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...

• Type: Conference
• Year: 2004
• Where: JBI
• Authors: Paul Kellam, Stephen Swift, Allan Tucker, Veronica Vinciotti, Nigel J. Martin, Christine A. Orengo, Xiaohui Liu
• Keywords: Bioinformatics, Consensus Clustering, JBI 2004, Microarray Analysis, Real-world Clustering Problems