MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

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 conditio...

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Vydáno v:Molecular & cellular proteomics Ročník 19; číslo 10; s. 1706
Hlavní autoři: Huang, Ting, Choi, Meena, Tzouros, Manuel, Golling, Sabrina, Pandya, Nikhil Janak, Banfai, Balazs, Dunkley, Tom, Vitek, Olga
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.10.2020
Témata:
Humans
Isotope Labeling
Proteome - metabolism
Proteomics
Statistics as Topic
Tandem Mass Spectrometry
quantification
hypothesis testing
protein quantification
multiple mixtures
mathematical modeling
TMT
Mass spectrometry
bioinformatics software
statistics
ISSN:1535-9484, 1535-9484
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Shrnutí: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 , 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 balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.
Bibliografie:ObjectType-Article-1
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ISSN:1535-9484
1535-9484
DOI:10.1074/mcp.RA120.002105