Studies of the brain's transcriptome have become prominent in recent years, resulting in an accumulation of datasets with somewhat distinct attributes. These datasets, which are often analyzed only in isolation, also are often collected with divergent goals, which are reflected in their sampling properties. While many researchers have been interested in sampling gene expression in one or a few brain areas in a large number of subjects, recent efforts from the Allen Institute for Brain Sciences and others have focused instead on dense neuroanatomical sampling, necessarily limiting the number of individual donor brains studied. The purpose of the present work is to develop methods that draw on the complementary strengths of these two types of datasets for study of the human brain, and to characterize the anatomical specificity of gene expression profiles and gene co-expression networks derived from human brains using different specific technologies. The approach is applied using two publicly accessible datasets: (1) the high anatomical resolution Allen Human Brain Atlas (AHBA, Hawrylycz et al., 2012) and (2) a relatively large sample size, but comparatively coarse neuroanatomical dataset described previously by Gibbs et al. (2010). We found a relatively high degree of correspondence in differentially expressed genes and regional gene expression profiles across the two datasets. Gene co-expression networks defined in individual brain regions were less congruent, but also showed modest anatomical specificity. Using gene modules derived from the Gibbs dataset and from curated gene lists, we demonstrated varying degrees of anatomical specificity based on two classes of methods, one focused on network modularity and the other focused on enrichment of expression levels. Two approaches to assessing the statistical significance of a gene set's modularity in a given brain region were studied, which provide complementary information about the anatomical specificity of a gene network of interest. Overall, the present work demonstrates the feasibility of cross-dataset analysis of human brain microarray studies, and offers a new approach to annotating gene lists in a neuroanatomical context.