Consensus clustering and functional interpretation of gene-expression data.
File(s)
Author(s)
Type
Journal Article
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.
Date Issued
2004-11-01
Date Acceptance
2004-09-13
Citation
Genome Biology, 2004, 5 (11), pp.R94-R94
ISSN
1474-760X
Publisher
BioMed Central
Start Page
R94
End Page
R94
Journal / Book Title
Genome Biology
Volume
5
Issue
11
Copyright Statement
© 2004 Swift et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/15535870
PII: gb-2004-5-11-r94
Subjects
Cluster Analysis
Computer Simulation
Consensus Sequence
Gene Expression Profiling
Gene Expression Regulation
Microarray Analysis
Models, Genetic
Publication Status
Published
Coverage Spatial
England
Article Number
R94