The quantitative comparison of two or more microarrays can reveal, for example, the distinct patterns of gene expression that define different cellular phenotypes or the genes that are induced in the cellular response to certain stimulations. Normalization of the measured intensities is a prerequisite of such comparisons. However, a fundamental problem in cDNA microarray analysis is the lack of a common standard to compare the expression levels of different samples. Several normalization protocols have been proposed to overcome the variabilities inherent in this technology. We have developed a normalization procedure based on within-array replications via a semilinear in-slide model, which adjusts objectively experimental variations without making critical biological assumptions. The significant analysis of gene expressions is based on a weighted t statistic, which accounts for the heteroscedasticity of the observed log ratios of expressions, and a balanced sign permutation test. We illustrated the use of the techniques in a comparison of the expression profiles of neuroblastoma cells that were stimulated with a growth factor, macrophage migration inhibitory factor (MIF). The analysis of expression changes at mRNA levels showed that approximately 99 genes were up-regulated and 24 were reduced significantly (P <0.001) in MIF-stimulated neuroblastoma cells. The regulated genes included several oncogenes, growth-related genes, tumor metastatic genes, and immuno-related genes. The findings provide clues as to the molecular mechanisms of MIF-mediated tumor progression and supply therapeutic targets for neuroblastoma treatment.