title: Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data.
abstract: Dimensionality reduction is largely and successfully employed for the visualization and discrimination of patterns, hidden in multidimensional proteomics datasets. Principal component analysis (PCA), which is the preferred approach for linear dimensionality reduction, may present serious limitations, in particular when samples are nonlinearly related, as often occurs in several two-dimensional electrophoresis (2-DE) datasets. An aggravating factor is that PCA robustness is impaired when the number of samples is small in comparison to the number of proteomic features, and this is the case in high-dimensional proteomic datasets, including 2-DE ones. Here, we describe the use of a nonlinear unsupervised learning machine for dimensionality reduction called minimum curvilinear embedding (MCE) that was successfully applied to different biological samples datasets. In particular, we provide an example where we directly compare MCE performance with that of PCA in disclosing neuropathic pain patterns, hidden in a multidimensional proteomic dataset.
authors.affiliation: Proteome Biochemistry, IRCCS-San Raffaele Scientific Institute, Milan, Italy. m.alessio@hsr.it.
authors.forename: Massimo
authors.initials: M
authors.lastname: Alessio
authors.affiliation: Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Technische Universität Dresden, Tatzberg 47/49, 01307, Dresden, Germany. kalokagathos.agon@gmail.com.
authors.forename: Carlo Vittorio
authors.initials: CV
authors.lastname: Cannistraci
mesh_terms: D000465:Algorithms; D015180:Electrophoresis, Gel, Two-Dimensional / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D040901:Proteomics / Q000379:methods* / Q000706:statistics & numerical data
keywords: High-dimensional data; Minimum curvilinear embedding; Minimum curvilinearity; Multivariate analysis; Nonlinear dimensionality reduction; Pattern recognition; Principal component analysis; Two-dimensional gel electrophoresis; Unsupervised machine learning; Visualization