Cell-cell interaction analysis and visualization

1. processing steps

library(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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2. Run Cell-cell interaction

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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3. Visualize Cell-cell interaction

# 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))

4. visualize interactions at the spatial level

# 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'))