Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1368
Title: Consensus clustering and functional interpretation of gene expression data
Authors: Swift, S
Tucker, A
Vinciotti, V
Martin, N
Orengo, C
Liu, X
Kellam, P
Keywords: Data clustering;Gene expression data
Issue Date: 2004
Publisher: BioMed Central
Citation: Genome Biology, 5: R94, Nov 2004
Abstract: Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene expression analysis. Here we introduce Consensus Clustering which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel Nuclear Factor-kB and Unfolded Protein Response regulated genes in certain B-cell lymphomas.
URI: http://bura.brunel.ac.uk/handle/2438/1368
DOI: http://dx.doi.org/10.1186/gb-2004-5-11-r94
Appears in Collections:Mathematical Science
Computer Science
Dept of Computer Science Research Papers

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