Modified significance analysis of microarrays in heterogeneous diseases

Abstract

Significance analysis of microarrays (SAM) provides researchers with a non-parametric score for each gene based on repeated measurements. However, it may lose certain power in general statistical tests to correctly detect differentially expressed genes (DEGs) which violate homogeneity. Monte Carlo simulation shows that the “half SAM score” can maintain type I error rates of about 0.05 based on assumptions of normal and non-normal distributions. The author found 265 DEGs using the half SAM scoring, more than the 119 DEGs detected by SAM, with the false discovery rate controlled at 0.05. In conclusion, the author recommends the half SAM scoring method to detect DEGs in data that show heterogeneity.