Integrating Gene Expression Data from Microarrays Using the Self-Organising Map and the Gene Ontology
The self-organizing map (SOM) is useful within bioinformatics research because of its clustering and visualization capabilities. The SOM is a vector quantization method that reduces the dimensionality of original measurement and visualizes individual tumor sample in a SOM component plane. The data is taken from cDNA microarray experiments on Diffuse Large B-Cell Lymphoma (DLBCL) data set of Alizadeh. The objective is to get the SOM to discover biologically meaningful clusters of genes that are active in this particular form of cancer. Despite their powers of visualization, SOMs cannot provide a full explanation of their structure and composition without further detailed analysis. The only method to have gone someway towards filling this gap is the unified distance matrix or U-matrix technique. This method will be used to provide a better understanding of the nature of discovered gene clusters. We enhance the work of previous researchers by integrating the clustering results with the Gene Ontology for deeper analysis of biological meaning, identification of diversity in gene expression of the DLBCL tumors and reflecting the variations in tumor growth rate.
KeywordsGene Ontology Gene Expression Data Lateral Connection Biological Process Term Visualization Capability
- 14.Kaski, S., Nikkilä, J., Törönen, P., Castrén, E., Wong, G.: Analysis and visualization of gene expression data using self-organizing maps. In: Proceedings of NSIP-01, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing 2001, Baltimore, USA (2001)Google Scholar
- 18.Ultsch, A., Siemon, H.P.: Kohonens self organizing feature maps for exploratory data analysis. In: Proceedings of the International Neural Network Conference, pp. 305–308 (1990)Google Scholar
- 19.Kaski, S.: Dimensionality reduction by random mapping: Fast similarity computation for clustering. In: Proceedings of IJCNN 1998, International Joint Conference on Neural Networks, Piscataway, NJ, vol. 1, pp. 413–418 (1998)Google Scholar