The gene expression profiling could aid the physicians to better understand the cellular morphology, resistance to chemotherapy and overall clinical outcome of disease [1,2]. Such individualized treatment may significantly increase survival due to the optimization of treatment procedure according to clinical pathogenesis. Ein-Dor et al [3] have pointed out that the gene sorted for the same clinical types of patients but different groups differed widely and possessed only few genes in common. An explanation to this lack of overlap between predictive signatures from different studies with the same goal may be due to the presence of more predictive genes than required to design an accurate predictor [4]. However, the microarray technique itself has been shown to be highly reproducible within and across two high volume laboratories [5]. Numerous statistical procedures including t-test [6,7], analysis of variance [8], Pearson correlation [9], Wilcoxon signed-rank test [10,11] and Mann Whitney U test [12,13] have been used for comparison of microarray data. However, the validity of various conventional statistical methods for two-group comparison of gene signatures was never evaluated using carefully selected data sets. A novel algorithm with software support is presented herein for more realistic and comprehensive interpretation of gene signatures.