There is a great need to develop analytical methodologies to analyze and exploit the information contained in gene expression data obtained from microarray-based experiments. Because of large number of genes and complexity of biological networks, clustering is a useful exploratory technique for analysis of such data. Different data analysis techniques and algorithms have been developed which are used to cluster the gene expression data. Various tools have been developed that implement these algorithms. Clusters of co-expressed genes provide useful basis for further investigation of gene function, regulation and their possible involvement in causing different diseases. ClustPK has been developed using C# .NET and implementing k-means and PCA algorithms. Analysis of microarray data using the already existing tools is difficult and the results are also hard to be analyzed. While, ClustPK is an easy-to-use and user friendly tool that provides the easy visualization and analysis of the results obtained from eitherk-means or PCA.
Key words: Microarray, gene expression, data sets, cluster analysis, k-means, principle component analysis.
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