Given the broader dynamic range of RNASeq and its ability to detect low abundance transcripts – because it does not suffer from hybridization-based limitations associated with microarray such as background noise – we would expect to see large fold-changes, if they existed, even for genes displaying low expression; or expression and differential expression restricted to a specific subset of cells. ISH images for representative genes taken from the same brain region used in our experiment can add extra information when related to RNASeq intensity data. Supplementary Fig. 10 shows in situ hybridization images from Allen Human Brain Atlas for a selected set of genes showing significant differential expression in CMC; the figure shows different cell type specific expression (from high to low specificity). These are from the largest dataset for DLPFC, the Neurotransmitter Study (176 genes across cortical regions and 88 genes across subcortical regions in 4 control cases), 12 of which were in common with our list. The data suggest that genes identified by DE analysis display various degrees of cell-type specificity.
