Skip Navigation


Briefings in Bioinformatics Advance Access originally published online on October 4, 2008
Briefings in Bioinformatics 2009 10(1):24-34; doi:10.1093/bib/bbn042
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
10/1/24    most recent
bbn042v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Dinu, I.
Right arrow Articles by Yasui, Y.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Dinu, I.
Right arrow Articles by Yasui, Y.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Gene-set analysis and reduction

Irina Dinu, John D. Potter, Thomas Mueller, Qi Liu, Adeniyi J. Adewale, Gian S. Jhangri, Gunilla Einecke, Konrad S. Famulski, Philip Halloran and Yutaka Yasui

Corresponding author. Irina Dinu, PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G 2G3, Canada. Tel: +1-780-492-8336; Fax: +1-780-492-0364; E-mail: idinu{at}ualberta.ca

Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between ‘self-contained’ versus ‘competitive’ methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of ‘core members’ that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.

Keywords: DNA microarray, gene sets, gene set n, multivariate means, pathways, significance analysis of microarray, two-sample test

Submitted: May 29, 2008. Received (in revised form): July 29, 2008.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.