Integration of gene expression and ontology for clustering functionally similar genes

Clustering functionally similar genes helps in understanding the mechanism of a biological pathway. It also provides information of those genes whose biological importance is previously not known. Clustering of genes is highly dependent on the similarity or dissimilarity criterion. Usually, microarray gene expression data is used to cluster genes. However, a microarray data may contain noise that may lead to undesired results. Therefore, incorporating gene ontology information may improve the clustering solutions. In this regard, an integrated dissimilarity measure is introduced for grouping functionally similar genes. It is comprised of city block distance and gene ontology based semantic dissimilarity. While, the city block distance is used to compute distance between two gene expression vectors, gene ontology based semantic dissimilarity measure is used for incorporating biological knowledge. The importance of the integrated dissimilarity measure is shown by incorporating it in different c-means clustering algorithms including rough-fuzzy clustering algorithms. In this work it has been shown that incorporation of integrated dissimilarity measure increases the functional similarity of cluster of genes as compared to the methods that are based on either type of dissimilarity measure. It is also observed that the rough-fuzzy clustering algorithm performs better with the new dissimilarity measure compared to different c-means clustering algorithms.