Figure 2A

Code

# Enrique Blanco Carmona
# e.blancocarmona@kitz-heidelberg.de
# PhD Student – Clinical Bioinformatics
# Division of Pediatric Neurooncology (B062)
# DKFZ-KiTZ | Germany

# Read in sample.
sample <- readRDS("/omics/odcf/analysis/OE0145_projects/idh_gliomas/Figures_Science/revision/IDH_gliomas_book/datasets/integrated_LGGs.rds")

# Define set of markers.
markers.suva <- readRDS("/omics/odcf/analysis/OE0145_projects/idh_gliomas/Figures_Science/revision/IDH_gliomas_book/datasets/IDH_gliomas_TB_annotation_kit_with_Suva_programs_and_metaprogram_iterations.rds")

markers <- list()
markers$Stemness_program <- markers.suva$Suva_Stemness_Program
markers$Oligo_program <- markers.suva$Suva_Oligo_Program
markers$Astro_program <- markers.suva$Suva_Astro_Program

input_gene_list <- markers

# Load diffusion analysis.
diff.map <- readRDS("/omics/odcf/analysis/OE0145_projects/idh_gliomas/Figures_Science/revision/IDH_gliomas_book/datasets/diffusion_map_only_suva.rds")

# Set the diffusion analysis as a dimensional reduction of the seurat object.
sample@reductions$diffusion <- Seurat::CreateDimReducObject(embeddings = diff.map@eigenvectors[colnames(sample), ],
                                                            key = "DC_",
                                                            assay = "SCT")

# Cluster colors.
cluster_cols <- c("RE" = "#ECA809",
                  "OPC-like" = "#043362",                    
                  "Gradient" = "#D6D6D6",                    
                  "Astro-like" = "#9A031E",                 
                  "Cycling" = "#5F0F40")


# Exclude gradient for a better visualization.
sample <- sample[, sample$relabelling != "Gradient"]
sample$Annotation <- sample$relabelling
sample$Annotation[sample$Annotation == "RA"] <- "RE"
sample$Annotation <- factor(sample$Annotation, levels = c("Astro-like", "Cycling", "OPC-like", "RE"))

Code

p1 <- SCpubr::do_DimPlot(sample = sample,
                         reduction = "diffusion",
                         colors.use = cluster_cols,
                         group.by = "Annotation",
                         font.size = 20,
                         raster = TRUE,
                         raster.dpi = 2048,
                         pt.size = 4,
                         legend.icon.size = 8,
                         legend.ncol = 2,
                         na.value = "grey50",
                         axis.title.face = "plain")

out <- SCpubr::do_DiffusionMapPlot(sample = sample,  
                                   input_gene_list = markers, 
                                   group.by = "Annotation", 
                                   colors.use = list("Annotation" = cluster_cols), 
                                   flavor = "Seurat",
                                   font.size = 20,
                                   interpolate = FALSE,
                                   main.heatmap.size = 0.80,
                                   axis.text.face = "plain",
                                   legend.ncol = 1,
                                   legend.length = 12,
                                   legend.width = 0.5,
                                   legend.framecolor = "grey50",
                                   legend.tickcolor = "white",
                                   legend.framewidth = 0.2,
                                   legend.tickwidth = 0.2,
                                   axis.title.face = "plain")
p2 <- out$DC_1
p3 <- out$DC_2
p2$labels$x <- "DC 1"
p3$labels$x <- "DC 2"
p2$labels$y <- "Gene set"
p3$labels$y <- "Gene set"
p2$guides$fill$title <- "Enrichment"
p3$guides$fill$title <- "Enrichment"
p2[[1]]$guides$fill$title <- ""
p3[[1]]$guides$fill$title <- ""
p2[[2]] <- p2[[2]] + ggplot2::theme(axis.title.y = ggplot2::element_text(angle = 90))
p3[[2]] <- p3[[2]] + ggplot2::theme(axis.title.y = ggplot2::element_text(angle = 90))
p2 <- p2 & ggplot2::theme(legend.key.size = ggplot2::unit(0.3, 'cm'))
p3 <- p3 & ggplot2::theme(legend.key.size = ggplot2::unit(0.3, 'cm'))
p2 <- p2 + patchwork::plot_annotation(theme = ggplot2::theme(plot.margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 0)))
p3 <- p3 + patchwork::plot_annotation(theme = ggplot2::theme(plot.margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 0)))