单细胞免疫细胞基因marker

Marker Gene List

gene.list =c("CD3D",'CD3E','CD2',
'COL1A1','DCN','C1R',
'LYZ','CD68','TYROBP',
'CD79A','MZB1','MS4A1',
'CLDN5','FLT1','RAMP2',
'CPA3','TPSAB1','TPSB2',
'LILRA4','CXCR3','IRF7')


T.cell.marker = c("CD3D",'CD3E','CD2')
Fib.cell.marker = c('COL1A1','DCN','C1R')
Myeioid.cell.marker = c('LYZ','CD68','TYROBP')
B.cell.marker= c('CD79A','MZB1','MS4A1')
Endothelial.cell.marker= c('CLDN5','FLT1','RAMP2')
Mast.cell.marker= c('CPA3','TPSAB1','TPSB2')
DC.cell.marker= c('LILRA4','CXCR3','IRF7')

gene.list = c('LEF1','TCF7','SELL',
'IL7R','CD40LG','ANXA1','FOS','JUN',
'FOXP3','SAT1','IL2RA','CTLA4',
'PDCD1','CXCL13','CD200','TNFRSF18',
'CCR7','NELL2','CD55','KLF2',
'TOB1',
'GZMK','EOMES','ITM2C',
'GZMH',
'GZMB','LAG3','CCL4L2','CCL5',
'FCGR3A','FGFBP2','TYROBP','CX3CR1',
'AREG','XCL1','KLRC1','GNLY','KLRD1',
'TRDV2','TRGV9','MTRNR2L8',
'TRDV1','KLRC3','CTSW','CD7',
'MK167','STMN1','TUBA1B','HIST1H4C')

CD4.naive.marker = c('LEF1','TCF7','SELL')
CD4.effector.memory.marker = c('IL7R','CD40LG','ANXA1','FOS','JUN')
CD4.Trg.Reg.marker = c('FOXP3','SAT1','IL2RA','CTLA4')
CD4.experience.marker = c('PDCD1','CXCL13','CD200','TNFRSF18')
CD8.naive.marker = c('CCR7','NELL2','CD55','KLF2')
CD8.resident.memory.marker = c('TOB1')
CD8.effector.memory.marker = c('GZMK','EOMES','ITM2C')
CD8.recently.activate.effector.memory.marker = c('GZMH')
CD8.experience.marker = c( 'GZMB','LAG3','CCL4L2','CCL5')
NK.cytotoxic.marker = c('FCGR3A','FGFBP2','TYROBP','CX3CR1','GNLY','KLRD1')
NK.resting.marker = c('AREG','XCL1','KLRC1','GNLY','KLRD1')
Gama.marker = c('TRDV2','TRGV9','MTRNR2L8')
Semi.marker = c('TRDV1','KLRC3','CTSW','CD7')
Pro.marker = c('MK167','STMN1','TUBA1B','HIST1H4C')


# PBMC Marker
包括T细胞（CD3D，CD3E，CD3G，60.00％）
CD4 + T细胞（CD4，36.94％）
- Th1 细胞属于CD4 细胞属，（INF-γ）
CD8 + T细胞（CD8A , CD8B , 18.53%)
 自然杀伤 (NK) 细胞 (NCAM1或CD56，KLRB1，NKG7，5.44％）
 B细胞（CD19，MS4A1或CD20，CD38，22.27％）
 单核细胞（CD14，CD68，FCGR3A或CD16，9.60％）
 髓样树突状细胞（mDC的）（CD1C， 0.26%)
 浆细胞样树突细胞 (pDCs) ( LILRA4 , 0.18%)
 和造血干细胞和祖细胞 (HSPCs) ( CD34 , 0.12%)
 红细胞（HBB，0.99%）
 巨核细胞（PPBP）, 0.92%) 

Cell Annotion

### Cell Markers
T.cell.marker = c("CD3D",'CD3E','CD2')
Fib.cell.marker = c('COL1A1','DCN','C1R')
Myeioid.cell.marker = c('LYZ','CD68','TYROBP')
Cancer.cell.marker = c('CD24','KRT19','SCGB2A2')
B.cell.marker= c('CD79A','MZB1','MS4A1')
Endothelial.cell.marker= c('CLDN5','FLT1','RAMP2')
Mast.cell.marker= c('CPA3','TPSAB1','TPSB2')
DC.cell.marker= c('LILRA4','CXCR3','IRF7')

### Define CellType
cell_type = ls() %>% grep('marker',.,value = T) %>% gsub('\\.marker','',.)
markers =  ls() %>% grep('marker',.,value = T)

#cell_type = c('T-cell','Fibroblast','Myeloid cell','Cancer cell','B-cell','Endothelial cell','Mast cell',"pDC")
#markers = c("T.cell.marker","Fib.cell.marker","Myeioid.cell.marker","Cancer.cell.marker", "B.cell.marker","Endothelial.cell.marker", "Mast.cell.marker","DC.cell.marker")

  for(i in 1:length(cell_type)){
    p = DotPlot(sce, features = get(markers[i])) + coord_flip()
    ids = as.numeric(p$data[which(p$data$avg.exp.scaled > 0 ),]$id)-1
    ids = table(ids)[which(table(ids) >=2)] %>% names() %>% as.numeric()
    ids = rownames([email protected][which([email protected]$seurat_clusters %in% ids),])
    assign(cell_type[i], ids)
  }

# Cell types were preliminarily identified according to expression levels
rest = setdiff(rownames([email protected]),ids)

ids = NULL
for(i in cell_type){
  ids = paste0(ids,'ifelse(rownames([email protected]) %in% get(\'',i,'\'),\'',i,'\',')
}
ids = paste0(ids,"\'rest\'",str_c(rep(')',length(cell_type)),collapse=''))
[email protected]$cell_annotion = eval(parse(text = ids))


### Check Unique & consistency
check.unique = NULL
for(i in 1:7){
  for(j in (i+1):8){
    len = intersect(get(cell_type[i]),get(cell_type[j]))
    if(length(len) != 0 ){
      ids = c(cell_type[i],cell_type[j],markers[i],markers[j],length(len))
      check.unique = rbind(check.unique,ids)
    }
  }
}
check.unique


##### Manual Adjust
i=5 # for i in 1:dim(check.unique)[1]
ids1 = check.unique[i,1]
ids2 = check.unique[i,2]
gen1 = check.unique[i,3]
gen2 = check.unique[i,4]
ids = intersect(get(ids1),get(ids2))
DotPlot(sce[,ids], features = c(get(gen1),get(gen2)))  + coord_flip()
ids1
ids2
ids = [email protected][which([email protected]$seurat_clusters == 8),] %>% rownames() 
DotPlot(sce[,ids], features =gene.list)  + coord_flip()
DotPlot(sce[,rest], features =gene.list)  + coord_flip()


[email protected][which(sce$seurat_clusters == 19),'cell_annotion'] = "T-cell"


##### consistency
[email protected]$cell_annotion = str_replace_all([email protected]$cell_annotion, c("B-cell" = "B_cell",
                                               "Cancer cell" = "Cancer_cell",
                                               "Endothelial cell" = "Endothelial_cell",
                                               "Fibroblast" = "Fibroblast",
                                               "Mast cell" = "Mast_cell",
                                               "Myeloid cell" = "Myeloid_cell",
                                               "pDC" = "pDC",
                                               "T-cell" = "T_cell"))

### plot
p1 = DimPlot(sce,reduction = "tsne",label=T, group.by = "cell_annotion",cols=mycolors[6:15],label.size=2.5)
p2 = DimPlot(sce,reduction = "umap",label=T, group.by = "cell_annotion",cols=mycolors[6:15],label.size=2.5)
CombinePlots(plots =list(p1, p2))
ggsave('plot/Cohort1_cell_annotion_plot.png', width = 12, height = 4)

### ---------------
###
### Create: Yuan.Sh
### Date: 2021-10-14 23:52:41
### Email: [email protected]
### Blog: https://blog.csdn.net/qq_40966210
### Fujian Medical University
###
### ---------------

> 课题项目合作以及咨询请联系：[email protected]

After advisement, if you still have questions, you can send me an E-mail asking for help
Best Regards,
Yuan.SH
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please contact me via the following ways:
(a) E-mail: yuansh3354@gmail/163/outlook.com
(b) QQ: 1044532817
(c) WeChat: YuanSh181014
(d) Address: School of Basic Medical Sciences, 
Fujian Medical University, Fuzhou, 
Fujian 350108, China
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