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Consensus clustering and functional interpretation of gene-expression data

By S. Swift, A. Tucker, V. Vinciotti, Nigel Martin, C.A. Orengo, X. Liu and P. Kellam


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 genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas

Topics: csis
Publisher: Springer
Year: 2004
OAI identifier: oai:eprints.bbk.ac.uk.oai2:2991

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