Expression of proteins can be quantified in high throughput using different types of mass spectrometers. In recent years, there have emerged label-free methods to determine protein abundance. Although the expression is initially measured at the peptide level, a common approach is to combine the peptide-level measurements into protein-level values before differential expression analysis. However, simple combination is prone to inconsistencies between peptides and may lose valuable information. To this end, we introduce here a method to detect differentially expressed proteins by combining peptide-level expression change statistics. Using controlled spike-in experiments, we show that the approach of averaging peptide-level expression changes yields more accurate lists of differentially expressed proteins than the conventional protein-level approach. This is particularly true when there are only few replicate samples or the differences between the sample groups are small. The proposed technique is implemented in the Bioconductor package PECA and it can be downloaded from http://www.bioconductor.org.