{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T01:35:33Z","timestamp":1790991333573,"version":"4.1.0"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2015,8,14]],"date-time":"2015-08-14T00:00:00Z","timestamp":1439510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"funder":[{"name":"NIH","award":["R01EB008400, U01AI089859, and HHSN272201200005C"],"award-info":[{"award-number":["R01EB008400, U01AI089859, and HHSN272201200005C"]}]}],"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>Flow cytometry (FCM) is a fluorescence\u2010based single\u2010cell experimental technology that is routinely applied in biomedical research for identifying cellular biomarkers of normal physiological responses and abnormal disease states. While many computational methods have been developed that focus on identifying cell populations in individual FCM samples, very few have addressed how the identified cell populations can be matched across samples for comparative analysis. This article presents FlowMap\u2010FR, a novel method for cell population mapping across FCM samples. FlowMap\u2010FR is based on the Friedman\u2013Rafsky nonparametric test statistic (FR statistic), which quantifies the equivalence of multivariate distributions. As applied to FCM data by FlowMap\u2010FR, the FR statistic objectively quantifies the similarity between cell populations based on the shapes, sizes, and positions of fluorescence data distributions in the multidimensional feature space. To test and evaluate the performance of FlowMap\u2010FR, we simulated the kinds of biological and technical sample variations that are commonly observed in FCM data. The results show that FlowMap\u2010FR is able to effectively identify equivalent cell populations between samples under scenarios of proportion differences and modest position shifts. As a statistical test, FlowMap\u2010FR can be used to determine whether the expression of a cellular marker is statistically different between two cell populations, suggesting candidates for new cellular phenotypes by providing an objective statistical measure. In addition, FlowMap\u2010FR can indicate situations in which inappropriate splitting or merging of cell populations has occurred during gating procedures. We compared the FR statistic with the symmetric version of Kullback\u2013Leibler divergence measure used in a previous population matching method with both simulated and real data. The FR statistic outperforms the symmetric version of KL\u2010distance in distinguishing equivalent from nonequivalent cell populations. FlowMap\u2010FR was also employed as a distance metric to match cell populations delineated by manual gating across 30 FCM samples from a benchmark FlowCAP data set. An F\u2010measure of 0.88 was obtained, indicating high precision and recall of the FR\u2010based population matching results. FlowMap\u2010FR has been implemented as a standalone R\/Bioconductor package so that it can be easily incorporated into current FCM data analytical workflows. \u00a9 2015 International Society for Advancement of Cytometry<\/jats:p>","DOI":"10.1002\/cyto.a.22735","type":"journal-article","created":{"date-parts":[[2015,8,14]],"date-time":"2015-08-14T14:24:56Z","timestamp":1439562296000},"page":"71-88","source":"Crossref","is-referenced-by-count":22,"title":["Mapping cell populations in flow cytometry data for cross\u2010sample comparison using the Friedman\u2013Rafsky test statistic as a distance measure"],"prefix":"10.1002","volume":"89","author":[{"given":"Chiaowen","family":"Hsiao","sequence":"first","affiliation":[{"name":"Center for Bioinformatics and Computational Biology University of Maryland College Park Maryland"},{"name":"Applied Mathematics, Applied Statistics, and Scientific Computing University of Maryland College Park Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengya","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistical Science Southern Methodist University Dallas Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rick","family":"Stanton","sequence":"additional","affiliation":[{"name":"Department of Informatics J. Craig Venter Institute La Jolla California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monnie","family":"McGee","sequence":"additional","affiliation":[{"name":"Department of Statistical Science Southern Methodist University Dallas Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Informatics J. Craig Venter Institute La Jolla California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard H.","family":"Scheuermann","sequence":"additional","affiliation":[{"name":"Department of Informatics J. Craig Venter Institute La Jolla California"},{"name":"Department of Pathology University of California San Diego California"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2015,8,14]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature10673"},{"key":"e_1_2_6_3_1","unstructured":"ChattopadhyayP PerfettoS GaylordB StallA DuckettL HillJ NguyenR AmbrozakD BalderasR RoedererM. \u201cToward 40+ Parameter Flow Cytometry \u201d in CYTO Conference Plenary Presentation and Abstract 388 2014."},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2009.02.030"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2005.09.036"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2365"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003130"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0903028106"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.20531"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0100334"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.20554"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-11-403"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0320(20010901)45:1<47::AID-CYTO1143>3.0.CO;2-A"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-11-546"},{"issue":"2","key":"e_1_2_6_15_1","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-13-S2-S10","article-title":"Matching phosphorylation response patterns of antigen\u2010receptor\u2010stimulated T cells via flow cytometry","volume":"13","author":"Azad A","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176344722"},{"key":"e_1_2_6_17_1","unstructured":"ZhaoTi SotoS MurphyRF.Improved comparison of protein subcellar location patterns. 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