MOTIVATION: An important object in the analysis of high-throughput genomic data is to find an association between the expression profile of functional gene sets and the different levels of a group response. Instead of multiple testing procedures which focus on single genes, global tests are usually used to detect a group effect in an entire gene set. In a simulation study, we compare the power and computation times of four different approaches for global testing. The applicability of one of these methods to gene expression data is demonstrated for the first time. In addition, we propose an algorithm for the detection of those genes which might be responsible for a group effect. RESULTS: We could detect that the power of three of the approaches is comparable in many settings but considerable differences were detected in the computation times. Our proposed gene selection algorithm was able to detect potentially effect-causing genes in artificial sets with high power when many genes were altered with a small effect, while classical multiple testing was more powerful when few genes were altered with a large effect. AVAILABILITY: An R-package called 'RepeatedHighDim' which implements our new global test procedures is made available from http://cran.r-project.org/.

The main goal of microarray experiments is to select a small subset of genes that are differentially expressed among competing mRNA samples. For a given set of such mRNA samples, it is possible to consider a number of two-color cDNA microarray designs with a fixed number of arrays. Appropriate criteria can be used to select an efficient design from such a set of alternative experimental designs. In practice, however, microarray expression data often contain missing observations and the most efficient design (with complete observations) for a specific setup may not be efficient in the presence of missing observations. In this article, we propose two criteria to address the robustness of microarray designs against missing observations. We demonstrate the simultaneous use of efficiency and robustness criteria to select good microarray designs for both one-factor and multi-factor experiments.