Brunel University Research Archive (BURA) >
Research Areas >
Computer Science >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1368

Title: Consensus clustering and functional interpretation of gene expression data
Authors: Swift, S
Tucker, A
Vinciotti, V
Martin, N
Orengo, C
Liu, X
Kellam, P
Keywords: Data clustering
Gene expression data
Publication Date: 2004
Publisher: BioMed Central
Citation: Genome Biology, 5: R94, Nov 2004
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 Nuclear Factor-kB and Unfolded Protein Response regulated genes in certain B-cell lymphomas.
URI: http://bura.brunel.ac.uk/handle/2438/1368
DOI: http://dx.doi.org/10.1186/gb-2004-5-11-r94
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Mathematical Science
Computer Science

Files in This Item:

File Description SizeFormat
GBConsensus.pdf780.49 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan