#> Warning: This tutorial was written with Giotto version 0.3.6.9046, your version
#> is 1.0.3.This is a more recent version and results should be reproduciblelibrary(Giotto) # 1. set working directory results_folder = '/path/to/directory/' # 2. set giotto python path # set python path to your preferred python version path # set python path to NULL if you want to automatically install (only the 1st time) and use the giotto miniconda environment python_path = NULL if(is.null(python_path)) { installGiottoEnvironment() }
Dataset explanation
10X genomics recently launched a new platform to obtain spatial expression data using a Visium Spatial Gene Expression slide.
The Visium kidney data to run this tutorial can be found here
Visium technology:
High resolution png from original tissue:
Part 1: Giotto global instructions and preparations
## create instructions instrs = createGiottoInstructions(save_dir = results_folder, save_plot = TRUE, show_plot = FALSE, python_path = python_path) ## provide path to visium folder data_path = '/path/to/Kidney_data/'
part 2: Create Giotto object & process data
## directly from visium folder visium_kidney = createGiottoVisiumObject(visium_dir = data_path, expr_data = 'raw', png_name = 'tissue_lowres_image.png', gene_column_index = 2, instructions = instrs) ## update and align background image # problem: image is not perfectly aligned spatPlot(gobject = visium_kidney, cell_color = 'in_tissue', show_image = T, point_alpha = 0.7, save_param = list(save_name = '2_a_spatplot_image'))
# check name showGiottoImageNames(visium_kidney) # "image" is the default name # adjust parameters to align image (iterative approach) visium_kidney = updateGiottoImage(visium_kidney, image_name = 'image', xmax_adj = 1300, xmin_adj = 1200, ymax_adj = 1100, ymin_adj = 1000) # now it's aligned spatPlot(gobject = visium_kidney, cell_color = 'in_tissue', show_image = T, point_alpha = 0.7, save_param = list(save_name = '2_b_spatplot_image_adjusted'))
## check metadata pDataDT(visium_kidney) ## compare in tissue with provided jpg spatPlot(gobject = visium_kidney, cell_color = 'in_tissue', point_size = 2, cell_color_code = c('0' = 'lightgrey', '1' = 'blue'), save_param = list(save_name = '2_c_in_tissue'))
## subset on spots that were covered by tissue metadata = pDataDT(visium_kidney) in_tissue_barcodes = metadata[in_tissue == 1]$cell_ID visium_kidney = subsetGiotto(visium_kidney, cell_ids = in_tissue_barcodes) ## filter visium_kidney <- filterGiotto(gobject = visium_kidney, expression_threshold = 1, gene_det_in_min_cells = 50, min_det_genes_per_cell = 1000, expression_values = c('raw'), verbose = T) ## normalize visium_kidney <- normalizeGiotto(gobject = visium_kidney, scalefactor = 6000, verbose = T) ## add gene & cell statistics visium_kidney <- addStatistics(gobject = visium_kidney) ## visualize spatPlot2D(gobject = visium_kidney, show_image = T, point_alpha = 0.7, save_param = list(save_name = '2_d_spatial_locations'))
spatPlot2D(gobject = visium_kidney, show_image = T, point_alpha = 0.7, cell_color = 'nr_genes', color_as_factor = F, save_param = list(save_name = '2_e_nr_genes'))
part 3: dimension reduction
## highly variable genes (HVG) visium_kidney <- calculateHVG(gobject = visium_kidney, save_param = list(save_name = '3_a_HVGplot'))
## run PCA on expression values (default) visium_kidney <- runPCA(gobject = visium_kidney, center = TRUE, scale_unit = TRUE) screePlot(visium_kidney, ncp = 30, save_param = list(save_name = '3_b_screeplot'))
## run UMAP and tSNE on PCA space (default) visium_kidney <- runUMAP(visium_kidney, dimensions_to_use = 1:10) plotUMAP(gobject = visium_kidney, save_param = list(save_name = '3_d_UMAP_reduction'))
visium_kidney <- runtSNE(visium_kidney, dimensions_to_use = 1:10) plotTSNE(gobject = visium_kidney, save_param = list(save_name = '3_e_tSNE_reduction'))
part 4: cluster
## sNN network (default) visium_kidney <- createNearestNetwork(gobject = visium_kidney, dimensions_to_use = 1:10, k = 15) ## Leiden clustering visium_kidney <- doLeidenCluster(gobject = visium_kidney, resolution = 0.4, n_iterations = 1000) plotUMAP(gobject = visium_kidney, cell_color = 'leiden_clus', show_NN_network = T, point_size = 2.5, save_param = list(save_name = '4_a_UMAP_leiden'))
part 5: co-visualize
# expression and spatial spatDimPlot(gobject = visium_kidney, cell_color = 'leiden_clus', dim_point_size = 2, spat_point_size = 2.5, save_param = list(save_name = '5_a_covis_leiden'))
spatDimPlot(gobject = visium_kidney, cell_color = 'nr_genes', color_as_factor = F, dim_point_size = 2, spat_point_size = 2.5, save_param = list(save_name = '5_b_nr_genes'))
part 6: cell type marker gene detection
gini
gini_markers_subclusters = findMarkers_one_vs_all(gobject = visium_kidney, method = 'gini', expression_values = 'normalized', cluster_column = 'leiden_clus', min_genes = 20, min_expr_gini_score = 0.5, min_det_gini_score = 0.5) topgenes_gini = gini_markers_subclusters[, head(.SD, 2), by = 'cluster']$genes # violinplot violinPlot(visium_kidney, genes = unique(topgenes_gini), cluster_column = 'leiden_clus', strip_text = 8, strip_position = 'right', save_param = c(save_name = '6_a_violinplot_gini', base_width = 5, base_height = 10))
# cluster heatmap plotMetaDataHeatmap(visium_kidney, selected_genes = topgenes_gini, metadata_cols = c('leiden_clus'), x_text_size = 10, y_text_size = 10, save_param = c(save_name = '6_b_metaheatmap_gini'))
# umap plots dimGenePlot2D(visium_kidney, expression_values = 'scaled', genes = gini_markers_subclusters[, head(.SD, 1), by = 'cluster']$genes, cow_n_col = 3, point_size = 1, save_param = c(save_name = '6_c_gini_umap', base_width = 8, base_height = 5))
scran
scran_markers_subclusters = findMarkers_one_vs_all(gobject = visium_kidney, method = 'scran', expression_values = 'normalized', cluster_column = 'leiden_clus') topgenes_scran = scran_markers_subclusters[, head(.SD, 2), by = 'cluster']$genes # violinplot violinPlot(visium_kidney, genes = unique(topgenes_scran), cluster_column = 'leiden_clus', strip_text = 10, strip_position = 'right', save_param = c(save_name = '6_d_violinplot_scran', base_width = 5))
# cluster heatmap plotMetaDataHeatmap(visium_kidney, selected_genes = topgenes_scran, metadata_cols = c('leiden_clus'), save_param = c(save_name = '6_e_metaheatmap_scran'))
# umap plots dimGenePlot(visium_kidney, expression_values = 'scaled', genes = scran_markers_subclusters[, head(.SD, 1), by = 'cluster']$genes, cow_n_col = 3, point_size = 1, save_param = c(save_name = '6_f_scran_umap', base_width = 8, base_height = 5))
part 7: cell-type annotation
Visium spatial transcriptomics does not provide single-cell resolution, making cell type annotation a harder problem. Giotto provides 3 ways to calculate enrichment of specific cell-type signature gene list:
- PAGE
- rank
- hypergeometric test
See the mouse Visium brain dataset for an example.
part 8: spatial grid
visium_kidney <- createSpatialGrid(gobject = visium_kidney, sdimx_stepsize = 400, sdimy_stepsize = 400, minimum_padding = 0) spatPlot(visium_kidney, cell_color = 'leiden_clus', show_grid = T, grid_color = 'red', spatial_grid_name = 'spatial_grid', save_param = c(save_name = '8_grid'))
part 9: spatial network
## delaunay network: stats + creation plotStatDelaunayNetwork(gobject = visium_kidney, maximum_distance = 400, save_param = c(save_name = '9_a_delaunay_network'))
visium_kidney = createSpatialNetwork(gobject = visium_kidney, minimum_k = 0) showNetworks(visium_kidney) spatPlot(gobject = visium_kidney, show_network = T, network_color = 'blue', spatial_network_name = 'Delaunay_network', save_param = c(save_name = '9_b_delaunay_network'))
part 10: spatial genes
Spatial genes
## kmeans binarization kmtest = binSpect(visium_kidney) spatGenePlot(visium_kidney, expression_values = 'scaled', genes = kmtest$genes[1:6], cow_n_col = 2, point_size = 1.5, save_param = c(save_name = '10_a_spatial_genes_km'))
## rank binarization ranktest = binSpect(visium_kidney, bin_method = 'rank') spatGenePlot(visium_kidney, expression_values = 'scaled', genes = ranktest$genes[1:6], cow_n_col = 2, point_size = 1.5, save_param = c(save_name = '10_b_spatial_genes_rank'))
Spatial co-expression patterns
## spatially correlated genes ## ext_spatial_genes = kmtest[1:500]$genes # 1. calculate gene spatial correlation and single-cell correlation # create spatial correlation object spat_cor_netw_DT = detectSpatialCorGenes(visium_kidney, method = 'network', spatial_network_name = 'Delaunay_network', subset_genes = ext_spatial_genes) # 2. identify most similar spatially correlated genes for one gene Napsa_top10_genes = showSpatialCorGenes(spat_cor_netw_DT, genes = 'Napsa', show_top_genes = 10) spatGenePlot(visium_kidney, expression_values = 'scaled', genes = c('Napsa', 'Kap', 'Defb29', 'Prdx1'), point_size = 3, save_param = c(save_name = '10_d_Napsa_correlated_genes'))
# 3. cluster correlated genes & visualize spat_cor_netw_DT = clusterSpatialCorGenes(spat_cor_netw_DT, name = 'spat_netw_clus', k = 8) heatmSpatialCorGenes(visium_kidney, spatCorObject = spat_cor_netw_DT, use_clus_name = 'spat_netw_clus', save_param = c(save_name = '10_e_heatmap_correlated_genes', save_format = 'pdf', base_height = 6, base_width = 8, units = 'cm'), heatmap_legend_param = list(title = NULL))
# 4. rank spatial correlated clusters and show genes for selected clusters netw_ranks = rankSpatialCorGroups(visium_kidney, spatCorObject = spat_cor_netw_DT, use_clus_name = 'spat_netw_clus', save_param = c(save_name = '10_f_rank_correlated_groups', base_height = 3, base_width = 5))
top_netw_spat_cluster = showSpatialCorGenes(spat_cor_netw_DT, use_clus_name = 'spat_netw_clus', selected_clusters = 6, show_top_genes = 1) # 5. create metagene enrichment score for clusters 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 visium_kidney = createMetagenes(visium_kidney, gene_clusters = cluster_genes, name = 'cluster_metagene') spatCellPlot(visium_kidney, spat_enr_names = 'cluster_metagene', cell_annotation_values = netw_ranks$clusters, point_size = 1.5, cow_n_col = 4, save_param = c(save_name = '10_g_spat_enrichment_score_plots', base_width = 13, base_height = 6))
# example for gene per cluster top_netw_spat_cluster = showSpatialCorGenes(spat_cor_netw_DT, use_clus_name = 'spat_netw_clus', selected_clusters = 1:8, show_top_genes = 1) first_genes = top_netw_spat_cluster[, head(.SD, 1), by = clus]$gene_ID cluster_names = top_netw_spat_cluster[, head(.SD, 1), by = clus]$clus names(first_genes) = cluster_names first_genes = first_genes[as.character(netw_ranks$clusters)] spatGenePlot(visium_kidney, genes = first_genes, expression_values = 'scaled', cow_n_col = 4, midpoint = 0, point_size = 2, save_param = c(save_name = '10_h_spat_enrichment_score_plots_genes', base_width = 11, base_height = 6))
part 11: HMRF domains
# HMRF requires a fully connected network! visium_kidney = createSpatialNetwork(gobject = visium_kidney, minimum_k = 2, name = 'Delaunay_full') # spatial genes my_spatial_genes <- kmtest[1:100]$genes # do HMRF with different betas hmrf_folder = paste0(results_folder,'/','11_HMRF/') if(!file.exists(hmrf_folder)) dir.create(hmrf_folder, recursive = T) HMRF_spatial_genes = doHMRF(gobject = visium_kidney, expression_values = 'scaled', spatial_network_name = 'Delaunay_full', spatial_genes = my_spatial_genes, k = 5, betas = c(0, 1, 6), output_folder = paste0(hmrf_folder, '/', 'Spatial_genes/SG_topgenes_k5_scaled')) ## view results of HMRF for(i in seq(0, 5, by = 1)) { viewHMRFresults2D(gobject = visium_kidney, HMRFoutput = HMRF_spatial_genes, k = 5, betas_to_view = i, point_size = 2) }
## alternative way to view HMRF results #results = writeHMRFresults(gobject = ST_test, # HMRFoutput = HMRF_spatial_genes, # k = 5, betas_to_view = seq(0, 25, by = 5)) #ST_test = addCellMetadata(ST_test, new_metadata = results, by_column = T, column_cell_ID = 'cell_ID') ## add HMRF of interest to giotto object visium_kidney = addHMRF(gobject = visium_kidney, HMRFoutput = HMRF_spatial_genes, k = 5, betas_to_add = c(0, 2), hmrf_name = 'HMRF') ## visualize spatPlot(gobject = visium_kidney, cell_color = 'HMRF_k5_b.0', point_size = 5, save_param = c(save_name = '11_a_HMRF_k5_b.0'))
Export and create Giotto Viewer
# check which annotations are available combineMetadata(visium_kidney) # select annotations, reductions and expression values to view in Giotto Viewer viewer_folder = paste0(results_folder, '/', 'mouse_visium_kidney_viewer') exportGiottoViewer(gobject = visium_kidney, output_directory = viewer_folder, spat_enr_names = 'PAGE', factor_annotations = c('in_tissue', 'leiden_clus'), numeric_annotations = c('nr_genes', 'clus_25'), dim_reductions = c('tsne', 'umap'), dim_reduction_names = c('tsne', 'umap'), expression_values = 'scaled', expression_rounding = 2, overwrite_dir = T)