At present, there are many algorithms and tools for identifying and quantifying proteomic data, but for most biological researchers who do not have the programming skills, analysis and visualization of downstream data remains a challenge due to the lack of appropriate and easy-to-use tools and the steep learning curve of proteomicsrelated data analysis. By summarizing and comparing existing tools, our review can serve as a starting point for comparisons of label-free quantitation. . The models and tools evaluated include: MSstats' imputation methods NaN and zero, normalization equalize medians (EM), quantile (Q) and no normalization (NN); Proteus imputation mean-variance by limma (MVL) and normalization equalize medians (EM) and quantile (Q); prolfqua using group mean imputation (GMI) and robust scale normalization; ProVision using a normal distribution (ND) as Perseus and no normalization (NN); LFQ-Analyst uses a normal distribution (ND) as Perseus, K-nearest neighbours (KNN), zero with no normalization; Eatomics imputation methods normal distributions (ND) as Perseus, K-nearest neighbours, imputation based on quantile regression (QRILC) and the limma normalization method VSN (VSN); ProStar uses the global quantile alignment (GQA) and Det quantile normalization (DQ); finally, Perseus was evaluated with NaN, down-shifted gaussian distribution (GD) and zero imputation with no normalization applied. In addition, we included the positive predicted value (PPV) and negative predicted value (NPV) for MSstats and Proteus based on peptide analysis with only at least two unique peptides per protein.