Alexander Begun1. 1. Institute of Medical Informatics and Statistics, Kiel University Brunswiker Strasse 10, D-24105 Kiel, Germany. a.begun@ikmb.uni-kiel.de
Abstract
MOTIVATION: A steadily increasing number of experiments with microarrays stimulate the further development of the statistical methods of the analysis of gene expression data. One of the central problems in this area is detecting differential gene expression under two or more conditions. Unfortunately, up to now it has not been studied how the correlations between related individuals, such as twins influence the estimates of differential gene expression. RESULTS: In this paper, we discuss this problem and propose a new method that is robust with respect to correlations of gene expression data for twins.
MOTIVATION: A steadily increasing number of experiments with microarrays stimulate the further development of the statistical methods of the analysis of gene expression data. One of the central problems in this area is detecting differential gene expression under two or more conditions. Unfortunately, up to now it has not been studied how the correlations between related individuals, such as twins influence the estimates of differential gene expression. RESULTS: In this paper, we discuss this problem and propose a new method that is robust with respect to correlations of gene expression data for twins.