Analysis and visualization of gene expression data using self-organizing maps

Authors:
Janne Nikkila;Petri Törönen;Samuel Kaski;Jarkko Venna;Eero Castrén;Garry Wong
Affiliations:
Helsinki University of Technology, Neural Networks Research Centre, P.O. Box 9800, 02015 HUT, Finland;University of Kuopio, A.I. Virtanen Institute, P.O. Box 1627, 70211 Kuopio, Finland;Helsinki University of Technology, Neural Networks Research Centre, P.O. Box 9800, 02015 HUT, Finland;Helsinki University of Technology, Neural Networks Research Centre, P.O. Box 9800, 02015 HUT, Finland;University of Kuopio, A.I. Virtanen Institute, P.O. Box 1627, 70211 Kuopio, Finland;University of Kuopio, A.I. Virtanen Institute, P.O. Box 1627, 70211 Kuopio, Finland
Venue:
Neural Networks - New developments in self-organizing maps
Year:
2002

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Abstract

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by computing a SOM of yeast genes. The relationships of known functional classes of genes are investigated by analyzing their distribution on the SOM, the cluster structure is visualized by the U- matrix method, and the clusters are characterized in terms of the properties of the expression profiles of the genes. Finally, it is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.