Package: plgem 1.85.0

Norman Pavelka

plgem: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM)

The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets.

Authors:Mattia Pelizzola <[email protected]> and Norman Pavelka <[email protected]>

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manual.pdf |manual.html✨
card.svg |card.png
plgem/json (API)
NEWS

# Install 'plgem' in R:
install.packages('plgem', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • LPSeset - ExpressionSet for Testing PLGEM

On BioConductor:plgem-1.85.0(bioc 3.24)plgem-1.84.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologymicroarraydifferentialexpressionproteomicsgeneexpressionmassspectrometry

4.49 score 1 packages 13 scripts 574 downloads 4 mentions 8 exports 4 dependencies

Last updated from:01627314fc. Checks:1 ERROR, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksERROR138
linux-devel-x86_64OK136
source / vignettesOK190
linux-release-x86_64OK116
macos-release-arm64OK95
macos-oldrel-arm64OK96
windows-develOK101
windows-releaseOK90
windows-oldrelOK98
wasm-releaseOK86

Exports:plgem.degplgem.fitplgem.obsStnplgem.pValueplgem.resampledStnplgem.write.summaryrun.plgemsetGpar

Dependencies:BiobaseBiocGenericsgenericsMASS

An introduction to PLGEM

Rendered fromplgem.Rnwusingutils::Sweaveon May 30 2026.

Last update: 2013-02-06
Started: 2013-02-06