跟着Nature Genetics学作图：R语言ggplot2普

论文

Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies

https://www.nature.com/articles/s41588-022-01051-w

本地pdf s41588-022-01051-w.pdf

代码链接

https://zenodo.org/record/6332981#.YroV0nZBzic

https://github.com/Jingning-Zhang/PlasmaProtein/tree/v1.2

今天的推文重复一下论文中的Figure3，涉及到4个图，普通箱线图，分组箱线图，箱线图分面，最后一个知识点是如何将这5个图组合到一起

首先是定义了ggplot2的主题

library(ggplot2)

My_Theme <- theme(
  panel.background = element_blank(), 
  title = element_text(size = 7),
  text = element_text(size = 6))

第一个普通的箱线图

部分示例数据集

image.png

读取数据集

library(readxl)
dat01<-read_excel("data/20220627/Fig3.xlsx",
                  sheet = "3a")

作图代码

p1 <- ggplot(data = dat01, aes(x = group)) + 
  geom_boxplot(alpha=0.6, 
               notch = TRUE, 
               notchwidth = 0.5, 
               aes(y=hsq, fill=kind)) +
  coord_cartesian(ylim = c(0,0.5)) +  
  labs(y = expression(paste("cis-",h^2)),
       x=NULL, title=NULL) +
  theme(legend.position="top",
        legend.title=element_blank(), 
        axis.text.x = element_text(color = c("#4a1486", 
                                             "#4a1486", 
                                             "#cb181d",
                                             "#cb181d"),
                                   vjust = 0.5, 
                                   hjust = 0.5, 
                                   angle = 15))+
  My_Theme+
  scale_fill_manual(values=c("#4a1486","#cb181d"))+
  theme(axis.line = element_line())
p1

image.png

分组箱线图

作图代码

dat02<-read_excel("data/20220627/Fig3.xlsx",
                  sheet = "3b")
head(dat02)

p2 <- ggplot(data = dat02, aes(x = group)) +
  geom_boxplot(alpha=0.8, 
               notch = TRUE, 
               notchwidth = 0.5, 
               aes(y=acc, fill=Model)) + 
  coord_cartesian(ylim = c(0,1.2)) +
  labs(title = NULL, x=NULL,
       y=expression(paste(R^2,"/cis-",h^2))) +
  theme(legend.position="top",
        axis.text.x = element_text(color = c("#4a1486", 
                                             "#4a1486", 
                                             "#cb181d",
                                             "#cb181d"),
                                   vjust = 0.5, 
                                   hjust = 0.5, 
                                   angle = 15))+
  My_Theme+
  scale_fill_manual(values=c("#feb24c","#41b6c4"))+
  theme(axis.line = element_line())
p2

箱线图分面

dat03<-read_excel("data/20220627/Fig3.xlsx",
                  sheet = "3c")
head(dat03)
p3 <- ggplot(data = dat03, aes(x = model)) + 
  geom_boxplot(alpha=0.8, 
               notch = TRUE, 
               notchwidth = 0.5, 
               aes(y=acc, fill=model)) + 
  facet_wrap(~race,  ncol=2)+
  labs(title = NULL, x=NULL,
       y=expression(paste(R^2,"/cis-",h^2))) +
  coord_cartesian(ylim = c(0,1.2))  +
  theme(axis.text.x = element_text(color = c("#238b45", 
                                             "#2171b5"),
                                   vjust = 0.5, 
                                   hjust = 0.5, 
                                   angle = 15),
        legend.position="none") +
  My_Theme+
  scale_fill_manual(values=c("#238b45","#2171b5"))+
  theme(axis.line = element_line(),
        panel.spacing.x = unit(0,'lines'),
        strip.background = element_rect(color="white"))
p3

这里两个小知识点，

• 默认分面两个图之间是有空白的，如果想没有这个空白可以在主题里进行设置 panel.spacing.x = unit(0,'lines')

• 两个图中间没有空白，上面灰色区域的地方如果想区分开，可以将边框颜色设置为白色strip.background = element_rect(color="white")

image.png

最后一个箱线图

dat04<-read_excel("data/20220627/Fig3.xlsx",
                  sheet = "3d")
head(dat04)
gtex.colors <- read_excel("data/20220627/gtex_colors.xlsx")
gtex.colors

myColors <- gtex.colors$V2
names(myColors) <- gtex.colors$V1
colScale <- scale_fill_manual(name = "gtex.colors", values = myColors)

p4 <- ggplot(data = dat04, aes(x = tissue, fill=tissue)) +
  geom_boxplot(alpha=0.8, 
               notch = TRUE, 
               notchwidth = 0.5, 
               aes(y=cor)) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1),
        legend.position="none",
        axis.title.y = element_text(hjust=1))+
  My_Theme+
  coord_cartesian(ylim = c(-0.25,1))+
  colScale +
  labs(x = "GTEx V7 tissue", 
       y = "Correlation between cis-regulated gene       \nexpression and plasma protein SOMAmers      ",
       title=NULL)+
  theme(axis.line = element_line())
p4

image.png

将四个图组合到一起

library(ggpubr)
p <- ggarrange(ggarrange(p1, p2,
                         p3,
                         ncol = 3, labels = c("a", "b","c"),
                         widths = c(0.29,0.4,0.31)),
               p4,
               nrow = 2, heights = c(0.5,0.5),
               labels = c(NA,"d"))
p

image.png

示例数据和代码可以自己到论文中获取，或者给本篇推文点赞，点击在看，然后留言获取

欢迎大家关注我的公众号

小明的数据分析笔记本

小明的数据分析笔记本 公众号 主要分享：1、R语言和python做数据分析和数据可视化的简单小例子；2、园艺植物相关转录组学、基因组学、群体遗传学文献阅读笔记；3、生物信息学入门学习资料及自己的学习笔记！