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浅谈R中相关性网络热图绘制小细节

2023-02-18 16:35:33 时间

❝最近在绘制相关性网络热图的时候突然有一个小的发现，可以使用相关性热图的数据来结合「linkET」来绘图，以前一直认为为必须使用「mantel_test」才行；果然绘图还得多思考；本节就来通过一个案例将两份数据结合起来进行绘图；

加载R包

library(tidyverse)
library(linkET)
library(RColorBrewer)
library(ggtext)
library(magrittr)
library(psych)
library(reshape)

导入数据

table1 <- read.delim("env.xls",header =T,sep="\t",row.names = 1,check.names = F)

table2 <- read.delim("genus.xls",header =T,sep="\t",row.names = 1,check.names = F) %>% 
  t() %>% as.data.frame()

相关性分析

pp <- corr.test(table1,table2,method="pearson",adjust = "fdr")

整合数据

cor <- pp$r
pvalue <- pp$p

df <- melt(cor) %>% mutate(pvalue=melt(pvalue)[,3],
                     p_signif=symnum(pvalue, corr = FALSE, na = FALSE,  
                                     cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), 
                                     symbols = c("***", "**", "*", "", " "))) %>% 
  set_colnames(c("env","genus","r","p","p_signif"))

转换数据格式

❝在此处以前一直以为必须使用「linkET::mantel_test」函数生成特定格式才能用于后面绘图，直到某次看了数据才明白导入外部的相关性分析数据也能用于后期绘图；此处的范围可根据需要自定义 ❞

cordata <- df %>% left_join(.,read_tsv('annotation.xls'),by=c("genus")) %>% 
  select(group,env:p,-genus) %>% 
  set_colnames(c("spc","env","r","p")) %>% 
  mutate(rd = cut(r, breaks = c(-Inf, 0, 0.4, Inf),
                  labels = c("< 0", "0 - 0.4", ">= 0.4")),
         pd = cut(p, breaks = c(-Inf, 0.01, 0.05, Inf),
                  labels = c("< 0.01", "0.01 - 0.05", ">= 0.05")))

绘制相关性网络图

qcorrplot(correlate(table1,method = "spearman"),diag=F,type="upper")+
  geom_tile()+
  geom_mark(size=2.5,sig.thres=0.05,sep="\n")+
  geom_couple(aes(colour=pd,size=rd),data=cordata,label.colour = "black",
              curvature=nice_curvature(0.15),
              nudge_x=0.2,
              label.fontface=2,
              label.size =4,
              drop = T)+
  scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11,"RdBu"))+
  scale_size_manual(values = c(0.5, 1, 2)) +
  scale_colour_manual(values =c("#D95F02","#1B9E77","#A2A2A288")) +
  guides(size = guide_legend(title = "cor",override.aes = list(colour = "grey35"), order = 2),
         colour = guide_legend(title = "P_value",override.aes = list(size = 3), order = 1),
         fill = guide_colorbar(title = "spearman's r",order = 3))+
  theme(plot.margin = unit(c(0,0,0,-1),units="cm"),
        panel.background = element_blank(),
        axis.text=element_markdown(color="black",size=10),
        legend.background = element_blank(),
        legend.key = element_blank())

❝本节介绍到此结束

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