Abstract
High-throughput experiments in proteomics, such as 2-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS), yield usually high-dimensional data sets of expression values for hundreds or thousands of proteins which are, however, observed on only a relatively small number of biological samples. Statistical methods for the planning and analysis of experiments are important to avoid false conclusions and to receive tenable results. In this chapter, the most frequent experimental designs for proteomics experiments are illustrated. In particular, focus is put on studies for the detection of differentially regulated proteins. Furthermore, issues of sample size planning, statistical analysis of expression levels as well as methods for data preprocessing are covered.
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Jung, K. (2011). Statistics in Experimental Design, Preprocessing, and Analysis of Proteomics Data. In: Hamacher, M., Eisenacher, M., Stephan, C. (eds) Data Mining in Proteomics. Methods in Molecular Biology, vol 696. Humana Press. https://doi.org/10.1007/978-1-60761-987-1_16
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DOI: https://doi.org/10.1007/978-1-60761-987-1_16
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