## ----echo=FALSE, results="hide"----------------------------------------------- knitr::opts_chunk$set(error=FALSE, warning=FALSE, message=FALSE, crop = NULL) library(BiocStyle) ## ----library, echo=FALSE------------------------------------------------------ library(imcRtools) ## ----access-spillover-files--------------------------------------------------- path <- system.file("extdata/spillover", package = "imcRtools") list.files(path, recursive = TRUE) ## ----access-txt-files--------------------------------------------------------- txt_files <- list.files(system.file("extdata/mockData/raw", package = "imcRtools")) txt_files ## ----imcsegmentationpipeline-data--------------------------------------------- path <- system.file("extdata/mockData/cpout", package = "imcRtools") list.files(path, recursive = TRUE) ## ----steinbock-data----------------------------------------------------------- path <- system.file("extdata/mockData/steinbock", package = "imcRtools") list.files(path, recursive = TRUE) ## ----show_cpout_features------------------------------------------------------ path <- system.file("extdata/mockData/cpout", package = "imcRtools") show_cpout_features(path) ## ----read_cpout--------------------------------------------------------------- cur_path <- system.file("extdata/mockData/cpout", package = "imcRtools") # Read as SpatialExperiment (spe <- read_cpout(cur_path, graph_file = "Object_relationships.csv")) # Read as SingleCellExperiment (sce <- read_cpout(cur_path, graph_file = "Object_relationships.csv", return_as = "sce")) ## ----read_steinbock----------------------------------------------------------- cur_path <- system.file("extdata/mockData/steinbock", package = "imcRtools") # Read as SpatialExperiment (spe <- read_steinbock(cur_path)) # Read as SingleCellExperiment (sce <- read_steinbock(cur_path, return_as = "sce")) ## ----read-txt-1--------------------------------------------------------------- path <- system.file("extdata/mockData/raw", package = "imcRtools") cur_CytoImageList <- readImagefromTXT(path) cur_CytoImageList ## ----read-single-metals------------------------------------------------------- path <- system.file("extdata/spillover", package = "imcRtools") sce <- readSCEfromTXT(path) sce ## ----plotSpotHeatmap, fig.width=5, fig.height=5------------------------------- plotSpotHeatmap(sce) ## ----plotSpotHeatmap-2, fig.width=5, fig.height=5----------------------------- plotSpotHeatmap(sce, log = FALSE, threshold = 200) ## ----pixel-binning, fig.width=5, fig.height=5--------------------------------- sce2 <- binAcrossPixels(sce, bin_size = 5) plotSpotHeatmap(sce2, log = FALSE, threshold = 200) ## ----assign-pixels------------------------------------------------------------ library(CATALYST) bc_key <- as.numeric(unique(sce$sample_mass)) assay(sce, "exprs") <- asinh(counts(sce)/5) sce <- assignPrelim(sce, bc_key = bc_key) sce <- estCutoffs(sce) sce <- applyCutoffs(sce) # Filter out mislabeled pixels sce <- filterPixels(sce) table(sce$bc_id, sce$sample_mass) ## ----estimate-spillover------------------------------------------------------- sce <- computeSpillmat(sce) metadata(sce)$spillover_matrix ## ----buildSpatialGraph, message=FALSE----------------------------------------- library(cytomapper) data("pancreasSCE") pancreasSCE <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb", type = "expansion", threshold = 20) pancreasSCE <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb", type = "knn", k = 5) pancreasSCE <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb", type = "delaunay") colPairNames(pancreasSCE) ## ----plotSpatial-------------------------------------------------------------- library(ggplot2) library(ggraph) plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "CellType", node_shape_by = "ImageNb", node_size_by = "Area", draw_edges = TRUE, colPairName = "knn_interaction_graph", directed = FALSE, scales = "free") # Colored by expression and with arrows plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "PIN", assay_type = "exprs", node_size_fix = 3, edge_width_fix = 0.2, draw_edges = TRUE, colPairName = "knn_interaction_graph", directed = TRUE, arrow = grid::arrow(length = grid::unit(0.1, "inch")), end_cap = ggraph::circle(0.05, "cm"), scales = "free") # Subsetting the SingleCellExperiment plotSpatial(pancreasSCE[,pancreasSCE$Pattern], img_id = "ImageNb", node_color_by = "CellType", node_size_fix = 1, draw_edges = TRUE, colPairName = "knn_interaction_graph", directed = TRUE) ## ----aggregateNeigbors-------------------------------------------------------- pancreasSCE <- aggregateNeighbors(pancreasSCE, colPairName = "knn_interaction_graph", aggregate_by = "metadata", count_by = "CellType") head(pancreasSCE$aggregatedNeighbors) pancreasSCE <- aggregateNeighbors(pancreasSCE, colPairName = "knn_interaction_graph", aggregate_by = "expression", assay_type = "exprs") head(pancreasSCE$mean_aggregatedExpression) ## ----aggregateNeigbors-clustering--------------------------------------------- set.seed(22) cur_cluster <- kmeans(pancreasSCE$aggregatedNeighbors, centers = 3) pancreasSCE$clustered_neighbors <- factor(cur_cluster$cluster) # Visualize CellType and clustered_neighbors plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "CellType", node_size_fix = 4, edge_width_fix = 2, edge_color_by = "clustered_neighbors", draw_edges = TRUE, colPairName = "knn_interaction_graph", directed = FALSE, nodes_first = FALSE, scales = "free") + scale_color_brewer(palette = "Set2") + scale_edge_color_brewer(palette = "Set1") # Visualize clustered_neighbors plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "clustered_neighbors", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "knn_interaction_graph", directed = FALSE, nodes_first = FALSE, scales = "free")+ scale_color_brewer(palette = "Set1") ## ----detectSpatialContext----------------------------------------------------- # Generate k-nearest neighbor graph pancreasSCE <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb", type = "knn", name = "knn_spatialcontext_graph", k = 15) colPairNames(pancreasSCE) # Aggregate based on clustered_neighbors pancreasSCE <- aggregateNeighbors(pancreasSCE, colPairName = "knn_spatialcontext_graph", aggregate_by = "metadata", count_by = "clustered_neighbors", name = "aggregatedNeighborhood") # Detect spatial contexts pancreasSCE <- detectSpatialContext(pancreasSCE, entry = "aggregatedNeighborhood", threshold = 0.9, name = "spatial_context") # Define SC color scheme col_SC <- setNames(c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F"), sort(unique(pancreasSCE$spatial_context))) # Visualize spatial contexts on images plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "spatial_context", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "knn_spatialcontext_graph", directed = FALSE, nodes_first = FALSE, scales = "free") + scale_color_manual(values = col_SC) ## ----filterSpatialContext----------------------------------------------------- # Filter spatial contexts # By number of group entries pancreasSCE <- filterSpatialContext(pancreasSCE, entry = "spatial_context", group_by = "ImageNb", group_threshold = 2, name = "spatial_context_filtered_group") metadata(pancreasSCE)$filterSpatialContext plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "spatial_context_filtered_group", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "knn_spatialcontext_graph", directed = FALSE, nodes_first = FALSE, scales = "free") + scale_color_manual(values = col_SC) # By total number of cells pancreasSCE <- filterSpatialContext(pancreasSCE, entry = "spatial_context", group_by = "ImageNb", cells_threshold = 15, name = "spatial_context_filtered_cells") metadata(pancreasSCE)$filterSpatialContext plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "spatial_context_filtered_cells", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "knn_spatialcontext_graph", directed = FALSE, nodes_first = FALSE, scales = "free") + scale_color_manual(values = col_SC) ## ----plotSpatialContext------------------------------------------------------- ## Plot spatial context graph # Default plotSpatialContext(pancreasSCE, entry = "spatial_context", group_by = "ImageNb") # Colored by name and size by n_cells plotSpatialContext(pancreasSCE, entry = "spatial_context", group_by = "ImageNb", node_color_by = "name", node_size_by = "n_cells", node_label_color_by = "name") # Colored by n_cells and size by n_group plotSpatialContext(pancreasSCE, entry = "spatial_context", group_by = "ImageNb", node_color_by = "n_cells", node_size_by = "n_group", node_label_color_by = "n_cells")+ scale_color_viridis() ## ----plotSpatialContext - return data----------------------------------------- # Return data plotSpatialContext(pancreasSCE, entry = "spatial_context", group_by = "ImageNb", return_data = TRUE) ## ----detectCommunity---------------------------------------------------------- ## Detect spatial community set.seed(22) pancreasSCE <- detectCommunity(pancreasSCE, colPairName = "expansion_interaction_graph") plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "spatial_community", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "expansion_interaction_graph", directed = FALSE, nodes_first = FALSE, scales = "free") ## Detect spatial community - specify size_threshold set.seed(22) pancreasSCE <- detectCommunity(pancreasSCE, colPairName = "expansion_interaction_graph", size_threshold = 20) plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "spatial_community", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "expansion_interaction_graph", directed = FALSE, nodes_first = FALSE, scales = "free") ## ----detectCommunity-walktrap------------------------------------------------- ## Detect spatial community - walktrap community detection set.seed(22) pancreasSCE <- detectCommunity(pancreasSCE, colPairName = "expansion_interaction_graph", cluster_fun = "walktrap") plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "spatial_community", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "expansion_interaction_graph", directed = FALSE, nodes_first = FALSE, scales = "free") ## ----detectCommunity-bygroup-------------------------------------------------- ## Detect spatial community - specify group_by pancreasSCE <- detectCommunity(pancreasSCE, colPairName = "expansion_interaction_graph", group_by = "CellType", size_threshold = 10, BPPARAM = BiocParallel::SerialParam(RNGseed = 22)) plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "spatial_community", node_size_fix = 4, edge_width_fix = 1, draw_edges = TRUE, colPairName = "expansion_interaction_graph", directed = FALSE, nodes_first = FALSE, scales = "free") ## ----findBorderCells---------------------------------------------------------- pancreasSCE <- findBorderCells(pancreasSCE, img_id = "ImageNb", border_dist = 10) plotSpatial(pancreasSCE[,!pancreasSCE$border_cells], img_id = "ImageNb", node_color_by = "CellType", node_size_fix = 4, edge_width_fix = 2, edge_color_by = "clustered_neighbors", draw_edges = TRUE, colPairName = "knn_interaction_graph", directed = FALSE, nodes_first = FALSE, scales = "free") + scale_color_brewer(palette = "Set2") + scale_edge_color_brewer(palette = "Set1") ## ----patchDetection----------------------------------------------------------- pancreasSCE <- patchDetection(pancreasSCE, patch_cells = pancreasSCE$CellType == "celltype_B", colPairName = "expansion_interaction_graph", expand_by = 20, img_id = "ImageNb") plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "patch_id", scales = "free") ## ----patchSize---------------------------------------------------------------- patchSize(pancreasSCE) ## ----minDistToCells----------------------------------------------------------- pancreasSCE <- minDistToCells(pancreasSCE, x_cells = pancreasSCE$CellType == "celltype_B", coords = c("Pos_X","Pos_Y"), img_id = "ImageNb") plotSpatial(pancreasSCE, img_id = "ImageNb", node_color_by = "distToCells", scales = "free") + scale_color_viridis() ## ----countInteractions-------------------------------------------------------- out <- countInteractions(pancreasSCE, group_by = "ImageNb", label = "CellType", method = "classic", colPairName = "knn_interaction_graph") out ## ----testInteractions--------------------------------------------------------- out <- testInteractions(pancreasSCE, group_by = "ImageNb", label = "CellType", method = "classic", colPairName = "knn_interaction_graph", BPPARAM = BiocParallel::SerialParam(RNGseed = 123)) out ## ----sessionInfo, echo=FALSE-------------------------------------------------- sessionInfo()