跟着Nature Metabolism学作图:R语言ggplot2水平堆积柱形图并添加同组连线

原创于 2025-12-23 16:14:48 发布 · 428 阅读

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#r语言 #开发语言

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

今天的推文我们复现一下论文中的figure2b 水平堆积柱形图并添加连线

image.png

示例数据如下

image.png

读取数据

library(readxl)
library(tidyverse)
dat<-read_excel("data/20230207/figure2b.xlsx")  
dat

指定因子水平和配色

x_levels<-c("Prolif","EC-art","EC-cap1","EC-cap2",
            "EC-lymph1","EC-lymph2","EC-ven","EC-venule","EC-ang")

fill.colors<-c("#f09004","#e30528","#f5aaad","#ef7b64","#7ec7bd",
               "#63b32f","#527dbf","#8eb1de","#de2080")

堆积柱形图的代码

width<-0.4

dat %>% 
  pivot_longer(!group) %>% 
  mutate(name=factor(name,levels = c("Western","chow"))) %>% 
  mutate(group=factor(group,levels = rev(x_levels))) %>% 
  ggplot(aes(x=value,y=name))+
  geom_bar(aes(fill=group),
           stat="identity",
           position = "fill",
           width = width)+
  scale_fill_manual(values = rev(fill.colors)) -> p1

p1

image.png

计算添加线段的位置坐标

dat[match(x_levels,dat$group),] %>% 
  mutate(x1=cumsum(chow/sum(chow)),
         x2=cumsum(Western/sum(Western))) -> new.df

在p1的基础上添加线段并整体美化

p1+
  geom_segment(data=new.df,
               aes(x=x1,xend=x2,y=2-width/2,yend=1+width/2),
               lty="dashed",
               color="gray",
               size=1)+
  theme_classic()+
  scale_x_continuous(position = "top",
                     expand = expansion(mult=c(0,0)),
                     breaks = seq(0,1,by=0.1),
                     labels=c(0,"",20,"",40,"",60,"",80,"",100))+
  theme(axis.ticks.y = element_blank(),
        panel.grid.major.x = element_line(),
        axis.title = element_blank(),
        plot.title = element_text(hjust=0.5),
        axis.text.y = element_text(face="bold",size=25),
        legend.title = element_blank())+
  labs(title="Percentage of cells per cluster")