Package: ProteoMM
Title: Multi-Dataset Model-based Differential Expression Proteomics
        Analysis Platform
Version: 1.28.0
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: 2025-10-29 14:48:25 UTC
RemoteUrl: https://github.com/bioc/ProteoMM
RemoteRef: RELEASE_3_22
RemoteSha: e3b80cabd916ea7c3b5b8741bdc120702ccb2687
NeedsCompilation: no
Packaged: 2025-11-18 07:27:18 UTC; root
Built: R 4.5.2; ; 2025-11-18 07:29:08 UTC; windows
