單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復現(xiàn)（一）批量讀取數(shù)據(jù)

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復現(xiàn)（二）批量創(chuàng)建Seurat對象及質(zhì)控

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復現(xiàn)（三）降維、聚類和細胞注釋

前面對細胞進行了注釋，在腫瘤樣本中發(fā)現(xiàn)有很多Mo/MΦ細胞，這時需要借助圖形來直觀的表達，下面就來嘗試畫一下文獻中的Fig. 1d。

一、加載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、讀入數(shù)據(jù)

sex_condition_objects = readRDS("sex_condition_objects.RDS")

三、將細胞注釋結(jié)果整理成一個EXCEL表并讀入

前面得到了四個樣本的注釋結(jié)果，將它們整理成一個excel表，部分截圖如下

1.png

注意這里cluster列和cell_type的命名需按照截圖里的規(guī)則，不然后面的代碼會報錯，當然也可以根據(jù)自己的命名修改后面的代碼。

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

四、在sex_condition_objects中添加細胞類型

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、畫圖

# Figure 1d（Pie charts）
# 定義細胞的顏色
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()

與文獻中的圖比較一下

2.png

3.png

每種細胞的比例跟文獻中基本保持一致，在腫瘤樣本中，MG仍然是最豐富的細胞群，但比例有所下降，出現(xiàn)了很多其它種類的細胞，這也就是腫瘤的異質(zhì)性。

往期單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復現(xiàn)（一）批量讀取數(shù)據(jù)

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復現(xiàn)（二）批量創(chuàng)建Seurat對象及質(zhì)控

單細胞數(shù)據(jù)挖掘?qū)崙?zhàn)：文獻復現(xiàn)（三）降維、聚類和細胞注釋