Differential expression analysis is one of the most common types of analyses performed on various biological and biomedical data, including mass spectrometry proteomics. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To address the challenge, our reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic on the basis of the inherent properties of the data and provides a ranking of the proteins based on their statistical evidence for differential expression between sample groups. We have shown the robust performance of ROTS in multiple studies, covering both bulk and single cell measurements. The ROTS package is freely available in Bioconductor.