Data Visualization and Feature Selection Methods in Gel-based Proteomics

Title:Data Visualization and Feature Selection Methods in Gel-based Proteomics

Volume: 15 Issue: 1

Author(s): Tome S. Silva, Nadege Richard, Jorge P. Dias and Pedro M. Rodrigues

Affiliation:

Keywords: Independent component analysis, multidimensional scaling, partial least squares regression, principal component analysis, self-organized maps, two-dimensional gel electrophoresis.

Abstract: 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.

Cite this article as:

Silva S. Tome, Richard Nadege, Dias P. Jorge and Rodrigues M. Pedro, Data Visualization and Feature Selection Methods in Gel-based Proteomics, Current Protein & Peptide Science 2014; 15 (1) . https://dx.doi.org/10.2174/1389203715666140221112334