In this study, we investigate multiple R-packages that enable the normalization, imputation, and performance of differential expression analysis on LFQ-based intensity proteomics experiments starting from peptide or protein-level intensity information. Previous works have been mainly focused on the evaluation of the software that performs peptide identification, protein inference and the generation of protein intensity tables . We first shortly describe the main packages and tools that enable the statistical analysis of LFQ datasets from peptide or protein intensity tables. While multiple packages and tools are available for statistical analysis of data, we selected some of the most relevant ones and novel implementations including MSstats 20 , Perseus , Proteus 22 prolfqua 23 , ProVision , LFQ-Analyst , Eatomics and ProStaR . Finally, we used three different datasets (PXD000279 28 , PXD007145 and PXD020248 ) to evaluate the performance of each tool and discuss some of the advantages and disadvantages of their use with different types of datasets.