{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T01:23:25Z","timestamp":1790558605649,"version":"4.1.0"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2015,10,22]],"date-time":"2015-10-22T00:00:00Z","timestamp":1445472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001407","name":"Department of Biotechnology, Ministry of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cytometry Pt A"],"published-print":{"date-parts":[[2016,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We present an algorithm for modeling flow cytometry data in the presence of large inter\u2010sample variation. Large\u2010scale cytometry datasets often exhibit some within\u2010class variation due to technical effects such as instrumental differences and variations in data acquisition, as well as subtle biological heterogeneity within the class of samples. Failure to account for such variations in the model may lead to inaccurate matching of populations across a batch of samples and poor performance in classification of unlabeled samples. In this paper, we describe the Joint Clustering and Matching (JCM) procedure for simultaneous segmentation and alignment of cell populations across multiple samples. Under the JCM framework, a multivariate mixture distribution is used to model the distribution of the expressions of a fixed set of markers for each cell in a sample such that the components in the mixture model may correspond to the various populations of cells, which have similar expressions of markers (that is, clusters), in the composition of the sample. For each class of samples, an overall class template is formed by the adoption of random\u2010effects terms to model the inter\u2010sample variation within a class. The construction of a parametric template for each class allows for direct quantification of the differences between the template and each sample, and also between each pair of samples, both within or between classes. The classification of a new unclassified sample is then undertaken by assigning the unclassified sample to the class that minimizes the distance between its fitted mixture density and each class density as provided by the class templates. For illustration, we use a symmetric form of the Kullback\u2010Leibler divergence as a distance measure between two densities, but other distance measures can also be applied. We show and demonstrate on four real datasets how the JCM procedure can be used to carry out the tasks of automated clustering and alignment of cell populations, and supervised classification of samples. \u00a9 2015 International Society for Advancement of Cytometry<\/jats:p>","DOI":"10.1002\/cyto.a.22789","type":"journal-article","created":{"date-parts":[[2015,10,22]],"date-time":"2015-10-22T14:51:40Z","timestamp":1445525500000},"page":"30-43","source":"Crossref","is-referenced-by-count":26,"title":["Modeling of inter\u2010sample variation in flow cytometric data with the joint clustering and matching procedure"],"prefix":"10.1002","volume":"89","author":[{"given":"Sharon X.","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Mathematics University of Queensland St. Lucia Queensland 4072 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey J.","family":"McLachlan","sequence":"additional","affiliation":[{"name":"Department of Mathematics University of Queensland St. Lucia Queensland 4072 Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saumyadipta","family":"Pyne","sequence":"additional","affiliation":[{"name":"Indian Institute of Public Health Hyderabad (IIPHH), Plot No. 1, A.N.V. Arcade, Amar Co\u2010op Society, Kavuri Hills, Madhapur Hyderabad AP 500033 India"},{"name":"CR Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus, Hyderabad AP 500046 India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2015,10,22]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.1351\/pac200880122627"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1198704"},{"key":"e_1_2_5_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2365"},{"key":"e_1_2_5_5_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0903028106"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxp062"},{"key":"e_1_2_5_7_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003130"},{"key":"e_1_2_5_8_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0100334"},{"key":"e_1_2_5_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-15-314"},{"key":"e_1_2_5_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr468"},{"key":"e_1_2_5_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-13-S5-S5"},{"key":"e_1_2_5_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-010-9225-9"},{"key":"e_1_2_5_13_1","doi-asserted-by":"crossref","unstructured":"NaimI DattaS SharmaG CavenaughJS MosmannTR.Swift: Scalable weighted iterative sampling for flow cytometry clustering. 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