Package: msqrob2
Title: Robust statistical inference for quantitative LC-MS proteomics
Version: 1.10.0
Authors@R: c(
      person(given = "Lieven",
           family = "Clement",
           role = c("aut", "cre"),
           email = "lieven.clement@ugent.be",
           comment = c(ORCID = "0000-0002-9050-4370")),
      person(given = "Laurent",
             family = "Gatto",
             role = c("aut"),
             email = "laurent.gatto@uclouvain.be",
             comment = c(ORCID = "0000-0002-1520-2268")),
      person(family = "Crook",
             given = "Oliver M.",
       		   email = "oliver.crook@stats.ox.ac.uk",
       		   comment = c(ORCID = "0000-0001-5669-8506"),
       		   role = "aut"),
      person(given = "Adriaan",
             family = "Sticker",
             email = "adriaan.sticker@ugent.be",
             role = "ctb"),
      person(given = "Ludger",
             family = "Goeminne",
             email = " ludgergoeminne@gmail.com",
             role = "ctb"),
      person(given = "Milan",
             family = "Malfait",
             email = "milan.malfait94@gmail.com",
             comment = c(ORCID = "0000-0001-9144-3701"),
             role = "ctb"),
      person(given = "Stijn",
             family = "Vandenbulcke",
             email = "vandenbulcke.stijn@gmail.com",
             role = "aut")
      )
Description: 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.
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
License: Artistic-2.0
Collate: 'msqrob-framework.R' 'allGenerics.R' 'accessors.R' 'msqrob.R'
        'msqrob-utils.R' 'StatModel-methods.R'
        'hypothesisTest-methods.R' 'msqrob-methods.R'
        'msqrobAggregate.R' 'topFeatures.R' 'data.R' 'msqrobQB.R'
        'msqrobHurdle-methods.R'
Encoding: UTF-8
VignetteBuilder: knitr
Roxygen: list(markdown=TRUE)
RoxygenNote: 7.2.1
biocViews: Proteomics, MassSpectrometry, DifferentialExpression,
        MultipleComparison, Regression, ExperimentalDesign, Software,
        ImmunoOncology, Normalization, TimeCourse, Preprocessing
URL: https://github.com/statOmics/msqrob2
BugReports: https://github.com/statOmics/msqrob2/issues
git_url: https://git.bioconductor.org/packages/msqrob2
git_branch: RELEASE_3_18
git_last_commit: 6bf2b78
git_last_commit_date: 2023-10-24
Date/Publication: 2023-10-24
NeedsCompilation: no
Packaged: 2023-10-24 22:04:34 UTC; biocbuild
Author: Lieven Clement [aut, cre] (<https://orcid.org/0000-0002-9050-4370>),
  Laurent Gatto [aut] (<https://orcid.org/0000-0002-1520-2268>),
  Oliver M. Crook [aut] (<https://orcid.org/0000-0001-5669-8506>),
  Adriaan Sticker [ctb],
  Ludger Goeminne [ctb],
  Milan Malfait [ctb] (<https://orcid.org/0000-0001-9144-3701>),
  Stijn Vandenbulcke [aut]
Maintainer: Lieven Clement <lieven.clement@ugent.be>
Built: R 4.3.2; ; 2023-12-07 11:00:16 UTC; unix
