The single nuclei RNA-seq dataset in this study provides a resource to generate gene pathways. To discover pathways from our single cell RNA-seq, we applied the pySCENIC Protocol to build transcription factor (TF)- gene regulatory networks47,145. To potentially capture individual-specific TF-gene regulatory relationships, we ran the GRNBoost2 with multiprocessing to infer TF-gene relationships from the pySCENIC package on each individual separately145,146. To speed up the GRNBoost2 run time, and reduce bias by cell type proportion, we down-sampled oligodendrocytes (~60% of all cells) to the next most prevalent cell type, Astrocytes (~8%), reducing the GRNBoost2 run time by 50%. Furthermore, we subset the ~20k genes that are expressed and analyzed in the pseudobulk differential expression analysis further reducing GRNBoost2 runtime by 30%. Since GRNBoost2 is a stochastic method, we ran this step 3–4 times for each individual and selected the TF-gene relationships that were reproducible > 80% across runs. Similarly, to aggregate the TF-gene relationships across individuals, we aggregated TF-gene relationships that were detected in 80% of 12 individuals. Across these aggregation steps, we averaged the importance scores for selected TF-gene
