Consensus clustering and functional interpretation of gene-expression data
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
Genome Biology 2004, 5:R94 doi:10.1186/gb-2004-5-11-r94Published: 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.