Statistical design and analysis for reproducible quantitative mass spectrometry-based experiments

Statistical methodology is key for reproducible research. This is particularly true in quantitative mass spectrometry-based proteomic experiments, which must overcome many sources of bias and unwanted variation. This talk will illustrate challenges of reproducible research in quantitative mass spectrometry-based proteomics, and will discuss ways in which reproducibility is promoted by appropriate statistical methodology. We will present the methods behind MSstats, an open-source R package for statistical relative quantification of proteins and peptides, and will demonstrate that they reduce the dependency of biological conclusions on tools used for initial data processing. Finally, we will discuss the importance of statistical approaches to experimental design, and of methods for assay characterization and quality control that can assist in conducting reproducible large-scale research.