DNA microarray analysis using Equalized Orthogonal Mapping

Gene expression data obtained from DNA microarray experiments consists of expression levels of thousands of genes of only a few samples. Thus, accurate analysis of these datasets for classification of cancer is a big challenge. In this paper, we apply a novel Equalized Orthogonal Map (EOM) as a dimension reduction technique to produce topologically correct map of DNA microarray data for visualization and classification of cancer types. Effectiveness of the EOM has been investigated for visualization and self organization using a benchmark microarray dataset and its performance was compared with the Kohonen´s Self Organizing Map (SOM). EOM was able to produce 100% accuracy for both FL and CLL samples of the lymphoma dataset. Furthermore, EOM is computationally more efficient, e.g., it took only 3 seconds for training of FL-CLL samples of Alizadeh et al. dataset, whereas SOM took more than 8 seconds. With extensive simulation results we have illustrated superiority of the EOM over SOM in terms of quality of maps, classification accuracy and computational complexity.