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

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Figure 2.
Figure 2.

First ablation study. Comparing different data transformation methods using the same k-means clustering. (A) The violin plots show the ARI and NMI results of all 12 slices of DLPFC. The runtimes are shown in a bar chart. (B) The detailed clustering results of different embedding methods together with the ground-truth labels are shown for the slice 151673. Here, SpatialPCA, WL, and PCA ran on CPU only. Other methods ran on GPU.

This Article

  1. Genome Res. 35: 355-367

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