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One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as\n                    <jats:italic>k<\/jats:italic>\n                    -means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the\n                    <jats:italic>mbkmeans<\/jats:italic>\n                    R\/Bioconductor package, an open-source implementation of the mini-batch\n                    <jats:italic>k<\/jats:italic>\n                    -means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the\n                    <jats:italic>mbkmeans<\/jats:italic>\n                    package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of\n                    <jats:italic>mbkmeans<\/jats:italic>\n                    against the standard implementation of\n                    <jats:italic>k<\/jats:italic>\n                    -means and other popular single-cell clustering methods. Our software package is available in Bioconductor at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/bioconductor.org\/packages\/mbkmeans\" xlink:type=\"simple\">https:\/\/bioconductor.org\/packages\/mbkmeans<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008625","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T13:46:35Z","timestamp":1611668795000},"page":"e1008625","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":59,"title":["mbkmeans: Fast clustering for single cell data using mini-batch k-means"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7858-0231","authenticated-orcid":true,"given":"Stephanie C.","family":"Hicks","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1488-5789","authenticated-orcid":true,"given":"Ruoxi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3038-3682","authenticated-orcid":true,"given":"Yuwei","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9455-7990","authenticated-orcid":true,"given":"Elizabeth","family":"Purdom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8508-5012","authenticated-orcid":true,"given":"Davide","family":"Risso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"key":"pcbi.1008625.ref001","doi-asserted-by":"crossref","first-page":"237","DOI":"10.4137\/BBI.S38316","article-title":"Clustering Algorithms: Their Application to Gene Expression Data","volume":"10","author":"J Oyelade","year":"2016","journal-title":"Bioinform Biol Insights"},{"issue":"2","key":"pcbi.1008625.ref002","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine Learning for Medical Imaging","volume":"37","author":"BJ Erickson","year":"2017","journal-title":"Radiographics"},{"key":"pcbi.1008625.ref003","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.mam.2017.07.002","article-title":"Identifying cell populations with scRNASeq","volume":"59","author":"TS Andrews","year":"2018","journal-title":"Mol Aspects Med"},{"key":"pcbi.1008625.ref004","article-title":"Challenges in unsupervised clustering of single-cell RNA-seq data","author":"VY Kiselev","year":"2019","journal-title":"Nature Reviews Genetics"},{"key":"pcbi.1008625.ref005","article-title":"Orchestrating single-cell analysis with Bioconductor","author":"RA Amezquita","year":"2019","journal-title":"Nat Methods"},{"key":"pcbi.1008625.ref006","unstructured":"J MacQueen. 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Parallel K-Means Clustering Based on MapReduce. In: Proceedings of the 1st International Conference on Cloud Computing. CloudCom\u201909. Berlin, Heidelberg: Springer-Verlag; 2009. p. 674\u2013679. Available from: https:\/\/doi.org\/10.1007\/978-3-642-10665-1_71.","DOI":"10.1007\/978-3-642-10665-1_71"},{"key":"pcbi.1008625.ref015","doi-asserted-by":"crossref","unstructured":"Anchalia PP. Improved MapReduce K-Means Clustering Algorithm with Combiner. In: Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. UKSIM 2014. USA: IEEE Computer Society; 2014. p. 386\u2013391. Available from: https:\/\/doi.org\/10.1109\/UKSim.2014.11.","DOI":"10.1109\/UKSim.2014.11"},{"key":"pcbi.1008625.ref016","doi-asserted-by":"crossref","unstructured":"Gursoy A. Data Decomposition for Parallel K-means Clustering. In: Proceeding of the International Conference on Parallel Processing and Applied Mathematics; 2003. p. 241\u2013248.","DOI":"10.1007\/978-3-540-24669-5_31"},{"key":"pcbi.1008625.ref017","unstructured":"Jin S, Cui Y, Yu C. A New Parallelization Method for K-means; 2016. arXiv: 1608.06347"},{"key":"pcbi.1008625.ref018","unstructured":"Kerdprasop K, Kerdprasop N. Parallelization of K-means clustering on multi-core processors. International Conference on Applied Computer Science\u2014Proceedings. 2010;."},{"key":"pcbi.1008625.ref019","doi-asserted-by":"crossref","unstructured":"Sculley D. Web-Scale k-Means Clustering. In: Proceedings of the 19th International Conference on World Wide Web. WWW\u201910. New York, NY, USA: Association for Computing Machinery; 2010. p. 1177\u20131178. Available from: https:\/\/doi.org\/10.1145\/1772690.1772862.","DOI":"10.1145\/1772690.1772862"},{"key":"pcbi.1008625.ref020","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"pcbi.1008625.ref021","unstructured":"Mouselimis L, Sanderson C, Curtin R, Agrawal S, Frey B, Dueck D. ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering; 2020. Available from: https:\/\/cran.r-project.org\/web\/packages\/ClusterR\/"},{"key":"pcbi.1008625.ref022","unstructured":"Ni Y, Risso D, Hicks S, Purdom E. mbkmeans: Mini-batch K-means Clustering for Single-Cell RNA-seq; 2020. Available from: https:\/\/doi.org\/doi:10.18129\/B9.bioc.mbkmeans"},{"issue":"2","key":"pcbi.1008625.ref023","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nmeth.3252","article-title":"Orchestrating high-throughput genomic analysis with Bioconductor","volume":"12","author":"W Huber","year":"2015","journal-title":"Nat Methods"},{"key":"pcbi.1008625.ref024","unstructured":"The HDF Group. Hierarchical Data Format, version 5; 1997. Available from: http:\/\/www.hdfgroup.org\/HDF5\/."},{"key":"pcbi.1008625.ref025","doi-asserted-by":"crossref","first-page":"14049","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"GXY Zheng","year":"2017","journal-title":"Nat Commun"},{"issue":"10","key":"pcbi.1008625.ref026","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"VD Blondel","year":"2008","journal-title":"Journal of Statistical Mechanics: Theory and Experiment"},{"issue":"1","key":"pcbi.1008625.ref027","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-41695-z","article-title":"From Louvain to Leiden: guaranteeing well-connected communities","volume":"9","author":"VA Traag","year":"2019","journal-title":"Scientific Reports"},{"key":"pcbi.1008625.ref028","unstructured":"Tang C, Monteleoni C. Convergence rate of stochastic k-means. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017. 2017;54."},{"key":"pcbi.1008625.ref029","unstructured":"Bates D, Maechler M. Matrix: Sparse and Dense Matrix Classes and Methods; 2018. Available from: https:\/\/CRAN.R-project.org\/package=Matrix."},{"key":"pcbi.1008625.ref030","unstructured":"Morgan M, Obenchain V, Hester J, Pag\u00e8s H. SummarizedExperiment: SummarizedExperiment container; 2018. Available from: https:\/\/bioconductor.org\/packages\/SummarizedExperiment."},{"key":"pcbi.1008625.ref031","unstructured":"Lun ATL, Risso D, Korthauer K. SingleCellExperiment: S4 Classes for Single Cell Data; 2019. Available from: https:\/\/bioconductor.org\/packages\/SingleCellExperiment."},{"issue":"8","key":"pcbi.1008625.ref032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v040.i08","article-title":"Rcpp: Seamless R and C++ Integration","volume":"40","author":"D Eddelbuettel","year":"2011","journal-title":"Journal of Statistical Software"},{"issue":"5","key":"pcbi.1008625.ref033","doi-asserted-by":"crossref","first-page":"e1006135","DOI":"10.1371\/journal.pcbi.1006135","article-title":"beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types","volume":"14","author":"ATL Lun","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1008625.ref034","unstructured":"Pag\u00e8s H, with contributions from Peter Hickey, Lun A. DelayedArray: Delayed operations on array-like objects; 2019. Available from: https:\/\/bioconductor.org\/packages\/DelayedArray."},{"key":"pcbi.1008625.ref035","unstructured":"Pag\u00e8s H. HDF5Array: HDF5 backend for DelayedArray objects; 2018. Available from: https:\/\/bioconductor.org\/packages\/HDF5Array."},{"key":"pcbi.1008625.ref036","unstructured":"Arthur D, Vassilvitskii S. K-Means++: The Advantages of Careful Seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SODA\u201907. USA: Society for Industrial and Applied Mathematics; 2007. p. 1027\u20131035."},{"key":"pcbi.1008625.ref037","unstructured":"Lun A, Morgan M. TENxBrainData: Data from the 10X 1.3 Million Brain Cell Study; 2019. Available from: https:\/\/doi.org\/doi:10.18129\/B9.bioc.TENxBrainData"},{"issue":"8","key":"pcbi.1008625.ref038","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1093\/bioinformatics\/btw777","article-title":"Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R","volume":"33","author":"DJ McCarthy","year":"2017","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1008625.ref039","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1137\/04060593X","article-title":"Augmented implicitly restarted Lanczos bidiagonalization methods","volume":"27","author":"J Baglama","year":"2005","journal-title":"SIAM Journal on Scientific Computing"},{"issue":"1","key":"pcbi.1008625.ref040","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13059-016-0947-7","article-title":"Pooling across cells to normalize single-cell RNA sequencing data with many zero counts","volume":"17","author":"AT 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