cerebellum, where only 30 components were used), using the fastICA package in R. Clustering of these components was performed by a very similar process to that of the R package Seurat(Gierahn et al., 2017; Satija et al., 2015): a shared nearest neighbor (SNN) graph was generated, setting the k parameter to 25 from a distance matrix computed in IC space. Next, clustering of this graph was performed with the smart local moving algorithm (SLM)(Waltman and van Eck, 2013), a modularity-based approach to detecting communities, using a resolution setting of 0.01. This produced 11–22 Global clusters across the nine different tissues, partitioning cells into broad “classes.”
