DNA microarray technology is used in biology to study the simultaneous expression of hundreds to thousands of genes in an organism under specific experimental conditions (treatments, time, genotypes, etc.) Experiments using microarrays to study global gene expression often produce massive amounts of abstract multivariate, multidimensional(MDMV)data. Considering the size and biological meanings can be extremely challenging [Nadon and Shoemaker 2002; J. 2001]. There are multiple sources of 'noise' (error) in microarray data, including both biological and technical. Proper statistical techniques are required to measure and separate the real biological effects (the changes in gene expression in response to experimental conditions) from the sources of error. Therefore the challenge with gobal gene expression data sets is twofold: their nature (large, abstract MDMV) and separation of real biological effects from error. To begin to explore the biological meaning of these complex data sets, our group has developed an interactive data visualization technique in 3D that depends on a mapping considering the experimental variables time, genotype, and treatment.