Package: ProteoMM
Title: Multi-Dataset Model-based Differential Expression Proteomics
        Analysis Platform
Version: 1.30.0
Description: ProteoMM is a statistical method to perform model-based
        peptide-level differential expression analysis of single or
        multiple datasets. For multiple datasets ProteoMM produces a
        single fold change and p-value for each protein across multiple
        datasets. ProteoMM provides functionality for normalization,
        missing value imputation and differential expression.
        Model-based peptide-level imputation and differential
        expression analysis component of package follows the analysis
        described in “A statistical framework for protein quantitation
        in bottom-up MS based proteomics" (Karpievitch et al.
        Bioinformatics 2009). EigenMS normalisation is implemented as
        described in "Normalization of peak intensities in bottom-up
        MS-based proteomics using singular value decomposition."
        (Karpievitch et al. Bioinformatics 2009).
Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed
Maintainer: Yuliya V Karpievitch <yuliya.k@gmail.com>
License: MIT
LazyData: TRUE
Depends: R (>= 3.5)
Encoding: UTF-8
RoxygenNote: 6.1.0
Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats,
        graphics
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization,
        DifferentialExpression
Suggests: BiocStyle, knitr, rmarkdown
VignetteBuilder: knitr
Config/pak/sysreqs: libicu-dev libpng-dev libxml2-dev libssl-dev
        zlib1g-dev
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2026-04-28 12:48:59 UTC
RemoteUrl: https://github.com/bioc/ProteoMM
RemoteRef: RELEASE_3_23
RemoteSha: a9158b9462a9b478f7e4813aabf016e7972eb356
NeedsCompilation: no
Packaged: 2026-04-29 23:57:37 UTC; root
Built: R 4.6.0; ; 2026-04-29 23:59:15 UTC; windows
