Chemometric Multivariate Tools for Candidate Biomarker Identification: LDA, PLS-DA, SIMCA, Ranking-PCA

  • Elisa RobottiEmail author
  • Emilio Marengo
Part of the Methods in Molecular Biology book series (MIMB, volume 1384)


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.

Key words

Principal component analysis Classification SIMCA LDA PLS-DA Ranking-PCA Spot volume data 



The authors gratefully acknowledge the collaboration of Dr. Daniela Cecconi (University of Verona) who provided the biological samples and the 2D-maps used in this study.


  1. 1.
    Massart DL, Vandeginste BGM, Deming SM, Michotte Y, Kaufman L (1988) Chemometrics: a textbook. Elsevier, AmsterdamGoogle Scholar
  2. 2.
    Vandeginste BGM, Massart DL, Buydens LMC, De Yong S, Lewi PJ, Smeyers-Verbeke J (1988) Handbook of chemometrics and qualimetrics: part B. Elsevier, AmsterdamGoogle Scholar
  3. 3.
    Frank IE, Lanteri S (1989) Classification models: discriminant analysis, SIMCA, CART. Chemometr Intell Lab Syst 5:247–256CrossRefGoogle Scholar
  4. 4.
    Marengo E, Robotti E, Righetti PG, Campostrini N, Pascali J, Ponzoni M, Hamdan M, Astner H (2004) Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods. Clin Chim Acta 345:55–67CrossRefPubMedGoogle Scholar
  5. 5.
    Marengo E, Robotti E, Bobba M, Liparota MC, Rustichelli C, Zamò A, Chilosi M, Righetti PG (2006) Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis. Electrophoresis 27:484–494CrossRefPubMedGoogle Scholar
  6. 6.
    Marengo E, Robotti E, Bobba M, Righetti PG (2008) Evaluation of the variables characterized by significant discriminating power in the application of SIMCA classification method to proteomic studies. J Proteome Res 7:2789–2796CrossRefPubMedGoogle Scholar
  7. 7.
    Martens H, Naes T (1989) Multivariate calibration. Wiley, LondonGoogle Scholar
  8. 8.
    Seasholtz MB, Kowalski B (1993) The parsimony principle applied to multivariate calibration. Anal Chim Acta 277:165–177CrossRefGoogle Scholar
  9. 9.
    Booksh KS, Kowalski BR (1997) Calibration method choice by comparison of model basis functions to the theoretical instrumental response function. Anal Chim Acta 348(1–3):1–9CrossRefGoogle Scholar
  10. 10.
    Gributs CE, Burns DH (2006) Parsimonious calibration models for near-infrared spectroscopy using wavelets and scaling functions. Chemometr Intell Lab Syst 83(1):44–53CrossRefGoogle Scholar
  11. 11.
    Lo Re VIII, Bellini LM (2002) William of Occam and Occam’s razor. Ann Intern Med 136(8):634–635PubMedGoogle Scholar
  12. 12.
    Robotti E, Demartini M, Gosetti F, Calabrese G, Marengo E (2011) Development of a classification and ranking method for the identification of possible biomarkers in two-dimensional gel-electrophoresis based on principal component analysis and variable selection procedures. Mol Biosyst 7(3):677–686CrossRefPubMedGoogle Scholar
  13. 13.
    Marengo E, Robotti E, Bobba M, Gosetti F (2010) The principle of exhaustiveness versus the principle of parsimony: a new approach for the identification of biomarkers from proteomic spot volume datasets based on principal component analysis. Anal Bioanal Chem 397(1):25–41CrossRefPubMedGoogle Scholar
  14. 14.
    Polati R, Menini M, Robotti E, Millioni R, Marengo E, Novelli E, Balzan S, Cecconi D (2012) Proteomic changes involved in tenderization of bovine Longissimus dorsi muscle during prolonged ageing. Food Chem 135:2052–2069 CrossRefPubMedGoogle Scholar
  15. 15.
    Esbensen KH, Guyot D, Westad F, Houmoller LP (2002) Multivariate data analysis—in practice: an introduction to multivariate data analysis and experimental design. CAMO Process Inc., Oslo, NorwayGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Sciences and Technological InnovationUniversity of Piemonte OrientaleAlessandriaItaly

Personalised recommendations