跟着Nature Metabolism学作图:R语言ggplot2柱形图和下三角热图完整示例

论文

Single-cell profiling of vascular endothelial cells reveals progressive organ-specific vulnerabilities during obesity

https://www.nature.com/articles/s42255-022-00674-x#Sec58

s42255-022-00674-x.pdf

https://github.com/Osynchronika/sc_EC_obesity_atlas

大部分 作图的数据都有，可以试着用论文中提供的数据复现一下论文中的图

今天的推文重复一下论文中的Figure1e 柱形图 和 Figure1f的下三角热图

Figure1e的数据论文中是提供的，格式如下

这是3个柱形图的数据，需要我们手动整理成作图格式

柱形图的作图代码

df02<-read_excel("data/20230207/42255_2022_674_MOESM3_ESM.xlsx",
                 sheet = "Sheet1")
df02
df02$x<-factor(df02$x,levels = df02$x)

pe1<-ggplot()+
  geom_col(data=df02,aes(x=x,y=y),
           fill="red",color="black")+
  theme_classic()+
  scale_y_continuous(expand = expansion(mult=c(0,0)),
                     limits = c(0,120),
                     breaks = seq(0,120,20))+
  labs(x=NULL,y="Number of DEGs",title="Art")+
  theme(plot.title = element_text(hjust=0.5,face="bold"))


df03<-read_excel("data/20230207/42255_2022_674_MOESM3_ESM.xlsx",
                 sheet = "Sheet2")
df03
df03$x<-factor(df03$x,levels = df03$x)

pe2<-ggplot()+
  geom_col(data=df03,aes(x=x,y=y),
           fill="#46b198",color="black")+
  theme_classic()+
  scale_y_continuous(expand = expansion(mult=c(0,0)),
                     limits = c(0,900),
                     breaks = seq(0,900,300))+
  labs(x=NULL,y="Number of DEGs",title="Cap")+
  theme(plot.title = element_text(hjust=0.5,face="bold"))


df04<-read_excel("data/20230207/42255_2022_674_MOESM3_ESM.xlsx",
                 sheet = "Sheet3")
df04
df04$x<-factor(df04$x,levels = df04$x)

pe3<-ggplot()+
  geom_col(data=df04,aes(x=x,y=y),
           fill="#4472c4",color="black")+
  theme_classic()+
  scale_y_continuous(expand = expansion(mult=c(0,0)),
                     limits = c(0,350),
                     breaks = seq(0,350,50))+
  labs(x=NULL,y="Number of DEGs",title="Ven")+
  theme(plot.title = element_text(hjust=0.5,face="bold"))

三个柱形图的代码基本一样

下三角相关系数热图

这个论文中没有提供数据，我手动整理下来了格式如下

作图代码

library(readxl)
library(ggplot2)
library(tidyverse)
library(paletteer)
library(latex2exp)

df<-read_excel("data/20230207/figure1f.xlsx")
x_axis<-c('Brain','Heart','Lungs','Kidney','Liver','Vis AT')
y_axis<-c('Sc AT','Vis AT','Liver','Kidney','Lungs','Heart')

table(df$var1)
table(df$var2)


df<-df %>% 
  mutate(var1=factor(var1,levels = x_axis),
         var2=factor(var2,levels = y_axis))

txt.df<-data.frame(x=1:7,
                   y=7:1,
                   label=c('Brain','Heart','Lungs','Kidney','Liver','Vis AT','Sc AT'))
p1<-ggplot(data=df,aes(x=var1,y=var2))+
  geom_tile(aes(fill=value),
            color="black")+
  geom_text(aes(label=value))+
  geom_text(data=txt.df,
            aes(x=x,y=y,label=label))+
  #scale_x_discrete(expand = expansion(mult = c(0,0)))+
  #scale_y_discrete(expand = expansion(mult = c(0,0)))+
  theme_bw()+
  theme(axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        panel.border = element_blank(),
        legend.position = "left",
        axis.title = element_blank())+
  coord_cartesian(xlim = c(0,8),y=c(0,7))+
  scale_fill_gradient2(low="blue",
                       mid="white",
                       high="red",
                       breaks=c(-0.11,0,0.17),
                       name=TeX(r"(\textit{r} value)"),
                       midpoint=0)+
  guides(fill=guide_colorbar(barheight = 10,
                             ticks.colour = "black"))

p1

怎么把图例做成和论文中的一样我暂时想不到了，ggplot2这个这个图例好像只能是最小值和最大值，比如现在最大值是0.17,我先让图例映射到1，这个好像实现不了

做三个一样的，然后拼图

p1+
  labs(title="Art")+
  theme(plot.title = element_text(hjust=0.5,
                                  face="bold",
                                  size=20)) -> pA


p1+
  labs(title="Cap")+
  theme(plot.title = element_text(hjust=0.5,
                                  face="bold",
                                  size=20),
        legend.position = "none") -> pB

p1+
  labs(title="Ven")+
  theme(plot.title = element_text(hjust=0.5,
                                  face="bold",
                                  size=20),
        legend.position = "none") ->pC

library(patchwork)

pA+pB+pC

然后将柱形图和热图拼到一起

(pe1+pe2+pe3)/(pA+pB+pC)

示例数据和代码可以给推文点赞，然后点击在看，最后留言获取

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