Analysis of DNA microarray data using self-organizing map and kernel based clustering

We describe a method of combining a self-organizing map (SOM) and a kernel based clustering for analyzing and categorizing the gene expression data obtained from DNA microarray. The SOM is an unsupervised neural network learning algorithm and forms a mapping a high-dimensional data to a two-dimensional space. However, it is difficult to find clustering boundaries from results of the SOM. On the other hand, the kernel based clustering can partition the data nonlinearly. In order to understand the results of SOM easily, we apply the kernel based clustering to finding the clustering boundaries and show that the proposed method is effective for categorizing the gene expression data.

[1]  Manabu Kotani,et al.  Analysis of gene expression data by using self-organizing maps and k-means clustering , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[2]  James I. Garrels,et al.  The Yeast Proteome Database (YPD): a model for the organization and presentation of genome-wide functional data , 1999, Nucleic Acids Res..

[3]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[4]  P. Törönen,et al.  Analysis of gene expression data using self‐organizing maps , 1999, FEBS letters.

[5]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Mark A. Girolami,et al.  Mercer kernel-based clustering in feature space , 2002, IEEE Trans. Neural Networks.

[7]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[9]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[10]  Samuel Kaski,et al.  SOM-Based Exploratory Analysis of Gene Expression Data , 2001, WSOM.