论文
Graph pangenome captures missing heritability and empowers tomato breeding
https://www.nature.com/articles/s41586-022-04808-9#MOESM8
没有找到论文里的作图的代码，但是找到了部分做图数据，我们可以用论文中提供的原始数据模仿出论文中的图
今天的推文重复一下论文中的 Figure4b Figure4c 箱线图叠加蜂群图
Figure4b的部分数据截图
image.png
读取数据
library(readxl)
dat.fig4b<-read_excel("data/20220711/41586_2022_4808_MOESM8_ESM.xlsx",
sheet = "Fig4b",
skip = 1)
head(dat.fig4b)
作图代码
(ggplot2)
library(latex2exp)
library(ggbeeswarm)
segment.data<-data.frame(x=c(0.8,1.8,2.8),
xend=c(1.2,2.2,3.2),
y=c(73,97,83)+1,
yend=c(73,97,83)+1)
ggplot(data=dat.fig4b,aes(x=VarID,
y=`Standardized gene expression`,
color=Genotype))+
geom_boxplot(show.legend = FALSE)+
geom_beeswarm(dodge.width = 0.8,shape=21)+
theme_bw()+
theme(panel.grid = element_blank(),
legend.position = c(0.1,0.95),
legend.title = element_blank(),
legend.background = element_rect(fill="transparent"),
legend.key = element_rect(fill="transparent"))+
scale_y_continuous(breaks = c(-50,0,50,100),
limits = c(-50,100))+
scale_x_discrete(labels=paste0("SV",unique(dat.fig4b$VarID)))+
labs(x=NULL)+
annotate(geom = "text",x=0.8,y=70,label="(n=177)")+
annotate(geom = "text",x=1.2,y=45,label="(n=9)")+
annotate(geom = "text",x=1.8,y=60,label="(n=174)")+
annotate(geom = "text",x=2.2,y=95,label="(n=14)")+
annotate(geom = "text",x=2.8,y=60,label="(n=155)")+
annotate(geom = "text",x=3.2,y=80,label="(n=134)")+
geom_segment(data=segment.data,
aes(x=x,xend=xend,y=y,yend=yend),
inherit.aes = FALSE)+
annotate(geom = "text",x=1,y=76,
label=TeX(r"(\textit{P} = 0.76)"),vjust=0)+
annotate(geom = "text",x=2,y=99.5,
label=TeX(r"(\textit{P} = 8.37 \times 10${^-}{^3}$)"),vjust=0)+
annotate(geom = "text",x=3,y=85.5,
label=TeX(r"(\textit{P} = 6.84 \times 10${^-}{^8}$)"),vjust=0)
image.png
Figure4c数据的部分截图
image.png
读取数据
library(readxl)
dat.fig4c<-read_excel("data/20220711/41586_2022_4808_MOESM8_ESM.xlsx",
sheet = "Fig4c",
skip = 1)
head(dat.fig4c)
作图代码
dat.fig4c$Type<-factor(dat.fig4c$Type,
levels = c("non-favourable","favourable"))
dat.fig4c$Variation<-factor(dat.fig4c$Variation,
levels = c("SV2","SV4","SV2+SV4"))
x<-c(0.7,1.3,1.7,2.3,2.7,3.3)
y<-c(0.24,0.42,0.15,0.42,0.24,0.42)
label_z<-c(" = 174)"," = 14)"," = 155)"," = 34)"," = 178)"," = 10)")
ggplot(data = dat.fig4c,
aes(x=Variation,y=BLUP))+
geom_boxplot(aes(color=Type),show.legend = FALSE)+
geom_beeswarm(aes(color=Type),
dodge.width = 0.8,shape=21)+
scale_color_manual(values = c("#648fff","#d36b1c"),
name="",
label=c("Non-favourable alleles",
"Favourable alleles"))+
scale_x_discrete(label=c("SV2_44168216",
"SV4_54067283",
"Both"))+
labs(x=NULL,y="BLUP of SSC")+
theme_bw()+
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.justification = c(0,0)) -> p2.1
for (i in 1:6){
p2.1+
annotate(geom = "text",x=x[i],y=y[i],label=TeX(r"((\textit{n})"),
vjust=0,hjust=1) -> p2.1
}
for (i in 1:6){
p2.1+
annotate(geom = "text",x=x[i],y=y[i],label=label_z[i],
vjust=0,hjust=0) -> p2.1
}
p2.1
image.png
最后是拼图
library(patchwork)
p1+p2.1+
theme(legend.position = "none")
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
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