Knowledge discovery in DNA microarray data of cancer patients with emergent self organizing maps

DNA microarrays provide a powerful means of monitoring thousands of gene expression levels at the same time. They consist of high dimensional data sets which challenge conventional clustering methods. The data’s high dimensionality calls for Self Organizing Maps (SOMs) to cluster DNA microarray data. This paper shows that a precise estimation of the variables’ variances is, however, the key to successful clustering of such data with SOMs. We propose PDEplots to verify the estimation of variances. PDEplots are probability density estimations based on information optimal sets. This paper demonstrates the application of PDEplots for clustering DNA microarray data of leukemia with the U-Matrix. Our approach reveals new insights into the structure of the leukemia dataset: PDEplots show two different distributions in the raw data. Three new subclasses are found with the U-Matrix.