EXCLUVIS: A MATLAB GUI Software for Comparative Study of Clustering and Visualization of Gene Expression Data

The result of one clustering algorithm varies from that of another for the same input dataset as the input parameters of an algorithms can substantially affect the behavior and execution of the algorithms. Cluster validity measures can be used to find the partitioning that best fits the underlying data. In most realistic applications, this analysis can be visualized using simple Computer-Aided-Design package specifying various constraints, as for example MATLAB GUI. In gene clustering, grouping related genes in the same cluster based on their expression patterns, or clustering different samples based on expression values of genes is the foundation of different genomic studies that aim at analyzing the function of genes. Microarray technology has made it possible to measure gene expression levels for thousands of genes simultaneously. Gene clustering methods help in grouping similarly expressed genes together. EXCLUVIS is an application developed in the MATLAB GUI environment that represents an interface between the user and the results of various clustering algorithms. In this application package, users select a number of parameters like internal validity indices, external validity indices, number of clusters etc. from the active windows for evaluating the performance of the clustering algorithms. EXCLUVIS compares the performance of K-means, fuzzy C-means, hierarchical clustering and multiobjective clustering with support vector machine. Heatmap is also included for visualizing the results of the cluster analysis. This application package, developed in Matlab R2009b, allows the users to easily find the goodness of the clustering solutions and immediately see the difference of those algorithmic solutions graphically.