Fig. 2

Performance of GraphPCA on human dorsolateral prefrontal cortex (DLPFC) samples. a Clustering results of various methods on sample 151673 of the DLPFC dataset. The ground truth of tissue regions is the manual annotation of six cortex layers and white matter (WM), as provided by the original study. Manual annotations and clustering results of other DLPFC slices are shown in Additional file 1: Fig. S5. b Boxplots illustrating clustering accuracies across all 12 sections by eight methods. c Runtimes of different methods for spatial domain detection across all 12 sections. d UMAP visualizations of the sample 151673 generated by GraphPCA, PCA, NMF, and SpatialPCA embeddings, respectively. e Line plots showing the dynamic change in clustering accuracy at varying numbers of input low-dimensional components (x-axis) inferred by GraphPCA and other methods. f Scatter plot displaying the mean expression of layer-specific markers including MBP and CLDND1 (white matter), KRT17 (layer 6), PCP4 (layer 5), PVPLB and TMSB10 (layer 4), MFGE8 (layer 3), CXCL14 (layer 2), and MALAT1 and FABP3 (layer 1). Clusters in y-axis correspond to the spatial domains inferred by GraphPCA. g Trajectory inference on sample 151673 based on the inferred low-dimensional components of GraphPCA. Arrows point from tissue locations with low pseudo-time to those with high pseudo-time