1 - 11 of 11 results


A probabilistic framework that directly models the peptide-protein hierarchy and rewards the proteins with reproducible evidence of differential expression (DE) over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide-level analysis of EBprot provides better receiver-operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein-level ratios. We also demonstrate superior classification performance of peptide-level EBprot analysis in a spike-in dataset. EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling-based quantitative datasets.