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Open Access Highly Accessed Open Badges Methodology article

Testing for mean and correlation changes in microarray experiments: an application for pathway analysis

Mayer Alvo1, Zhongzhu Liu1, Andrew Williams2* and Carole Yauk2

Author Affiliations

1 Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5 Canada

2 Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Health Canada, Ottawa ON K1A 0K9 Canada

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BMC Bioinformatics 2010, 11:60  doi:10.1186/1471-2105-11-60

Published: 28 January 2010



Microarray experiments examine the change in transcript levels of tens of thousands of genes simultaneously. To derive meaningful data, biologists investigate the response of genes within specific pathways. Pathways are comprised of genes that interact to carry out a particular biological function. Existing methods for analyzing pathways focus on detecting changes in the mean or over-representation of the number of differentially expressed genes relative to the total of genes within the pathway. The issue of how to incorporate the influence of correlation among the genes is not generally addressed.


In this paper, we propose a non-parametric rank test for analyzing pathways that takes into account the correlation among the genes and compared two existing methods, Global and Gene Set Enrichment Analysis (GSEA), using two publicly available data sets. A simulation study was conducted to demonstrate the advantage of the rank test method.


The data indicate the advantages of the rank test. The method can distinguish significant changes in pathways due to either correlations or changes in the mean or both. From the simulation study the rank test out performed Global and GSEA. The greatest gain in performance was for the sample size case which makes the application of the rank test ideal for microarray experiments.