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.
Nonlinear dimensionality reduction Unsupervised machine learning Pattern recognition Multivariate analysis Principal component analysis Minimum curvilinearity Minimum curvilinear embedding Visualization High-dimensional data Two-dimensional gel electrophoresis
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M.A. is supported by AIRC Special Program Molecular Clinical Oncology 5 per mille n.9965.C.V.C. is supported by the independent group leader starting grant of the Technische Universität Dresden (TUD).
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