Consensus clustering and functional interpretation of gene-expression data.
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.
Swift
Stephen
S
Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK.
Tucker
Allan
A
Vinciotti
Veronica
V
Martin
Nigel
N
Orengo
Christine
C
Liu
Xiaohui
X
Kellam
Paul
P
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