Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic 
research. It enables relative quantification of proteins from multiple 
biological samples in a single MS 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. This manuscript 
proposes a general statistical approach for relative protein quantification in 
MS- based experiments with TMT labeling. It is applicable to experiments with 
multiple conditions, multiple biological replicate runs and multiple technical 
replicate runs, and unbalanced 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. Evaluation 
on a controlled mixture, simulated datasets, and three biological investigations 
with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and 
the specificity of detecting differentially abundant proteins, in large-scale 
experiments with multiple biological mixtures.