CD45_analysis_V2

1 Functions

2 Data

acc_immune = LoadH5Seurat(file = "./Data/acc_immune_5KvarGenes_V2_cellIdentity.h5seurat")

acc_immune$origin  %<>%  gsub(pattern = "Primary",replacement = "pri")
ident_origin = FetchData(object = acc_immune,vars = c("origin","cell_identity"))
ident_origin$combine = paste(ident_origin$cell_identity , ident_origin$origin)
ident_origin$general_identity = ident_origin$cell_identity
acc_immune %<>% AddMetaData(metadata = ident_origin[,"combine", drop=F],col.name = "cell_identity")
DimPlot(acc_immune,group.by  = "cell_identity")

3 Antigen presenting machinery

apm_genes = c("HLA-A","HLA-B","HLA-C","B2M","TAP1","TAP2", "TAPBP")
apm_score = FetchData(acc_immune,vars = apm_genes,slot = "data") %>% rowMeans()
acc_immune = AddMetaData(object = acc_immune,metadata = apm_score,col.name = "APM_score")

print_tab(plt = FeaturePlot(acc_immune,features = apm_genes),title = "genes")

genes

print_tab(plt = FeaturePlot(acc_immune,features = "APM_score"),title = "score")

score

NA

4 Exhaustion markers

exhausted_genes = c("PDCD1","CD244","CD160","CTLA4","HAVCR2")
FeaturePlot(acc_immune,features = exhausted_genes)

5 Immune receptors

receptors = c("CCR3", "CCR4", "CCR10","CXCR2", "CXCR3", "CXCR4", "IL17A")
FeaturePlot(acc_immune,features = receptors)

6 CellphoneDB

acc_cancer_cells = readRDS("/sci/labs/yotamd/lab_share/avishai.wizel/R_projects/ACC_microenv/Data/acc_tpm_nCount_mito_no146_15k_cancercells.rds")
acc_caf = readRDS("/sci/labs/yotamd/lab_share/ACC/ACC_sc/analysis/acc_tpm_nCount_mito_no146_cafs.rds")

# merge cancer, immune and CAF
common_genes = rownames(acc_cancer_cells) %>% intersect (rownames(acc_immune)) %>% intersect(rownames(acc_caf_cells))
acc_cancer_and_cd45 = merge(acc_cancer_cells[common_genes,],acc_immune[common_genes,])
overlapping_cells = colnames(acc_cancer_cells) %>% intersect(colnames(acc_caf)) 
acc_cancer_cd45_caf = merge(acc_cancer_and_cd45[common_genes,],acc_caf_cells[common_genes,!colnames(acc_caf_cells) %in% overlapping_cells] )

#create origin metadata in acc
ln_plates = c("ACC22.LN.P11", "ACC22.P12.LN",  "ACC7.P13" )
acc_origin = FetchData(object = acc_cancer_cells,vars = "orig.ident") %>% mutate(origin = if_else(condition = orig.ident %in% ln_plates,true = "LN",false = "pri"))
acc_cancer_cells %<>% AddMetaData(metadata = acc_origin[,"origin",drop=F],col.name = "origin")

#create origin metadata in caf
ln_plates = c("ACC22.LN.P11", "ACC22.P12.LN",  "ACC7.P13" )
caf_origin = FetchData(object = acc_caf,vars = "orig.ident") %>% mutate(origin = if_else(condition = orig.ident %in% ln_plates,true = "LN",false = "pri"))
acc_caf %<>% AddMetaData(metadata = caf_origin[,"origin",drop=F],col.name = "origin")

  #create metadata

#create luminal_over_myo
gs=acc_cancer_cells@assays$RNA@var.features
myoscore=apply(acc_cancer_cells@assays$RNA@scale.data[intersect(c("TP63","TP73","CAV1","CDH3","KRT5","KRT14","ACTA2","TAGLN","MYLK","DKK3"),gs),],2,mean)
lescore=apply(acc_cancer_cells@assays$RNA@scale.data[intersect(c("KIT","EHF","ELF5","KRT7","CLDN3","CLDN4","CD24","LGALS3","LCN2","SLPI"),gs),],2,mean)
acc_cancer_cells=AddMetaData(acc_cancer_cells,lescore-myoscore,"luminal_over_myo")

#create lum_or_myo
lum_over_myo = FetchData(object = acc_cancer_cells,vars = "luminal_over_myo")
lum_over_myo$lum_or_myo = "Unknown"
lum_over_myo$lum_or_myo [lum_over_myo$luminal_over_myo>1]  = "Luminal"
lum_over_myo$lum_or_myo [lum_over_myo$luminal_over_myo<(-1)]  = "Myo"
lum_or_myo = lum_over_myo[,"lum_or_myo",drop = F]

lum_or_myo$combine = paste(lum_or_myo$lum_or_myo , acc_cancer_cells$origin) #add environment
acc_cancer_cells %<>% AddMetaData(metadata = lum_or_myo[,"combine",drop=F],col.name = "cell_identity")

#add envirinment to CAF identity
acc_caf$cell_identity = paste(acc_caf$cell.type , acc_caf$origin) #add environment

#create  metadata

# combine
immune_identity =FetchData(object = acc_immune,vars = "cell_identity")
caf_identity =FetchData(object = acc_caf,vars = "cell_identity")
cancer_identity =FetchData(object = acc_cancer_cells,vars = "cell_identity")%>% filter(!cell_identity %in% c("Unknown pri","Unknown LN")) #remove unknown cells

all_identity = do.call("rbind", list(cancer_identity, immune_identity, caf_identity))

#rename and sort columns
all_identity$barcode_sample = rownames(all_identity)
all_identity = all_identity %>% rename(cell_type = cell_identity)
all_identity = all_identity[,c(2,1)]

a = data.frame(cell_type = all_environment$cell_type %>% unique()) %>% mutate(microenviroment = if_else(condition = grepl(pattern = all_environment$cell_type, "LN"),true = "LN",false = "pri"))

Warning: Problem while computing `microenviroment = if_else(...)`.
ℹ argument 'pattern' has length > 1 and only the first element will be used

write.table(x = all_identity,file = "./Data/CellphoneDB/input_V2/metadata.tsv",row.names =F,sep = "\t")
write.table(x = all_environment,file = "./Data/CellphoneDB/input_V2/enviroment.tsv",row.names =F,sep = "\t")

acc_cancer_cd45_caf = acc_cancer_cd45_caf[,rownames(all_identity)]
fwrite(as.data.frame(acc_cancer_cd45_caf@assays[["RNA"]]@data), file = "./Data/CellphoneDB/input_V2/counts.txt",sep = "\t",row.names = T)

trace(plot_cpdb_heatmap,edit = T)
untrace(plot_cpdb_heatmap)
undebug(plot_cpdb_heatmap)
mat = plot_cpdb_heatmap(scdata = acc_cancer_cd45_caf, idents = 'cell_type',pvals =  pvals,main = "Number of significant interactions",alpha = 0.05,treeheight_row = 50,return_tables = T)

rownames(all_environment) = all_environment$cell_type
all_environment$cell_type <- NULL

ln_types = all_environment %>% filter(microenviroment == "LN") %>% rownames()
pri_types = all_environment %>% filter(microenviroment == "pri") %>% rownames()

print_tab(plt =  ComplexHeatmap::pheatmap(mat$count_network[pri_types,pri_types])
          ,title = "primary significant interactions")

primary significant interactions

print_tab(plt = ComplexHeatmap::pheatmap(mat$count_network[ln_types,ln_types])
          ,title = "LN significant interactions")

LN significant interactions

NA

uniq_interactions <- function(cell_type1, cell_type2_a ,cell_type2_b, gene.family = NULL,genes = NULL) {
  require(purrr)
  a = plot_cpdb(cell_type1 = cell_type1, cell_type2 =cell_type2_a, scdata = acc_cancer_cd45_caf,
                      idents = 'cell_type', means = means, pvals = pvals,
                      gene.family = gene.family,return_table = T, p.adjust.method = "fdr",keep_significant_only = F, genes = genes)
  
  b = plot_cpdb(cell_type1 = cell_type1, cell_type2 = cell_type2_b, scdata = acc_cancer_cd45_caf,
                      idents = 'cell_type', means = means, pvals = pvals,
                      gene.family = gene.family,return_table = T,p.adjust.method = "fdr",keep_significant_only = F, genes = genes)
  
  
  all = list()
  i=0
  nelements = a$Var2 %>% unique() %>% length()
  a_couples = a$Var2 %>% unique()%>% as.vector()  
  b_couples = b$Var2 %>% unique()%>% as.vector()  
  for (i in 1:nelements) {
    sig_couples_a = a %>% filter(Var2 == a_couples[i]) %>% filter(pvals_adj <= 0.05) %>%  pull(Var1) %>% as.vector()  
    sig_couples_b = b %>% filter(Var2 == b_couples[i]) %>% filter(pvals_adj <= 0.05) %>%  pull(Var1) %>% as.vector()  
    only_in_a = sig_couples_a[! sig_couples_a %in% sig_couples_b]
    all[[a_couples[i]]] = only_in_a 
    i = i+1
  }
  all = t(map_dfr(all, ~as_tibble(t(.)))) %>% as.data.frame() %>%  set_names(a_couples)
  return(all)
}

7 Costimulatory interactions

print_tab(plt = 
            plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Luminal', scdata = acc_cancer_cd45_caf,
                      idents = 'cell_type', means = means, pvals = pvals,
                      gene.family = 'costimulatory',return_table = F,max_size = 3,p.adjust.method = "fdr",keep_significant_only = T,cluster_rows = T)+
            ggtitle("Costimulatory Luminal")
  ,title = "Luminal")

Luminal

print_tab(plt = 
            plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Myo', scdata = acc_cancer_cd45_caf,
                      idents = 'cell_type', means = means, pvals = pvals,
                      gene.family = 'costimulatory',return_table = F,max_size = 3,p.adjust.method = "fdr",keep_significant_only = T,cluster_rows = F)+
            ggtitle("Costimulatory Myo")
  ,title = "Myo")

Myo

print_tab(plt = 
            plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'CAF', scdata = acc_cancer_cd45_caf,
                      idents = 'cell_type', means = means, pvals = pvals,
                      gene.family = 'costimulatory',return_table = F,max_size = 3,p.adjust.method = "fdr",keep_significant_only = T,cluster_rows = F)+
            ggtitle("Costimulatory CAF")
  ,title = "CAF")

CAF

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal pri',cell_type2_b = "Myo",gene.family = "Costimulatory")
,title = "unique in luminal pri")

unique in luminal pri

Loading required package: purrr

Warning message: The x argument of as_tibble.matrix() must have unique column names if .name_repair is omitted as of tibble 2.0.0. Using compatibility .name_repair. This warning is displayed once every 8 hours. Call lifecycle::last_lifecycle_warnings() to see where this warning was generated.

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo pri',cell_type2_b = "Luminal",gene.family = "Costimulatory")
,title = "unique in myo pri")

unique in myo pri

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal LN',cell_type2_b = "Myo",gene.family = "Costimulatory")
,title = "unique in luminal LN")

unique in luminal LN

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo LN',cell_type2_b = "Luminal",gene.family = "Costimulatory")
,title = "unique in myo LN")

unique in myo LN

NA

8 Coinhibitory interactions

print_tab(plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Luminal', scdata = acc_cancer_cd45_caf,
          idents = 'cell_type', means = means, pvals = pvals,
          gene.family = 'coinhibitory',return_table = F,max_size = 4,p.adjust.method = "fdr",keep_significant_only = F,cluster_rows = F)+
  ggtitle("coinhibitory Luminal"),title = "Luminal")

Luminal

print_tab(plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Myo', scdata = acc_cancer_cd45_caf,
          idents = 'cell_type', means = means, pvals = pvals,
          gene.family = 'coinhibitory',return_table = F,max_size = 4,p.adjust.method = "fdr",keep_significant_only = F,cluster_rows = F)+
  ggtitle("coinhibitory Myo"),title = "Myo")

Myo

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal LN',cell_type2_b = "Myo LN",gene.family = "coinhibitory")
,title = "unique in luminal LN")

unique in luminal LN

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo LN',cell_type2_b = "Luminal LN",gene.family = "coinhibitory")
,title = "unique in myo LN")

unique in myo LN

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal pri',cell_type2_b = "Myo pri",gene.family = "coinhibitory")
,title = "unique in luminal pri")

unique in luminal pri

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo pri',cell_type2_b = "Luminal pri",gene.family = "coinhibitory")
,title = "unique in myo pri")

unique in myo pri

NA

9 Chemokines interactions

print_tab(
  plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Luminal', scdata = acc_cancer_cd45_caf,
          idents = 'cell_type', means = means, pvals = pvals,
          gene.family = 'chemokines',return_table = F,max_size = 4,p.adjust.method = "fdr",keep_significant_only = F,cluster_rows = F)+
  ggtitle("chemokines Luminal"),title = "Luminal")

Luminal

print_tab(
  plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Myo', scdata = acc_cancer_cd45_caf,
          idents = 'cell_type', means = means, pvals = pvals,
          gene.family = 'chemokines',return_table = F,max_size = 4,p.adjust.method = "fdr",keep_significant_only = F,cluster_rows = F)+
  ggtitle("chemokines Myo"),title = "Myo")

Myo

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal LN',cell_type2_b = "Myo LN",gene.family = "coinhibitory")
,title = "unique in luminal LN")

unique in luminal LN

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo LN',cell_type2_b = "Luminal LN",gene.family = "coinhibitory")
,title = "unique in myo LN")

unique in myo LN

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal pri',cell_type2_b = "Myo pri",gene.family = "coinhibitory")
,title = "unique in luminal pri")

unique in luminal pri

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo pri',cell_type2_b = "Luminal pri",gene.family = "coinhibitory")
,title = "unique in myo pri")

unique in myo pri

NA

10 Chemokine ligands

genes = c("CXCL1\\D", "CXCL2\\D","CXCL3\\D","CXCL17","C3","CXCL14")
print_tab(plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Myo', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
 genes = genes,return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F) 
 ,title = "Myo")

Myo

print_tab(plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Luminal', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
  genes = genes,return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F) 
,title = "Luminal")

Luminal

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal LN',cell_type2_b = "Myo LN",genes = genes)
,title = "unique in luminal LN")

unique in luminal LN

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo LN',cell_type2_b = "Luminal LN",genes = genes)
,title = "unique in myo LN")

unique in myo LN

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Luminal pri',cell_type2_b = "Myo pri",genes = genes)
,title = "unique in luminal pri")

unique in luminal pri

print_tab(plt = 
            uniq_interactions(cell_type1 = 'CD|B|Macro|mono',cell_type2_a  = 'Myo pri',cell_type2_b = "Luminal pri",genes = genes)
,title = "unique in myo pri")

unique in myo pri

NA

11 CCL22 and CCL28

print_tab(plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Myo', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
 genes = c("CCL22", "CCL28" ),return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F),title = "Myo")

Myo

print_tab(plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Luminal', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
  genes = c("CCL22", "CCL28" ),return_table = F,max_size = 6,p.adjust.method = "fdr" ,keep_significant_only = F),title = "Luminal")

Luminal

NA

12 Notch signaling interactions with immune

plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Myo', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
 genes =c("JAG", "MYB" , "NOTCH","HES1","HEY"),return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F) 

plot_cpdb(cell_type1 = 'CD|B|Macro|mono', cell_type2 = 'Luminal', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
  genes = c("JAG", "MYB" , "NOTCH","HES1","HEY"),return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F) 

13 Notch signaling interactions with CAF

plot_cpdb(cell_type1 = 'CAF', cell_type2 = 'Myo', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
 genes = c("JAG", "MYB" ),return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F) 

plot_cpdb(cell_type1 = 'CAF', cell_type2 = 'Luminal', scdata = acc_cancer_cd45_caf,
    idents = 'cell_type', means = means, pvals = pvals,
  genes = c("JAG", "MYB" , "NOTCH","HES1","HEY"),return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F)