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'))

Peer review:

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 412 downloads 4 mentions 8 exports 3 dependencies

Last updated 26 days agofrom:4123c5bf02. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

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

Dependencies:BiobaseBiocGenericsMASS

An introduction to PLGEM

Rendered fromplgem.Rnwusingutils::Sweaveon Oct 31 2024.

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