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Comparing Clustering Techniques for Real Microarray Data

The clustering of genes detected as significant or differentially expressed provides useful information to biologists about functions and functional relationship of genes. There are variant types of clustering methods that can be applied in genomic data. These are mainly divided into the two groups, namely, hierarchical and partitional methods. In this paper, as the novelty, we perform a detailed clustering analysis for the recently collected boron microarray dataset to investigate biologically more interesting results and to construct a basis for the selection of the most effective method in the analysis of different microarray datum. In the calculation, we implement the agglomerative hierarchical clustering among hierarchical techniques and use the k-means and the PAMSAM methods within partitional clustering approaches, and finally use a recently improved method, called HIPAM, which is not only a hierarchical but also partitional approach. In the assessment, we compare and discuss the significant genes of the boron data whose estimated signals are found by the FGX normalization method.