Abstract: Since transcriptional control is the result of complex networks that interpret a variety of inputs, analyzing dynamical states of gene expression is of paramount importance to detect the multivariate nature of complex biological mechanisms. Although hundreds of studies fully demonstrated the relevancy of microarrays in describing the transcriptional status of different physiological conditions, to access and reconstruct complex interaction pathways it is necessary to analyze the temporal evolution of transcriptional states. However, an appropriate experimental design to accurately identify differentially expressed genes over a meaningful temporal window would require large amounts of microarrays and computational procedures able to assess the correlation structure among data at different time points. Unfortunately, replicates for each time point and experimental condition are not always available, because of cost limitations and/or biological sample scarcity, while common data analysis tools, e.g., ANOVA, do require replicates.