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. 2010 Jan 28:11:60.
doi: 10.1186/1471-2105-11-60.

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

Mayer Alvo  1 , Zhongzhu Liu, Andrew Williams, Carole Yauk
Affiliations

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

Mayer Alvo et al. BMC Bioinformatics. .

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

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Figures

Figure 1
Figure 1
Summary of the results from the male data for the 4 methods.
Figure 2
Figure 2
Summary of the results from the female data for the 4 methods.
Figure 3
Figure 3
C21-Steroid hormone metabolism pathway. Parallel co-ordinate plots of the genes in the C21-Steroid hormone metabolism pathway are displayed. The genes Akr1c18 and Hsd3b1 are identified by red lines.
Figure 4
Figure 4
Changes in gene correlations in the Alpha-Linolenic acid metabolism pathway. Histograms of the gene correlations for the control and treated samples are presented with the histogram of the differences in correlation between the controls and treated.
See this image and copyright information in PMC

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