Clustering methods are widely used in the analysis of microar- ray data for their ability to discover co–regulated genes. In a previous work we introduced, in a hierarchical clustering context, a theoretical framework for the comparison of dissimilarity measures on the basis of their ability to identify functional modules consisting of a transcription factor and the associated target genes. In this paper we extend these results by including in the analysis a set of dissimilarity measures based on the “1 − absolute value of correlation coefficient” proximity between genes. We show that such theoretical framework allows one to obtain a partial ordering of the considered measures in which we identify three minimal elements that are then compared on the basis of both simulated and real data.

Partial ordering of dissimilarity measures for gene clustering

DI LASCIO, FRANCESCA MARTA LILJA;ROVERATO, ALBERTO
2010

Abstract

Clustering methods are widely used in the analysis of microar- ray data for their ability to discover co–regulated genes. In a previous work we introduced, in a hierarchical clustering context, a theoretical framework for the comparison of dissimilarity measures on the basis of their ability to identify functional modules consisting of a transcription factor and the associated target genes. In this paper we extend these results by including in the analysis a set of dissimilarity measures based on the “1 − absolute value of correlation coefficient” proximity between genes. We show that such theoretical framework allows one to obtain a partial ordering of the considered measures in which we identify three minimal elements that are then compared on the basis of both simulated and real data.
Proceedings of the 45th Scientific Meeting of the Italian Statistical Society
1
8
Di Lascio F.M.L.; Roverato A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/103854
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