Double self-organizing maps to cluster gene expression data

Clustering is a very useful and important technique for analyzing gene expression data. Self-organizing map (SOM) is one of the most useful clustering algorithms. SOM requires the number of clusters to be one of the initialization parameters prior to clustering. However, this information is unavailable in most cases, particularly in gene expression data. Thus, the validation results from SOM are commonly employed to choose the appropriate number of clusters. This approach is very inconvenient and time-consuming. This paper applies a novel model of SOM, called double self-organizing map (DSOM) to cluster gene expression data. DSOM helps to find the appropriate number of clusters by clearly and visually depicting the appropriate number of clusters. We use DSOM to cluster an artificial data set and two kinds of real gene expression data sets. To validate our results, we employed a novel validation technique, which is known as figure of merit (FOM).