Analysis of gene expression data by using self-organizing maps and k-means clustering

There is a growing need for a method to analyze massive gene expression data obtained from DNA microarrays. We introduce a method of combining a self-organizing map (SOM) and a k-means clustering for analyzing and categorizing the gene expression data. The SOM is an unsupervised neural network learning algorithm and forms a mapping the high-dimensional data to two-dimensional space. However, it is difficult to find clustering boundaries from results of the SOM. On the other hand, the k-means clustering can partition the data into the clusters under the assumption of the known number of clusters. In order to understand easily the results of SOM, we apply the k-means clustering to finding the clustering boundaries from results of SOM. We have applied the proposed method to the published data of yeast gene expression and show that the method is effective for categorizing the data.