Skip Navigation



Bioinformatics Advance Access published online on September 7, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm441
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary data
Right arrow All Versions of this Article:
23/20/2733    most recent
btm441v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
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 Möller-Levet, C. S
Right arrow Articles by Miller, C. J
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Möller-Levet, C. S
Right arrow Articles by Miller, C. J
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Exploiting sample variability to enhance multivariate analysis of microarray data

Carla S Möller-Levet a,b,*, Catharine M West b and Crispin J Miller a

Cancer Research UK, Paterson Institute for Cancer Researcha and Academic Radiation Oncologyb The University of Manchester, Christie Hospital, Manchester, M20 4BX, UK

*To whom correspondence should be addressed. Dr. Carla S Möller-Levet, E-mail: cmoller{at}picr.man.ac.uk


  Abstract

Motivation: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses.

Results: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to 'borrow’ information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterised relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data.

Availability:Matlab script files are available from the author.

Contact: cmoller{at}picr.man.ac.uk

Supplementary information: A file with additional material is available at Bioinformatics online.

Associate Editor: Dr. Trey Ideker

Received on June 12, 2007; revised on July 31, 2007; accepted on August 20, 2007

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.