Chunk #67 — STAR* METHODS — QUANTIFICATION AND STATISTICAL ANALYSIS — Enrichment analysis using brain developmental, regional, and cell-type-specific data
Developmental expression trajectories for candidate genes were plotted using a published transcriptome atlas constructed from post-mortem brain data (Kang et al. 2011). As this dataset contains expression values from multiple brain regions, we selected transcriptomic profiles of cerebral cortex with developmental epochs that span prenatal (6–37 post-conception weeks, PCW) and postnatal (4 months-42 years) periods. Expression values were log-transformed and centered to the mean expression level for each sample using a scale(center=T, scale=F)+1 function in R. This normalization method has been frequently used in other papers to plot developmental expression trajectories (e.g. (Grove et al., 2019b; Li et al., 2018; Mah and Won, 2019; Satterstrom et al., 2019). Instead of measuring the expression values of individual disease associated gene, we measured the average expression values of the entire gene set. To do this, disease risk genes were selected for each sample and their average centered expression values were calculated and plotted (individual dots in the plot denote different samples or individuals, not different genes). It is of note that the average expression values each gene set correspond to representative expression patterns of the disease risk genes, so individual genes may behave differently.
