  We review quantitative methods and software developed to analyze
genome-scale, brain-wide spatially-mapped gene-expression data. We expose new
methods based on the underlying high-dimensional geometry of voxel space and
gene space, and on simulations of the distribution of co-expression networks of
a given size. We apply them to the Allen Atlas of the adult mouse brain, and to
the co-expression network of a set of genes related to nicotine addiction
retrieved from the NicSNP database. The computational methods are implemented
in {\ttfamily{BrainGeneExpressionAnalysis}}, a Matlab toolbox available for
download.
