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Meena Choi, M.S., is a PhD candidate in the Department of Statistics at Purdue University, in the group of Dr. Olga Vitek. Meena received a B.S. in Biology at KAIST (Korea), and a M.S. in Applied Statistics at Purdue. Her research focuses on the development of statistical methods and software for relative protein quantification in mass spectrometric workflows, and for discovery of biomarkers of disease. She is the main developer and maintainer of MSstats.
MSstats as an external tool in Skyline – an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments
MSstats is an open-source R package for statistical relative quantification of proteins and peptides. It supports experiments with complex designs, such as comparisons of multiple groups or time course mparisons. It handles quantitative shotgun DDA (data-dependent acquisition) experiments, targeted SRM (selected reaction monitoring), and SWATH/DIA (data independent acquisition) experiments. It can be used in conjunction with label-free experimental workflows, or with workflows that utilize stable isotope reference proteins or peptides. MSstats provides three main functionalities: (1) data processing, visualization and quality control, (2) model-based statistical analysis, in particular summarization of all the quantitative values of a protein across features and runs, to test for differential abundance between conditions or to estimate its abundance in individual biological samples or conditions on a relative scale, and (3) model-based calculation of a sample size for a future experiment, while using the current dataset as a pilot. MSstats is available as a stand-alone tool from www.msstats.org or from Bioconductor. Importantly, MSstats is now available to the users of Skyline as an external tool via graphical user interface. The external tool supports one-click installation of R and all the associated dependencies. Tables with numerical outputs and visualization plots are automatically generated, and the analysis steps are documented in a log file. In the future we plan to expand the statistical functionalities of the package, to enable an efficient analysis of diverse experiments.
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