Package: plgem 1.79.0

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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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-1.79.0(bioc 3.21)plgem-1.78.0(bioc 3.20)

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

immunooncologymicroarraydifferentialexpressionproteomicsgeneexpressionmassspectrometry

4.38 score 1 packages 8 scripts 480 downloads 4 mentions 8 exports 4 dependencies

Last updated 4 months agofrom:4123c5bf02. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 28 2025
R-4.5-winOKFeb 28 2025
R-4.5-macOKFeb 28 2025
R-4.5-linuxOKFeb 28 2025
R-4.4-winOKFeb 28 2025
R-4.4-macOKFeb 28 2025
R-4.3-winOKFeb 28 2025
R-4.3-macOKFeb 28 2025

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

Dependencies:BiobaseBiocGenericsgenericsMASS

An introduction to PLGEM

Rendered fromplgem.Rnwusingutils::Sweaveon Feb 28 2025.

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