Scaleup test result for different clustering algorithms on the Slide-seq V2 mouse hippocampus data set (Stickels et al. 2021). This data set facilitates the creation of increasing larger subsets for the test. The data sizes range from 1000 to 160,000. We reduce the dimensionality of the data set using principal component analysis (PCA) and retain the top 20 principal components, which capture the majority of the variance in the data set. This is used for all algorithms. The data set size has 1000 points at data size ratio = 1. BayesSpace has no results on larger data sets because it took >48 h. Note that SpatialPCA and stLearn employ Walktrap and k-means/Louvain, respectively, as their clustering algorithms. Only SpaGCN's runtime is in GPU seconds. Note that the linear time has a gradient of one in the runtime ratio plot (shown by the line labeled as linear). Those runtimes that are worse than linear have a higher gradient.
