Abstract
Two-dimensional gel electrophoresis remains the most widely used technique for protein separation in plant proteomics experiments. Despite the continuous technical advances and improvements in current 2-DE protocols, an adequate and correct experimental design and statistical analysis of the data tend to be ignored or not properly documented in current literature. Both proper experimental design and appropriate statistical analysis are requested in order to confidently discuss our results and to conclude from experimental data.
In this chapter, we describe a model procedure for a correct experimental design and a complete statistical analysis of proteomic dataset. Our model procedure covers all of the steps in data mining and processing, starting with the data preprocessing (transformation, missing value imputation, definition of outliers) and univariate statistics (parametric and nonparametric tests), and finishing with multivariate statistics (clustering, heat-mapping, PCA, ICA, PLS-DA).
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Acknowledgements
L.V.’s work was generously supported by a Marie Curie IEF Grant (FP7-PEOPLE-IEF, European Union).
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Valledor, L., Romero-Rodríguez, M.C., Jorrin-Novo, J.V. (2014). Standardization of Data Processing and Statistical Analysis in Comparative Plant Proteomics Experiment. In: Jorrin-Novo, J., Komatsu, S., Weckwerth, W., Wienkoop, S. (eds) Plant Proteomics. Methods in Molecular Biology, vol 1072. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-631-3_5
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DOI: https://doi.org/10.1007/978-1-62703-631-3_5
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