title: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
authors.affiliation: Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia. mrobinson@wehi.edu.au
authors.forename: Mark D
authors.initials: MD
authors.lastname: Robinson
authors.forename: Davis J
authors.initials: DJ
authors.lastname: McCarthy
authors.forename: Gordon K
authors.initials: GK
authors.lastname: Smyth
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011381:Programming Languages; D012815:Signal Processing, Computer-Assisted; D012984:Software