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

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ID: 289068
2009
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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.
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openalex_W2114104545 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mark D. Robinson, Davis J. McCarthy, Gordon K. Smyth
Journal BMC Bioinformatics
Year 2009
DOI
10.1093/bioinformatics/btp616
URL
https://academic.oup.com/bioinformatics/article-pdf/26/1/139/48851299/bioinformatics_26_1_139.pdf
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