Quality control of raw proteomics data is an essential step in ensuring that pipelines and analyses give believable and useful results. A large number of metrics and plots of data are needed to get a view of whether a particular experiment has been a success, and that means carrying out a lot of analysis before we start to actually derive any new knowledge from the data. In this recipe, we'll look at an integrated pipeline that carries out a wide range of relevant and useful QC steps and presents the result as a single helpful and readable report.