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Gene Expression Data Analysis Using Seed Clustering  

Shin Myoung (Bioinformatics Research Team, Electronics and Telecommunication Research Institute)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.42, no.1, 2005 , pp. 1-7 More about this Journal
Abstract
Cluster analysis of microarray data has been often used to find biologically relevant Broups of genes based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this Paper we address a novel clustering approach, called seed clustering, and investigate its applicability for microarray data analysis. In the seed clustering method, seed genes are first extracted by computational analysis of their expression profiles and then clusters are generated by taking the seed genes as prototype vectors for target clusters. Since it has strong mathematical foundations, the seed clustering method produces the stable and consistent results in a systematic way. Also, our empirical results indicate that the automatically extracted seed genes are well representative of potential clusters hidden in the data, and that its performance is favorable compared to current approaches.
Keywords
microarray data; gene expression data analysis; clustering algorithm; seed clustering;
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