Consensus clustering and functional interpretation of gene-expression data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-10

AUTHORS

Stephen Swift, Allan Tucker, Veronica Vinciotti, Nigel Martin, Christine Orengo, Xiaohui Liu, Paul Kellam

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. More... »

PAGES

r94

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/gb-2004-5-11-r94

DOI

http://dx.doi.org/10.1186/gb-2004-5-11-r94

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023643640

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15535870


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