{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:35Z","timestamp":1772138015890,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Bavarian State Ministry of Science"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data are not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the earth mover\u2019s distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS \u2013 CYtometry ANalysis Using Shiny \u2013 a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https:\/\/exbio.wzw.tum.de\/cyanus\/.<\/jats:p>","DOI":"10.1093\/bib\/bbab471","type":"journal-article","created":{"date-parts":[[2021,10,16]],"date-time":"2021-10-16T08:22:17Z","timestamp":1634372537000},"source":"Crossref","is-referenced-by-count":16,"title":["A systematic comparison of novel and existing differential analysis methods for CyTOF data"],"prefix":"10.1093","volume":"23","author":[{"given":"Lis","family":"Arend","sequence":"first","affiliation":[{"name":"Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany"}]},{"given":"Judith","family":"Bernett","sequence":"additional","affiliation":[{"name":"Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany"}]},{"given":"Quirin","family":"Manz","sequence":"additional","affiliation":[{"name":"Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany"}]},{"given":"Melissa","family":"Klug","sequence":"additional","affiliation":[{"name":"Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany"},{"name":"Department of Internal Medicine I, School of Medicine, University Hospital rechts der Isar, Technical University of Munich, Munich, Germany"},{"name":"German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany"}]},{"given":"Olga","family":"Lazareva","sequence":"additional","affiliation":[{"name":"Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany"}]},{"given":"Jan","family":"Baumbach","sequence":"additional","affiliation":[{"name":"Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany"},{"name":"Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark"}]},{"given":"Dario","family":"Bongiovanni","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine I, School of Medicine, University Hospital rechts der Isar, Technical University of Munich, Munich, Germany"},{"name":"German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany"},{"name":"Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS and Humanitas University, Rozzano, Milan, Italy"}]},{"given":"Markus","family":"List","sequence":"additional","affiliation":[{"name":"Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany"}]}],"member":"286","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"2022012000300173800_ref1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.3389\/fonc.2019.00415","article-title":"Validation of cytof against flow cytometry for immunological studies and monitoring of 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