Abstract
Testing for significant differences in quantities on protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a fixed proteomics quantification software, there exists a multitude of tools and R packages to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effect of packages and settings in their sub-steps on the final list of significant proteins, we studied several packages on three public datasets with known expected protein fold changes. We found that the final results between packages and even across different parameters of the same package can vary significantly and therefore hope that this benchmark helps in identifying the right package for a certain dataset in question. In addition to usability aspects and feature/compatibility lists of different packages, this manuscript highlights sensitivity and specificity trade-offs that come with certain packages and settings.
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