13. Plotting Tabula Muris UMAPs (Supplemental Figures S17-S36)

suppressPackageStartupMessages({
library(recallreproducibility)

library(Seurat)

library(ggplot2)
library(patchwork)
library(grid)
library(gridExtra)
})

Define a function for plotting the UMAPs.

get_umap_with_scace <- function(tissue, tissue_name) {
  umap_fig <- one_umap(tissue, "cell_ontology_class", "Cell Ontology Class", add_legend=TRUE) + 
    one_umap(tissue, "callback_idents", "recall", add_legend=TRUE) + 
    one_umap(tissue, "scSHC_clusters", "sc-SHC", add_legend=TRUE) + 
    one_umap(tissue, "CHOIR_clusters_0.05", "CHOIR", add_legend=TRUE) +
    one_umap(tissue, "scace_cluster", "scAce", add_legend=TRUE) +

    patchwork::plot_layout(widths = c(1, 1),
                           heights = c(1,1, 1)) + 
    plot_annotation(title = tissue_name,
                    theme = theme(plot.title = element_text(size = 64, hjust = 0.5, vjust = 1.0)))

  return(umap_fig)

}

Get the clustering results from running callback, sc-SHC, and CHOIR and scAce.

tissue_seurat_files <- list.files(pattern = "cluster_results_seurat.rds")

Loop over the clustering results and plot all of the UMAP grids.

for (tissue_rds_file in tissue_seurat_files) {
  
  tissue_name <- sub('cluster_results_seurat.rds', "", tissue_rds_file)
  
  print(tissue_name)
  
  tissue_seurat_obj <- readRDS(paste0("original_results/", tissue_rds_file))
  scace_adata <- anndata::read_h5ad(paste0("scace_results/", tissue_name, ".h5ad"))
  tissue_scace <- Seurat::CreateSeuratObject(counts = t(scace_adata$X), meta.data = scace_adata$obs)


  tissue_seurat_obj@meta.data$scace_cluster <- tissue_scace@meta.data$scace_cluster  
  if (tissue_name == "Heart") {
    # clean up cell type labels for heart
    tissue_seurat_obj@meta.data$cell_ontology_class[tissue_seurat_obj@meta.data$cell_ontology_class == ""] <- "cardiac neuron"
  }
  
  if (tissue_name == "Fat") {
    # clean up cell type labels for fat
    tissue_seurat_obj@meta.data$cell_ontology_class[tissue_seurat_obj@meta.data$cell_ontology_class == ""] <- "unlabeled"
  }
  
  if (tissue_name == "Lung") {
    # clean up cell type labels for heart
    tissue_seurat_obj@meta.data$cell_ontology_class[tissue_seurat_obj@meta.data$cell_ontology_class == ""] <- "unlabeled"
  }
  
  # make legends fit better
  legend_line_length <- 15
  
  if (tissue_name == "Lung") {
    legend_line_length <- 22
  }

  # make some cell types multiple lines
  tissue_seurat_obj@meta.data$cell_ontology_class <- stringr::str_wrap(tissue_seurat_obj@meta.data$cell_ontology_class, legend_line_length)

  # remove NAs
  tissue_seurat_obj <- subset(tissue_seurat_obj, subset = cell_ontology_class %in% levels(factor(tissue_seurat_obj@meta.data$cell_ontology_class)))


  umap_fig <- get_umap_with_scace(tissue_seurat_obj, tissue_name)
  
  if (tissue_name == "Marrow") {
    
    legend_line_length <- 23
    
    tissue_seurat_obj@meta.data$cell_ontology_class <- stringr::str_wrap(tissue_seurat_obj@meta.data$cell_ontology_class, legend_line_length)
    
    layout <- "AAA###\nAAA###\nBBCCDD\nBBCCDD"

    marrow_ontology <- one_umap(tissue_seurat_obj, "cell_ontology_class", "Cell Ontology Class", add_legend=TRUE) + guides(color=guide_legend(nrow=7))

    marrow_recall <- one_umap(tissue_seurat_obj, "recall_idents", "recall", add_legend=TRUE) + guides(color=guide_legend(nrow=12))
    marrow_scSHC <- one_umap(tissue_seurat_obj, "scSHC_clusters", "sc-SHC", add_legend=TRUE) + guides(color=guide_legend(nrow=12))
    marrow_CHOIR <- one_umap(tissue_seurat_obj, "CHOIR_clusters_0.05", "CHOIR", add_legend=TRUE) + guides(color=guide_legend(nrow=12))
    marrow_scAce <- one_umap(tissue_seurat_obj, "scace_cluster", "scAce", add_legend=TRUE) + guides(color=guide_legend(nrow=12))

    umap_fig <- arrangeGrob(
      marrow_ontology, marrow_recall, marrow_scSHC, marrow_CHOIR, marrow_scAce
      #  widths = c(2, 1, 1),
      top=textGrob("Marrow", gp=gpar(fontsize = 64, hjust = 0.5, vjust = 1.0)),
      layout_matrix = rbind(c(1, 1),
                            c(2, 3),
                            c(4, 5))
    )
    
  }
  
  ggsave(paste0(tissue_name, "_umap.png"), plot = umap_fig, height = 1.5 * 3 * 1440, width = 1.3 * 5 * 1440, units = "px")
}