Testing for significant differences in quantities at
the 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 given proteomics quantification software, many tools and R
packages exist to perform the final tasks of imputation, summarization,
normalization, and statistical testing. To evaluate the effects of
packages and settings in their substeps on the final list of significant
proteins, we studied several packages on three public data sets with
known expected protein fold changes. We found that the results between
packages and even across different parameters of the same package
can vary significantly. In addition to usability aspects and feature/compatibility
lists of different packages, this paper highlights sensitivity and
specificity trade-offs that come with specific packages and settings.