听说你还缺单细胞亚群标记基因

生信技能树

发布于 2021-05-27 15:48:08

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代码可运行

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我们做肿瘤研究的单细胞数据，一般来说初步定义细胞亚群，第一次分群是通用规则，按照：

immune (CD45+,PTPRC),
epithelial/cancer (EpCAM+,EPCAM),
stromal (CD10+,MME,fibo or CD31+,PECAM1,endo)

然后绝大部分都是抓住免疫细胞亚群进行细分，包括淋巴系（T,B,NK细胞）和髓系（单核，树突，巨噬，粒细胞）的两大类作为第二次细分亚群。第三次细分亚群就会深入到T淋巴细胞的CD8和CD4细分，这个时候很多人就很难搞起来各个细分亚群的标记基因了，最近（ 06 May 2021）出来了一个文章：《A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer》，链接是：https://www.nature.com/articles/s41591-021-01323-8

这方面就做的很细致，如下所示的基因：

但是这么多基因名字，如果你也想把它用起来，得一个个手敲。这当然是不符合咱们现代人的习惯啦，我们善假于物，所以我随手找了一个网页工具：http://www.ocrmaker.com/

结果可以接受！其实主要是1和I这两个很难区分，修改回来即可：

The following requested variables were not found (10 out of 13 shown): 
LEFI, ANXAI, SATI, PDCDI, TOBI, FCGRGA, KLRCI, KLRDI, TRDVI, MK167

需要一些手动修改，最后绘图代码如下：

rm(list=ls())
options(stringsAsFactors = F)
library(Seurat)
load(file = 'main_sce_recluster.Rdata')
marker_genes= c("LEF1","TCF7","SELL","IL7R","CD40LG","ANXA1","FOS",
                "JUN","FOXP3","SAT1","IL2RA","CTLA4","PDCD1","CXCL13","CD200",
                "TNFRSF18","CCR7","NELL2","CD55","KLF2","TOB1","ZNF683","CCL5",
                "GZMK","EOMES","ITM2C","CX3CR1","GNLY","GZMH","GZMB","LAG3","CCL4L2",
                "FCGR3A","FGFBP2","TYROBP","AREG","XCL1","KLRC1","TRDV2","TRGV9","MTRNR2L8",
                "KLRD1","TRDV1","KLRC3",
                "CTSW","CD7","MKI67","STMN1","TUBA1B","HIST1H4C" )


p <- DotPlot(sce, features = marker_genes,
             assay='RNA' ,group.by = 'cellSubType' )  + coord_flip() +
  theme(axis.text.x = element_text(angle = 90))

p
ggsave('check_g1_markers_by_cellSubType.pdf')


marker_genes =c("CD3D","CD4","CD8A","CCR7","LEF1","SELL","TCF7","GNLY","IFNG","NKG7","PRF1",
   "GZMA","GZMB","GZMH","GZMK","HAVCR2","LAG3","PDCD1","CTLA4","TIGIT","BTLA","KLRC1",
   "ANXA1","ANKRD28","CD69","CD40LG","ZNF683","FOXP3","IL2RA","IKZF2","NCR1","NCAM1",
   "TYROBP","KLRD1","KLRF1","KLRB1","CX3CR1","FCGR3A",
   "XCL1","XCL2","TRDV2","TRGV9","TRGC2","MKI67","TOP2A")

p <- DotPlot(sce, features = marker_genes,
             assay='RNA' ,group.by = 'cellSubType' )  + coord_flip() +
  theme(axis.text.x = element_text(angle = 90))

p
ggsave('check_g2_markers_by_cellSubType.pdf')

可以看到效果还不错！

如果你也有T淋巴细胞的CD8和CD4细分需求，这些基因拿去使用吧！

工具并不是万能的

比如当我想上传的图片里面的基因名字有一些重叠的时候：

这个时候肉眼其实是可以区分的，所以你还可以退而求其次，选择比较廉价的本科生帮你手敲这些基因名字哦！

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原始发表：2021-05-14，如有侵权请联系 cloudcommunity@tencent.com 删除

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