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

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...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Mayer Alvo, Zhongzhu Liu, Andrew Williams, Carole Yauk
• Keywords: BMCBI 2010, Non-parametric Rank Test, Particular Biological Function, Rank Test