We next investigated the dominant features of transcriptional variation across the brain, beginning with global, brain-wide analyses and moving towards targeted local analyses of specific regions. An informative method for identifying biologically relevant patterns in high-dimensional microarray data sets is weighted gene co-expression network analysis (WGCNA)16, 17, which groups genes into modules that have strongly covarying patterns across the sample set. This method can identify gene expression patterns related to specific cell types such as neurons and glia from heterogeneous samples such as whole human cortex18, due to the highly distinct transcriptional profiles of these cell types and variation in their relative proportions across samples. Each module is represented by an ‘eigengene’ corresponding to its expression pattern across structures (first left singular vector of the gene × structure matrix16), and genes highly correlated with the module eigengene are called ‘hub’ genes. This unbiased approach allows a module’s function or cellular specificity to be imputed based on hub gene function, and allows statistical comparison either across studies to assign function or between brains to examine preservation between individuals.
