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We propose a quantitative method to estimate the statistical properties ofsets of genes for which expression data are available and co-registered to areference atlas of the brain. It is based on graph-theoretic properties ofco-expression coefficients between pairs of genes. We apply this method tomouse genes from the Allen Gene Expression Atlas. Co-expression patterns of alist of several hundreds of genes related to addiction are analyzed, using ISHdata produced for the mouse brain at the Allen Institute. It appears that largesubsets of this set of genes are much more highly co-expressed than expected bychance.

Statistical analysis of co-expression properties of sets of genes in the mouse brain