Skip Navigation


Bioinformatics Advance Access originally published online on December 19, 2008
Bioinformatics 2009 25(7):904-909; doi:10.1093/bioinformatics/btn650
This Article
Right arrow Full Text
Right arrow Full Text (Print PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
25/7/904    most recent
btn650v1
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 Heller, R.
Right arrow Articles by Small, D. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Heller, R.
Right arrow Articles by Small, D. S.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

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

Matching methods for observational microarray studies

Ruth Heller 1,*, Elisabetta Manduchi 2 and Dylan S. Small 1

1Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6340 and 2Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104-6021, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: We address the problem of identifying differentially expressed genes between two conditions in the scenario where the data arise from an observational study, in which confounding factors are likely to be present.

Results: We suggest to use matching methods to balance two groups of observed cases on measured covariates, and to identify differentially expressed genes using a test suited to matched data. We illustrate this approach on two microarray studies: the first study consists of data from patients with two cancer subtypes, and the second study consists of data from AMKL patients with and without Down syndrome.

Availability: R code (www.r-project.org) for implementing our approach is included as Supplementary Material.

Contact: ruheller{at}whatron.upenn.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

Received on July 9, 2008; revised on December 8, 2008; accepted on December 17, 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.