## ----global.options, include = FALSE------------------------------------------ knitr::opts_knit$set( collapse = TRUE, comment = "#>", fig.align = 'center' ) knitr::opts_chunk$set( out.extra = 'style="display:block; margin:auto;"', warning = FALSE, message = FALSE, # Compact, HTML-friendly rendering. JPEG (rather than PNG) is the # right choice for the H&E histology overlays — photographic raster # compresses ~5x better as JPEG at quality 80, keeping the rendered # vignette under Bioconductor's 5 MB per-file size guideline. dev = "jpeg", dev.args = list(quality = 80), dpi = 72, fig.width = 5, fig.height = 4, fig.retina = 1 ) ## ----eval = FALSE------------------------------------------------------------- # if (!require("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("SpaceMarkers") ## ----message = FALSE, warning = FALSE----------------------------------------- library(SpaceMarkers) ## ----------------------------------------------------------------------------- data_dir <- "visiumBrCA" unlink(file.path(data_dir), recursive = TRUE) dir.create(data_dir, showWarnings = FALSE) main_10xlink <- "https://cf.10xgenomics.com/samples/spatial-exp/1.3.0" counts_folder <- "Visium_Human_Breast_Cancer" counts_file <- "Visium_Human_Breast_Cancer_filtered_feature_bc_matrix.h5" counts_url <- paste(c(main_10xlink, counts_folder, counts_file), collapse = "/") sp_folder <- "Visium_Human_Breast_Cancer" sp_file <- "Visium_Human_Breast_Cancer_spatial.tar.gz" sp_url <- paste(c(main_10xlink, sp_folder, sp_file), collapse = "/") ## ----------------------------------------------------------------------------- bfc <- BiocFileCache::BiocFileCache(ask = FALSE) counts_cache <- BiocFileCache::bfcrpath(bfc, counts_url) sp_cache <- BiocFileCache::bfcrpath(bfc, sp_url) file.copy(counts_cache, file.path(data_dir, counts_file), overwrite = TRUE) untar(sp_cache, exdir = data_dir) ## ----message=FALSE, warning=FALSE--------------------------------------------- cogaps_result <- readRDS(system.file("extdata", "CoGAPS_result.rds", package = "SpaceMarkers", mustWork = TRUE)) sme <- load10X(data_dir, features = cogaps_result, method = "CoGAPS") sme ## ----eval=FALSE--------------------------------------------------------------- # # Step 1: Load without features # sme <- load10X(data_dir) # # # Step 2: Add features later # sme <- add_features(sme, cogaps_result, method = "CoGAPS") ## ----------------------------------------------------------------------------- # Expression matrix dim(assay(sme, "logcounts")) # Spatial coordinates head(spatialCoords(sme)) # Spatial patterns stored in colData head(spatial_patterns(sme)) ## ----------------------------------------------------------------------------- data("curated_genes") good_gene_threshold <- 3 keep_genes <- rownames(sme)[ apply(assay(sme, "logcounts"), 1, function(x) sum(x > 0) >= good_gene_threshold) ] keep_genes <- intersect(keep_genes, curated_genes) sme <- sme[keep_genes, ] # Keep all patterns for now; we will subset per analysis below sme ## ----message=FALSE, warning=FALSE--------------------------------------------- sme_undirected <- SpaceMarkers( x = sme, directed = FALSE, min.gene.expr = 3, pattern_pairs = rbind(c("Pattern_1", "Pattern_2")) ) ## ----------------------------------------------------------------------------- # Analysis type analysis_type(sme_undirected) # IM scores (summary: genes x pattern pairs) head(undirected_scores(sme_undirected)) ## ----------------------------------------------------------------------------- # Undirected hotspots hs <- hotspots(sme_undirected, type = "undirected") head(hs) ## ----------------------------------------------------------------------------- # Per-pair interaction details pair_names <- names(interactions(sme_undirected)) pair_names # Detailed stats for the first pair (if any interactions were found) if (length(pair_names) > 0) { pair1 <- interactions(sme_undirected, pair = pair_names[1]) if (length(pair1$interacting_genes) > 0) { head(pair1$interacting_genes[[1]]) } else { message("No interacting genes found for this pair.") } } ## ----message=FALSE, warning=FALSE--------------------------------------------- sme_directed <- SpaceMarkers( x = sme, directed = TRUE, min.gene.expr = 3, pattern_pairs = rbind(c("Pattern_1", "Pattern_5")) ) ## ----------------------------------------------------------------------------- analysis_type(sme_directed) head(directed_scores(sme_directed)) ## ----------------------------------------------------------------------------- # Pattern hotspots head(hotspots(sme_directed, type = "pattern")) # Influence hotspots head(hotspots(sme_directed, type = "influence")) # Influence map head(influence_map(sme_directed)) ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme, feature_col = "Pattern_1", title = "Pattern_1 (immune)") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme, feature_col = "Pattern_5", title = "Pattern_5 (cancer)") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme_undirected, feature_col = "Pattern_1", source = "hotspots", hotspot_type = "undirected", colors = "steelblue", title = "Pattern_1 hotspots") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme_undirected, feature_col = "Pattern_2", source = "hotspots", hotspot_type = "undirected", colors = "firebrick", title = "Pattern_2 hotspots") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_overlap_scores(sme_undirected, title = "Undirected overlap") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme_undirected, source = "interaction", hotspot_type = "undirected", interaction_patterns = c("Pattern_1", "Pattern_2"), colors = c(Pattern_1 = "steelblue", interacting = "purple", Pattern_2 = "firebrick")) ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ pair_col <- setdiff(colnames(undirected_scores(sme_undirected)), "Gene")[1] plot_im_scores(sme_undirected, interaction = pair_col, nGenes = 10) ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ ims <- undirected_scores(sme_undirected) top_genes <- head(ims[order(ims[[pair_col]], decreasing = TRUE), "Gene"], 5) for (g in top_genes) { print(plot_spatial(sme_undirected, feature_col = g, title = g)) } ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme_directed, feature_col = "Pattern_1", source = "hotspots", hotspot_type = "pattern", colors = "steelblue", title = "Pattern_1 GMM hotspots") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme_directed, feature_col = "Pattern_5", source = "hotspots", hotspot_type = "pattern", colors = "firebrick", title = "Pattern_5 GMM hotspots") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_overlap_scores(sme_directed, title = "Directed overlap (relative abundance)") ## ----------------------------------------------------------------------------- labs_fwd <- overlap_map(sme_directed, c("Pattern_1", "Pattern_5"), direction = "forward") table(labs_fwd, useNA = "ifany") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme_directed, source = "interaction", interaction_patterns = c("Pattern_1", "Pattern_5"), direction = "forward", colors = c(Pattern_1 = "steelblue", "Pattern_1 near Pattern_5" = "purple", "Pattern_5 influence" = "gray60")) ## ----------------------------------------------------------------------------- labs_rev <- overlap_map(sme_directed, c("Pattern_1", "Pattern_5"), direction = "reverse") table(labs_rev, useNA = "ifany") ## ----message=FALSE, warning=FALSE, dpi=60, fig.width=6------------------------ plot_spatial(sme_directed, source = "interaction", interaction_patterns = c("Pattern_1", "Pattern_5"), direction = "reverse", colors = c(Pattern_5 = "firebrick", "Pattern_5 near Pattern_1" = "goldenrod", "Pattern_1 influence" = "gray60")) ## ----------------------------------------------------------------------------- mat <- matrix(rnorm(100), nrow = 10, ncol = 10) rownames(mat) <- paste0("gene_", 1:10) colnames(mat) <- paste0("spot_", 1:10) coords <- matrix(runif(20) * 100, ncol = 2) colnames(coords) <- c("y", "x") rownames(coords) <- colnames(mat) cd <- S4Vectors::DataFrame( CellType_A = runif(10), CellType_B = runif(10), row.names = colnames(mat) ) my_sme <- SpaceMarkersExperiment( assays = list(logcounts = mat), colData = cd, spatialCoords = coords, spaceMarkers = list( params = list(pattern_names = c("CellType_A", "CellType_B")) ) ) my_sme ## ----------------------------------------------------------------------------- spe <- SpatialExperiment::SpatialExperiment( assays = list(logcounts = mat), colData = cd, spatialCoords = coords ) # Extract numeric colData columns as spatial features features <- get_spatial_features(spe) head(features) ## ----eval = FALSE------------------------------------------------------------- # # Legacy usage (returns data.frame, not SME) # result <- SpaceMarkers( # features = "path/to/features.rds", # data = "path/to/visium_dir", # directed = FALSE, # returnSME = FALSE # ) ## ----------------------------------------------------------------------------- unlink(file.path(data_dir), recursive = TRUE) ## ----------------------------------------------------------------------------- sessionInfo()