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BMCBI
2005

Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments

11 years 1 months ago
Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments
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. Results: 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 competitor...
Xin Gao, Peter X. K. Song
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where BMCBI
Authors Xin Gao, Peter X. K. Song
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