Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics.
Three-way PCA has been applied to proteomic pattern images to identify the classes of samples present in the dataset. The developed method has been applied to two different datasets: a rat sera dataset, constituted by five samples of healthy Wistar rat sera and five samples of nicotine-treated Wistar rat sera; a human lymph-node dataset constituted by four healthy lymph-nodes and four lymph-nodes affected by a non-Hodgkin's lymphoma. The method proved to be successful in the identification of the classes of samples present in both of the groups of 2D-PAGE images, and it allowed us to identify the regions of the two-dimensional maps responsible for the differences occurring between the classes for both rat sera and human lymph-nodes datasets.
Marengo
Emilio
E
Dipartimento di Scienze e Tecnologie Avanzate, Università del Piemonte Orientale, Spalto Marengo 33, 15100 Alessandria, Italy. marengoe@tin.it
Leardi
Riccardo
R
Robotti
Elisa
E
Righetti
Pier Giorgio
PG
Antonucci
Francesca
F
Cecconi
Daniela
D
D000465:Algorithms; D000818:Animals; D001798:Blood Proteins / Q000032:analysis / Q000187:drug effects; D015180:Electrophoresis, Gel, Two-Dimensional; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008198:Lymph Nodes / Q000737:chemistry; D020522:Lymphoma, Mantle-Cell / Q000737:chemistry; D015999:Multivariate Analysis; D009538:Nicotine / Q000494:pharmacology; D010449:Peptide Mapping; D025341:Principal Component Analysis / Q000379:methods*; D040901:Proteomics; D051381:Rats; D017208:Rats, Wistar; D012984:Software
D001798:Blood Proteins; D009538:Nicotine