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.
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DOI:10.1214/11-AOAS526SUPP.
