{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T08:06:52Z","timestamp":1783670812212,"version":"3.55.0"},"reference-count":7,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":567,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption\/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.<\/jats:p>\n               <jats:p>Contact: o.vitek@neu.edu<\/jats:p>\n               <jats:p>Availability and implementation: The code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv146","type":"journal-article","created":{"date-parts":[[2015,3,17]],"date-time":"2015-03-17T00:26:10Z","timestamp":1426551970000},"page":"2418-2420","source":"Crossref","is-referenced-by-count":262,"title":["<i>Cardinal<\/i>: an R package for statistical analysis of mass spectrometry-based imaging experiments"],"prefix":"10.1093","volume":"31","author":[{"given":"Kyle D.","family":"Bemis","sequence":"first","affiliation":[{"name":"1 Department of Statistics and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"April","family":"Harry","sequence":"additional","affiliation":[{"name":"1 Department of Statistics and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Livia S.","family":"Eberlin","sequence":"additional","affiliation":[{"name":"2 Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Ferreira","sequence":"additional","affiliation":[{"name":"2 Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie M.","family":"van de Ven","sequence":"additional","affiliation":[{"name":"3 Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parag","family":"Mallick","sequence":"additional","affiliation":[{"name":"3 Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Stolowitz","sequence":"additional","affiliation":[{"name":"3 Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Vitek","sequence":"additional","affiliation":[{"name":"4 College of Science and"},{"name":"5 College of Computer and Information Science, Northeastern University, Boston, MA 02115 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,3,15]]},"reference":[{"key":"2023020202161332500_btv146-B1","doi-asserted-by":"crossref","first-page":"i230","DOI":"10.1093\/bioinformatics\/btr246","article-title":"Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering","volume":"27","author":"Alexandrov","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020202161332500_btv146-B2","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.1007\/s00216-010-4259-6","article-title":"Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry","volume":"398","author":"Dill","year":"2010","journal-title":"Anal. 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