Integrating Gene Expression Data from Microarrays Using the Self-Organising Map and the Gene Ontology

  • Ken McGarry
  • Mohammad Sarfraz
  • John MacIntyre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

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.

Keywords

Gene Ontology Gene Expression Data Lateral Connection Biological Process Term Visualization Capability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ken McGarry
    • 1
  • Mohammad Sarfraz
    • 1
  • John MacIntyre
    • 1
  1. 1.School of Computing and Technology, University of Sunderland, St Peters Campus, St Peters Way, SR6 ODDUK

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