Bayesian Models for Gene Expression With DNA Microarray Data

Two of the critical issues that arise when examining DNA microarray data are (1) determination of which genes best discriminate among the different types of tissue, and (2) characterization of expression patterns in tumor tissues. For (1), there are many genes that characterize DNA expression, and it is of critical importance to try and identify a small set of genes that best discriminate between normal and tumor tissues. For (2), it is critical to be able to characterize the DNA expression of the normal and tumor tissue samples and develop suitable models that explain patterns of DNA expression for these types of tissues. Toward this goal, we propose a novel Bayesian model for analyzing DNA microarray data and propose a model selection methodology for identifying subsets of genes that show different expression levels between normal and cancer tissues. In addition, we propose a novel class of hierarchical priors for the parameters that allow us to borrow strength across genes for making inference. The properties of the priors are examined in detail. We introduce a Bayesian model selection criterion for assessing the various models, and develop Markov chain Monte Carlo algorithms for sampling from the posterior distributions of the parameters and for computing the criterion. We present a detailed case study in endometrial cancer to demonstrate our proposed methodology.

[1]  Purushottam W. Laud,et al.  A Predictive Approach to the Analysis of Designed Experiments , 1994 .

[2]  M. Lawera.  Predictive inference : an introduction , 1995 .

[3]  Purushottam W. Laud,et al.  Predictive Model Selection , 1995 .

[4]  Seymour Geisser,et al.  8. Predictive Inference: An Introduction , 1995 .

[5]  Y. Chen,et al.  Ratio-based decisions and the quantitative analysis of cDNA microarray images. , 1997, Journal of biomedical optics.

[6]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[7]  Alan E. Gelfand,et al.  Model choice: A minimum posterior predictive loss approach , 1998, AISTATS.

[8]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[9]  R. Tibshirani,et al.  Clustering methods for the analysis of DNA microarray data , 1999 .

[10]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[11]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Trevor Hastie,et al.  Gene Shaving: a new class of clustering methods for expression arrays , 2000 .

[13]  Robert Tibshirani,et al.  Microarrays and Their Use in a Comparative Experiment , 2000 .

[14]  Mike West,et al.  Bayesian Regression Analysis in the "Large p, Small n" Paradigm with Application in DNA Microarray S , 2000 .

[15]  Christina Kendziorski,et al.  On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..

[16]  Joseph G. Ibrahim,et al.  Criterion-based methods for Bayesian model assessment , 2001 .

[17]  R. Gentleman,et al.  Global expression changes of constitutive and hormonally regulated genes during endometrial neoplastic transformation. , 2001, Gynecologic oncology.