## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----message=FALSE------------------------------------------------------------ library(SPIAT) ## ----------------------------------------------------------------------------- data("simulated_image") # define cell types formatted_image <- define_celltypes( simulated_image, categories = c("Tumour_marker","Immune_marker1,Immune_marker2", "Immune_marker1,Immune_marker3", "Immune_marker1,Immune_marker2,Immune_marker4", "OTHER"), category_colname = "Phenotype", names = c("Tumour", "Immune1", "Immune2", "Immune3", "Others"), new_colname = "Cell.Type") ## ----fig.height = 2.5--------------------------------------------------------- my_colors <- c("red", "blue", "darkcyan", "darkgreen") plot_cell_categories(spe_object = formatted_image, categories_of_interest = c("Tumour", "Immune1", "Immune2", "Immune3"), colour_vector = my_colors, feature_colname = "Cell.Type") ## ----fig.width=3, fig.height = 2.2-------------------------------------------- p_cells <- calculate_cell_proportions(formatted_image, reference_celltypes = NULL, feature_colname ="Cell.Type", celltypes_to_exclude = "Others", plot.image = TRUE) p_cells ## ----fig.height=1.2, fig.width = 3.8------------------------------------------ plot_cell_percentages(cell_proportions = p_cells, cells_to_exclude = "Tumour", cellprop_colname="Proportion_name") ## ----------------------------------------------------------------------------- distances <- calculate_pairwise_distances_between_celltypes( spe_object = formatted_image, cell_types_of_interest = c("Tumour", "Immune1", "Immune3"), feature_colname = "Cell.Type") ## ----fig.height = 4, fig.width=6, out.width="75%"----------------------------- plot_cell_distances_violin(distances) ## ----------------------------------------------------------------------------- summary_distances <- calculate_summary_distances_between_celltypes(distances) summary_distances ## ----fig.height = 2.5, out.width = "75%"-------------------------------------- plot_distance_heatmap(phenotype_distances_result = summary_distances, metric = "mean") ## ----------------------------------------------------------------------------- min_dist <- calculate_minimum_distances_between_celltypes( spe_object = formatted_image, cell_types_of_interest = c("Tumour", "Immune1", "Immune2","Immune3", "Others"), feature_colname = "Cell.Type") ## ----fig.height = 5, fig.width=8, out.width="75%"----------------------------- plot_cell_distances_violin(cell_to_cell_dist = min_dist) ## ----------------------------------------------------------------------------- min_summary_dist <- calculate_summary_distances_between_celltypes(min_dist) # show the first five rows min_summary_dist[seq_len(5),] ## ----fig.height = 2.5, out.width = "75%"-------------------------------------- plot_distance_heatmap(phenotype_distances_result = min_summary_dist, metric = "mean") ## ----------------------------------------------------------------------------- sessionInfo()