To explore more details of statistical tools, the metrics of positive predictive value (PPV) and negative predictive values (NPV) with different combinations of parameters are calculated for each tool (Figure ). The MSstats tool achieved the best 100% PPV and 100% NPV performance when the NaN imputation method is used without normalization. Perseus, Eatomics, LFQ-Analyst and Provision all detected most of the true positive proteins (38), but Perseus achieved better PPV than others. It is worth noting that ProStaR achieved the worst 84.1% PPV even if it had 100% NPV. Therefore, users may want to adopt a stricter threshold to avoid high false positive rates with the ProStaR tool in real datasets. It is important to notice that the differences between two combinations of parameters for the same tool (MSstats) can produce very different results. For example, when enabling the quantile normalization method, MSstats detected more true positive proteins (33 versus 28 UPS proteins), while the number of false positives increased.