Consensus clustering and functional interpretation of gene-expression data.

Stephen Swift; Allan Tucker; Veronica Vinciotti; Nigel Martin; Christine Orengo; Xiaohui Liu; Paul Kellam

doi:10.1186/gb-2004-5-11-r94

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Consensus clustering and functional interpretation of gene-expression data.

Stephen Swift·Allan Tucker·Veronica Vinciotti·Nigel Martin·Christine Orengo·Xiaohui Liu·Paul Kellam

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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.

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Cluster Analysis Computer Simulation Consensus Sequence Gene Expression Profiling Gene Expression Regulation Microarray Analysis Models, Genetic Journal Article Research Support, Non-U.S. Gov't

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Stephen Swift, Allan Tucker, Veronica Vinciotti, Nigel Martin, Christine Orengo, Xiaohui Liu, Paul Kellam. 2004-11-01. Consensus clustering and functional interpretation of gene-expression data.. https://doi.org/10.1186/gb-2004-5-11-r94

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