Multivariate Statistical Tools for the Evaluation of Proteomic 2D-maps:Recent Achievements and Applications

Emilio Marengo; Elisa Robotti; Marco Bobba

doi:10.2174/157016407781387366

Abstract

Two dimensional polyacrylamide gel electrophoresis (2D-PAGE) maps represent an unavoidable tool in many fields connected with proteome research, such as development of new diagnostic assays or new drugs. Unfortunately the information contained in the maps is often so complex that its recognition and extraction usually requires complex statistical treatments. Statistics accompanies many phases of 2D-PAGE maps management - from the spot revelation to maps matching, as well as the extraction and rationalisation of useful information. This review describes and reports the most recent achievements in the field of statistical tools applied to proteome research by two-dimensional gel electrophoresis (2D-GE). The first section is devoted to briefly describe the theoretical aspects of the multivariate methods mostly adopted in this field such as Principal Component Analysis, Cluster Analysis, Classification methods, Artificial Neural Networks. The most recent applications are then described explaining the analysis of spot volume datasets from standard differential analysis as well as the direct analysis of 2D maps images. Applications are also reported about the use of multivariate tools in the analysis of DNA and RNA profiles.

Keywords: Principal component analysis, classification methods, linear discriminant analysis, soft-independent model of class analogy, image analysis, moment functions, fuzzy logic, spot volume data