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Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis
July-Aug. 2013 (vol. 10 no. 4)
pp. 845-857
Pablo A. Jaskowiak, Inst. of Math. & Comput. Sci., Univ. of Sao Paulo, Sao Carlos, Brazil
Ricardo J. G. B. Campello, Inst. of Math. & Comput. Sci., Univ. of Sao Paulo, Sao Carlos, Brazil
Ivan G. Costa, Center of Inf., Fed. Univ. of Pernambuco, Recife, Brazil
Cluster analysis is usually the first step adopted to unveil information from gene expression microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity measure (similarity or distance) is of great importance to achieve satisfactory clustering results. Nevertheless, up to date, there are no comprehensive guidelines concerning how to choose proximity measures for clustering microarray data. Pearson is the most used proximity measure, whereas characteristics of other ones remain unexplored. In this paper, we investigate the choice of proximity measures for the clustering of microarray data by evaluating the performance of 16 proximity measures in 52 data sets from time course and cancer experiments. Our results support that measures rarely employed in the gene expression literature can provide better results than commonly employed ones, such as Pearson, Spearman, and euclidean distance. Given that different measures stood out for time course and cancer data evaluations, their choice should be specific to each scenario. To evaluate measures on time-course data, we preprocessed and compiled 17 data sets from the microarray literature in a benchmark along with a new methodology, called Intrinsic Biological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of new measures for gene time-course data.
Index Terms:
pattern clustering,bioinformatics,cancer,genetics,Intrinsic Biological Separation Ability,proximity measures,gene expression microarray data clustering,validation methodology,cluster analysis,cancer,Pearson coefficient,Spearman coefficient,euclidean distance,time course data,Correlation,Equations,Cancer,Gene expression,Clustering algorithms,Time complexity,time course,Proximity measure,distance,similarity,correlation coefficient,clustering,gene expression,cancer
Citation:
Pablo A. Jaskowiak, Ricardo J. G. B. Campello, Ivan G. Costa, "Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 4, pp. 845-857, July-Aug. 2013, doi:10.1109/TCBB.2013.9
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