vignettes/tut12_giotto_cell_interaction.Rmd
tut12_giotto_cell_interaction.Rmdlibrary(Giotto) path_to_matrix = system.file("extdata", "seqfish_field_expr.txt", package = 'Giotto') path_to_locations = system.file("extdata", "seqfish_field_locs.txt", package = 'Giotto') my_giotto_object = createGiottoObject(raw_exprs = path_to_matrix, spatial_locs = path_to_locations) # processing my_giotto_object <- filterGiotto(gobject = seqfish_mini, expression_threshold = 0.5, gene_det_in_min_cells = 20, min_det_genes_per_cell = 0) my_giotto_object <- normalizeGiotto(gobject = my_giotto_object) # dimension reduction my_giotto_object <- calculateHVG(gobject = my_giotto_object) my_giotto_object <- runPCA(gobject = my_giotto_object) my_giotto_object <- runUMAP(my_giotto_object, dimensions_to_use = 1:5) # leiden clustering my_giotto_object = doLeidenCluster(my_giotto_object, name = 'leiden_clus') # annotate metadata = pDataDT(my_giotto_object) uniq_clusters = length(unique(metadata$leiden_clus)) clusters_cell_types = paste0('cell ', LETTERS[1:uniq_clusters]) names(clusters_cell_types) = 1:uniq_clusters my_giotto_object = annotateGiotto(gobject = my_giotto_object, annotation_vector = clusters_cell_types, cluster_column = 'leiden_clus', name = 'cell_types') # create network (required for binSpect methods) my_giotto_object = createSpatialNetwork(gobject = my_giotto_object, minimum_k = 2) # identify genes with a spatial coherent expression profile km_spatialgenes = binSpect(my_giotto_object, bin_method = 'kmeans')
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set.seed(seed = 2841) cell_proximities = cellProximityEnrichment(gobject = my_giotto_object, cluster_column = 'cell_types', spatial_network_name = 'Delaunay_network', adjust_method = 'fdr', number_of_simulations = 1000)
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# barplot cellProximityBarplot(gobject = my_giotto_object, CPscore = cell_proximities, min_orig_ints = 3, min_sim_ints = 3) # heatmap cellProximityHeatmap(gobject = my_giotto_object, CPscore = cell_proximities, order_cell_types = T, scale = T, color_breaks = c(-1.5, 0, 1.5), color_names = c('blue', 'white', 'red')) # network cellProximityNetwork(gobject = my_giotto_object, CPscore = cell_proximities, remove_self_edges = T, only_show_enrichment_edges = T) # network with self-edges cellProximityNetwork(gobject = my_giotto_object, CPscore = cell_proximities, remove_self_edges = F, self_loop_strength = 0.3, only_show_enrichment_edges = F, rescale_edge_weights = T, node_size = 8, edge_weight_range_depletion = c(1, 2), edge_weight_range_enrichment = c(2,5))
# Option 1 spec_interaction = "cell D--cell F" cellProximitySpatPlot2D(gobject = my_giotto_object, interaction_name = spec_interaction, show_network = T, cluster_column = 'cell_types', cell_color = 'cell_types', cell_color_code = c('cell D' = 'lightblue', 'cell F' = 'red'), point_size_select = 4, point_size_other = 2) # Option 2: create additional metadata my_giotto_object = addCellIntMetadata(my_giotto_object, spatial_network = 'Delaunay_network', cluster_column = 'cell_types', cell_interaction = spec_interaction, name = 'D_F_interactions') spatPlot(my_giotto_object, cell_color = 'D_F_interactions', legend_symbol_size = 3, select_cell_groups = c('other_cell D', 'other_cell F', 'select_cell D', 'select_cell F'))