Despite the increasing popularity of gel-free proteomic strategies, two-dimensional gel electrophoresis (2DE) is
still the most widely used approach in top-down proteomic studies, for all sorts of biological models. In order to achieve
meaningful biological insight using 2DE approaches, importance must be given not only to ensure proper experimental
design, experimental practice and 2DE technical performance, but also a valid approach for data acquisition, processing
and analysis. This paper reviews and illustrates several different aspects of data analysis within the context of gel-based
proteomics, summarizing the current state of research within this field. Particular focus is given on discussing the usefulness
of available multivariate analysis tools both for data visualization and feature selection purposes. Visual examples are
given using a real gel-based proteomic dataset as basis.
Keywords: Independent component analysis, multidimensional scaling, partial least squares regression, principal component
analysis, self-organized maps, two-dimensional gel electrophoresis.
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