{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T03:09:51Z","timestamp":1771556991973,"version":"3.50.1"},"reference-count":7,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2020,2,12]],"date-time":"2020-02-12T00:00:00Z","timestamp":1581465600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ELIXIR CZ","award":["LM2015047"],"award-info":[{"award-number":["LM2015047"]}]},{"name":"Charles University Project Progress Q48"},{"DOI":"10.13039\/501100009553","name":"Czech Health Research Council","doi-asserted-by":"publisher","award":["NV18-08-00385"],"award-info":[{"award-number":["NV18-08-00385"]}],"id":[{"id":"10.13039\/501100009553","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Organic Chemistry and Biochemistry","award":["61388963"],"award-info":[{"award-number":["61388963"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>ShinySOM is free and open-source, available online at gitlab.com\/exaexa\/ShinySOM.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa091","type":"journal-article","created":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T12:25:04Z","timestamp":1580819104000},"page":"3288-3289","source":"Crossref","is-referenced-by-count":9,"title":["ShinySOM: graphical SOM-based analysis of single-cell cytometry data"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7356-4075","authenticated-orcid":false,"given":"Miroslav","family":"Kratochv\u00edl","sequence":"first","affiliation":[{"name":"Institute of Organic Chemistry and Biochemistry AS CR , 166 10 Praha 6, Czech Republic"},{"name":"Department of Software Engineering , MFF, Charles University, 118 00 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7740-0158","authenticated-orcid":false,"given":"David","family":"Bedn\u00e1rek","sequence":"additional","affiliation":[{"name":"Department of Software Engineering , MFF, Charles University, 118 00 Prague, Czech Republic"}]},{"given":"Tom\u00e1\u0161","family":"Sieger","sequence":"additional","affiliation":[{"name":"Department of Cybernetics , Faculty of Electrical Engineering, Czech Technical University in Prague, 121 35 Prague 2, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7265-3268","authenticated-orcid":false,"given":"Karel","family":"Fi\u0161er","sequence":"additional","affiliation":[{"name":"Childhood Leukaemia Investigation Prague (CLIP) , 2nd Faculty of Medicine, Charles University and University Hospital Motol, 150 06 Praha 5, Czech Republic"}]},{"given":"Ji\u0159\u00ed","family":"Vondr\u00e1\u0161ek","sequence":"additional","affiliation":[{"name":"Institute of Organic Chemistry and Biochemistry AS CR , 166 10 Praha 6, Czech Republic"}]}],"member":"286","published-online":{"date-parts":[[2020,2,12]]},"reference":[{"key":"2023013112030667200_btaa091-B1","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.cels.2018.02.010","article-title":"Compensation of signal spillover in suspension and imaging mass cytometry","volume":"6","author":"Chevrier","year":"2018","journal-title":"Cell Syst"},{"key":"2023013112030667200_btaa091-B2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1002\/cyto.a.21148","article-title":"Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data","volume":"81","author":"Fi\u0161er","year":"2012","journal-title":"Cytometry A"},{"key":"2023013112030667200_btaa091-B3","doi-asserted-by":"crossref","first-page":"2120","DOI":"10.12688\/f1000research.21642.1","article-title":"Generalized EmbedSOM on quadtree-structured self-organizing maps","volume":"8","author":"Kratochv\u00edl","year":"2019","journal-title":"F1000Res"},{"key":"2023013112030667200_btaa091-B4","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1093\/bioinformatics\/btw191","article-title":"flowAI: automatic and interactive anomaly discerning tools for flow cytometry data","volume":"32","author":"Monaco","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013112030667200_btaa091-B5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v076.i10","article-title":"Interactive dendrograms: the R packages idendro and idendr0","volume":"76","author":"Sieger","year":"2017","journal-title":"J. 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