MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures
| Autor: | Ting Huang, Meena Choi, Sabrina Golling, Nikhil J. Pandya, Manuel Tzouros, Balazs Banfai, Olga Vitek, Tom Dunkley |
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| Rok vydání: | 2020 |
| Předmět: |
Proteomics
protein quantification Proteome Computer science Statistics as Topic Quantitative proteomics Computational biology Tandem mass tag Biochemistry Analytical Chemistry Bioconductor 03 medical and health sciences Tandem Mass Spectrometry hypothesis testing Humans Molecular Biology 030304 developmental biology Statistical hypothesis testing 0303 health sciences Mass spectrometry 030302 biochemistry & molecular biology Technological Innovation and Resources mathematical modeling multiple mixtures Replicate quantification Isobaric labeling statistics Isotope Labeling TMT bioinformatics software |
| Zdroj: | Molecular & Cellular Proteomics : MCP |
| ISSN: | 1535-9476 |
| Popis: | MSstatsTMT implements a general statistical approach for relative protein quantification and tests for differential abundance in mass spectrometry-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. 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. Graphical Abstract Highlights • Statistical approach for differential abundance analysis for proteomic experiments with TMT labeling. • Applicable to large-scale experiments with complex or unbalanced design. • An open-source R/Bioconductor package compatible with popular data processing tools. 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. |
| Databáze: | OpenAIRE |
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