edgeR

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 23 articles )

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  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. Bonafede, Elisabetta; Picard, Franck; Robin, Stéphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)
  3. 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)
  4. Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data (2016)
  5. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  6. 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)
  7. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  8. Zhou, Yi-Hui: Pathway analysis for RNA-Seq data using a score-based approach (2016)
  9. Cui, Shiqi; Guha, Subharup; Ferreira, Marco A.R.; Tegge, Allison N.: HmmSeq: a hidden Markov model for detecting differentially expressed genes from RNA-seq data (2015)
  10. Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence; Rau, Andrea: A model selection criterion for model-based clustering of annotated gene expression data (2015)
  11. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  12. Niemi, Jarad; Mittman, Eric; Landau, Will; Nettleton, Dan: Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis (2015)
  13. Niu, Liang; Lin, Shili: A Bayesian mixture model for chromatin interaction data (2015)
  14. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  15. Thorne, Thomas: Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data (2015)
  16. Cleynen, Alice; Dudoit, Sandrine; Robin, Stéphane: Comparing segmentation methods for genome annotation based on RNA-seq data (2014)
  17. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)
  18. Ji, Tieming; Liu, Peng; Nettleton, Dan: Estimation and testing of gene expression heterosis (2014)
  19. Matthias Katzfuss, Andreas Neudecker, Simon Anders, Julien Gagneur: BADER: Bayesian analysis of differential expression in RNA sequencing data (2014) arXiv
  20. van Iterson, Maarten; van de Wiel, Mark A.; Boer, Judith M.; de Menezes, Renée X.: General power and sample size calculations for high-dimensional genomic data (2013)

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