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

References in zbMATH (referenced in 15 articles )

Showing results 1 to 15 of 15.
Sorted by year (citations)

  1. Lun, Aaron T.L.; Smyth, Gordon K.: No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data (2017)
  2. Kruppa, Jochen; Kramer, Frank; Beißbarth, Tim; Jung, Klaus: A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments (2016)
  3. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  4. Phipson, Belinda; Lee, Stanley; Majewski, Ian J.; Alexander, Warren S.; Smyth, Gordon K.: Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression (2016)
  5. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  6. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  7. Niemi, Jarad; Mittman, Eric; Landau, Will; Nettleton, Dan: Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis (2015)
  8. Niu, Liang; Lin, Shili: A Bayesian mixture model for chromatin interaction data (2015)
  9. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  10. Cleynen, Alice; Dudoit, Sandrine; Robin, Stéphane: Comparing segmentation methods for genome annotation based on RNA-seq data (2014)
  11. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)
  12. Ji, Tieming; Liu, Peng; Nettleton, Dan: Estimation and testing of gene expression heterosis (2014)
  13. Matthias Katzfuss, Andreas Neudecker, Simon Anders, Julien Gagneur: BADER: Bayesian analysis of differential expression in RNA sequencing data (2014) arXiv
  14. Witten, Daniela M.: Classification and clustering of sequencing data using a Poisson model (2011)
  15. Robinson, Mark D.; Mccarthy, Davis J.; Smyth, Gordon K.: edger: a bioconductor package for differential expression analysis of digital gene expression data (2010) ioport