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
git_url: https://git.bioconductor.org/packages/ProteoMM
git_branch: RELEASE_3_22
git_last_commit: e3b80ca
git_last_commit_date: 2025-10-29
Repository: Bioconductor 3.22
Date/Publication: 2025-10-29
NeedsCompilation: no
Packaged: 2025-10-30 04:59:23 UTC; biocbuild
Built: R 4.5.1; ; 2025-10-30 12:32:12 UTC; unix
