Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments |
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| Authors: | Email author" target="_blank">Xin?GaoEmail author Peter?XK?Song |
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| Institution: | (1) Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada;(2) Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave. W., Waterloo, ON, N2L 3G1, Canada |
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| Abstract: | Background Numerous nonparametric approaches have been proposed in literature to detect differential gene expression in the setting of
two user-defined groups. However, there is a lack of nonparametric procedures to analyze microarray data with multiple factors
attributing to the gene expression. 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. |
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