Parametric Empirical Bayes Methods for Microarrays. We have developed an empirical Bayes methodology for gene expression data to account for replicate arrays, multiple conditions, and a range of modeling assumptions. The methodology is implemented in an R library called EBarrays. Functions in the library calculate posterior probabilities of patterns of differential expression across multiple conditions. This chapter provides an overview of the methodology and its implementation in EBarrays.

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  1. Boca, Simina M.; Céorrada Bravo, Héctor; Caffo, Brian; Leek, Jeffrey T.; Parmigiani, Giovanni: A decision-theory approach to interpretable set analysis for high-dimensional data (2013)
  2. Charnigo, Richard; Fan, Qian; Bittel, Douglas; Dai, Hongying: Testing unilateral versus bilateral normal contamination (2013)
  3. Dawson, John A.; Kendziorski, Christina: An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments (2012)
  4. Lund, Steven P.; Nettleton, Dan: The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data (2012)
  5. Ausín, M.C.; Gómez-Villegas, M.A.; González-Pérez, B.; Rodríguez-Bernal, M.T.; Salazar, I.; Sanz, L.: Bayesian analysis of multiple hypothesis testing with applications to microarray experiments (2011)
  6. Ruan, Lingyan; Yuan, Ming: An empirical Bayes’ approach to joint analysis of multiple microarray gene expression studies (2011)
  7. Cho, HyungJun; Kang, Jaewoo; Lee, Jae K.: Empirical Bayes analysis of unreplicated microarray data (2009)
  8. Gustafson, Paul; Greenland, Sander: Interval estimation for messy observational data (2009)
  9. Muir, W.M.; Rosa, G.J.M.; Pittendrigh, B.R.; Xu, Z.; Rider, S.D.; Fountain, M.; Ogas, J.: A mixture model approach for the analysis of small exploratory microarray experiments (2009)
  10. Qin, Li-Xuan; Satagopan, Jaya M.: Normalization method for transcriptional studies of heterogeneous samples -- simultaneous array normalization and identification of equivalent expression (2009)
  11. Rossell, David: Gaga: a parsimonious and flexible model for differential expression analysis (2009)
  12. Chen, Ming-Hui; Ibrahim, Joseph G.; Chi, Yueh-Yun: A new class of mixture models for differential gene expression in DNA microarray data (2008)
  13. George, Florence; Ramachandran, Kandethody M.: A mixture model approach for gene selection using Johnson’s system and Bayes formula (2008)
  14. Pan, Wei; Jeong, Kyeong S.; Xie, Yang; Khodursky, Arkady: A nonparametric empirical Bayes approach to joint modeling of multiple sources of genomic data (2008)
  15. Rempala, Grzegorz A.; Pawlikowska, Iwona: Limit theorems for hybridization reactions on oligonucleotide microarrays (2008)
  16. Nott, David J.; Yu, Zeming; Chan, Eva; Cotsapas, Chris; Cowley, Mark J.; Pulvers, Jeremy; Williams, Rohan; Little, Peter: Hierarchical Bayes variable selection and microarray experiments (2007)
  17. Opgen-Rhein, Rainer; Strimmer, Korbinian: Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach (2007)
  18. Tong, Tiejun; Wang, Yuedong: Optimal shrinkage estimation of variances with applications to microarray data analysis (2007)
  19. Wu, Haiyan; Yuan, Ming; Kaech, Susan M.; Halloran, M.Elizabeth: A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment (2007)
  20. Gottardo, Raphael; Raftery, Adrian E.; Yeung, Ka Yee; Bumgarner, Roger E.: Bayesian robust inference for differential gene expression in microarrays with multiple samples (2006)

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