  Identifying differentially expressed (DE) genes associated with a sample
characteristic is the primary objective of many microarray studies. As more and
more studies are carried out with observational rather than well controlled
experimental samples, it becomes important to evaluate and properly control the
impact of sample heterogeneity on DE gene finding. Typical methods for
identifying DE genes require ranking all the genes according to a preselected
statistic based on a single model for two or more group comparisons, with or
without adjustment for other covariates. Such single model approaches
unavoidably result in model misspecification, which can lead to increased error
due to bias for some genes and reduced efficiency for the others. We evaluated
the impact of model misspecification from such approaches on detecting DE genes
and identified parameters that affect the magnitude of impact. To properly
control for sample heterogeneity and to provide a flexible and coherent
framework for identifying simultaneously DE genes associated with a single or
multiple sample characteristics and/or their interactions, we proposed a
Bayesian model averaging approach which corrects the model misspecification by
averaging over model space formed by all relevant covariates. An empirical
approach is suggested for specifying prior model probabilities. We demonstrated
through simulated microarray data that this approach resulted in improved
performance in DE gene identification compared to the single model approaches.
The flexibility of this approach is demonstrated through our analysis of data
from two observational microarray studies.
