Projection based clustering of gene expression data

Authors:
Sotiris K. Tasoulis;Vassilis P. Plagianakos;Dimitris K. Tasoulis
Affiliations:
Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, Greece;Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, Greece;Mathematics Department, Imperial College London, London, UK
Venue:
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Year:
2009

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Abstract

The microarray DNA technologies have given researchers the ability to examine, discover and monitor thousands of genes in a single experiment. Nonetheless, the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis, mainly due to the very high data dimensionality. A particular class of clustering algorithms has been very successful in dealing with such data, utilising information driven by the Principal Component Analysis. In this paper, we investigate the application of recently proposed projection based hierarchical clustering algorithms on gene expression microarray data. The algorithms apart from identifying the clusters present in a data set also calculate their number and thus require no special knowledge about the data.