Cell type interaction enrichment

cellProximityEnrichment function
cellProximityEnrichment <- function(gobject, spatial_network_name = 'Delaunay_network', cluster_column,number_of_simulations = 1000,adjust_method = c("none", "fdr", "bonferroni","BH","holm", "hochberg", "hommel","BY")) {

This function computes the cell-type to cell-type interactions. It outputs the number of interactions (i.e. interaction frequency) per cell-type pair, and the depletion/enrichment score per cell-type pair, to be defined as the observed over expected interactions.

The input is a spatial network defined by spatial_network_name. The cluster_column refers to the column in the cell metadata table containing the cell types to use. The number_of_simulations is the number of times the spatial network is simulated. In each simulation, the labels of the cells are reshuffled in the spatial network and the interactions are recounted. adjust_method uses various ways to correct for multiple hyothesis testing.

Returns a list of cell proximity scores in data.table format. The first (raw_sim_table) shows the raw observations of both the original and simulated networks. The second (enrichm_res) shows the enrichment results.

It is recommended to use the functions cellProximityBarplot, cellProximityHeatmap, cellProximityNetwork to visualize the enrichment/depletion result.

#seqFISH+ dataset
cell_proximities = cellProximityEnrichment(gobject = VC_test, cluster_column = 'cell_types',spatial_network_name = 'Delaunay_network',adjust_method = 'fdr',number_of_simulations = 2000)
cellProximityBarplot(gobject = VC_test, CPscore = cell_proximities, min_orig_ints = 5, min_sim_ints = 5)
cellProximityNetwork(gobject = VC_test, CPscore = cell_proximities, remove_self_edges = T, only_show_enrichment_edges = T)

findICG function
findICG = function(gobject, expression_values = 'normalized',selected_genes = NULL, cluster_column, spatial_network_name = 'Delaunay_network',minimum_unique_cells = 1, minimum_unique_int_cells = 1, diff_test = c('permutation', 'limma', 't.test', 'wilcox'), mean_method = c('arithmic', 'geometric'), offset = 0.1,adjust_method = c("bonferroni","BH", "holm", "hochberg", "hommel","BY", "fdr", "none"),nr_permutations = 100,exclude_selected_cells_from_test = T,do_parallel = TRUE, cores = NA)

findICG identifies genes that are differentially expressed due to proximity to other cell types. CPG stands for cell proximity genes. This analysis is particularly meaningful for whole transcriptome dataset such as seqFISH+.

The options cluster_column, spatial_network_name, adjust_method are explained previously (see cellProximityEnrichment()).

param explanations
minimum_unique_cells minimum number of target cells required
minimum_unique_int_cells minimum number of interacting cells required
diff_test which differential statistical test to use, among “permutation”, “limma”, “t.test”, or “wilcox”
nr_permutations applies if diff_test is set to “permutation”
mean_method whether to use arithmetic mean or geometric mean
offset for calculating log2 ratio
do_parallel whether or not to run in parallel using mcapply
cores number of cores when do_parallel=TRUE

Returns a data.table with the following information:

  1. genes (tested genes)
  2. target cell type
  3. interacting cell type
  4. number of cells for target cell type
  5. number of cells for interacting cell type
  6. average expression in interacting cells from the target cell type
  7. average expression in the non-interacting cells from the target cell type
  8. log2 fold change between (6) and (7)
  9. difference of expression between (6) and (7)
  10. pvalue
  11. p.adj
gene_metadata = fDataDT(VC_test)
high_expressed_genes = gene_metadata[mean_expr_det > 1.31]$gene_ID
CPGscoresHighGenes =  findICG(gobject = VC_test,selected_genes = high_expressed_genes,spatial_network_name = 'Delaunay_network', cluster_column = 'cell_types',diff_test = 'permutation',adjust_method = 'fdr',nr_permutations = 2000, do_parallel = T, cores = 2)
plotCellProximityGenes(VC_test, cpgObject = CPGscoresHighGenes, method = 'dotplot')