The central nervous system (CNS) is composed of hundreds of distinct cell types, each expressing different subsets of genes from the genome. High throughput gene expression analysis of complex tissues like the CNS from patients and controls is a common method to screen for potentially pathological molecular mechanisms of psychiatric disease. One mechanism by which gene expression might be seen to vary across samples would be alterations in the cellular composition of the tissue. While there are a few gene 'markers' from literature for each cell type, their expression patterns vary significantly resulting in poor sensitivity and specificity. Here, we propose a method utilizing prior information from cell specific transcriptome profiling experiments in mice and co-expression network analysis to select cell type specific gene markers, and further to analytically detect changing cellular composition in human tissues. Our method successfully detects changes in cellularity over time that roughly correspond to known epochs of human brain development.