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Consensus clustering and functional interpretation of gene-expression data

Stephen Swift1, Allan Tucker1, Veronica Vinciotti1, Nigel Martin2, Christine Orengo3, Xiaohui Liu1 and Paul Kellam4*

Author Affiliations

1 Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK

2 School of Computer Science and Information Systems, Birkbeck College, London WC1E 7HX, UK

3 Department of Biochemistry and Molecular Biology, University College London, London WC1E 6BT, UK

4 Virus Genomics and Bioinformatics Group, Department of Infection, Windeyer Institute, 46 Cleveland Street, University College London, London W1T 4JF, UK

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Genome Biology 2004, 5:R94  doi:10.1186/gb-2004-5-11-r94

Published: 1 November 2004


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.