Assessment of hierarchical clustering methodologies for proteomic data mining

Meunier, Bruno, Dumas, Emilie, Piec, Isabelle ORCID:, Bechet, Daniel, Hebraud, Michel and Hocquette, Jean-Francois (2007) Assessment of hierarchical clustering methodologies for proteomic data mining. Journal of Proteome Research, 6 (1). 358–366. ISSN 1535-3893

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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.

Item Type: Article
Faculty \ School: Faculty of Science > School of Biological Sciences
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Depositing User: LivePure Connector
Date Deposited: 15 Sep 2022 09:31
Last Modified: 29 Sep 2022 19:32
DOI: 10.1021/PR060343H

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