Application of partial least squares discriminant analysis and variable selection procedures: a 2D-PAGE proteomic study.
2D gel electrophoresis is a tool for measuring protein regulation, involving image analysis by dedicated software (PDQuest, Melanie, etc.). Here, partial least squares discriminant analysis was applied to improve the results obtained by classic image analysis and to identify the significant spots responsible for the differences between two datasets. A human colon cancer HCT116 cell line was analyzed, treated and not treated with a new histone deacetylase inhibitor, RC307. The proteins regulated by RC307 were detected by analyzing the total lysates and nuclear proteome profiles. Some of the regulated spots were identified by tandem mass spectrometry. The preliminary data are encouraging and the protein modulation reported is consistent with the antitumoral effect of RC307 on the HCT116 cell line. Partial least squares discriminant analysis coupled with backward elimination variable selection allowed the identification of a larger number of spots than classic PDQuest analysis. Moreover, it allows the achievement of the best performances of the model in terms of prediction and provides therefore more robust and reliable results. From this point of view, the multivariate procedure applied can be considered a good alternative to standard differential analysis, also taking into account the interdependencies existing among the variables.
Marengo
Emilio
E
Dipartimento di Scienze dell'Ambiente e della Vita, Università degli Studi del Piemonte Orientale, Via Bellini 25/G, 15100, Alessandria, Italy. marengoe@tin.it
Robotti
Elisa
E
Bobba
Marco
M
Milli
Alberto
A
Campostrini
Natascia
N
Righetti
Sabina Carla
SC
Cecconi
Daniela
D
Righetti
Pier Giorgio
PG
D045744:Cell Line, Tumor; D002467:Cell Nucleus / Q000737:chemistry; D016002:Discriminant Analysis; D015180:Electrophoresis, Gel, Two-Dimensional / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D013058:Mass Spectrometry; D008954:Models, Biological; D040901:Proteomics / Q000379:methods*