{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:38:43Z","timestamp":1774125523695,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2005,5,12]],"date-time":"2005-05-12T00:00:00Z","timestamp":1115856000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"},{"start":{"date-parts":[[2005,5,12]],"date-time":"2005-05-12T00:00:00Z","timestamp":1115856000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                        <jats:title>Background<\/jats:title>\n                        <jats:p>The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop\/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets) and is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/function.princeton.edu\/GeneVAnD\">http:\/\/function.princeton.edu\/GeneVAnD<\/jats:ext-link>.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusion<\/jats:title>\n                        <jats:p>Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1471-2105-6-115","type":"journal-article","created":{"date-parts":[[2005,5,13]],"date-time":"2005-05-13T06:13:25Z","timestamp":1115964805000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Visualization methods for statistical analysis of microarray clusters"],"prefix":"10.1186","volume":"6","author":[{"given":"Matthew A","family":"Hibbs","sequence":"first","affiliation":[]},{"given":"Nathaniel C","family":"Dirksen","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[]},{"given":"Olga G","family":"Troyanskaya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2005,5,12]]},"reference":[{"issue":"16","key":"440_CR1","doi-asserted-by":"publisher","first-page":"8961","DOI":"10.1073\/pnas.161273698","volume":"98","author":"MK Kerr","year":"2001","unstructured":"Kerr MK, Churchill GA: Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc Natl Acad Sci U S A 2001, 98(16):8961\u20135.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"4","key":"440_CR2","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1093\/bioinformatics\/17.4.309","volume":"17","author":"KY Yeung","year":"2001","unstructured":"Yeung KY, Haynor DR, Ruzzo WL: Validating clustering for gene expression data. Bioinformatics 2001, 17(4):309\u201318.","journal-title":"Bioinformatics"},{"issue":"1\u20133","key":"440_CR3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/S0014-5793(02)02873-9","volume":"522","author":"MA Mendez","year":"2002","unstructured":"Mendez MA, Hodar C, Vulpe C, Gonzalez M, Cambiazo V: Discriminant analysis to evaluate clustering of gene expression data. FEBS Lett 2002, 522(1\u20133):24\u20138.","journal-title":"FEBS Lett"},{"issue":"4","key":"440_CR4","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1093\/bioinformatics\/btg025","volume":"19","author":"S Datta","year":"2003","unstructured":"Datta S, Datta S: Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 2003, 19(4):459\u201366.","journal-title":"Bioinformatics"},{"key":"440_CR5","unstructured":"Munich Information Center for Protein Sequences (MIPS)[http:\/\/mips.gsf.de\/]"},{"key":"440_CR6","unstructured":"Gene Ontology Consortium[http:\/\/www.geneontology.org\/]"},{"key":"440_CR7","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/INFVIS.2004.10","volume-title":"IEEE Symposium on Information Visualization","author":"R Amar","year":"2004","unstructured":"Amar R, Stasko J: A knowledge task-based framework for design and evaluation of information visualizations. IEEE Symposium on Information Visualization 2004, 143\u2013150."},{"issue":"14","key":"440_CR8","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1093\/bioinformatics\/btg232","volume":"19","author":"R Sharan","year":"2003","unstructured":"Sharan R, Maron-Katz A, Shamir R: CLICK and EXPANDER: a system for clustering and visualizing gene expression data. Bioinformatics 2003, 19(14):1787\u201399.","journal-title":"Bioinformatics"},{"issue":"10","key":"440_CR9","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1093\/bioinformatics\/btg136","volume":"19","author":"JE Johnson","year":"2004","unstructured":"Johnson JE, Stromvik MV, Silverstein KA, Crow JA, Shoop E, Retzel EF: TableView: portable genomic data visualization. Bioinformatics 2004, 19(10):1292\u20133. 2003 Jul 1","journal-title":"Bioinformatics"},{"issue":"17","key":"440_CR10","doi-asserted-by":"publisher","first-page":"3246","DOI":"10.1093\/bioinformatics\/bth349","volume":"20","author":"AJ Saldanha","year":"2003","unstructured":"Saldanha AJ: Java treeview \u2013 extensible visualization of microarray data. Bioinformatics 2003, 20(17):3246\u20138.","journal-title":"Bioinformatics"},{"issue":"7","key":"440_CR11","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MC.2002.1016905","volume":"35","author":"J Seo","year":"2002","unstructured":"Seo J, Shneiderman B: Interactively Exploring Hierarchical Clustering Results. IEEE Computer 2002, 35(7):80\u201386.","journal-title":"IEEE Computer"},{"issue":"10","key":"440_CR12","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1101\/gr.225402","volume":"12","author":"M Werner-Washburne","year":"2002","unstructured":"Werner-Washburne M, Wylie B, Boyack K, Fuge E, Galbraith J, Weber J, Davidson G: Comparative Analysis of Multiple Genome-Scale Data Sets. Genome Res 2002, 12(10):1564\u201373.","journal-title":"Genome Res"},{"issue":"1","key":"440_CR13","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/1471-2105-5-84","volume":"5","author":"E Baehrecke","year":"2004","unstructured":"Baehrecke E, Dang N, Babaria K, Shneiderman B: Visualization and analysis of microarray and gene ontology data with treemaps. BMC Bioinformatics 2004, 5(1):84.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"440_CR14","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1186\/1471-2105-5-141","volume":"5","author":"CA Rees","year":"2004","unstructured":"Rees CA, Demeter J, Matese J, Botstein D, Sherlock G: GeneXplorer: an interactive web application for microarray data visualization and analysis. BMC Bioinformatics 2004, 5(1):141.","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"440_CR15","doi-asserted-by":"crossref","first-page":"374","DOI":"10.2144\/03342mt01","volume":"34","author":"AI Saeed","year":"2003","unstructured":"Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J: TM4: a free, open-source system for microarray data management and analysis. Biotechniques 2003, 34(2):374\u20138.","journal-title":"Biotechniques"},{"issue":"1","key":"440_CR16","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1093\/bioinformatics\/18.1.207","volume":"18","author":"A Sturn","year":"2002","unstructured":"Sturn A, Quackenbush J, Trajanoski Z: Genesis: cluster analysis of microarray data. Bioinformatics 2002, 18(1):207\u20138.","journal-title":"Bioinformatics"},{"key":"440_CR17","unstructured":"Genespring[http:\/\/www.silicongenetics.com\/cgi\/SiG.cgi\/Products\/GeneSpring\/index.smf]"},{"key":"440_CR18","unstructured":"Spotfire[http:\/\/www.spotfire.com\/]"},{"issue":"25","key":"440_CR19","doi-asserted-by":"publisher","first-page":"14863","DOI":"10.1073\/pnas.95.25.14863","volume":"95","author":"MB Eisen","year":"1998","unstructured":"Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998, 95(25):14863\u20138.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"440_CR20","first-page":"455","volume-title":"Pac Symp Biocomput","author":"S Raychaudhuri","year":"2000","unstructured":"Raychaudhuri S, Stuart JM, Altman RB: Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 2000, 455\u201366."},{"issue":"18","key":"440_CR21","doi-asserted-by":"publisher","first-page":"10101","DOI":"10.1073\/pnas.97.18.10101","volume":"97","author":"O Alter","year":"2000","unstructured":"Alter O, Brown PO, Botstein D: Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 2000, 97(18):10101\u20136.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"12","key":"440_CR22","doi-asserted-by":"publisher","first-page":"3273","DOI":"10.1091\/mbc.9.12.3273","volume":"9","author":"PT Spellman","year":"1998","unstructured":"Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9(12):3273\u201397.","journal-title":"Mol Biol Cell"},{"key":"440_CR23","unstructured":"Java3D[http:\/\/java.sun.com\/products\/java-media\/3D\/]"},{"issue":"8","key":"440_CR24","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TSE.2004.44","volume":"30","author":"BB Bederson","year":"2004","unstructured":"Bederson BB, Grosjean J, Meyer J: Toolkit Design for Interactive Structured Graphics. IEEE Transactions on Software Engineering 2004, 30(8):535\u2013546.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"440_CR25","unstructured":"JAva MAtrix Package (JAMA)[http:\/\/math.nist.gov\/javanumerics\/jama\/]"},{"issue":"24","key":"440_CR26","doi-asserted-by":"publisher","first-page":"13784","DOI":"10.1073\/pnas.241500798","volume":"98","author":"ME Garber","year":"2001","unstructured":"Garber ME, Troyanskaya OG, Schluens K, Petersen S, Thaesler Z, Pacyna-Gengelbach M, van de Rijn M, Rosen GD, Perou CM, Whyte RI, Altman RB, Brown PO, Botstein D, Petersen I: Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci U S A 2001, 98(24):13784\u20139.","journal-title":"Proc Natl Acad Sci U S A"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-6-115.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/1471-2105-6-115\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-6-115.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T12:11:55Z","timestamp":1728303115000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-6-115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2005,5,12]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2005,12]]}},"alternative-id":["440"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-6-115","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2005,5,12]]},"assertion":[{"value":"17 December 2004","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2005","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2005","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"115"}}