edgeR
Empirical analysis of digital gene expression data in R
Bioconductor version: Release (2.9)
Differential expression analysis of RNA-seq and digital gene expression profiles with biological replication. Uses empirical Bayes estimation and exact tests based on the negative binomial distribution. Also useful for differential signal analysis with other types of genome-scale count data.
Author: Mark Robinson, Davis McCarthy , Yunshun Chen, Gordon Smyth
Maintainer: Mark Robinson, Davis McCarthy , Yunshun Chen, Gordon Smyth
To install this package, start R and enter:
source("http:///biocLite.R")
biocLite("edgeR")
To cite this package in a publication, start R and enter:
citation("edgeR")
Documentation
| R Script | edgeR Vignette | |
| edgeRUsersGuide.pdf | ||
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Bioinformatics, DifferentialExpression, SAGE, HighThroughputSequencing, RNAseq |
| Depends | R, methods, limma |
| Imports | |
| Suggests | MASS, statmod, splines |
| System Requirements | |
| License | LGPL (>= 2) |
| URL | |
| Depends On Me | |
| Imports Me | ArrayExpressHTS, DiffBind, Repitools, rnaSeqMap, tweeDEseq |
| Suggests Me | EDASeq, GenomicRanges, Repitools, baySeq, goseq, oneChannelGUI |
| Version | 2.4.3 |
| Since | Bioconductor 2.3 (R-2.8) |
Package Downloads
| Package Source | edgeR_2.4.3.tar.gz |
| Windows Binary | edgeR_2.4.3.zip (32- & 64-bit) |
| MacOS 10.5 (Leopard) binary | edgeR_2.4.3.tgz |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Annotation
- Flow Cytometry and other assays
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