My colleagues and I have been interested in model-based methods to detect differential expression signals in microarray data.

Papers in the area

• M.A. Newton, A. Noueiry, D. Sarkar, and P. Ahlquist (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5, 155-176.

• Newton, M.A. and C.M. Kendziorski. Parametric Empirical Bayes Methods for Microarrays in The analysis of gene expression data: methods and software. Eds. G. Parmigiani, E.S. Garrett, R. Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.

• Kendziorski, C.M., M.A. Newton, H. Lan, and M.N. Gould (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22 3899-3914. See EBarrays below for code.

• M.A. Newton, C.M. Kendziorski, C.S. Richmond, F.R. Blattner, K.W. Tsui (2001). On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 8 37-52. See also Biostatistics Technical Report #139 .

R code

• EBarrays is an R package for empirical Bayesian analysis of gene expression data. See the developmental packages link at Bioconductor to download this package and obtain more information.

  You can download the g-zipped version of EBarrays. Installation directions are here. Once installed, type demo(ebarrays) to see a demo of the package. A vignette is also available once the package is installed. You can download that vignette here.

• Two-sample comparison via semiparametric hierarchical mixture method used in Newton et al. 2004, Biostatistics . (R source code)