免疫浸润可视化

liyue 2021/6/30

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本次画图使用的数据来自于CIBERSOFT和ssGSEA的结果，需要自己根据给出的格式整理。

画个图

rm(list = ls())
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.1     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
load(file = "./rdata/coadread_cibersoft.RData")
res_cibersort1 <- as.data.frame(res_cibersort,stringsAsFactors = F)
knitr::kable(head(res_cibersort1))

#添加分组信息
coread_cibersoft <- res_cibersort1 %>% 
  rownames_to_column(var = "sample_id") %>% 
  mutate(sampletype = ifelse(as.numeric(as.character(str_sub(sample_id,14,15)))<10,
                             "tumor","normal")) %>% 
  select("P-value",Correlation,RMSE,sampletype,everything()) %>% 
  pivot_longer(cols = 6:27,names_to = "celltype",values_to = "proportion")
#save(coad_cibersoft,file = "coad_cibersoft.RData")
library(ggplot2)
library(ggpubr)
library(ggsci)
#直方图
dd1 <- coread_cibersoft
library(RColorBrewer)
mypalette <- colorRampPalette(brewer.pal(8,"Set1"))
ggplot(data =dd1, aes(x = sample_id, y = proportion,fill = celltype))+
  geom_bar(stat = "identity",position = "stack")+
  labs(fill = "Cell Type",x = "",y = "Estiamted Proportion") + 
  theme_bw() +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "bottom") + 
  scale_y_continuous(expand = c(0.01,0)) +
  scale_fill_manual(values = mypalette(22))

# 有顺序的箱线图
library(forcats)
ggplot(dd1,aes(fct_reorder(celltype, proportion),proportion,fill = celltype)) + 
  geom_boxplot(outlier.shape = 21,color = "black") + 
  theme_bw() + 
  labs(x = "Cell Type", y = "Estimated Proportion") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "bottom") + 
  scale_fill_manual(values = mypalette(22))

分组箱线图

dd1$Group = ifelse(as.numeric(str_sub(dd1$sample_id,14,15))<10,"tumor","normal")
library(ggpubr)
ggplot(dd1,aes(fct_reorder(celltype,proportion),proportion,fill = Group)) + 
  geom_boxplot(outlier.shape = 21,color = "black") + 
  theme_bw() + 
  labs(x = "Cell Type", y = "Estimated Proportion") +
  theme(legend.position = "top") + 
  theme(axis.text.x = element_text(angle=80,vjust = 0.5))+
  scale_fill_manual(values = mypalette(22)[c(6,1)])+ 
  stat_compare_means(aes(group = Group,label = ..p.signif..),method = "kruskal.test")

热图

library(pheatmap)
k <- apply(res_cibersort1[,-c(23:25)],2,function(x) {sum(x == 0) < nrow(res_cibersort1)/2})
table(k)

## k
## FALSE  TRUE 
##     7    15

re2 <- as.data.frame(t(res_cibersort1[,-c(23:25)][,k]))
Group <- ifelse(as.numeric(as.character(str_sub(rownames(res_cibersort1),14,15)))<10,  "tumor","normal")
an <- data.frame(group = Group,
                row.names = rownames(res_cibersort1))
pheatmap(re2,scale = "row",
         show_colnames = F,
         annotation_col = an,
         color = colorRampPalette(c("navy", "white", "firebrick3"))(50))

### 画图
rm(list = ls())
load(file = "./rdata/gsva_data_TCGA_COADREAD.Rdata")
coread_gsva <- as.data.frame(t(gsva_data))
test <- coread_gsva[1:10,1:10]
# 添加分组信息
library(tidyverse)
coread_gsva <- coread_gsva %>% 
  rownames_to_column(var = "sample_id") %>% 
  mutate(sampletype = ifelse(as.numeric(as.character(str_sub(sample_id,14,15)))<10,
                             "tumor","normal")) %>% 
  select(sampletype,everything())
#画图
dd1 <- coread_gsva %>% 
  pivot_longer(cols=3:30,
               names_to= "celltype",
               values_to = "NES")
library(ggplot2)
library(ggpubr)
library(ggsci)
#箱线图,还可以画小提琴图等
ggplot(data =dd1, aes(x = celltype, y = NES))+
  geom_boxplot(aes(color = sampletype),outlier.shape = NA)+
  scale_color_lancet()+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1, colour = "black"))+
  stat_compare_means(aes(group=sampletype), label = "p.signif")

获取更多R语言知识，请关注公众号：医学和生信笔记

医学和生信笔记 公众号主要分享：1.医学小知识、肛肠科小知识；2.R语言和Python相关的数据分析、可视化、机器学习等；3.生物信息学学习资料和自己的学习笔记！