Abstract
The DIGE technology allows the detection of small differences in the expression level of abundant proteins. Many diseases are associated with quantitative deviations of proteins which might represent useful biomarkers for diagnosis or prognosis. DIGE is therefore a highly convenient method for the characterization of disease-related expression changes. This chapter focuses on the study design in DIGE-based biomarker discovery. It introduces the statistical implications of testing thousands of proteins in parallel and discusses the solutions proposed by the literature. The outline provided in the method section tries to guide the researcher through the different statistical considerations, which have to be taken into account in biomarker detection. Special emphasis is given to the use of sample sizes of sufficient statistical power and to the statistical evaluation of the results.
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We thank Sonja Zehetmayer for helpful comments.
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Graf, A., Oehler, R. (2012). Study Design in DIGE-Based Biomarker Discovery. In: Cramer, R., Westermeier, R. (eds) Difference Gel Electrophoresis (DIGE). Methods in Molecular Biology, vol 854. Humana Press. https://doi.org/10.1007/978-1-61779-573-2_14
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DOI: https://doi.org/10.1007/978-1-61779-573-2_14
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