To run this vignette you need to install all the necessary Python modules.
This can be done manually, see https://rubd.github.io/Giotto_site/articles/installation_issues.html#python-manual-installation
This can be done within R using our installation tools (installGiottoEnvironment), see https://rubd.github.io/Giotto_site/articles/tut0_giotto_environment.html for more information.
# to automatically save figures in save_dir set save_plot to TRUE temp_dir = '~/Temp/' myinstructions = createGiottoInstructions(save_dir = temp_dir, save_plot = FALSE, show_plot = F)
minimum requirements:
- matrix with expression information (or path to)
- x,y(,z) coordinates for cells or spots (or path to)
# giotto object expr_path = system.file("extdata", "visium_DG_expr.txt", package = 'Giotto') loc_path = system.file("extdata", "visium_DG_locs.txt", package = 'Giotto') mini_visium <- createGiottoObject(raw_exprs = expr_path, spatial_locs = loc_path, instructions = myinstructions)
How to work with Giotto instructions that are part of your Giotto object:
- show the instructions associated with your Giotto object with showGiottoInstructions
- change one or more instructions with changeGiottoInstructions
- replace all instructions at once with replaceGiottoInstructions
- read or get a specific giotto instruction with readGiottoInstructions
Of note, the python path can only be set once in an R session. See the reticulate package for more information.
# show instructions associated with giotto object (mini_visium) showGiottoInstructions(mini_visium)
## 1. read image png_path = system.file("extdata", "deg_image.png", package = 'Giotto') mg_img = magick::image_read(png_path) ## 2. test and modify image alignment mypl = spatPlot(mini_visium, return_plot = T, point_alpha = 0.8) orig_png = createGiottoImage(gobject = mini_visium, mg_object = mg_img, name = 'image', xmax_adj = 450, xmin_adj = 550, ymax_adj = 200, ymin_adj = 200) mypl_image = addGiottoImageToSpatPlot(mypl, orig_png) mypl_image ## 3. add images to Giotto object ## image_list = list(orig_png) mini_visium = addGiottoImage(gobject = mini_visium, images = image_list) showGiottoImageNames(mini_visium)
# explore gene and cell distribution filterDistributions(mini_visium, detection = 'genes') filterDistributions(mini_visium, detection = 'cells') filterCombinations(mini_visium, expression_thresholds = c(1), gene_det_in_min_cells = c(20, 20, 50, 50), min_det_genes_per_cell = c(100, 200, 100, 200)) # filter and normalize mini_visium <- filterGiotto(gobject = mini_visium, expression_threshold = 1, gene_det_in_min_cells = 50, min_det_genes_per_cell = 100, expression_values = c('raw'), verbose = T) mini_visium <- normalizeGiotto(gobject = mini_visium, scalefactor = 6000, verbose = T) mini_visium <- addStatistics(gobject = mini_visium)
mini_visium <- calculateHVG(gobject = mini_visium) mini_visium <- runPCA(gobject = mini_visium) screePlot(mini_visium, ncp = 30) plotPCA(gobject = mini_visium) mini_visium <- runUMAP(mini_visium, dimensions_to_use = 1:10) plotUMAP(gobject = mini_visium) mini_visium <- runtSNE(mini_visium, dimensions_to_use = 1:10) plotTSNE(gobject = mini_visium)
mini_visium <- createNearestNetwork(gobject = mini_visium, dimensions_to_use = 1:10, k = 15) mini_visium <- doLeidenCluster(gobject = mini_visium, resolution = 0.4, n_iterations = 1000) # visualize UMAP cluster results plotUMAP(gobject = mini_visium, cell_color = 'leiden_clus', show_NN_network = T, point_size = 2.5) # visualize UMAP and spatial results spatDimPlot(gobject = mini_visium, cell_color = 'leiden_clus', spat_point_shape = 'voronoi') # heatmap and dendrogram showClusterHeatmap(gobject = mini_visium, cluster_column = 'leiden_clus') showClusterDendrogram(mini_visium, h = 0.5, rotate = T, cluster_column = 'leiden_clus')
scran_markers = findMarkers_one_vs_all(gobject = mini_visium, method = 'scran', expression_values = 'normalized', cluster_column = 'leiden_clus') # violinplot topgenes_scran = scran_markers[, head(.SD, 2), by = 'cluster']$genes violinPlot(mini_visium, genes = topgenes_scran, cluster_column = 'leiden_clus', strip_text = 10, strip_position = 'right') # metadata heatmap topgenes_scran = scran_markers[, head(.SD, 6), by = 'cluster']$genes plotMetaDataHeatmap(mini_visium, selected_genes = topgenes_scran, metadata_cols = c('leiden_clus'))
clusters_cell_types = c('Gfap_cells', 'Tbr1_cells', 'Tcf7l2_cells', 'Wfs1_cells', 'Nptxr_cells') names(clusters_cell_types) = 1:5 mini_visium = annotateGiotto(gobject = mini_visium, annotation_vector = clusters_cell_types, cluster_column = 'leiden_clus', name = 'cell_types') # check new cell metadata pDataDT(mini_visium) # visualize annotations spatDimPlot(gobject = mini_visium, cell_color = 'cell_types', spat_point_size = 3, dim_point_size = 3)
Here we will use known markers for different mouse brain cell types to identify which cell types are enriched in the individual spots or identified clusters.
Paper: eisel, A. et al. Molecular Architecture of the Mouse Nervous System. Cell 174, 999-1014.e22 (2018).
## cell type signatures ## ## combination of all marker genes identified in Zeisel et al sign_matrix_path = system.file("extdata", "sig_matrix.txt", package = 'Giotto') brain_sc_markers = data.table::fread(sign_matrix_path) # file don't exist in data folder sig_matrix = as.matrix(brain_sc_markers[,-1]); rownames(sig_matrix) = brain_sc_markers$Event ## enrichment tests mini_visium = runSpatialEnrich(mini_visium, sign_matrix = sig_matrix, enrich_method = 'PAGE') #default = 'PAGE' ## heatmap of enrichment versus annotation (e.g. clustering result) cell_types = colnames(sig_matrix) plotMetaDataCellsHeatmap(gobject = mini_visium, metadata_cols = 'leiden_clus', value_cols = cell_types, spat_enr_names = 'PAGE', x_text_size = 8, y_text_size = 8) enrichment_results = mini_visium@spatial_enrichment$PAGE enrich_cell_types = colnames(enrichment_results) enrich_cell_types = enrich_cell_types[enrich_cell_types != 'cell_ID'] ## spatplot spatCellPlot(gobject = mini_visium, spat_enr_names = 'PAGE', cell_annotation_values = enrich_cell_types, cow_n_col = 3,coord_fix_ratio = NULL, point_size = 1)
Create a grid based on defined stepsizes in the x,y(,z) axes.
mini_visium <- createSpatialGrid(gobject = mini_visium, sdimx_stepsize = 300, sdimy_stepsize = 300, minimum_padding = 50) showGrids(mini_visium) spatPlot(gobject = mini_visium, show_grid = T, point_size = 1.5) # extract grid and associated metadata spots annotated_grid = annotateSpatialGrid(mini_visium) annotated_grid_metadata = annotateSpatialGrid(mini_visium, cluster_columns = c('leiden_clus', 'cell_types', 'nr_genes'))
plotStatDelaunayNetwork(gobject = mini_visium, maximum_distance = 300) mini_visium = createSpatialNetwork(gobject = mini_visium, minimum_k = 2, maximum_distance_delaunay = 400) mini_visium = createSpatialNetwork(gobject = mini_visium, minimum_k = 2, method = 'kNN', k = 10) showNetworks(mini_visium) # visualize the two different spatial networks spatPlot(gobject = mini_visium, show_network = T, network_color = 'blue', spatial_network_name = 'Delaunay_network', point_size = 2.5, cell_color = 'leiden_clus') spatPlot(gobject = mini_visium, show_network = T, network_color = 'blue', spatial_network_name = 'kNN_network', point_size = 2.5, cell_color = 'leiden_clus')
Identify spatial genes with 3 different methods:
- binSpect with kmeans binarization (default)
- binSpect with rank binarization
- silhouetteRank
Visualize top 4 genes per method.
km_spatialgenes = binSpect(mini_visium) spatGenePlot(mini_visium, expression_values = 'scaled', genes = km_spatialgenes[1:4]$genes, point_shape = 'border', point_border_stroke = 0.1, show_network = F, network_color = 'lightgrey', point_size = 2.5, cow_n_col = 2) rank_spatialgenes = binSpect(mini_visium, bin_method = 'rank') spatGenePlot(mini_visium, expression_values = 'scaled', genes = rank_spatialgenes[1:4]$genes, point_shape = 'border', point_border_stroke = 0.1, show_network = F, network_color = 'lightgrey', point_size = 2.5, cow_n_col = 2)
silh_spatialgenes = silhouetteRank(gobject = mini_visium) # TODO: suppress print output spatGenePlot(mini_visium, expression_values = 'scaled', genes = silh_spatialgenes[1:4]$genes, point_shape = 'border', point_border_stroke = 0.1, show_network = F, network_color = 'lightgrey', point_size = 2.5, cow_n_col = 2)
Identify robust spatial co-expression patterns using the spatial network or grid and a subset of individual spatial genes.
1. calculate spatial correlation scores
2. cluster correlation scores
# 1. calculate spatial correlation scores ext_spatial_genes = km_spatialgenes[1:100]$genes spat_cor_netw_DT = detectSpatialCorGenes(mini_visium, method = 'network', spatial_network_name = 'Delaunay_network', subset_genes = ext_spatial_genes) # 2. cluster correlation scores spat_cor_netw_DT = clusterSpatialCorGenes(spat_cor_netw_DT, name = 'spat_netw_clus', k = 8) heatmSpatialCorGenes(mini_visium, spatCorObject = spat_cor_netw_DT, use_clus_name = 'spat_netw_clus') netw_ranks = rankSpatialCorGroups(mini_visium, spatCorObject = spat_cor_netw_DT, use_clus_name = 'spat_netw_clus') top_netw_spat_cluster = showSpatialCorGenes(spat_cor_netw_DT, use_clus_name = 'spat_netw_clus', selected_clusters = 6, show_top_genes = 1) cluster_genes_DT = showSpatialCorGenes(spat_cor_netw_DT, use_clus_name = 'spat_netw_clus', show_top_genes = 1) cluster_genes = cluster_genes_DT$clus; names(cluster_genes) = cluster_genes_DT$gene_ID mini_visium = createMetagenes(mini_visium, gene_clusters = cluster_genes, name = 'cluster_metagene') spatCellPlot(mini_visium, spat_enr_names = 'cluster_metagene', cell_annotation_values = netw_ranks$clusters, point_size = 1.5, cow_n_col = 3)
hmrf_folder = paste0(temp_dir,'/','11_HMRF/') if(!file.exists(hmrf_folder)) dir.create(hmrf_folder, recursive = T) # perform hmrf my_spatial_genes = km_spatialgenes[1:100]$genes HMRF_spatial_genes = doHMRF(gobject = mini_visium, expression_values = 'scaled', spatial_genes = my_spatial_genes, spatial_network_name = 'Delaunay_network', k = 8, betas = c(28,2,2), output_folder = paste0(hmrf_folder, '/', 'Spatial_genes_brain/SG_top100_k8_scaled')) # check and select hmrf for(i in seq(28, 30, by = 2)) { viewHMRFresults2D(gobject = mini_visium, HMRFoutput = HMRF_spatial_genes, k = 8, betas_to_view = i, point_size = 2) } mini_visium = addHMRF(gobject = mini_visium, HMRFoutput = HMRF_spatial_genes, k = 8, betas_to_add = c(28), hmrf_name = 'HMRF') giotto_colors = getDistinctColors(8) names(giotto_colors) = 1:8 spatPlot(gobject = mini_visium, cell_color = 'HMRF_k8_b.28', point_size = 3, coord_fix_ratio = 1, cell_color_code = giotto_colors)
viewer_folder = paste0(temp_dir, '/', 'Mouse_cortex_viewer') # select annotations, reductions and expression values to view in Giotto Viewer exportGiottoViewer(gobject = mini_visium, output_directory = viewer_folder, factor_annotations = c('cell_types', 'leiden_clus', 'HMRF_k8_b.28'), numeric_annotations = 'total_expr', dim_reductions = c('umap'), dim_reduction_names = c('umap'), expression_values = 'scaled', expression_rounding = 3, overwrite_dir = T)