Extended Data Fig. 2: Effect of number of models of the ensemble procedure on the performance of CytoCommunity.

(a-c) Unsupervised CytoCommunity was applied to the mouse hypothalamic preoptic region MERFISH dataset using different number of models learned from the soft TCN assignment module. The effect of number of models on the robustness (a) and accuracy including Macro-F1 score (b) and adjusted mutual information (AMI) score (c) was assessed. Macro-F1 score and AMI score were computed by comparing TCN partitions generated using different number of models with manually annotated hypothalamic nuclei (Fig. 3b). Robustness score was computed as the average Jaccard index between those TCN partitions and new TCN partitions generated by additional three replicated experiments. Each black point represents the robustness or the accuracy performance on a given MERFISH image (n = 5). (d, e) Supervised CytoCommunity was applied to the human TNBC MIBI-TOF dataset using different number of models trained based on a 10-fold cross-validation. The effect of number of models on the robustness (d) and accuracy (e) was assessed. Robustness score was computed the same as before and each black point represents the robustness performance on a given compartmentalized or mixed tumor image (n = 34). Accuracy performance was evaluated using the fractions of neoplastic and immune cells correctly assigned to the neoplastic- and immune-dominated TCNs. Each black point represents the accuracy performance on a given compartmentalized tumor image (n = 15). For all panels, blue horizontal bars represent the means of each group with grey dashed lines connecting them.