Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test
Comparative profiling proteomics experiments are important tools in biological research. In such experiments, tens to hundreds of thousands of peptides are measured simultaneously, with the goal of inferring protein abundance levels. Statistical evaluation of these datasets are required to determine proteins that are differentially abundant between the test samples. Previously we have reported the non-normal distribution of SILAC datasets, and demonstrated the permutation test to be a superior method for the statistical evaluation of non-normal peptide ratios. This chapter outlines the steps and the R scripts that can be used for performing permutation analysis with false discovery rate control via the Benjamini–Yekutieli method.
Key wordsComparative profiling Simultaneous testing SILAC Hypothesis test Permutation test False discovery rate
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