title: Consensus clustering and functional interpretation of gene-expression data.
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.
authors.affiliation: Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: Swift
authors.forename: Allan
authors.initials: A
authors.lastname: Tucker
authors.forename: Veronica
authors.initials: V
authors.lastname: Vinciotti
authors.forename: Nigel
authors.initials: N
authors.lastname: Martin
authors.forename: Christine
authors.initials: C
authors.lastname: Orengo
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Liu
authors.forename: Paul
authors.initials: P
authors.lastname: Kellam
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D016384:Consensus Sequence / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data*; D005786:Gene Expression Regulation / Q000502:physiology*; D046228:Microarray Analysis / Q000379:methods / Q000706:statistics & numerical data*; D008957:Models, Genetic