edgeR: Empirical analysis of digital gene expression data in R
A software package for the differential expression analysis of digital gene expression data, that is, of count data arising from DNA sequencing technologies. It is especially designed for differential expression analyses of RNA-Seq or SAGE data, or differential marking analyses of ChIP-Seq data.
edgeR is available as part of Bioconductor project. To install edgeR from the R command line, use the BiocManager package from CRAN:
> library("BiocManager")
> install("edgeR")
Comprehensive documentation is distributed with the package. The edgeR User's Guide is also available from the Bioconductor edgeR package page. Help using edgeR can be obtained by posting questions or problems to the Bioconductor support site https://support.bioconductor.org.
Data Sets Used in the User's Guide
- RNA-Seq profiles of oral carcinomas vs matched normal tissue [TuchData]
- RNA-Seq of pathogen inoculated arabidopsis [ArabData]
- RNA-Seq profiles of mouse mammary gland [FuData]
- CRISPR-Cas9 knockout screen analysis [CRISPRData]
- Time course experiments of drosophila melanogaster [TimecourseData]
- Single-cell RNA-seq pseudo-bulking DE analysis [SeuratObj]
Workflow articles
- From reads to genes to pathways (differential expression analysis of RNA-Seq experiments using the edgeR quasi-likelihood pipeline)
- F1000Research 2017 (methylation workflow)
- F1000Research 2016 (RNA-seq workflow)
- Methods in Molecular Medicine 2016 (RNA-seq workflow)
- Springer 2014 (RNA-seq workflow and tutorial)
- F1000Research 2014 (shRNA-seq and CRISPR)