Assigning significance in label-free quantitative proteomics to include single-peptide-hit proteins with low replicates.

Selecting differentially regulated proteins with an assignment of statistical significance remains difficult for proteins with a single-peptide hit or a small fold-change when sample replicates are limited. This study presents a label-free quantitative proteomics scheme that was used to select differentially regulated proteins with single-peptide hits and with <2-fold change at a 5% false discovery rate. The scheme incorporated a labeled internal control into two unlabeled samples to facilitate error modeling when there were no replicates for the unlabeled samples. The results showed that, while both a power law global error model with a signal-to-noise ratio statistic (PLGEM-STN) and a constant fold-change threshold could be used, neither of them alone was stringent enough to select differentially regulated proteins at a 5% false discovery rate. Thus, the rule of minimum number of permuted significant pairings (MPSP) was introduced to reduce false discovery rates in combination with PLGEM-STN or a fold-change threshold. MPSP played a critical role in extending the selection of differentially regulated proteins to those with single-peptide hits or with lower fold-changes. Although the approaches were demonstrated for limited sample replicates, they should also be applicable to the situation where more sample replicates are available.