baySeq: Empirical Bayesian analysis of patterns of differential expression in count data. Results: We propose a framework for defining patterns of differential expression and develop a novel algorithm, baySeq, which uses an empirical Bayes approach to detect these patterns of differential expression within a set of sequencing samples. The method assumes a negative binomial distribution for the data and derives an empirically determined prior distribution from the entire dataset. We examine the performance of the method on real and simulated data.

References in zbMATH (referenced in 13 articles )

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  1. Park, Jincheol; Lin, Shili: A random effect model for reconstruction of spatial chromatin structure (2017)
  2. Bonafede, Elisabetta; Picard, Franck; Robin, St├ęphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)
  3. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  4. 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)
  5. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  6. Liu, Fangfang; Wang, Chong; Liu, Peng: A semi-parametric Bayesian approach for differential expression analysis of RNA-seq data (2015)
  7. Nguyen, Yet; Nettleton, Dan; Liu, Haibo; Tuggle, Christopher K.: Detecting differentially expressed genes with RNA-seq data using backward selection to account for the effects of relevant covariates (2015)
  8. Niemi, Jarad; Mittman, Eric; Landau, Will; Nettleton, Dan: Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis (2015)
  9. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  10. Thorne, Thomas: Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data (2015)
  11. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)
  12. Matthias Katzfuss, Andreas Neudecker, Simon Anders, Julien Gagneur: BADER: Bayesian analysis of differential expression in RNA sequencing data (2014) arXiv
  13. Si, Yaqing; Liu, Peng: An optimal test with maximum average power while controlling FDR with application to RNA-seq data (2013)