Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains

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Figure 9.
Figure 9.

Application of KBC to the DLPFC data. (A,B) The violin plots of the results obtained from six different methods on the DLPFC data set. (C) The boxplot of clustering LISI of the six different methods on the DLPFC data set. (D) Histology image, manual annotation (Maynard et al. 2021), and the clustering results of KBC, BayesSpace, SpaGCN, SpatialPCA, Stagate, and stLearn plotted on DLPFC slice 151669.

This Article

  1. Published in Advance February 5, 2025, doi: 10.1101/gr.278983.124 Genome Res. 35: 355-367

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