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

• Published in: Bioinformatics Vol. 26; no. 1; pp. 139 - 140
• Main Authors: Robinson, Mark D., McCarthy, Davis J., Smyth, Gordon K.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01-01-2010
• Subjects: Algorithms; Applications Note; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Oligonucleotide Array Sequence Analysis - methods; Programming Languages; Signal Processing, Computer-Assisted; Software; Data; Gene; Gene expression; Analysis
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp616

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). Contact: [e-mail]