tutorial cci

DESCRIPTION

@@ -11,7 +11,7 @@ License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Imports: 
     pheatmap,
     pbapply,

NAMESPACE

@@ -1,6 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
 export(CACIMAR_cols)
+export(Caculate_cell_pair_cci_score)
+export(Caculate_cell_pair_cci_score2)
 export(ChordDiagram)
 export(FormatConservedMarkers)
 export(Format_Markers_Frac)
@@ -10,25 +12,34 @@ export(Identify_ConservedCellTypes)
 export(Identify_ConservedMarkers)
 export(Identify_ConservedNetworks)
 export(Identify_Conserved_CCI)
+export(Identify_Conserved_CCI1)
+export(Identify_Conserved_CCI2)
 export(Identify_Conserved_LR)
 export(Identify_Markers)
-export(Merge_data_for_heatmap)
+export(Make_ChordDiagram_data)
+export(Make_ChordDiagram_data2)
 export(Plot_Celltype.Communication)
 export(Plot_MarkersHeatmap)
 export(Plot_Species_CellType_Tree)
 export(Plot_phylogenetic_tree)
 export(add_cellchat_prob)
 export(buildHomDatabase)
 export(calculate_Weights)
+export(conserved_interaction_score)
 export(create_sankey)
 export(get_average_expression)
 export(identify_conserved_gene)
 export(identify_conserved_marker)
 export(identify_conserved_pair)
 export(identify_ct_ConservedNetworks)
 export(identify_network_relationships)
+export(make_pheatmap_LR_data)
+export(make_pheatmap_LR_data1)
 export(perform_CCI_analysis)
-export(pheatmap_LR)
+export(pheatmap_LR1)
+export(pheatmap_LR_multi)
+export(phylogenetic_tree_circle)
+export(plot_interaction_heatmap)
 import(igraph)
 importFrom(Seurat,FindAllMarkers)
 importFrom(Seurat,GetAssayData)

R/Cell_Cell_interaction_sankey.R

@@ -154,6 +154,8 @@ create_sankey <- function(links,
 #' @param select_DB character, databased used for analysis, more option can run liana::show_resources()
 #' @param target_organism ncbi_taxid' or 'name' of the target organism. See ‘show_homologene' for available organisms via OmnipathR’s 'HomoloGene'
 #' @param method character, method use for cell-cell interaction analysis. By default, we set it to SingleCellSignalR
+#' @param LRscore_threshold numeric, the threshold the filter the cell-cell interactions, the larger the lesser interactions left
+#' @param scale_score logical, whether scale the score of the interaction,default is TRUE
 #'
 #' @return data frame of cell cell interaction analysis result
 #' @export
@@ -164,14 +166,18 @@ create_sankey <- function(links,
 #'
 # Zebrafish analysis
 #'SingleCellSignalR_zebrafish_result <- perform_CCI_analysis(seurat_obj=Zf_seurat, target_organism=7955)
-perform_CCI_analysis <- function(seurat_obj, expre_cell = 10, select_DB = "Consensus", target_organism, method = 'sca') {
+perform_CCI_analysis <- function(seurat_obj, expre_cell = 10, select_DB = "Consensus", target_organism, method = 'sca', LRscore_threshold = 0.5, scale_score = TRUE) {
   seurat_obj <- seurat_obj[apply(GetAssayData(seurat_obj, slot = "counts"), 1, function(x) sum(x > 0) > expre_cell),]
   op_resource <- liana::select_resource(select_DB)[[1]]
   ortholog_resource <- liana::generate_homologs(op_resource = op_resource, target_organism = target_organism)
   liana_result <- liana::liana_wrap(seurat_obj,
                                     method = method,
                                     resource = 'custom',
                                     external_resource = ortholog_resource)
+  liana_result <- liana_result[, LRscore > LRscore_threshold]
+  if (scale_score) {
+    liana_result$LRscore_scale <- liana_result$LRscore/sum(liana_result$LRscore)
+  }
   return(liana_result)
 }
 
@@ -206,7 +212,6 @@ calculate_Weights <- function(
                                                   specie_name = specie_name1)
   species2_all_weight_df_long <- calculateWeights(data = species2_cci,
                                                   specie_name = specie_name2)
-  # bind df
   all_weight_df_long <- rbind(species1_all_weight_df_long, species2_all_weight_df_long)
   all_weight_df_long <- subset(all_weight_df_long, weight > 0)
   all_weight_df_long$Source <- paste0(all_weight_df_long$species, all_weight_df_long$source)
@@ -219,9 +224,8 @@ calculate_Weights <- function(
   all_weight_df_long$target[which(all_weight_df_long$species == specie_name2)] <- edit_target
   return(all_weight_df_long)
 }
-
 calculateWeights <- function(data, specie_name) {
-  ccc_weight <- reshape2::dcast(data[, c(2, 3, 11)],
+  ccc_weight <- reshape2::dcast(data[, c("source", "target", "LRscore")],
                                 source ~ target,
                                 sum,
                                 value.var = 'LRscore')