Fig. 8

Workflow analysis plan. In blue, the single-nuclei data generation. The most important step is the quantification of the nuclei using a “pre-mRNA” annotation. This step will significantly increase the quantity of nuclei and the quantification of their expression profile. In green, the cleaning and quality control steps. The QC followed standard measurements such as removing mitochondrial genes (MT), removing doublets and multiples, and the normalization of the data using nUMI, percent mitochondrial reads sample origin as confounding factors. In orange, the clustering. In this step, we performed the identification of genes that are highly variable in common among brain nuclei from all of the subjects. Later on, the nuclei can be clustered differently using different resolution, but in general, they are assigned to clusters that are annotated to group nuclei from the same specific cell type. Next, we identified a hierarchical relationship among the clusters by performing coincidence analyses. The entropy, from Shannon’s information theory, provides a quantitative measure of even distribution of samples in a cluster. To annotate the clusters, we use a set of gene markers for each cell type collected from the literature. Finally, a hierarchical clustering of clusters should reproduce expected results, grouping together neuronal subtypes in one branch and in another branch glial cells