Consensus clustering and functional interpretation of gene-expression data
2004
Swift, Stephen | Tucker, Allan | Vinciotti, Veronica | Martin, Nigel | Orengo, Christine | Liu, Xiaohui | Kellam, Paul
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.
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