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...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Xin Gao, Peter X. K. Song
• Keywords: BMCBI 2005, Gene Expression, Interaction Effects, Microarray Data