A Multi-metric Similarity Based Analysis of Microarray Data

Clustering has been shown to be effective in analyzing functional relationships of genes. However, no single clustering method with single distance metric is capable of capturing all types of relationships that a gene may have with other genes. In this paper we introduce a framework which groups genes around a query gene, and ranks them in order corresponding to different levels of similarity utilizing multiple metrics. The focus of our efforts is to create gene centric clusters. The notion of Strong Group (SG) is presented as a cluster definition where no two genes are distant from each other, greater than a threshold value. The genes are then ranked on their frequency of co-occurrence. The grouping and rankings are drawn by applying set operations over results of multiple distance metrics, each capturing particular similarities such as shifted relationships, negative correlations and strong positive relationships. The effectiveness of the algorithm is demonstrated on two case studies. In the first one, a single yeast cell cycle dataset is used. It is shown that different combination of set operations reveals different kinds of interactions between genes. Finally, to provide further analysis on our techniques, we have tested them on multiple microarray datasets obtained from Stanford Microarray Database.