edgeR
Empirical analysis of digital gene expression data in R
Bioconductor version: 2.5
Estimation and Testing for Differential Expression in Multiple Digital Gene Expression Libraries.
Author: Mark Robinson <mrobinson at wehi.edu.au>, Davis McCarthy <dmccarthy at wehi.edu.au>, Gordon Smyth <smyth at wehi.edu.au>
Maintainer: Mark Robinson <mrobinson at wehi.edu.au>, Davis McCarthy <dmccarthy at wehi.edu.au>, Gordon Smyth <smyth at wehi.edu.au>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("edgeR")
To cite this package in a publication, start R and enter:
citation("edgeR")
Documentation
| R Script | edgeR User's Guide | |
| Reference Manual |
Details
| biocViews | Bioinformatics, DifferentialExpression, Statistics |
| Depends | R (>= 2.3.0), methods |
| Imports | limma |
| Suggests | MASS |
| System Requirements | |
| License | LGPL (>= 2) |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Version | 1.4.7 |
| Since | Bioconductor 2.3 (R-2.8) |
Package Downloads
| Package Source | edgeR_1.4.7.tar.gz |
| Windows Binary | edgeR_1.4.7.zip (32- & 64-bit) |
| MacOS 10.5 (Leopard) binary | edgeR_1.4.7.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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