Hierarchical clustering of gene expression data

Rapid development of biological technologies generates a huge amount of data, which provides a processing and global view of the gene expression levels across different conditions and over multiple stages. Analyzation and interpretation of these massive data is a challenging task. One of the most important steps is to extract useful and rational fundamental patterns of gene expression inherent in these huge data. Clustering technology is one of the useful and popular methods to obtain these patterns. In this paper we propose a new hierarchical clustering algorithm to obtain gene expression patterns. This algorithm constructs a hierarchy from top to bottom based on a self-organizing tree. It dynamically finds the number of clusters at each level. We compare our algorithm with the traditional hierarchical agglomerative clustering (HAC) algorithm. We apply our algorithm to an existing 112 rat central nervous system gene expression data. We observe that our algorithm extracts patterns with different levels of abstraction. Furthermore, our approach is useful on recognizing features in complex gene expression data.