论文
Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies
https://www.nature.com/articles/s41588-022-01051-w
本地pdf s41588-022-01051-w.pdf
代码链接
https://zenodo.org/record/6332981#.YroV0nZBzic
https://github.com/Jingning-Zhang/PlasmaProtein/tree/v1.2
今天的推文重复一下论文中的Extended Data Fig. 1
image.png
这里是散点图，然后突出显示其中某些点。论文里的处理方式是把指定的数据筛选出来，做好散点图后再叠加过滤后的数据。
这里还新接触到一个R包 mdthemes 可以解析代码中的markdown语法
https://github.com/thomas-neitmann/mdthemes
部分示例数据集截图
image.png
第一个图的代码
df<-read.delim("ExtendedDataFig1.txt",
sep="\t",
header = TRUE)
df.EA <- df[df$eth=="EA",]
df.EA_highlight <- df.EA[c(865,1391,1342,277), ]
df.EA_highlight
df.text<-df.EA_highlight
df.text$label<-letters[1:4]
df.text$label<-c("EA:0.021\nAA:0.035",
"EA:0.237\nAA:0.092",
"EA:0.056\nAA:0.139",
"EA:0.234\nAA:0.013")
library(ggplot2)
library(latex2exp)
#install.packages("mdthemes")
help(package="mdthemes")
library(mdthemes)
library(ggrepel)
My_Theme = theme(
panel.background = element_blank(),
title = element_text(size = 7),
text = element_text(size = 6)
# axis.title.x = element_text(size = 10),
# axis.text.x = element_text(size = 8),
# axis.title.y = element_text(size = 10),
# axis.text.y = element_text(size = 8),
# legend.title = element_text(size = 10)
# legend.text = element_text(size = 8)
)
p.EA <- ggplot(data = df.EA, aes(x = Beta_EA, y = Beta_AA)) +
geom_point(size=0.5, col="#2171b5") +
geom_abline(intercept = 0, slope = 1, col="red") +
theme(axis.line = element_line(color="black", size = 0.2)) +
ylim(-2,2)+xlim(-2,2)+
mdthemes::md_theme_classic() +
labs(x = "Effect size (EA)",
y = "Effect size (AA)",
title="Common sentinel SNP of *cis*-pQTLs in EA") +
My_Theme +
geom_point(data=df.EA_highlight,
aes(x = Beta_EA, y = Beta_AA),
size=5, col="darkorange")+
geom_text_repel(data=df.text,
aes(x = Beta_EA, y = Beta_AA,label=label),
color="darkorange",seed=5678)
p.EA
image.png
第二个图的代码
df.AA <- df[df$eth=="AA",]
df.AA_highlight <- df.AA[c(893,59,710,168), ]
df.text.AA<-df.AA_highlight
df.text.AA$label<-letters[1:4]
df.text.AA$label<-c("EA:0.075\nAA:0.104",
"EA:0.056\nAA:0.139",
"EA:0.015\nAA:0.093",
"EA:0.014\nAA:0.368")
p.AA <- ggplot(data = df.AA, aes(x = Beta_AA, y = Beta_EA)) +
geom_point(size=0.5,col="#238b45") +
geom_abline(intercept = 0, slope = 1, col="red") +
theme(axis.line = element_line(color="black", size = 0.2)) +
ylim(-2,2)+xlim(-2,2)+
mdthemes::md_theme_classic() +
labs(x = "Effect size (AA)",
y = "Effect size (EA)",
title="Common sentinel SNP of *cis*-pQTLs in AA"
) +
My_Theme +
geom_point(data=df.AA_highlight, aes(x = Beta_AA, y = Beta_EA),
size=5, col="darkorange")+
geom_text_repel(data=df.text.AA,
aes(x = Beta_AA, y = Beta_EA,
label=label),
seed=5678,
color="darkorange")
p.AA
image.png
拼图代码
library(cowplot)
p <- cowplot::plot_grid(p.EA, p.AA, ncol=2)
p
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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