Testing for differential expression in small sample microarray experiments

A typical microarray experiment involves comparing expression levels of thousands of genes across groups or experimental conditions simultaneously. The cost of microarray chips is high and the sample sizes associated with microarray experiments are usually low. This situation creates challenges in processing and analyzing data. In this study two important steps in microarray analysis, filtering and hypothesis testing, were assessed using simulation studies and real data. We propose a filtering approach that filters out non-expressed genes as opposed to other filtering methods which intend to filter out genes that are not differentially expressed before hypothesis testing. We compare the performance of this proposed method to other two commonly used filtering methods. We also develop a novel hypothesis testing procedure which provides better parameter estimates by taking into account the functional relationship between the variances of gene variances and gene expression levels. This relationship was ignored in the methods proposed in the literature for microarray analysis. We compare the proposed testing method with three other existing methods using simulated and spike-in data.