To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("plgem")
In most cases, you don't need to download the package archive at all.
plgem
Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM)
Bioconductor version: 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 gmail.com> and Norman Pavelka <normanpavelka at gmail.com>
Maintainer: Norman Pavelka <normanpavelka at gmail.com>
Citation (from within R,
enter citation("plgem")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("plgem")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("plgem")
| R Script | An introduction to PLGEM | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DifferentialExpression, GeneExpression, MassSpectrometry, Microarray, Proteomics, Software |
| Version | 1.38.0 |
| In Bioconductor since | BioC 1.6 (R-2.1) or earlier |
| License | GPL-2 |
| Depends | R (>= 2.10) |
| Imports | utils, Biobase(>= 2.5.5), MASS |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | http://www.genopolis.it |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | plgem_1.38.0.tar.gz |
| Windows Binary | plgem_1.38.0.zip |
| Mac OS X 10.6 (Snow Leopard) | plgem_1.38.0.tgz |
| Mac OS X 10.9 (Mavericks) | plgem_1.38.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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