prolfqua () integrates the basic steps of a mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modelling, hypothesis testing, and sample size estimation. The modular design of prolfqua enables users to select the optimal differential expression analysis algorithm, for example, adding generalized linear models (glm) to model the presence or absence of protein information. prolfqua supports four normalization methods: quantile, variance stabilizing normalization (vsn) , log2 transform and z-scale; and enables to perform imputation using a group-mean model. The package provides a set of reports for users such as peptide intensity variance across samples, a scatterplot matrix of intensity correlation across samples and replicates and a heatmap of missing values clustered by samples (example report ). prolfqua supports the output of multiple tools including MaxQuant 10 , Skyline , DIA-NN 36 and MSstats 20 .