Skip Navigation


Bioinformatics Advance Access originally published online on March 23, 2007
Bioinformatics 2007 23(10):1243-1250; doi:10.1093/bioinformatics/btm103
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
23/10/1243    most recent
btm103v1
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 Lai, Y.
Right arrow Articles by She, J.-X.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lai, Y.
Right arrow Articles by She, J.-X.
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

A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups

Yinglei Lai 1,*, Bao-ling Adam 2, Robert Podolsky 2 and Jin-Xiong She 2

1Department of Statistics and Biostatistics Center, The George Washington University, 2140 Pennsylvania Avenue, N.W. Washington, DC 20052, USA and 2Center for Biotechnology and Genomic Medicine, Medical College of Georgia, 1120 15th street, CA4098, GA 30912, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Due to advances in experimental technologies, such as microarray, mass spectrometry and nuclear magnetic resonance, it is feasible to obtain large-scale data sets, in which measurements for a large number of features can be simultaneously collected. However, the sample sizes of these data sets are usually small due to their relatively high costs, which leads to the issue of concordance among different data sets collected for the same study: features should have consistent behavior in different data sets. There is a lack of rigorous statistical methods for evaluating this concordance or discordance.

Methods: Based on a three-component normal-mixture model, we propose two likelihood ratio tests for evaluating the concordance and discordance between two large-scale data sets with two sample groups. The parameter estimation is achieved through the expectation-maximization (E-M) algorithm. A normal-distribution-quantile-based method is used for data transformation.

Results: To evaluate the proposed tests, we conducted some simulation studies, which suggested their satisfactory performances. As applications, the proposed tests were applied to three SELDI-MS data sets with replicates. One data set has replicates from different platforms and the other two have replicates from the same platform. We found that data generated by SELDI-MS showed satisfactory concordance between replicates from the same platform but unsatisfactory concordance between replicates from different platforms.

Availability: The R codes are freely available at http://home.gwu.edu/~ylai/research/Concordance

Contact: ylai{at}gwu.edu

Associate Editor: David Rocke

Received on December 5, 2006; revised on March 3, 2007; accepted on March 10, 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.