Whole brain expression data was used as an exploratory screen since the possible regions of DOC action on brain gene expression are unclear.
Because human brain expression data is derived from post-mortem brain tissue, special attention must be paid to RNA quality, post-mortem interval, and pH.
Those genes within the artificial locus for which spatial brain expression data was available were considered as candidate genes (simulating an 'orphan' locus obtained by linkage analysis or comparable techniques), and the prioritization method was applied as described above.
Previously published normal fetal brain regional expression data were additionally analyzed [ 41].
Brain (striatum) gene expression data were obtained using the Illumina Mouse WG 6.1 array, and the data sets were interrogated using a weighted gene co-expression network analysis (WGCNA).
Gene expression data were available for brain (cerebral cortex or whole brain without cerebellum), cerebellum, heart, kidney, liver, and testis (Brawand et al. 2011), which allowed us to determine the degree of expression divergence for each individual tissue, as well as the total expression divergence across all six tissues.
Overall, we observed that the clusters generated from the gene expression data were more consistent with the brain anatomy than the clusters generated from the connectivity data.
As a consequence, typical hub genes of microglia modules identified using brain tissue expression data are not necessarily hub genes in pure microglia expression data.
The overlap between the up-regulated blue modules in pure microglia and significantly up-regulated genes in brain tissue expression data was determined as a measure of preservation.
Changes caused by altered relative cell numbers in brain tissue expression data are readily detected using WGCNA, resulting in cell type-specific modules.
