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，但是找到了部门做图数据，我们可以用论文中提供的原始数据模仿出论文中的图
本日的推文重复一下论文中的 Figure4b Figure4c 箱线图叠加蜂群图
Figure4b的部门数据截图
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}$)"),vjust=0)Figure4c数据的部门截图
读取数据
library(readxl)dat.fig4c<-read_excel("data/20220711/41586_2022_4808_MOESM8_ESM.xlsx", sheet = "Fig4c", skip = 1)head(dat.fig4c)作图代码
dat.fig4c$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)) -> p2.1for (i in 1:6){ p2.1+ annotate(geom = "text",x=x,y=y,label=TeX(r"((\textit{n})"), vjust=0,hjust=1) -> p2.1}for (i in 1:6){ p2.1+ annotate(geom = "text",x=x,y=y,label=label_z, vjust=0,hjust=0) -> p2.1}p2.1末了是拼图
library(patchwork)p1+p2.1+ theme(legend.position = "none")示例数据和代码可以本身到论文中获取，大概给本篇推文点赞，点击在看，然后留言获取
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