Abstract
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.
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