Studying the effects of correlation on protein selection in proteomics data

Recently, Efron (2007) provided methods for assessing the effect of correlation on false discovery rate (FDR) in large‐scale testing problems in the context of microarray data. Although FDR procedure does not require independence of the tests, existence of correlation grossly under‐ or overestimates the number of critical genes. Here, we briefly review Efron's method and apply it to a relatively smaller spectrometry proteomics data. We show that even here the correlation can affect the FDR values and the number of proteins declared as critical.