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    Genome Biol. 2004;5(11):R94. Epub 2004 Nov 1.

    Consensus clustering and functional interpretation of gene-expression data.

    Source

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

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

    PMID:
    15535870
    [PubMed - indexed for MEDLINE]
    PMCID: PMC545785
    Free PMC Article

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