Table 1.

Comparison of ScribbleDom and AutoScribbleDom with state-of-the-art spatial and non-spatial clustering methods based on ARI.^a

Sample	ScribbleDom	AutoScribbleDom	SC-MEB	BayesSpace	SpaGCN	Giotto	GMM	Louvain
151507	0.53	0.34	0.42	0.33	0.49	0.33	0.40	0.32
151508	0.37	0.44	0.44	0.36	0.43	0.34	0.33	0.25
151509	0.65	0.42	0.52	0.44	0.44	0.35	0.29	0.30
151510	0.46	0.55	0.39	0.43	0.45	0.33	0.31	0.28
151669	0.66	0.32	0.32	0.41	0.26	0.25	0.22	0.20
151670	0.70	0.33	0.43	0.43	0.37	0.21	0.19	0.26
151671	0.71	0.60	0.42	0.38	0.52	0.40	0.23	0.36
151672	0.74	0.63	0.44	0.77	0.57	0.38	0.14	0.27
151673	0.50	0.52	0.49	0.55	0.53	0.37	0.29	0.29
151674	0.54	0.25	0.43	0.33	0.39	0.29	0.29	0.33
151675	0.52	0.39	0.31	0.41	0.46	0.32	0.24	0.24
151676	0.51	0.38	0.39	0.32	0.35	0.26	0.26	0.25

^aThe ARI values of SpaGCN have been collected from the respective paper (Hu et al. 2021). The ARI values of the remaining state-of-the-art methods were collected from SC-MEB (Yang et al. 2022) paper. The best ARI value for each sample has been marked in bold-face.