Assessment of hierarchical clustering methodologies for proteomic data mining.
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.
Meunier
Bruno
B
UR 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. bruno.meunier@clermont.inra.fr
Dumas
Emilie
E
Piec
Isabelle
I
Béchet
Daniel
D
Hébraud
Michel
M
Hocquette
Jean-François
JF
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D015180:Electrophoresis, Gel, Two-Dimensional; D040901:Proteomics / Q000379:methods*; D051381:Rats; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors; D014158:Transcription, Genetic