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J Proteome Res. 2007 Jan;6(1):358-66.

Assessment of hierarchical clustering methodologies for proteomic data mining.

Author information

  • 1UR 1213, Unité de Recherches sur les Herbivores, Equipe Croissance et Métabolisme du Muscle, INRA de Clermont-Ferrand/Theix, F-63122 [corrected] Saint-Genès Champanelle, France.

Erratum in

  • J Proteome Res. 2007 Mar;6(3):1215.


Hierarchical clustering methodology is a powerful data mining approach for a first exploration of proteomic data. It enables samples or proteins to be grouped blindly according to their expression profiles. Nevertheless, the clustering results depend on parameters such as data preprocessing, between-profile similarity measurement, and the dendrogram construction procedure. We assessed several clustering strategies by calculating the F-measure, a widely used quality metric. The combination, on logged matrix, of Pearson correlation and Ward's methods for data aggregation is among the best clustering strategies, at least with the data sets we studied. This study was carried out using PermutMatrix, a freely available software derived from transcriptomics.

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