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

@article{citeulike:6109634, 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). Contact: mrobinson@wehi.edu.au 10.1093/bioinformatics/btp616}, author = {Robinson, Mark D. and McCarthy, Davis J. and Smyth, Gordon K.}, citeulike-article-id = {6109634}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp616}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/btp616v1/.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/btp616v1/.full.pdf}, citeulike-linkout-3 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/1/139}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/19910308}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=19910308}, day = {1}, doi = {10.1093/bioinformatics/btp616}, journal = {Bioinformatics}, keywords = {bioconductor, expression, r}, month = {January}, number = {1}, pages = {139--140}, posted-at = {2009-11-13 14:00:00}, priority = {2}, title = {edgeR: a Bioconductor package for differential expression analysis of digital gene expression data}, url = {http://dx.doi.org/10.1093/bioinformatics/btp616}, volume = {26}, year = {2010} }

TY - JOUR ID - citeulike:6109634 L3 - citeulike-article-id:6109634 N2 - 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). Contact: mrobinson@wehi.edu.au 10.1093/bioinformatics/btp616 IS - 1 JF - Bioinformatics EP - 140 TI - edgeR: a Bioconductor package for differential expression analysis of digital gene expression data VL - 26 SP - 139 KW - bioconductor KW - expression KW - r AU - Robinson, Mark AU - McCarthy, Davis AU - Smyth, Gordon PY - 2010/01/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btp616 ER -