Fig. 2: Clustering of gene signature activation scores.

a Clustering of Gene Signature Scores Approach. The first step in this method is collecting the bulk or single-cell datasets of interest (Supplementary Fig. 4). For each dataset, clustering was used to identify relevant cell types or molecular subtypes. Then the top 100 marker genes were identified for each cluster to generate gene signatures. Those marker genes were used as gene sets for Gene Set Variation Analysis (GSVA), which was run on bulk transcriptomics data from 223 SHH tumors in the MAGIC cohort. This method converts the genes by samples matrix into a signatures by samples matrix by scoring each gene set signature for each of the 223 tumors. Consensus clustering was then run on those signature scores to identify which signatures are activated in the same SHH tumors. b Signature Score Consensus Clustering Summary. Consensus clustering results of 1000 trials using the signatures by samples output dataset. For each individual clustering, 50% of the SHH MBs were randomly chosen. Beige signifies signatures that never cluster together, and stronger red coloring indicates signatures that cluster together more often. The legend at the bottom indicates the dataset of origin, the species of the cells (human or mouse), the data type (scRNA-seq, snRNA-seq, or bulk), and material type (SHH MB tumor or healthy cerebellum). The groups of signatures on the right were manually annotated using the known cerebellar cell types from human and P14 mouse samples.