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Furthermore, incorporating interaction effects in the analysis of microarray data has long been of great interest to biological scientists, little of which has been investigated in the nonparametric framework.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this paper, we propose a set of nonparametric tests to detect treatment effects, clinical covariate effects, and interaction effects for multifactorial microarray data. When the distribution of expression data is skewed or heavy-tailed, the rank tests are substantially more powerful than the competing parametric F tests. On the other hand, in the case of light or medium-tailed distributions, the rank tests appear to be marginally less powerful than the parametric competitors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed rank tests enable us to detect differential gene expression and establish interaction effects for microarray data with various non-normally distributed expression measurements across genome. In the presence of outliers, they are advantageous alternative approaches to the existing parametric F tests due to the robustness feature.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-6-186","type":"journal-article","created":{"date-parts":[[2005,7,23]],"date-time":"2005-07-23T18:13:32Z","timestamp":1122142412000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments"],"prefix":"10.1186","volume":"6","author":[{"given":"Xin","family":"Gao","sequence":"first","affiliation":[]},{"given":"Peter XK","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2005,7,21]]},"reference":[{"issue":"Suppl 1","key":"511_CR1","doi-asserted-by":"publisher","first-page":"S115","DOI":"10.1093\/bioinformatics\/17.suppl_1.S115","volume":"17","author":"L Hunter","year":"2001","unstructured":"Hunter L, Taylor RC, Leach SM, Simon R: GEST: a gene expression search tool based on a novel Bayesian similarity metric. 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