DOI: 10.18129/B9.bioc.msqrob2  

This package is for version 3.16 of Bioconductor; for the stable, up-to-date release version, see msqrob2.

Robust statistical inference for quantitative LC-MS proteomics

Bioconductor version: 3.16

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.

Author: Lieven Clement [aut, cre] , Laurent Gatto [aut] , Oliver M. Crook [aut] , Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb] , Stijn Vandenbulcke [aut]

Maintainer: Lieven Clement <lieven.clement at>

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biocViews DifferentialExpression, ExperimentalDesign, ImmunoOncology, MassSpectrometry, MultipleComparison, Normalization, Preprocessing, Proteomics, Regression, Software, TimeCourse
Version 1.6.1
In Bioconductor since BioC 3.13 (R-4.1) (2 years)
License Artistic-2.0
Depends R (>= 4.1), QFeatures(>= 1.1.2)
Imports stats, methods, lme4, purrr, BiocParallel, Matrix, MASS, limma, SummarizedExperiment, MultiAssayExperiment, codetools
Suggests multcomp, gridExtra, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown, testthat, tidyverse, plotly, msdata, MSnbase, matrixStats, MsCoreUtils, covr
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