Fig. 2

Normalization of total detections/library sizes results in poorer spatial domain identification using clustering approaches. a Schematic of the benchmark performed on 25 samples spanning four spatial transcriptomics technologies showing the parameter space explored when using a single-cell clustering pipeline, as well as two spatially aware methods to identify spatial domains. b The adjusted Rand index (ARI) obtained when different normalization strategies are applied on the different datasets using three different clustering methods: graph-based clustering, SpaGCN, and BayesSpace. Explicit library size normalization using sctransform results in poorer domain identification across most datasets, indicating that library size confounds biology in spatial transcriptomics datasets. Choice of normalization methods is dependent on the clustering algorithm and dataset type