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dc.contributor.authorMar, Jessica
dc.contributor.authorWells, Christine
dc.contributor.authorQuackenbush, John
dc.date.accessioned2017-05-03T12:27:54Z
dc.date.available2017-05-03T12:27:54Z
dc.date.issued2011
dc.date.modified2012-05-15T22:33:30Z
dc.identifier.issn13674803
dc.identifier.doi10.1093/bioinformatics/btr074
dc.identifier.urihttp://hdl.handle.net/10072/44256
dc.description.abstractMotivation: Unsupervised 'cluster' analysis is an invaluable tool for exploratory microarray data analysis, as it organizes the data into groups of genes or samples in which the elements share common patterns. Once the data are clustered, finding the optimal number of informative subgroups within a dataset is a problem that, while important for understanding the underlying phenotypes, is one for which there is no robust, widely accepted solution. Results: To address this problem we developed an 'informativeness metric' based on a simple analysis of variance statistic that identifies the number of clusters which best separate phenotypic groups. The performance of the informativeness metric has been tested on both experimental and simulated datasets, and we contrast these results with those obtained using alternative methods such as the gap statistic.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent379354 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherOxford University Press
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1094
dc.relation.ispartofpageto1100
dc.relation.ispartofissue8
dc.relation.ispartofjournalBioinformatics
dc.relation.ispartofvolume27
dc.rights.retentionY
dc.subject.fieldofresearchBiological Sciences not elsewhere classified
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode069999
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleDefining an informativeness metric for clustering gene expression data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2011 Oxford University Press. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.date.issued2011
gro.hasfulltextFull Text
gro.griffith.authorWells, Christine


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