Proust improves the accuracy of predicting spatial domains compared to existing methods. Data from sample Br6432 from Visium SPG human DLPFC data set (Huuki-Myers et al. 2024), unless noted otherwise. (A) Immunofluorescence images of five protein channels: nuclei (DAPI), neurons (RBFOX3 [also known as NeuN]), oligodendrocytes (OLIG2), astrocytes (GFAP), and microglia (TMEM119). (B) Box plot of Adjusted Rand Index (ARI) across four samples. (C) Manual annotation of tissue slice from donor Br6432 and predicted spatial domains by the six methods. Labels do not indicate corresponding biological layers assigned by the algorithms. (D) UMAP visualization of spots from donor Br6432 colored by Proust predictions. (E) Stacked violin plot of marker gene distribution for white matter and sublayers of gray matter based on literature in each spatial domain assigned by Proust. Red rectangles are highlighted marker genes in F. (F) Violin plots of marker gene expression for Proust and manually annotated domains. (G) Heat maps of the top five differentially expressed genes (centered and scaled) across layers from Proust and manual annotations. A dendrogram on the right shows hierarchical clustering. (H) Selected cluster-based marker genes expression and visualization of individual clusters identified by Proust. Layers were annotated according to the laminar organization indicated by the manual annotation.
