科研搬砖日常，往往需要将各种图进行拼接，常用的组合包有patchwork、cowplot和gridExtra等。但是这几款都是类似将图片进行拼接，图与图之间的关系是平等的，尽管可通过调整参数突出主图，但参数相对较多，不好记忆；而aplot正好能解决对主图注释这一痛点。下面就来学习下吧。

核心函数：

• insert_left() insert_right() insert_top() insert_bottom()

• is.ggtree()

• plot_list()

• xlim2() ylim2()

• xrange() yrange()

1.核心函数

1.1 insert_**()

rm(list = ls())
library(ggplot2)
library(aplot)
p <- ggplot(mtcars,aes(mpg,disp))+
  geom_point()

p2 <- ggplot(mtcars,aes(mpg))+
  geom_density(fill="steelblue",alpha=0.5)+
  ggtree::theme_dendrogram()
p3 <- ggplot(mtcars,aes(x=1,y=disp))+
  geom_boxplot(fill="firebrick",alpha=0.5)+
  theme_void()
p+p2+p3

ap <- p %>% 
  insert_top(p2,height = 0.3) %>% 
  insert_right(p3,width = 0.1)
ap

1.2 xlim2

p1 <- ggplot(mtcars,aes(cyl))+
  geom_bar()
p2 <- ggplot(subset(mtcars,cyl!=4),aes(cyl))+
  geom_bar()
p1+p2

p2+xlim2(p1)

2.实战一：heatmap

rm(list = ls())
pacman::p_load(tidyverse,ggtree,aplot,reshape,ggExtra)

d <- matrix(rnorm(25),ncol = 5) %>% 
  data.frame()
rownames(d) <- paste0("g",1:5)
colnames(d) <- paste0("t",1:5)

hc <- hclust(dist(d))
hcc <- hclust(dist(t(d)))
phr <- ggtree(hc) #行聚类树
phc <- ggtree(hcc)+layout_dendrogram() #列聚类树

d$gene <- rownames(d)
dd <- melt(d)

p <- ggplot(dd,aes(variable,gene,fill=value))+
  geom_tile()+
  scale_fill_viridis_c()+
  scale_y_discrete(position = "right")+
  theme_minimal()+
  labs(x="",y="")

g <- ggplot(dplyr::filter(dd,gene!="g2"),
            aes(gene,value,fill=gene))+
  geom_boxplot()+
  coord_flip()+
  scale_fill_brewer(palette = "Set1")+
  theme_minimal()+
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank())+
  labs(x="",y="")

ca <- data.frame(variable=paste0("t",1:5),
                 A1=rep(LETTERS[1:2],times=c(3,2)),
                 A2=rep(letters[3:5],times=c(1,3,1)))
cad <- gather(ca,A1,A2,key = "anno",value = "type")

pc <- ggplot(cad,aes(variable,anno,fill=type))+
  geom_tile()+
  scale_y_discrete(position = "right")+
  theme_minimal()+
  theme(axis.text.x = element_blank(),
        axis.ticks.x=element_blank())+
  labs(x="",y="")

dp <- data.frame(gene=factor(rep(paste0("g",1:5),2)),
                 pathway=sample(paste0("pathway",1:5),10,replace = T))

pp <- ggplot(dp,aes(pathway,gene))+
  geom_point(size=5,color="steelblue")+
  theme_minimal()+
  theme(axis.text.x=element_text(angle=90,hjust=0),
        axis.text.y = element_blank(),
        axis.ticks.y=element_blank())+
  labs(x="",y="")

p %>% insert_left(phr,width = .3) %>% 
  insert_right(pp,width = .3) %>% 
  insert_right(g,width = .4) %>% 
  insert_top(pc,height =.1) %>% 
  insert_top(phc,height = .2)

看图确实让人感觉眼花缭乱的，但逻辑比较清晰，就是先用ggplot2绘制所有图，然后通过aplot将这些图放在主图的周围。

3.实战二：散点图+边际密度图

rm(list = ls())
library(ggsci)
pp <- ggplot(iris,aes(Sepal.Length,Sepal.Width,color=Species))+
  geom_point()+
  theme_bw()+
  scale_color_lancet()

x <- ggplot(iris,aes(Sepal.Length,fill=Species))+
  geom_density(alpha=0.5)+
  scale_fill_lancet()+
  expand_limits(x=0,y=0)+
  scale_y_continuous(expand = c(0,0))+
  theme_classic()+
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x=element_blank())+
  guides(fill=FALSE)

y <- ggplot(iris,aes(Sepal.Width,fill=Species))+
  geom_density(alpha=0.5)+
  scale_fill_lancet()+
  expand_limits(x=0,y=0)+
  scale_y_continuous(expand = c(0,0))+
  theme_classic()+
  theme(axis.title.y  = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y =element_blank())+
  guides(fill=FALSE)+
  coord_flip()

pp %>% 
  insert_right(y,width = .2) %>% 
  insert_top(x,height = .3)

还可以用ggExtra包直接展示

library(ggExtra)
p <- ggplot(iris,aes(Sepal.Length,Sepal.Width,color=Species))+
  geom_point()+
  guides(color=FALSE)+
  theme_bw()

ggMarginal(p,type = "density",groupFill = T)

参考链接：

1.R中的图片注释神包aplot

2.aplot: Decorate a ‘ggplot’ with Associated Information