Dabao Zhang
Department of Statistical Science
Cornell University
Wednesday, February 26, 2003
3:30 PM
Mayo C231 (Todd)
Minneapolis Campus
Abstract:
A new framework for normalizing spotted microarray data and identifying differentially expressed genes is developed by using a Bayesian analysis. First, we propose a measurement error model, which improves the usual semiparametric model for intensity-dependent normalization and takes account of the measurement errors in the observed overall intensities. Second, a Bayesian analysis of the semiparametric measurement-error model is constructed. The analysis avoids the potential risk in using the common two-step procedure for intensity dependent normalization. We also suggest a Bayesian identification of differentially expressed genes which automatically takes into consideration of the dimension of multiple tests of hypotheses by shrinking the alternative posteriors to zero. Both simulation and application to real microarray data demonstrate promising results.