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Abstract:
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Repeated measures experimental designs are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects, and increase the statistical power of detecting within-subject changes in protein abundance. At the same time, proteomic experiments increasingly incorporate Tandem Mass Tags (TMT) labeling, a multiplexing strategy that gains both accuracy of relative protein quantification and sample throughput. Combining repeated measures and TMT multiplexing leads to unique interplays of between-mixture, between-subject and within-subject variation. We propose a family of linear-mixed effects models for differential analysis of proteomic experiments that combine repeated measures and TMT multiplexing. The models decompose the variation in the data into the contributions from various sources as appropriate for the specifics of each experiment. Evaluations on simulated datasets and experimental datasets demonstrated the value of this approach as compared to the existing approaches and implementations. The models are implemented in the R/Bioconductor package MSstatsTMT.
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