Statistics for Quantitative Mass Spectrometry: Methods and Case Studies
Short Course, May Institute on Computation and Statistics for Mass Spectrometry and Proteomics, 2024
This short course introduced statistical experimental design and analysis strategies for quantitative mass spectrometry-based proteomics. Participants learned methods for normalization, missing value imputation, summarization of protein abundances, statistical inference, confidence interval estimation, and differential abundance testing, as well as multivariate analysis for biomarker discovery.
The program combined lectures with hands-on case studies using the MSstats family of packages (MSstats, MSstatsTMT, MSstatsPTM, and MSstatsShiny).
Case studies included label-free, TMT, and post-translational modification (PTM) experiments, illustrating how different data processing and modeling choices influence results. Participants left with practical skills to design, analyze, and interpret quantitative proteomics experiments using statistically rigorous methods.