analysis","author":"Chatfield","year":"1980"},{"key":"2020082408070154700_ref52","doi-asserted-by":"crossref","DOI":"10.4135\/9781412985130","volume-title":"Multidimensional Scaling","author":"Kruskal","year":"1978"},{"key":"2020082408070154700_ref53","doi-asserted-by":"crossref","DOI":"10.1109\/T-C.1969.222678","article-title":"A Nonlinear mapping for data structure analysis","author":"Sammon","year":"1969","journal-title":"IEEE Trans Comput"},{"issue":"2","key":"2020082408070154700_ref54","first-page":"179","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"2012","journal-title":"Ann Hum Genet"},{"issue":"5500","key":"2020082408070154700_ref55","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"2020082408070154700_ref56","doi-asserted-by":"crossref","DOI":"10.1162\/089976603321780317","volume-title":"Laplacian Eigenmaps for Dimensionality Reduction and Data Representation","author":"Belkin","year":"2003"},{"issue":"10","key":"2020082408070154700_ref57","doi-asserted-by":"crossref","first-page":"5591","DOI":"10.1073\/pnas.1031596100","article-title":"Hessian eigenmaps: locally linear embedding techniques for high-dimensional data","volume":"100","author":"Donoho","year":"2003","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"4","key":"2020082408070154700_ref58","first-page":"406","article-title":"Principal manifolds and nonlinear dimensionality reduction via tangent space alignment","volume":"8","author":"Zhang","year":"2004","journal-title":"Siam J Sci Comput"},{"issue":"10","key":"2020082408070154700_ref59","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1162\/089976600300014980","article-title":"Generalized discriminant analysis using a kernel approach","volume":"12","author":"Baudat","year":"2000","journal-title":"Neural Comput"},{"key":"2020082408070154700_ref60","article-title":"Neighborhood preserving embedding","volume-title":"Tenth IEEE International Conference on Computer Vision","author":"He","year":"2005"},{"key":"2020082408070154700_ref61","article-title":"Locality preserving projections","volume-title":"NIPS","author":"He","year":"2003"},{"issue":"20","key":"2020082408070154700_ref62","first-page":"e152","article-title":"Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks","volume":"44","author":"Xu","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2020082408070154700_ref63","doi-asserted-by":"crossref","DOI":"10.1007\/3-540-44491-2_3","article-title":"Extended k-means with an efficient estimation of the number of clusters","volume-title":"Seventeenth International Conference on Machine Learning","author":"Ishioka","year":"2000"},{"issue":"5","key":"2020082408070154700_ref64","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":"11","key":"2020082408070154700_ref65","first-page":"1083","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Cell"},{"issue":"5","key":"2020082408070154700_ref66","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":"Rahul","year":"2015","journal-title":"Nat Biotechnol"},{"issue":"10","key":"2020082408070154700_ref67","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":"4","key":"2020082408070154700_ref68","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/nmeth.4207","article-title":"Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning","volume":"14","author":"Wang","year":"2017","journal-title":"Nat Methods"},{"key":"2020082408070154700_ref69","first-page":"1","article-title":"Comparison of computational methods for imputing single-cell RNA-sequencing data","author":"Lihua","year":"2018","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2020082408070154700_ref70","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/978-1-4939-7710-9_15","article-title":"Single-cell Transcriptome analysis using SINCERA pipeline","volume":"1751","author":"Guo","year":"2018","journal-title":"Methods Mol Biol"},{"issue":"11","key":"2020082408070154700_ref71","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":"12","key":"2020082408070154700_ref72","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":"Xu","year":"2015","journal-title":"Bioinformatics"},{"issue":"2","key":"2020082408070154700_ref73","first-page":"btw607","article-title":"Robust classification of single-cell transcriptome data by nonnegative matrix factorization","volume":"33","author":"Shao","year":"2016","journal-title":"Bioinformatics"},{"issue":"4","key":"2020082408070154700_ref74","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nbt.2859","article-title":"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells","volume":"32","author":"Trapnell","year":"2014","journal-title":"Nat Biotechnol"},{"issue":"6226","key":"2020082408070154700_ref75","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.aaa1934","article-title":"Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq","volume":"347","author":"Zeisel","year":"2015","journal-title":"Science"},{"issue":"7","key":"2020082408070154700_ref76","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1089\/cmb.2017.0049","article-title":"Identifying cell subpopulations and their genetic drivers from single-cell RNA-Seq data using a biclustering approach","volume":"24","author":"Shi","year":"2017","journal-title":"J Comput Biol"},{"issue":"11","key":"2020082408070154700_ref77","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":"Blase","year":"2014","journal-title":"Genome Res"},{"issue":"9","key":"2020082408070154700_ref78","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":"2020082408070154700_ref79","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":"2020082408070154700_ref80","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":"4","key":"2020082408070154700_ref81","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.stem.2015.09.011","article-title":"Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation","volume":"17","author":"Kolodziejczyk","year":"2015","journal-title":"Cell Stem Cell"},{"issue":"7500","key":"2020082408070154700_ref82","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":"2020082408070154700_ref83","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":"2020082408070154700_ref84","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":"2020082408070154700_ref85","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":"2020082408070154700_ref86","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"},{"key":"2020082408070154700_ref87","doi-asserted-by":"crossref","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"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/21\/4\/1196\/33677680\/bbz062.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/21\/4\/1196\/33677680\/bbz062.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T11:47:36Z","timestamp":1721216856000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/21\/4\/1196\/5528236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,4]]},"references-count":87,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,7,4]]},"published-print":{"date-parts":[[2020,7,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbz062","relation":{},"ISSN":["1477-4054"],"issn-type":[{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,7]]},"published":{"date-parts":[[2019,7,4]]}}}