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.

Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("plgem")
AuthorMattia Pelizzola <mattia.pelizzola@gmail.com> and Norman Pavelka <normanpavelka@gmail.com>
Bioconductor views DifferentialExpression GeneExpression MassSpectrometry Microarray Proteomics
Date of publicationNone
MaintainerNorman Pavelka <normanpavelka@gmail.com>
LicenseGPL-2
Version1.48.0
http://www.genopolis.it

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