BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips. BGX is a new Bioconductor R package that implements an integrated Bayesian approach to the analysis of 3’ GeneChip data. The software takes into account additive and multiplicative error, non-specific hybridisation and replicate summarisation in the spirit of the model outlined in [1]. It also provides a posterior distribution for the expression of each gene. Moreover, BGX can take into account probe affinity effects from probe sequence information where available. The package employs a novel adaptive Markov chain Monte Carlo (MCMC) algorithm that raises considerably the efficiency with which the posterior distributions are sampled from. Finally, BGX incorporates various ways to analyse the results, such as ranking genes by expression level as well as statistically based methods for estimating the amount of up and down regulated genes between two conditions.

References in zbMATH (referenced in 14 articles , 1 standard article )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Luo, Xiangyu; Wei, Yingying: Batch effects correction with unknown subtypes (2019)
  2. Romeo, Giovanni; Thoresen, Magne: Model selection in high-dimensional noisy data: a simulation study (2019)
  3. Rosenthal, Jeffrey S.; Yang, Jinyoung: Ergodicity of combocontinuous adaptive MCMC algorithms (2018)
  4. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  5. Hellton, Kristoffer Herland; Thoresen, Magne: The impact of measurement error on principal component analysis (2014)
  6. Jow, Howsun; Boys, Richard J.; Wilkinson, Darren J.: Bayesian identification of protein differential expression in multi-group isobaric labelled mass spectrometry data (2014)
  7. Łatuszyński, Krzysztof; Roberts, Gareth O.; Rosenthal, Jeffrey S.: Adaptive Gibbs samplers and related MCMC methods (2013)
  8. Purutçuoǧlu, Vilda: Robust gene expression index (2012)
  9. Gupta, Rashi; Greco, Dario; Auvinen, Petri; Arjas, Elja: Bayesian integrated modeling of expression data: a case study on rhog (2010) ioport
  10. Chen, Ming-Hui; Ibrahim, Joseph G.; Chi, Yueh-Yun: A new class of mixture models for differential gene expression in DNA microarray data (2008)
  11. Turro, Ernest; Bochkina, Natalia; Hein, Anne-Mette K.; Richardson, Sylvia: BGX: a bioconductor package for the Bayesian integrated analysis of affymetrix genechips (2007) ioport
  12. Wu, Zhijin; Irizarry, Rafael A.: A statistical framework for the analysis of microarray probe-level data (2007)
  13. Lewin, Alex; Richardson, Sylvia; Marshall, Clare; Glazier, Anne; Aitman, Tim: Bayesian modeling of differential gene expression (2006)
  14. Hein, Anne-Mette K.; Richardson, Sylvia; Causton, Helen C.; Ambler, Graeme K.; Green, Peter J.: BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data (2005)