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MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

[Image: see text] The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strat...

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Autores principales: Kohler, Devon, Staniak, Mateusz, Tsai, Tsung-Heng, Huang, Ting, Shulman, Nicholas, Bernhardt, Oliver M., MacLean, Brendan X., Nesvizhskii, Alexey I., Reiter, Lukas, Sabido, Eduard, Choi, Meena, Vitek, Olga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629259/
https://www.ncbi.nlm.nih.gov/pubmed/37018319
http://dx.doi.org/10.1021/acs.jproteome.2c00834
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author Kohler, Devon
Staniak, Mateusz
Tsai, Tsung-Heng
Huang, Ting
Shulman, Nicholas
Bernhardt, Oliver M.
MacLean, Brendan X.
Nesvizhskii, Alexey I.
Reiter, Lukas
Sabido, Eduard
Choi, Meena
Vitek, Olga
author_facet Kohler, Devon
Staniak, Mateusz
Tsai, Tsung-Heng
Huang, Ting
Shulman, Nicholas
Bernhardt, Oliver M.
MacLean, Brendan X.
Nesvizhskii, Alexey I.
Reiter, Lukas
Sabido, Eduard
Choi, Meena
Vitek, Olga
author_sort Kohler, Devon
collection PubMed
description [Image: see text] The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package’s statistical models have been updated to a more robust workflow. Finally, MSstats’ code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.
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spelling pubmed-106292592023-11-08 MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale Kohler, Devon Staniak, Mateusz Tsai, Tsung-Heng Huang, Ting Shulman, Nicholas Bernhardt, Oliver M. MacLean, Brendan X. Nesvizhskii, Alexey I. Reiter, Lukas Sabido, Eduard Choi, Meena Vitek, Olga J Proteome Res [Image: see text] The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package’s statistical models have been updated to a more robust workflow. Finally, MSstats’ code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods. American Chemical Society 2023-04-05 /pmc/articles/PMC10629259/ /pubmed/37018319 http://dx.doi.org/10.1021/acs.jproteome.2c00834 Text en © 2023 American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Kohler, Devon
Staniak, Mateusz
Tsai, Tsung-Heng
Huang, Ting
Shulman, Nicholas
Bernhardt, Oliver M.
MacLean, Brendan X.
Nesvizhskii, Alexey I.
Reiter, Lukas
Sabido, Eduard
Choi, Meena
Vitek, Olga
MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale
title MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale
title_full MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale
title_fullStr MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale
title_full_unstemmed MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale
title_short MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale
title_sort msstats version 4.0: statistical analyses of quantitative mass spectrometry-based proteomic experiments with chromatography-based quantification at scale
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629259/
https://www.ncbi.nlm.nih.gov/pubmed/37018319
http://dx.doi.org/10.1021/acs.jproteome.2c00834
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