Fig. 3: Performance evaluation of the unsupervised CytoCommunity algorithm using single-cell spatial transcriptomics data.
From: Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes
a, Five single-cell spatial images—Bregma −0.14, Bregma −0.04, Bregma +0.06, Bregma +0.16 and Bregma +0.26—of mouse hypothalamic preoptic region generated using the MERFISH technology. The Bregma distance is given for each imaged brain section. Cells are colored based on the cell-type annotation from the original study17. b, Left, the 9, 10, 12, 12 and 11 hypothalamic nuclei or regions in the images were manually outlined by the authors of the original study. Right, cells were manually assigned TCN membership based on the nuclei outlined on the left. c–e, TCNs identified by CytoCommunity (c), Spatial-LDA and UTAG (d), and STAGATE, BayesSpace and stLearn (e). TCNs are labeled and colored based on the most similar manually annotated nuclei regions. TCNs without labels could not be matched to the manual annotation. f, Macro-F1 and AMI scores were computed using the manually annotated hypothalamic nuclei in b. Each point represents the performance on a given single-cell spatial image; the horizontal bars represent the mean across n = 5 images. Performances (points) on the same images are connected by gray dashed lines. P values were computed using a one-sided paired t-test. 3V, third ventricle; BAC, bed nucleus of the anterior commissure; Fx, fornix; LPO, lateral preoptic area; PS, parastrial nucleus; PVA, paraventricular thalamic nucleus; StHy, striohypothalamic nucleus.
