复现《nature communications》散点小提琴图+蜜蜂图

今天我们学做一下NC文章的小提琴图，有小提琴图，也有散点，其实看过之前系列文章的人如果能够联想，可以想到这个图是（ggplot分组散点图-坐标轴截断-添加四分位图-显著性检验）和（ggplot批量绘制小提琴图并添加趋势连线）的结合。只不过这篇文章的图有个特点是散点分布和小提琴图形状一致，在画散点的时候利用geom_quasirandom 代替geom_jitter即可。

image.png

原文提供了原始作图数据，可去官网下载。

示例数据和注释代码已上传群文件，免费获取可加群！

作图：

读入数据

setwd("D:/KS项目/复现NC")
A

计算平均值、sd等：

library(dplyr)
B % 
  group_by(Treatment) %>% 
  mutate(upper =  quantile(Integrated.density, 0.75),
         lower = quantile(Integrated.density, 0.25),
         mean = mean(Integrated.density),
         median = median(Integrated.density),
         sd = sd(Integrated.density))

ggplot作图：

ggplot(A,aes(x=Treatment,y=Integrated.density))+
  geom_violin(width =0.8,fill='#EDEDED',color='#EDEDED')+
  geom_quasirandom(aes(color=Biological.replicate),width = 0.4,size=2.5)+
  scale_color_manual(name = 'Rep.',
                     values = c('#FFD7A8','#F2A9A9','#BAB099'),
                     labels = c('1','2','3'))+ 
  theme_classic()+
  labs(x=" ",
       y=expression('Integrated density (x'~10^3~')'))+ 
  theme(axis.title.y = element_text(colour = 'black',size = 16),
        axis.text = element_text(colour = 'black',size = 14),
        axis.line = element_line(size = 1),
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 14))+
  guides(color=guide_legend(override.aes = list(size=4)))+
  geom_errorbar(data=B, aes(ymin = mean-sd, 
                            ymax = mean+sd),width = 0.2,size=0.5)+
  stat_summary(fun = "mean",
               geom = "crossbar",
               mapping = aes(ymin=..y..,ymax=..y..),
               width=0.4,
               size=0.3)+
  stat_summary(aes(fill=Biological.replicate), geom="point",
               fun = mean, shape=21, size=6,stroke=1.3)+
  scale_fill_manual(values = c('#FFAF51','#E65454','#756233'))+
  geom_signif(data=A,
             aes(xmin=1, xmax=2, annotations="0.3008367", 
                 y_position=310),
             textsize = 5,tip_length = c(0, 0),
             manual=TRUE, size = 0.5)+
  guides(fill=guide_legend(title = 'Mean'))