Supplementary Figure 11: Cluster analysis of top 1% prioritized genes across 16 immune traits.

a, A supra-hexagonal map labeled with the built-in index and the number of target genes per hexagon. Six target clusters were identified using the region-growing partition of the trained map. This partition allows for topology-preserving identification of target clusters, each of which is continuous over the map. b, Schematic flowchart illustrating multilayer data comparison. Target-trait matrix containing Pi rating is used as input data for training the map, while druggable pocket data (a binary vector) is used as additional data for overlaying onto the trained map. The resulting druggable map indicates the probability of each hexagon containing druggable genes, with the percentage (%) of druggable genes for each cluster shown (pie chart).