Visualize Spatial Data with Voronoi Plots

Voronoi plots are an attractive way to visualize spatial expression data since it reduces whitespace between single cells or spots, however the voronoi borders do NOT necessarily mimic the true cell borders. Nevertheless it has been used to ‘segment’ cells and assign transcripts to individual cells. Here we merely use it as a beautiful visualization alternative to simple ‘points’.

1. Analysis of the mini seqFISH+ dataset

  • This dataset is part of the Giotto package

  • It is very small and easy to run

library(Giotto)

# input data
VC_exprs_small = read.table(system.file("extdata", "seqfish_field_expr.txt", package = 'Giotto'))
VC_locs_small = read.table(system.file("extdata", "seqfish_field_locs.txt", package = 'Giotto'))

# giotto object
VC_small <- createGiottoObject(raw_exprs = VC_exprs_small, spatial_locs = VC_locs_small)
VC_small <- filterGiotto(gobject = VC_small, expression_threshold = 0.5, gene_det_in_min_cells = 0, min_det_genes_per_cell = 0)
VC_small <- normalizeGiotto(gobject = VC_small, scalefactor = 6000, verbose = T)
VC_small <- addStatistics(gobject = VC_small)

# clustering
VC_small <- calculateHVG(gobject = VC_small)
gene_metadata = fDataDT(VC_small)
featgenes = gene_metadata[hvg == 'yes' & perc_cells > 4 & mean_expr_det > 0.5]$gene_ID
VC_small <- runPCA(gobject = VC_small, genes_to_use = featgenes, scale_unit = F)
VC_small <- runUMAP(VC_small, dimensions_to_use = 1:5, n_threads = 2)
VC_small <- createNearestNetwork(gobject = VC_small, dimensions_to_use = 1:5, k = 5)
VC_small <- doLeidenCluster(gobject = VC_small, resolution = 0.4, n_iterations = 1000)

# spatial grid and network
VC_small = createSpatialNetwork(gobject = VC_small, minimum_k = 2)
VC_small = createSpatialGrid(gobject = VC_small, sdimx_stepsize = 500, sdimy_stepsize = 500)

2. Example: Voronoi Plot

# spatial voronoi plot with selected clusters
spatPlot(VC_small, point_shape = 'voronoi', cell_color ='leiden_clus', select_cell_groups = c(1,2,3))
voronoi plots 
# spatial voronoi plot without showing not selected clusters
spatPlot(VC_small, point_shape = 'voronoi', cell_color ='leiden_clus', select_cell_groups = c(1,2,3), show_other_cells = F)
voronoi plots 
# spatial voronoi plot without showing not selected cells, but showing the voronoi borders
spatPlot(VC_small, point_shape = 'voronoi', cell_color ='leiden_clus', select_cell_groups = c(1,2,3), show_other_cells = F, vor_border_color = 'black')
voronoi plots 
# visualization of both dimension reduction and spatial results
spatDimPlot(gobject = VC_small, cell_color = 'leiden_clus', spat_point_shape = 'voronoi', dim_point_size = 3)
voronoi plots

3. Creating and Showing Neighbors

nb_annot = findNetworkNeighbors(VC_small,
                spatial_network_name = 'Delaunay_network',
                source_cell_ids = c('cell_1', 'cell_6', 'cell_10', 'cell_91', 'cell_92', 'cell_93'))
VC_small = addCellMetadata(VC_small, new_metadata = nb_annot, by_column = T, column_cell_ID = 'cell_ID')

spatPlot(VC_small, point_shape = 'voronoi', cell_color ='nb_cells',
     cell_color_code = c(source = 'blue', target = 'red', both = 'yellow', others = 'lightgrey'))
voronoi plots

4. Gene Expression and Voronoi Plots

## overlay gene expression information ##
 selected_genes = c('Cldn5', 'Soga3')

 # selected genes original
 spatGenePlot(gobject = VC_small, expression_values = 'scaled', genes = selected_genes, point_size = 3)
voronoi plots 
# selected genes voronoi
spatGenePlot(gobject = VC_small, genes = selected_genes, point_shape = 'voronoi',
         expression_values = 'scaled', vor_border_color = 'black')
voronoi plots 
# one gene + black background and white borders
spatGenePlot(gobject = VC_small, genes = 'Cldn5', point_size = 3, point_shape = 'voronoi',
         expression_values = 'scaled', vor_border_color = 'white', show_network = T, network_color = 'green',
         background_color = 'black', cell_color_gradient = c('blue', 'black', 'red'), gradient_midpoint = 1, cow_n_col = 1)
voronoi plots