plgem
Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM)
Bioconductor version: 2.9
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 gmail.com> and Norman Pavelka <normanpavelka at gmail.com>
Maintainer: Norman Pavelka <normanpavelka at gmail.com>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("plgem")
To cite this package in a publication, start R and enter:
citation("plgem")
Documentation
| R Script | An introduction to PLGEM | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Microarray, DifferentialExpression, Proteomics |
| Depends | R (>= 2.6.0), Biobase(>= 2.5.5), MASS |
| Imports | utils |
| Suggests | |
| System Requirements | |
| License | GPL-2 |
| URL | http://www.genopolis.it |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Version | 1.26.0 |
| Since | Bioconductor 1.6 (R-2.1) or earlier |
Package Downloads
| Package Source | plgem_1.26.0.tar.gz |
| Windows Binary | plgem_1.26.0.zip (32- & 64-bit) |
| MacOS 10.5 (Leopard) binary | plgem_1.26.0.tgz |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Annotation
- Variants
- Flow Cytometry and other assays
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