To investigate the gene expression differences in OUD and unaffected individuals, we used the pseudo-bulk aggregation of gene expression profiles. Many have shown that pseudo-bulk-based case-control differential expression analyses robustly detect gene-level differences with lower false discovery due to repeated measures from single cells of the same individual137–139. The raw UMI counts were added together from the same individual, brain region, and cell type to create the pseudo bulk profiles. We aggregate the interneuron subtypes together as “Interneurons”. We filtered out pseudo-bulk profiles aggregated from more than 20 cells. We filtered out mitochondrial, ribosomal, and low-expressing genes with less than 5 average UMI counts. We retained 20,203 genes and 210 pseudobulk profiles to apply the voom-limma method140 for differential gene expression analyses and the sva method to construct surrogate variables to identify un-modeled sources of transcriptomic variation. These statistical methods together addressed several challenges in analyzing cell type differential expression across multiple axes of meaningful biological variation: 1) accounting for correlated pseudo-bulk samples shared across individuals with the duplicateCorrelation (block = Subject.ID), 2) estimating quality weights for adjusting for cell
