CD45_analysis_V1

1 Functions

2 Data

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

3 Immune markers

FeaturePlot(acc_immune, features = c("CD8A","MS4A1", "SELL", "CD3E",  "S100A4","CD14","GNLY","MS4A1"))

4 assign cell type

acc_immune <- RenameIdents(object = acc_immune, 
                               "0" = "Naive CD4+ T",
                               "1" = "Memory CD4+",
                               "2" = "CD14+ Mono",
                               "3" = "Memory CD4+",
                               "4" = "CD8+ T",
                               "5" = "B",
                               "6" = "Naive CD4+ T",
                               "7" = "Memory CD4+",
                               "8" = "Memory CD4+")
acc_immune$cell_identity = acc_immune@active.ident
DimPlot(object = acc_immune,label = T)

5 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

6 Exhaustion markers

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

7 Immune receptors

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

8 CellphoneDB

acc_cancer_cells = readRDS("/sci/labs/yotamd/lab_share/avishai.wizel/R_projects/ACC_microenv/Data/acc_cancer_no146_primaryonly15k_cancercells.rds")

# merge cancer and immune
common_genes = intersect(rownames(acc_cancer_cells),rownames(acc_immune))
acc_cancer_and_cd45 = merge(acc_cancer_cells[common_genes,],acc_immune[common_genes,])

  #write metadata

#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]
names(lum_or_myo)[1] = "cell_identity"

# combine
immune_identity =FetchData(object = acc_immune,vars = "cell_identity")
all_identity= rbind(lum_or_myo,immune_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)]


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

#write normalized counts
count_matrix = as.data.frame(acc_cancer_and_cd45@assays[["RNA"]]@data)
fwrite(count_matrix, file = "./Data/CellphoneDB/counts.txt",sep = "\t",row.names = T)

library(ktplots)
acc_cancer_and_cd45$cell_type = all_identity[,2,drop = F] # add cells identities to seurat

#read data:
pvals =  read.delim(file = "./Data/CellphoneDB/output/statistical_analysis_pvalues_07_19_2023_12:16:16.txt", check.names = FALSE)
means = read.delim(file = "./Data/CellphoneDB/output/statistical_analysis_means_07_19_2023_12:16:16.txt", check.names = FALSE)

9 significant interactions heatmap

plot_cpdb_heatmap(scdata = acc_cancer_and_cd45, idents = 'cell_type',pvals =  pvals,main = "Number of significant interactions",alpha = 0.05)

10 Costimulatory interactions

print_tab(plt = 
            plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Luminal', scdata = acc_cancer_and_cd45,
                      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 Luminal")
  ,title = "Luminal")

Luminal

print_tab(plt = 
            plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Myo', scdata = acc_cancer_and_cd45,
                      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

NA

11 Coinhibitory interactions

print_tab(plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Luminal', scdata = acc_cancer_and_cd45,
          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', cell_type2 = 'Myo', scdata = acc_cancer_and_cd45,
          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

NA

12 Chemokines interactions

print_tab(
  plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Luminal', scdata = acc_cancer_and_cd45,
          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', cell_type2 = 'Myo', scdata = acc_cancer_and_cd45,
          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

NA

13 Chemokine ligands

print_tab(plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Myo', scdata = acc_cancer_and_cd45,
    idents = 'cell_type', means = means, pvals = pvals,
 genes = c("CXCL1\\D", "CXCL2\\D","CXCL3\\D","CXCL17","C3","CXCL14"),return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F) 
 ,title = "Myo")

Myo

print_tab(plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Luminal', scdata = acc_cancer_and_cd45,
    idents = 'cell_type', means = means, pvals = pvals,
  genes = c("CXCL1\\D", "CXCL2\\D","CXCL3\\D","CXCL17","C3","CXCL14"),return_table = F,max_size = 4,p.adjust.method = "fdr" ,keep_significant_only = F) 
,title = "Luminal")

Luminal

NA

14 CCL22 and CCL28

print_tab(plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Myo', scdata = acc_cancer_and_cd45,
    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', cell_type2 = 'Luminal', scdata = acc_cancer_and_cd45,
    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

plot_cpdb(cell_type1 = 'CD', cell_type2 = 'Myo', scdata = acc_cancer_and_cd45,
    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 = 'CD', cell_type2 = 'Luminal', scdata = acc_cancer_and_cd45,
    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) 

NA
NA