P roducing microarray data starts with scanning in the glass, gel or plastic slides with a specialized scanner to obtain digital images of the results of an experiment after hybridization. With the help of image analysis software the DNA expression levels are then quantified. After the image processing and analysis step is completed we end up with a large number of quantified gene expression values. The data typically represents hundreds or thousands, in certain cases tens of thousands, of gene expressions across multiple experiments. To make sense of this much information it is unavoidable to use various visualization and statistical analysis techniques. One of the most typical microarray data analysis goals is to find statistically significant up or down regulated genes, in other words outliers or ‘interestingly’ behaving genes in the data. Other possible goals could be to find functional groupings of genes by discovering similarity or dissimilarity among gene expression profiles, or predicting the biochemical and physiological pathways of previously uncharacterized genes.