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Bioinformatics Advance Access published online on April 6, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti413
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received August 24, 2004
Revised January 6, 2005
Accepted March 27, 2005

Article

Probe rank approaches for gene selection in oligonucleotide arrays with a small number of replicates

Dung-Tsa Chen 1*, James J. Chen 2, and Seng-jaw Soong 1

1 Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, University of Alabama at Birmingham, 153 Wallace Tumor Institute, 1824 6th Avenue South, Birmingham, AL 35294, USA
2 Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA

* To whom correspondence should be addressed.
Dung-Tsa Chen, E-mail: dtchen{at}uab.edu


  Abstract

Motivation: One major area of interest in analyzing oligonucleotide gene array data is identifying differentially expressed genes. A challenge to biostatisticians is to develop an approach to summarizing probe-level information that adequately reflects the true expression level while accounting for probe variation, chip variation, and interaction effects. Various statistical tools, such as MAS and RMA, have been developed to address this issue. In these approaches, the probe level expression data are summarized into gene level data, which then are used for downstream statistical analysis. Since probe variation is often larger than chip variation (Li and Wong, 2001b; Irizarry et al., 2003), and there is also a potential interaction effect between probe affinity and treatment effect (Chen, et al., 2004), strategies such as a gene level analysis may not be optimal. In this study, we propose a procedure to analyze probe level data for selecting differentially expressed genes under two treatment conditions (groups) with a small number of replicates. The probe level discrepancy between two groups can be measured by a difference of the percentiles of probe perfect-match (PM) ranks or of probe PM weighted ranks. The difference is then compared to a pre-specified threshold to determine differentially expressed genes. The probe level approach takes into account of non-homogenous treatment effects and reduces possible cross-hybridization effects across a set of probes.

Results: The proposed approach is compared to MAS and RMA using two benchmark gene array datasets. Positive predictivity and sensitivity are used for evaluation. Results show the proposed approach has higher positive predictivity and higher sensitivity.

Availability: available on request from the authors.


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