Ready access to emerging databases of gene annotation and functional pathways
has shifted assessments of differential expression in DNA microarray studies
from single genes to groups of genes with shared biological function. This
paper takes a critical look at existing methods for assessing the differential
expression of a group of genes (functional category), and provides some
suggestions for improved performance. We begin by presenting a general
framework, in which the set of genes in a functional category is compared to
the complementary set of genes on the array. The framework includes tests for
overrepresentation of a category within a list of significant genes, and
methods that consider continuous measures of differential expression. Existing
tests are divided into two classes. Class 1 tests assume gene-specific measures
of differential expression are independent, despite overwhelming evidence of
positive correlation. Analytic and simulated results are presented that
demonstrate Class 1 tests are strongly anti-conservative in practice. Class 2
tests account for gene correlation, typically through array permutation that by
construction has proper Type I error control for the induced null. However,
both Class 1 and Class 2 tests use a null hypothesis that all genes have the
same degree of differential expression. We introduce a more sensible and
general (Class 3) null under which the profile of differential expression is
the same within the category and complement. Under this broader null, Class 2
tests are shown to be conservative. We propose standard bootstrap methods for
testing against the Class 3 null and demonstrate they provide valid Type I
error control and more power than array permutation in simulated datasets and
real microarray experiments.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS146 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
