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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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-11-60","type":"journal-article","created":{"date-parts":[[2010,1,28]],"date-time":"2010-01-28T19:15:36Z","timestamp":1264706136000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Testing for mean and correlation changes in microarray experiments: an application for pathway analysis"],"prefix":"10.1186","volume":"11","author":[{"given":"Mayer","family":"Alvo","sequence":"first","affiliation":[]},{"given":"Zhongzhu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Williams","sequence":"additional","affiliation":[]},{"given":"Carole","family":"Yauk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,1,28]]},"reference":[{"issue":"2","key":"3517_CR1","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1006\/geno.2002.6698","volume":"79","author":"P Khatri","year":"2002","unstructured":"Khatri P, Draghici S, Ostermeier G, Krawetz S: Profiling gene expression using onto-express. 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