图表复现再谈个性化热图绘制

R语言数据分析指南

发布于 2023-08-18 13:29:11

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发布于 2023-08-18 13:29:11

文章被收录于专栏：R语言数据分析指南R语言数据分析指南

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❝最近在群里看到一张图，其本质仍然是相关性热图作者在图形布局上做了一些细微的改动使得整体看来有些许特别之处；下面小编就来复现一下，数据为自己构建仅供参考❞

原图

论文

❝Environmental DNA metabarcoding reveals the influence of human activities on microeukaryotic plankton along the Chinese coastline ❞

复现结果

加载R包

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

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()

数据整合

❝此处跟以往一样还是进行相关性分析最终将数据整合 ❞

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

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"))

# 整合分组文件
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, 0.05, Inf),
                  labels = c("< 0.05",">= 0.05")))

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原始发表：2023-06-09，如有侵权请联系 cloudcommunity@tencent.com 删除

数据分析