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Bioinformatics Advance Access published online on October 10, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl500
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 22, 2006
Revised September 7, 2006
Accepted September 30, 2006

Article

Large scale data mining approach for gene-specific standardization of microarray gene expression data

Sukjoon Yoon 1 *, Young Yang 1, Jiwon Choi 2, and Jeeweon Seong 2

1 Department of Biological Sciences, Sookmyung Women's University, Hyochangwongil 52, Youngsan-gu, Seoul, Republic of Korea, 140-742; Research Center for Women's Diseases (RCWD), Sookmyung Women's University, Hyochangwongil 52, Youngsan-gu, Seoul, Republic of Korea, 140-742
2 Department of Biological Sciences, Sookmyung Women's University, Hyochangwongil 52, Youngsan-gu, Seoul, Republic of Korea, 140-742

* To whom correspondence should be addressed.
Sukjoon Yoon, E-mail: yoonsj{at}sookmyung.ac.kr


  Abstract

Motivation: The identification of the change of gene expression in multifactorial diseases such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of gene's behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples.

Results: The availability of a large collection of array samples sharing the same platform in public databases such as NCBI GEO, enabled us to re-standardize the expression intensity of a gene using its mean and variation in the wide variety of experimental conditions. This approach was evaluated via the re-identification of breast cancer-specific gene expression. It successfully prioritized several genes associated with breast tumor, for which the expression difference between normal and breast cancer cells was marginal and thus would have been difficult to recognize using conventional analysis methods. Maximizing the utility of microarray data in the public database, it provides a valuable tool particularly for the identification of previously unrecognized disease-related genes.

Availability: A user friendly web-interface (http://compbio.sookmyung.ac.kr/~lage/) was constructed to provide the present large-scale approach for the analysis of GEO microarray data (GS-LAGE server).

Associate Editor: Joaquin Dopazo
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