Statistical design and analysis of mass spectrometry-based proteomic experiments with isobaric labeling

Abstract

Tandem mass tag (TMT) is a multiplexing technology widely used in proteomic research. It enables the relative quantification of proteins from multiple biological samples in a single mass spectrometry run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. Therefore, there is a need for flexible statistical tools, which reflect diverse and complex designs of large-scale TMT experiments, and have good statistical performance. I develop a general statistical approach for relative protein quantification in mass spectrometry-based experiments with TMT labeling and various designs. It is applicable to experiments with multiple conditions, multiple biological mixtures and multiple technical replicated runs in a balanced or unbalanced group comparison, repeated measures, or hybrid designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. --Author's abstract