CD45_analysis_V1

1 Functions

2 Data

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

library("readxl")
signatures <- read_excel("./Data/Shengjin_et al_DataSheet_1.xlsx", sheet = "Gene Signature",skip = 42,col_names = F)
signatures = signatures %>% t() %>% as.data.frame() %>% janitor::row_to_names(1) %>% dplyr::filter(!row_number() %in% c(1))

3 log TMP signatures

acc_immune = FindVariableFeatures(object = acc_immune,nfeatures = 5000,verbose = F)
for (col in 1:ncol(signatures)) {
  genes = signatures[,col] %>% na.omit() %>% as.vector() %>% intersect(VariableFeatures(acc_immune))
  if (length(genes) == 0 ){
    print("skip")
    next
  }
  score = acc_immune@assays$RNA@data[genes,] %>% colMeans()
  acc_immune = AddMetaData(object = acc_immune,metadata = score, col.name = names(signatures)[col])
  print_tab(FeaturePlot(object = acc_immune,features = names(signatures)[col]),title = names(signatures)[col])

}

Activated_B_cell

Activated_CD4_T_cell

Activated_CD8_T_cell

Central_memory_CD4_T_cel

Central_memory_CD8_T_cell

Effector_memeory_CD4_T_cell

Effector_memeory_CD8_T_cell

Gamma_delta_T_cell

Immature_B_cell

Memory_B_cell

Regulatory_T_cell

T_follicular_helper_cell

Type_1_T_helper_cell

Type_17_T_helper_cell

Type_2_T_helper_cell

Activated_dendritic_cell

CD56bright_natural_killer_cell

[1] “skip” ## Eosinophil {.unnumbered }

Immature_dendritic_cell

Macrophage

Mast_cell

MDSC

Monocyte

Natural_killer_cell

Natural_killer_T_cell

Neutrophil

Plasmacytoid_dendritic_cell

NA

4 scaled data signatures

acc_immune = FindVariableFeatures(object = acc_immune,nfeatures = 5000,verbose = F)
for (col in 1:ncol(signatures)) {
  genes = signatures[,col] %>% na.omit() %>% as.vector() %>% intersect(VariableFeatures(acc_immune))
  if (length(genes) == 0 ){
    print("skip")
    next
  }
  score = acc_immune@assays$RNA@scale.data[genes,] %>% colMeans()
  acc_immune = AddMetaData(object = acc_immune,metadata = score, col.name = names(signatures)[col])
  print_tab(FeaturePlot(object = acc_immune,features = names(signatures)[col]),title = names(signatures)[col])

}

Activated_B_cell

Activated_CD4_T_cell

Activated_CD8_T_cell

Central_memory_CD4_T_cel

Central_memory_CD8_T_cell

Effector_memeory_CD4_T_cell

Effector_memeory_CD8_T_cell

Gamma_delta_T_cell

Immature_B_cell

Memory_B_cell

Regulatory_T_cell

T_follicular_helper_cell

Type_1_T_helper_cell

Type_17_T_helper_cell

Type_2_T_helper_cell

Activated_dendritic_cell

CD56bright_natural_killer_cell

[1] “skip” ## Eosinophil {.unnumbered }

Immature_dendritic_cell

Macrophage

Mast_cell

MDSC

Monocyte

Natural_killer_cell

Natural_killer_T_cell

Neutrophil

Plasmacytoid_dendritic_cell

# acc_immune <- AddModuleScore(
#   object = acc_immune,
#   features = list(genes),
#   ctrl = 5,
#   name = names(signatures)[col]
# )
# FeaturePlot(object = acc_immune,features = "Central_memory_CD4_T_cel1")

5 Clusters

acc_immune <- FindClusters(acc_immune, resolution = 1,verbose = F)
DimPlot(acc_immune,label = T)

lapply(c("dplyr","Seurat","HGNChelper"), library, character.only = T)
source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R"); source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R")

db_ = "https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/ScTypeDB_full.xlsx";
tissue = "Immune system" # e.g. Immune system,Pancreas,Liver,Eye,Kidney,Brain,Lung,Adrenal,Heart,Intestine,Muscle,Placenta,Spleen,Stomach,Thymus 

# prepare gene sets
gs_list = gene_sets_prepare(db_, tissue)

6 ScType

es.max = sctype_score(scRNAseqData = acc_immune[["RNA"]]@scale.data, scaled = TRUE, 
                      gs = gs_list$gs_positive, gs2 = gs_list$gs_negative) 
# merge by cluster
cL_resutls = do.call("rbind", lapply(unique(acc_immune@meta.data$seurat_clusters), function(cl){
    es.max.cl = sort(rowSums(es.max[ ,rownames(acc_immune@meta.data[acc_immune@meta.data$seurat_clusters==cl, ])]), decreasing = !0)
    head(data.frame(cluster = cl, type = names(es.max.cl), scores = es.max.cl, ncells = sum(acc_immune@meta.data$seurat_clusters==cl)), 10)
}))
sctype_scores = cL_resutls %>% group_by(cluster) %>% top_n(n = 1, wt = scores)  

# set low-confident (low ScType score) clusters to "unknown"
sctype_scores$type[as.numeric(as.character(sctype_scores$scores)) < sctype_scores$ncells/4] = "Unknown"
print(sctype_scores[,1:3])

acc_immune@meta.data$cell_identity = ""
for(j in unique(sctype_scores$cluster)){
  cl_type = sctype_scores[sctype_scores$cluster==j,]; 
  acc_immune@meta.data$cell_identity[acc_immune@meta.data$seurat_clusters == j] = as.character(cl_type$type[1])
}

DimPlot(acc_immune, reduction = "umap", label = TRUE, repel = TRUE, group.by = 'cell_identity')  

cL_resutls

acc_immune = SetIdent(object = acc_immune,value = "cell_identity")
g1_treat <- WhichCells(acc_immune, idents = c( "Macrophages"))
DimPlot(acc_immune, label=F, cells.highlight= g1_treat, cols.highlight = c( "red"), cols= "grey")

FindMarkers(object = acc_immune,ident.1 = "1",densify = T)

FeaturePlot(object = acc_immune,features = "GATA3")

scores_data = AverageExpression(object = acc_immune,features = names(signatures))
pheatmap(scores_data)

FeaturePlot(object = acc_immune,features = names(signatures)[1:6])
FeaturePlot(object = acc_immune,features = names(signatures)[7:13])
FeaturePlot(object = acc_immune,features = names(signatures)[14:17])
FeaturePlot(object = acc_immune,features = names(signatures)[20:26])
FeaturePlot(object = acc_immune,features = names(signatures)[27:28])

7 Immune markers

FeaturePlot(acc_immune, features = c("CD4","MS4A1", "SELL", "CD3E",  "S100A4","CD14","GNLY","MS4A1"))

8 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)

9 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")
print_tab(plt = FeaturePlot(acc_immune,features = "APM_score"),title = "score")

10 Exhaustion markers

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

11 Immune receptors

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

12 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)

13 significant interactions heatmap

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

14 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")

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")

15 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")

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")

16 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")

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")

17 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")

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")

18 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")

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")

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)