Differential gene expression was determined using DESeq2 (v.1.26.0) [25]. Minimal pre-filtering was applied, removing genes with normalized counts <10 for more than two samples. Technical replicates were merged prior to differential expression analysis using the collapseReplicates function as implemented in DESeq2. For the differential gene expression analysis, we included age, sex, RIN, pH-value of the brain, and postmortem interval (PMI) as covariates, because of their known influence on gene expression [26–28]. To assess residual bias after adjustment for covariates, we generated Q-Q plots and calculated genomic inflation factors (Supplementary Fig. 1). We further conducted a variance partition analysis using the variancePartition() function of the corresponding R package [29], which confirmed the covariates. Results of this analysis can be found in Supplementary Fig. 2. Results were filtered for differentially expressed (DE) genes with an absolute log2 fold change larger than 0.02. Volcano plots displaying up- and downregulation of genes for each brain region are shown in Supplementary Fig. 3.
