edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Abstract

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data.

Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

Figures

Fig. 1.

DGE data can be visualized as ‘MA’ plots (log ratio versus abundance), just as with microarray data where each dot represents a gene. This plot shows RNA-seq gene expression for DHT-stimulated versus Control LNCaP cells, as described in Li et al. (2008). The smear of points on the left side signifies that genes were observed in only one group of replicate samples and the points marked ‘×’ denote the top 500 differentially expressed genes.