1. 获得免疫浸润细胞组分的数据

如果是TCGA的数据，可以从TCGA网站获得计算好的免疫浸润细胞分数的数据，https://gdc.cancer.gov/about-data/publications/panimmune。
或者从cibersort网站利用自己整理好的数据进行计算，得出CIBERSORTx_Results。免疫细胞的名称可以调整也可以不用修改，检索出来的文章两种方式都有。

2. 数据整理

获得样本免疫细胞浸润分数的数据之后，下一步就是要根据自己的需要分组，然后再比较分组之间的差异。这里使用的是根据COX2基因log2TPM数据的中位数分组，分为高表达和低表达组。然后进行绘图。

1
2
3
4
5
6
7
8
9
10
11
12
rm(list = ls())
library(tidyverse)
load("log2_tpm_data.Rdata")#
cell_prop <- read_csv("CIBERSORTx_Results.csv")

#read_csv不会使列名的-变为.，但是得出的数据是tibble，如果需要改行名，还需要转换为data.frame

cell_prop$group <- ifelse(as.numeric(log2_tpm_data["COX2",])>median(as.numeric(log2_tpm_data["COX2",])), "high","low")#添加分组
cell_prop <- as.data.frame(cell_prop)
rownames(cell_prop) <- str_sub(cell_prop$Mixture, 1,12)
cell_prop <- cell_prop[,-1]

3. 绘图

3.1热图

1
2
3
4
5
6
7
8
9
10
11
12
#热图
library(pheatmap)
library(RColorBrewer)
png(file="heatmap.png")
annotation_col = data.frame(
  CellType =  cell_prop$group
)
rownames(annotation_col) = rownames(cell_prop)
pheatmap(t(cell_prop[,1:22]),
         annotation_col = annotation_col
         )
dev.off()

upload successful

具体的细节可以根据pheatmap包进行调整。

3.2 基于ggplot2的其他绘图

先将数据整理成ggplot适用的数据形式，这里用到了pivot_longer()函数，将免疫浸润细胞和比例整理成两列。

1
2
3
4
5
6
dd1 <- cell_prop %>% 
  as.data.frame() %>% 
  rownames_to_column("sample") %>% 
  pivot_longer(cols=2:23,
               names_to= "celltype",
               values_to = "Proportion")
1
2
3
4
5
library(ggplot2)
ggplot(dt1,aes(sample,Proportion,fill = celltype)) + 
  geom_bar(position = "stack",stat = "identity")+
  theme_bw()+
  guides(fill=guide_legend(ncol=1))

upload successful

3.3 各个细胞类型比例的箱式图

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
library(ggpubr)
# boxplot
ggboxplot(
  dt1,
  x = "celltype",
  y = "Proportion",
  color = "black",
  fill = "celltype",
  xlab = "",
  ylab = "Cell composition",
  main = "TME Cell composition"
) +
  theme(axis.text.x = element_text(
    angle = 90,
    hjust = 1,
    vjust = 1
  ))

upload successful

3.4 根据group分组的箱式图

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
ggboxplot(
  dt1,
  x = "celltype",
  y = "Proportion",
  color = "black",
  fill = "group",
  xlab = "",
  ylab = "Cell composition",
  main = "TME Cell composition group by COX2 expression"
) +
  stat_compare_means(
    aes(group = group),
    label = "p.signif", 
    method = "wilcox.test",
    hide.ns = T,
    size = 4.5
  ) +
  theme(axis.text.x = element_text(
    angle = 45,
    hjust = 1,
    vjust = 1
  ))

upload successful

3.5 将每种细胞组分分开进行绘图，采用分面的方法

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
gplot(data = dt1, aes(x = group, y = Proportion, fill = group)) +
  geom_boxplot()+
  stat_compare_means(aes(label = paste0("p = ", ..p.format..)), label.x = 1.35, vjust = 0.8, size = 4.5) +
  guides(fill = FALSE)+
  labs(x = "",
      y = "Cell composition fraction")+
  facet_wrap("celltype", scales = "free", nrow = 3)+
  theme(axis.title=element_text(face = 'bold',size=12),
        axis.text = element_text(size = 12, colour = 'black'),
        axis.ticks.length=unit(.2, "cm"),
        axis.ticks = element_line(colour = "black"),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black",size = 0, fill=NA),
        plot.margin = margin(1, 1, 1, 1, "cm"))

upload successful

有的免疫细胞的名称显示不全，可以通过保存成不同分辨率的tif格式的图来改变字体的相对大小，也可以在theme里面添加strip.text语句设置字体大小

1
strip.text = element_text(size = 10, face = "bold")

3.6 将每种细胞组分分开进行绘图，采用批量单个绘图的方法的方法。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
for (i in unique(dt1$celltype)){
  dt = filter(dt1,dt1$celltype == i)
  p = ggplot(data = dt, aes(x = group, y = Proportion, fill = group)) +
    geom_boxplot()+
    guides(fill = FALSE)+
    stat_compare_means(aes(label = paste0("p = ", ..p.format..)), label.x = 1.35, vjust = 0.8, size = 4.5) +
    labs(title = i,
      x = "",
         y = "Cell composition fraction")+
    theme(axis.title=element_text(face = 'bold',size=12),
          axis.text = element_text(size = 12, colour = 'black'),
          axis.ticks.length=unit(.2, "cm"),
          axis.ticks = element_line(colour = "black"),
          panel.background = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(colour = "black"),
          panel.border = element_rect(colour = "black",size = 0, fill=NA),
          plot.margin = margin(1, 1, 1, 1, "cm"))
  f = paste0(i, ".tif")
  png(filename = f, width = 320, height = 500, res = 100)
  print(p)
  dev.off()
}

upload successful

然后可以使用cowplot包将需要的图整合起来。