dc.contributor.author | Mar, Jessica | |
dc.contributor.author | Wells, Christine | |
dc.contributor.author | Quackenbush, John | |
dc.date.accessioned | 2017-05-03T12:27:54Z | |
dc.date.available | 2017-05-03T12:27:54Z | |
dc.date.issued | 2011 | |
dc.date.modified | 2012-05-15T22:33:30Z | |
dc.identifier.issn | 13674803 | |
dc.identifier.doi | 10.1093/bioinformatics/btr074 | |
dc.identifier.uri | http://hdl.handle.net/10072/44256 | |
dc.description.abstract | Motivation: 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.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.format.extent | 379354 bytes | |
dc.format.mimetype | application/pdf | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Oxford University Press | |
dc.publisher.place | United Kingdom | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofpagefrom | 1094 | |
dc.relation.ispartofpageto | 1100 | |
dc.relation.ispartofissue | 8 | |
dc.relation.ispartofjournal | Bioinformatics | |
dc.relation.ispartofvolume | 27 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Biological Sciences not elsewhere classified | |
dc.subject.fieldofresearch | Mathematical Sciences | |
dc.subject.fieldofresearch | Biological Sciences | |
dc.subject.fieldofresearch | Information and Computing Sciences | |
dc.subject.fieldofresearchcode | 069999 | |
dc.subject.fieldofresearchcode | 01 | |
dc.subject.fieldofresearchcode | 06 | |
dc.subject.fieldofresearchcode | 08 | |
dc.title | Defining an informativeness metric for clustering gene expression data | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - 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.issued | 2011 | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Wells, Christine | |