Back to the basics: Maximizing the information obtained by quantitative two dimensional gel electrophoresis analyses by an appropriate experimental design and statistical analyses.

Two dimensional gel electrophoresis has been one of the techniques most used for protein separation in proteomics experiments and still continues to be so for some species such as plants. Despite the constant technical advances and continuous improvements in the field of 2-DE, the experimental design and analysis of protein abundance data continue to be ignored or not properly documented in the literature. An appropriate experimental design, followed by decisive statistical methods is mandatory to extract all the information that is concealed in the complexity of 2-DE data. In this work we review, in a biologist's language, all the experimental design and statistical tests to be considered while planning a 2-DE based proteomics experiment and for the correct analysis and interpretation of the data. We aim to provide the researcher with an up to date introduction to these areas, starting with the experimental design and ending with the application of multivariate statistical methodologies such as PCA, ICA or neural network-based self-organizing maps. In between we have described, in an understandable way, the current methodologies available to deal with all the stages of the experimental design, data processing and analysis.