Application of KBC to the HER2 tumor data. (A,B) The violin plots of results obtained from different methods (for sections A1 to H1). (C) The boxplot in terms of local inverse Simpsons index (LISI) (Korsunsky et al. 2019) for different sections (from A1 to H1). A lower LISI value indicates a more uniform cluster of adjacent spatial domains. Thus, the smaller LISI the better. The red cross points are outliers of the LISI. (D) The histology image and manual annotation plot of section H1. (E) Clustering outcomes of four methods: KBC, BayesSpace (k-means), SpaGCN (Louvain), and SpatialPCA for section H1. The bottom row indicates three example cluster outcomes of BayesSpace and SpaGCN, but they employ the initial clusters produced by Mclust, k-means, and Louvain, respectively. Two results of SpaGCN use Louvain to produce the initial clusters but with different parameter settings.
