跟着Nature Communications学作图：R语言ggplot2做堆积柱形图展示群体基因组学的结果 |
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论文
Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia https://www.nature.com/articles/s41467-022-34206-8#Sec23 完整的数据分析代码 涉及到群体基因组学 作图数据 `` https://github.com/jingwanglab/Populus_genomic_prediction_climate_vulnerability 作者的github主页还有很多其他内容 https://github.com/jingwanglab 今天的图推文重复一下论文中的figure2a 论文中提供的代码是 https://github.com/jingwanglab/Populus_genomic_prediction_climate_vulnerability/blob/main/3-Population_genetics/1structure.sh 完整代码 Q2=read.table("pk230_ldpruned.2.Q.txt",header=F) dim(Q2) Q3=read.table("pk230_ldpruned.3.Q.txt",header=F) dim(Q3) myorder % pivot_longer(-V1) %>% mutate(name=factor(name,levels = c("V3","V2"))) %>% ggplot(aes(x=V1,y=value,fill=name))+ geom_bar(stat='identity',width=1,show.legend = FALSE)+ scale_fill_manual(values = c("V3"="#e9e9e9", "V2"="#e04d72"))+ theme_bw()+ theme(panel.grid = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank())+ scale_y_continuous(minor_breaks=seq(0,1,0.1), expand = c(0,0), breaks=seq(0,1,0.25))+ scale_x_discrete(breaks=NULL)+ labs(x=NULL,y="k=2") p2% mutate(V1=factor(V1, levels = myorder)) %>% pivot_longer(-V1) %>% #mutate(name=factor(name,levels = c("V3","V2"))) %>% ggplot(aes(x=V1,y=value,fill=name))+ geom_bar(stat='identity',width=1,show.legend = FALSE)+ scale_fill_manual(values = c("V2"="#e9e9e9", "V3"="#3280c3", "V4"="#e04d72"))+ theme_bw()+ theme(panel.grid = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank())+ scale_y_continuous(minor_breaks=seq(0,1,0.1), expand = c(0,0), breaks=seq(0,1,0.25))+ scale_x_discrete(breaks=NULL)+ labs(x=NULL,y="k=3") p3% mutate(V1=factor(V1, levels = myorder)) %>% ggplot()+ geom_ribbon(aes(x=V1,ymin=0.1,ymax=1),fill="#e04d72")+ #geom_ribbon(aes(x=164:230,ymin=0.1,ymax=1),fill="#3280c3")+ theme_bw()+ theme(panel.grid = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), panel.border = element_blank(), axis.title = element_blank())+ scale_y_continuous(minor_breaks=seq(0,1,0.1), expand = c(0,0), breaks=seq(0,1,0.25))+ #scale_x_continuous(breaks=NULL)+ annotate(geom="text",x=80,y=0,label="South",vjust=-0.5)+ annotate(geom="text",x=190,y=0,label="North",vjust=-0.5)+ annotate(geom = "ribbon",x=1:165,ymin=0.5,ymax=1,fill="#e04d72")+ annotate(geom = "ribbon",x=166:230,ymin=0.5,ymax=1,fill="#3280c3") library(patchwork) p1/p2/p3+ plot_layout(heights = c(4,4,1)) 最终结果 image.png示例数据和代码可以给公众号推文点赞，点击在看，最后留言获取 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！ |
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