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

Bioconductor version: Development (3.0)

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.

Author: Mattia Pelizzola <mattia.pelizzola at> and Norman Pavelka <normanpavelka at>

Maintainer: Norman Pavelka <normanpavelka at>

To install this package, start R and enter:


Citation (from within R, enter citation("plgem")):


PDF R Script An introduction to PLGEM
PDF   Reference Manual
Text   NEWS


biocViews DifferentialExpression, Microarray, Proteomics, Software
Version 1.37.0
In Bioconductor since BioC 1.6 (R-2.1) or earlier
License GPL-2
Depends R (>= 2.10), Biobase(>= 2.5.5), MASS
Imports utils
System Requirements
Depends On Me
Imports Me
Suggests Me

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Package Source plgem_1.37.0.tar.gz
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Mac OS X 10.6 (Snow Leopard) plgem_1.37.0.tgz
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