论文
https://www.science.org/doi/10.1126/science.abk0989
image.png
最近朋友圈好多人都在转这个论文，我也找来看了看，论文研究的内容看的还是一知半解。
论文用到的数据代码都是公开的，我们可以学习一下其中的代码
代码链接
https://github.com/James-S-Santangelo/glue_pc
今天的图文重复论文中的Figure 2B
image.png
这个图的图注写的是The eigenvectors for environmental variables, colored according to their contribution to PC2
这里为什么只展示对PC2的贡献暂时还不明白。主要是论文的研究内容看不明白
本篇推文只记录画图代码了
还是先做主成分分析
library(readr)
dat01<-read_csv("phenotypic-analyses/sciencefig2A.csv")
dim(dat01)
colnames(dat01)
dat02<-read_csv("phenotypic-analyses/sciencefig2A_group_info.csv")
dim(dat02)
colnames(dat02)
library(vegan)
enviroPCA <- rda(dat01,
scale = TRUE, na.action = "na.omit")
eig <- enviroPCA$CA$eig
percent_var <- eig * 100 / sum(eig)
PC1_varEx <- round(percent_var[1], 1) # Percent variance explained by PC1
PC2_varEx <- round(percent_var[2], 1) # Percent variance explained by PC2
计算物种贡献百分比
论文里提供的代码里放了一个参考链接
https://stackoverflow.com/questions/50177409/how-to-calculate-species-contribution-percentages-for-vegan-rda-cca-objects
contrib <- round(100*scores(enviroPCA, display = "sp", scaling = 0)[,2]^2, 3)
生成作图数据
library(tidyverse)
enviroPCA_vars <- scores(enviroPCA, display = 'species', choices = c(1, 2), scaling = 2) %>%
as.data.frame() %>%
rownames_to_column(., var = 'var') %>%
mutate(contrib = contrib)
准备配色
library(wesanderson)
pal <- wes_palette("Darjeeling1", 3, type = "continuous")
作图主题的一些设置
library(ggplot2)
ng1 <- theme(aspect.ratio=0.7,panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border=element_blank(),
axis.line.x = element_line(color="black",size=1),
axis.line.y = element_line(color="black",size=1),
axis.ticks=element_line(size = 1, color="black"),
axis.ticks.length=unit(0.25, 'cm'),
axis.text=element_text(color="black",size=15),
axis.title=element_text(color="black",size=1),
axis.title.y=element_text(vjust=2,size=17),
axis.title.x=element_text(vjust=0.1,size=17),
axis.text.x=element_text(size=15),
axis.text.y=element_text(size=15),
strip.text.x = element_text(size = 10, colour = "black",face = "bold"),
strip.background = element_rect(colour="black"),
legend.position = "top", legend.direction="vertical",
legend.text=element_text(size=17), legend.key = element_rect(fill = "white"),
legend.title = element_text(size=17),legend.key.size = unit(1.0, "cm"))
最后的作图代码
enviroPCA_variableContrib <- ggplot() +
geom_hline(yintercept = 0, linetype = "dotted") +
geom_vline(xintercept = 0, linetype = "dotted") +
geom_segment(data = enviroPCA_vars, aes(x = 0, xend = PC1, y=0, yend = PC2, color = contrib),
size = 2, arrow = arrow(length = unit(0.02, "npc")), alpha = 1) +
geom_text(data = enviroPCA_vars,
aes(x = PC1, y = PC2, label = var,
hjust = "inward", vjust = 0.5 * (1 - sign(PC1))),
color = "black", size = 3.5) +
xlab(sprintf("PC1 (%.1f%%)", PC1_varEx)) + ylab(sprintf("PC2 (%.1f%%)", PC2_varEx)) +
scale_colour_gradientn(colours = rev(pal), breaks = seq(from = 5, to = 25, by = 5)) +
# scale_x_continuous(breaks = seq(from = -1, to = 1, by = 0.25)) +
# scale_y_continuous(breaks = seq(from = -1, to = 1, by = 0.25)) +
ng1 + theme(legend.position = "top",
legend.direction="horizontal",
# legend.title = element_blank(),
legend.key.size = unit(0.5, "cm"),
legend.spacing.x = unit(0.1, "cm"),
legend.text = element_text(size=10)) +
guides(color = guide_colourbar(barwidth = 10, barheight = 0.5))
enviroPCA_variableContrib
image.png
本期推文的示例数据和代码可以给推文点赞 点击在看 然后留言20220406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！