title: Chemometric Multivariate Tools for Candidate Biomarker Identification: LDA, PLS-DA, SIMCA, Ranking-PCA.
abstract: 2-D gel electrophoresis usually provides complex maps characterized by a low reproducibility: this hampers the use of spot volume data for the identification of reliable biomarkers. Under these circumstances, effective and robust methods for the comparison and classification of 2-D maps are fundamental for the identification of an exhaustive panel of candidate biomarkers. Multivariate methods are the most suitable since they take into consideration the relationships between the variables, i.e., effects of synergy and antagonism between the spots. Here the most common multivariate methods used in spot volume datasets analysis are presented. The methods are applied on a sample dataset to prove their effectiveness.
authors.affiliation: Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy. elisa.robotti@uniupo.it.
authors.forename: Elisa
authors.initials: E
authors.lastname: Robotti
authors.affiliation: Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121, Alessandria, Italy.
authors.forename: Emilio
authors.initials: E
authors.lastname: Marengo
mesh_terms: D015415:Biomarkers; D015180:Electrophoresis, Gel, Two-Dimensional / Q000379:methods* / Q000706:statistics & numerical data; D025341:Principal Component Analysis; D011506:Proteins / Q000737:chemistry / Q000302:isolation & purification*; D040901:Proteomics
chemical_list: D015415:Biomarkers; D011506:Proteins
keywords: Classification; LDA; PLS -DA; Principal component analysis; Ranking-PCA; SIMCA; Spot volume data