vignettes/tut14_giotto_signaling.Rmd
tut14_giotto_signaling.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 = my_giotto_object, 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')
\(~\)
LR_data = data.table::fread(system.file("extdata", "mouse_ligand_receptors.txt", package = 'Giotto')) LR_data[, ligand_det := ifelse(mouseLigand %in% my_giotto_object@gene_ID, T, F)] LR_data[, receptor_det := ifelse(mouseReceptor %in% my_giotto_object@gene_ID, T, F)] LR_data_det = LR_data[ligand_det == T & receptor_det == T] select_ligands = LR_data_det$mouseLigand select_receptors = LR_data_det$mouseReceptor ## get statistical significance of gene pair expression changes based on expression ## expr_only_scores = exprCellCellcom(gobject = my_giotto_object, cluster_column = 'cell_types', random_iter = 500, gene_set_1 = select_ligands, gene_set_2 = select_receptors) ## get statistical significance of gene pair expression changes upon cell-cell interaction spatial_all_scores = spatCellCellcom(my_giotto_object, spatial_network_name = 'Delaunay_network', cluster_column = 'cell_types', random_iter = 500, gene_set_1 = select_ligands, gene_set_2 = select_receptors, adjust_method = 'fdr', do_parallel = T, cores = 4, verbose = 'none')
\(~\)
## select top LR ## selected_spat = spatial_all_scores[p.adj <= 0.5 & abs(log2fc) > 0.1 & lig_nr >= 2 & rec_nr >= 2] data.table::setorder(selected_spat, -PI) top_LR_ints = unique(selected_spat[order(-abs(PI))]$LR_comb)[1:33] top_LR_cell_ints = unique(selected_spat[order(-abs(PI))]$LR_cell_comb)[1:33] plotCCcomHeatmap(gobject = my_giotto_object, comScores = spatial_all_scores, selected_LR = top_LR_ints, selected_cell_LR = top_LR_cell_ints, show = 'LR_expr') plotCCcomDotplot(gobject = my_giotto_object, comScores = spatial_all_scores, selected_LR = top_LR_ints, selected_cell_LR = top_LR_cell_ints, cluster_on = 'PI') ## * spatial vs rank #### comb_comm = combCCcom(spatialCC = spatial_all_scores, exprCC = expr_only_scores) # top differential activity levels for ligand receptor pairs plotRankSpatvsExpr(gobject = my_giotto_object, comb_comm, expr_rnk_column = 'exprPI_rnk', spat_rnk_column = 'spatPI_rnk', midpoint = 10) ## * recovery #### ## predict maximum differential activity plotRecovery(gobject = my_giotto_object, comb_comm, expr_rnk_column = 'exprPI_rnk', spat_rnk_column = 'spatPI_rnk', ground_truth = 'spatial')