单细胞数据挖掘实战：文献复现（四）细胞比例饼图

单细胞数据挖掘实战：文献复现（一）批量读取数据

单细胞数据挖掘实战：文献复现（二）批量创建Seurat对象及质控

单细胞数据挖掘实战：文献复现（三）降维、聚类和细胞注释

前面对细胞进行了注释，在肿瘤样本中发现有很多Mo/MΦ细胞，这时需要借助图形来直观的表达，下面就来尝试画一下文献中的Fig. 1d。

一、加载R包

if(T){
  if(!require(BiocManager))install.packages("BiocManager")
  if(!require(Seurat))install.packages("Seurat")
  if(!require(Matrix))install.packages("Matrix")
  if(!require(ggplot2))install.packages("ggplot2")
  if(!require(cowplot))install.packages("cowplot")
  if(!require(magrittr))install.packages("magrittr")
  if(!require(dplyr))install.packages("dplyr")
  if(!require(purrr))install.packages("purrr")
  if(!require(ggrepel))install.packages("ggrepel")
  if(!require(ggpubr))install.packages("ggpubr")
}

二、读入数据

sex_condition_objects = readRDS("sex_condition_objects.RDS")

三、将细胞注释结果整理成一个EXCEL表并读入

前面得到了四个样本的注释结果，将它们整理成一个excel表，部分截图如下

1.png

注意这里cluster列和cell_type的命名需按照截图里的规则，不然后面的代码会报错，当然也可以根据自己的命名修改后面的代码。

cell_types<-read.csv("./anno_cell/cell_type_index.csv", header = T)

四、在sex_condition_objects中添加细胞类型

sex_condition_objects <- lapply(sex_condition_objects, function(x) {
  x$full_cluster_id <- paste(substring(x$shortID,12,12), x$condition, Idents(x), sep="_")
  x$cell_type <- cell_types[match(x$full_cluster_id, cell_types$cluster), "cell_type"]
  x$cell_type <- factor(x$cell_type, levels= c("micro", "pre-micro", "macro", "BAM", "NKT", "NK","B-cells", "T-cells","Ncam1+", "DC", "other"))
  x$cell_type_selection <- ""
  x$cell_type_selection[x$cell_type %in% c("micro", "pre-micro")] <- "Microglia"
  x$cell_type_selection[x$cell_type == "macro"] <- "Macrophages"
  x$cell_type_selection[x$cell_type == "BAM"] <- "BAM"
  x
})

五、画图

# Figure 1d（Pie charts）
# 定义细胞的颜色
micro<-"#53AFE6"
pre_micro<-"#2DA7C8"
BAM<- "#0DD1AD"
UN<-"grey"
Mo<-"#FCE80C"
Mo_Mg<-"#FABF00"
Mg<-"#E98934"
NK<-"#8c42a3"
ncam<-"#C2B4FC"
NKT<-"#DFA5F2"
DC<-"#bf7a58"
Tcells<-"#94112f" 
Bcells<-"#EC5CA5"

freq_list <- lapply(sex_condition_objects, function(x) {
  freq <- data.frame(cell_type = x$cell_type)
  freq <- freq %>%
    group_by(cell_type) %>%
    count() %>%
    ungroup %>%
    mutate(per = `n`/sum(`n`))
  freq$cell_type <- factor(freq$cell_type, levels= c("micro", "pre-micro", "macro", "BAM", "NKT","NK", "B-cells", "T-cells","Ncam1+", "DC", "other"))
  freq$label <- scales::percent(freq$per)
  freq
})

cf<-ggplot(freq_list$`GSM4039241-F-ctrl`, 
           aes(x="", y=per, fill=cell_type))+
  geom_bar(stat="identity", width=1, color="white")+
  coord_polar("y", start=0)+
  scale_fill_manual(values=c(micro, pre_micro, BAM))+
  theme_light()+
  geom_label_repel(aes(label = label), size=3, show.legend = F, nudge_x = 1)

cm<-ggplot(freq_list$`GSM4039245-M-ctrl`, 
           aes(x=" ", y=per, fill=cell_type))+
  geom_bar(stat="identity", width=1, color="white")+
  coord_polar("y", start=0)+
  scale_fill_manual(values=c(micro, pre_micro, BAM, NK, DC,UN))+
  theme_light()+
  geom_label_repel(aes(label = label), size=3, show.legend = F, nudge_x = 1)

tf<-ggplot(freq_list$`GSM4039243-F-tumor`, 
           aes(x="", y=per, fill=cell_type))+
  geom_bar(stat="identity", width=1, color="white")+
  coord_polar("y", start=0)+
  scale_fill_manual(values=c(micro, Mo_Mg, BAM,  NKT, NK, Bcells, Tcells, ncam, DC,UN))+
  theme_light()+
  geom_label_repel(aes(label = label), size=3, show.legend = F, nudge_x = 1)

tm<-ggplot(freq_list$`GSM4039247-M-tumor`, 
           aes(x="", y=per, fill=cell_type))+
  geom_bar(stat="identity", width=1, color="white")+
  coord_polar("y", start=0)+
  scale_fill_manual(values=c(micro, Mo_Mg, BAM,  NKT, Bcells,Tcells, DC, UN ))+
  theme_light()+
  geom_label_repel(aes(label = label), size=3, show.legend = F, nudge_x = 1)

pdf(file = "pie.pdf",width = 20,height = 10)
ggarrange(cf, cm, tf, tm, ncol = 4)
dev.off()

与文献中的图比较一下

2.png 3.png

每种细胞的比例跟文献中基本保持一致，在肿瘤样本中，MG仍然是最丰富的细胞群，但比例有所下降，出现了很多其它种类的细胞，这也就是肿瘤的异质性。

往期单细胞数据挖掘实战：

单细胞数据挖掘实战：文献复现（一）批量读取数据

单细胞数据挖掘实战：文献复现（二）批量创建Seurat对象及质控

单细胞数据挖掘实战：文献复现（三）降维、聚类和细胞注释