MAOSA: A new procedure for detection of differential gene expression

Gene expression data analysis provides scientists with a wealth of information about gene relationships, particularly the identification of significantly differentially expressed genes. However, there is no consensus on the analysis technique that will solve the inherent multiplicity problem (thousands of genes to be tested) and yield a reasonable and statistically justifiable number of differentially expressed genes. We propose the Multiplicity-Adjusted Order Statistics Analysis (MAOSA) to identify differentially expressed genes while adjusting for the multiple testing. The multiplicity problem will be eased by performing a Bonferroni correction on a small number of effects, since the majority of genes are not differentially expressed.