  We propose a quantitative method to estimate the statistical properties of
sets of genes for which expression data are available and co-registered to a
reference atlas of the brain. It is based on graph-theoretic properties of
co-expression coefficients between pairs of genes. We apply this method to
mouse genes from the Allen Gene Expression Atlas. Co-expression patterns of a
list of several hundreds of genes related to addiction are analyzed, using ISH
data produced for the mouse brain at the Allen Institute. It appears that large
subsets of this set of genes are much more highly co-expressed than expected by
chance.
