Abstract: The increasing pace at which DNA microarray-based genomic expression profiles are generated and published poses the issue of efficient and reliable comparison between datasets obtained by different laboratories and on different microarray platforms. Statistical analysis of microarray data is in continuous evolution, and several procedures have been described for detection and weighing of systematic and random errors coming from the highly parallel -but poorly replicated- microarray expression data. However, data obtained from different microarray platforms may be of substantially different nature. This is particularly evident when comparing two commonly used platforms, spotted cDNA microarrays and High-Density Oligonucleotide (HDO) microarrays of the Affymetrix type. cDNA microarrays yield a reproducible ratio between two signals, deriving respectively from the reference and from the sample. Conversely, absolute signals tend to vary across microarrays. Therefore, cDNA microarray data have to be analyzed with statistics handling repeated measurements or paired data, such as paired T-test. In the case of HDO microarrays, an absolute signal level is obtained from each single mRNA sample. As a consequence, non-paired statistics have to be applied to this type of data. Given the intrinsic differences between cDNA microarrays, data analysis procedures have generally been developed on one of the two platforms and only in some cases adapted to the other, however without a specific focus on systematic comparison and validation across platforms. It is still unclear whether data obtained in the two systems can be treated, compared and eventually merged under a common analysis framework. We addressed these issues by generating expression profiles from the same RNAs with both microarray platforms and by developing an analysis procedure in which inter-platform differences in data treatment are reduced to the minimum essential. We then developed a novel statistical test specifically designed to handle multiple comparisons against the same reference condition (eg many points of stimulation against one unstimulated control). In the Multiple Comparison (MulCom) test, regulated genes are identified by a ‘tunable’ statistic test weighing expression change in each stimulation point against replicate variability calculated across the whole set of stimulation points.