Bayesian Analysis of Variance for Microarrays

References

  1. Ishwaran H. and Rao J.S. (2003). Detecting differentially expressed genes in microarrays using Bayesian model selection. Journal of the American Statistical Association, Vol 98, 438-455.
  2. Ishwaran, H. and Rao J.S. (2005a). Spike and slab gene selection for multigroup microarray data. Journal of the American Statistical Association, 100, 764-780.
  3. Ishwaran H. and Rao J.S. (2005b). Spike and slab variable selection: frequentist and Bayesian strategies. Annals of Statistics, 33, 730-773.
  4. Ishwaran, H., Rao, J.S. and Kogalur U.B. (2006). BAMarray™: Java software for Bayesian analysis of variance for microarray data. BMC Bioinformatics, 7:59
  5. Papana, A. and Ishwaran, H. (2006). CART variance stabilization and regularization for high-throughput genomic data. Bioinformatics, 22 (18), 2254-2261.
  6. Ishwaran H. and Rao J.S. (2008). Clustering gene expression profile data by selective shrinkage. Stat. Prob. Letters, 78, 1490-1497. pdf

Papers referencing BAM

  1. Gene expression profiling of whole-blood samples from
    women exposed to hormone replacement therapy. Vanessa Dumeaux, Jostein Johansen, Anne-Lise Børresen-Dale, and Eiliv Lund. Molecular Cancer Therapeutics 2006 Apr;5(4):868-876.
  2. Van Lunteren, E., M. Moyer, and P. Leahy. Gene expression profiling of diaphragm muscle in α2-laminin (merosin) deficient dy/dy dystrophic mice. Physiol. Genomics 25: 85-95, 2006.
  3. The Bayesian revolution in genetics. Beaumont A. and Rannala B.
    Nature Review Genetics. 2004 Apr;5(4):251-61.
  4. Large-scale Bayesian logistic regression for text categorization.
    A Genkin, DD Lewis, D Madigan. (2005).
  5. Stephen Erickson and Chiara Sabatti (2005). Empirical Bayes estimation of a sparse vector of gene expression changes, Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 22.
  6. The Bayesian lasso. Trevor Park and George Casella. (2005).
  7. Sparse statistical modelling in gene expression genomics. Joe Lucas, Carlos Carvalho, Quanli Wang, Andrea Bild, Joe Nevins and Mike West. To appear in Bayesian Bioinformatics.
  8. Chen J. and Sarkar S. (2005). A Bayesian determination of threshold for identifying differentially expressed genes in microarray experiments. Stat. in Med., Published online Dec 12, 2005. Printed issue to appear.
  9. Erin M Conlon, Joon J Song, Jun S Liu (2006). Bayesian models for pooling microarray studies with multiple sources of replications. BMC Bioinformatic, 7:247.
  10. Kleivi K, Lind GE, Diep CB, Meling GI, Brandal LT, Nesland JM, Myklebost O, Rognum T, Giercksky KE, Skotheim RI, Lothe RA (2007). Gene expression profiles of primary colorectal carcionomas, liver metastases and carcinomatoses. Molecular Cancer, 6:2.
  11. Elston R, Spence MA (2006). Advances in statistical genetics over the last 25 years. Statistics in Medicine, 25: 3049-3080.
  12. Mehta TS, Zakharkin SO, Gadbury GL, Allison DB (2006). Epistemological issues in omics and high dimensional biology: give the people what they want. Physiological Genomics, 28: 24-32.
  13. Efron B. (2006). Correlation and large-scale simultaneous significance testing. Technical report, Department of Statistics, Stanford University.
  14. Abramovich F, Angelini C (2006). Bayesian maximum a posteriori multiple testing procedure. Sankhya, 68: 436-460.
  15. Moerkerke B and Goetghebeur E. (2006). Selecting "significant" differentially expressed genes from the combined perspective of the null and the alternative. Technical report, Harvard University Biostatistics Working Paper Series.
  16. Angelini C, De Canditiis D, Mutarelli M, Pensky M. (2006). Bayesian approach to estimation and testing in time course microarray experiments. Technical report, Instituto per le applicazioni del calcolo "Mauro Picone".
  17. Lewin A, Richardson S, Marshall C, Glazier A, Altman T (2006). Bayesian modeling of differential expression. Biometrics, 62:10-18.
  18. Lonnstedt I, Rimini, R and Nilsson P. (2005). Empirical Bayes microarray ANOVA and grouping cell lines by equal expression levels. Statistical Applications in Genetics and Molecular Biology, 4:7.
  19. Lewin A and Richardson S. (2005). Bayesian hierarchical modeling of differential gene expression. Technical report, Imperial College, UK.
  20. Chen Z, Chen J and Liu J (2006). A tournament approach to the detection of multiple associations in genome-wide studies with pedigree data. Technical report, Department of Statistics, University of Waterloo.
  21. Carvalho C, Chang J, Lucas J, Nevins J, Wang Q, West M (2006). High-dimensional sparse factor modeling: applications in gene expression genomics. Technical report, ISDS, Duke University.
  22. Bokka S and Mathur SK (2006). A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data. Applied Bioinformatics, 5:267-276.
  23. Seeger M, Steinke F, Tsuda K (2007) Bayesian inference and optimal design in the sparse linear model. Technical report, Max Planck Institute for Biological Cybernetics, Germany.
  24. Balding DJ (2006). A tutorial on statistical methods for population association studies. Nature Reviews Genetics, 7: 781-791.
  25. Hans-Olav Fjaerli, Geir Bukholm, Anne Krog, Camilla Skjaeret,
    Marit Holden and Britt Nakstad (2006). Whole blood gene expression in infants with respiratory syncytial virus bronchiolitis. BMC Infectious Diseases, 6:175.