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JBI
2004

Consensus Clustering and Functional Interpretation of Gene Expression Data

10 years 7 months ago
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 NFB and the unfolded protein response in certain B-cell lymphomas. Background There are many practical applications that involve the grouping of a set of objects into a number of mutually exclusive subsets. Methods to achieve the partitioning of objects related by correlation or distance metrics are collectively known as clustering algorithms. Any algorithm that applies a global search for optimal clusters in a given dataset will run in exponential time to the size of problem space, and therefore heuris...
Paul Kellam, Stephen Swift, Allan Tucker, Veronica
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where JBI
Authors Paul Kellam, Stephen Swift, Allan Tucker, Veronica Vinciotti, Nigel J. Martin, Christine A. Orengo, Xiaohui Liu
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