Fig. 2: Performance evaluation of the unsupervised CytoCommunity algorithm using single-cell spatial proteomics data.
From: Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes
a,b, Three single-cell spatial images, BALB/c-1, BALB/c-2 and BALB/c-3, generated from healthy mouse spleen samples using the CODEX technology. Cells are colored based on cell-type annotation (a) or manual tissue compartment annotation (b) from the original study16. c–e, TCNs identified by CytoCommunity (c), two methods (Spatial-LDA and UTAG) originally designed for spatial proteomics data (d) and three methods (STAGATE, BayesSpace and stLearn) originally designed for spatial transcriptomics data (e). f, Macro-F1 and AMI scores computed based on manually annotated TCNs. Each data point represents the performance on one image; the horizontal bars represent the mean across n = 3 images. Performances (points) on the same image are connected by gray dashed lines. P values were computed using a one-sided paired t-test. Note that only UTAG identified seven TCNs in the BALB/c-3 image, while all other methods identified four TCNs in all three images. mphs, macrophages; DNT, TCRα+CD4−CD8− double-negative T [cell]; NS, not significant.
