pheatmap带你轻松绘制聚类相关性热图

library(tidyverse)
library(psych)
library(pheatmap)
library(magrittr)
# devtools::install_github("thomasp85/scico")
library(scico)

# 读取环境数据文件并存储到env变量中，使用tab作为分隔符，第一列作为行名，不检查列名的合法性
env <- read.delim("env.xls", header = TRUE, sep = "\t", row.names = 1, check.names = FALSE)

# 读取物种数据文件并存储到genus变量中，使用tab作为分隔符，第一列作为行名，不检查列名的合法性
genus <- read.delim("genus.xls", header = TRUE, sep = "\t", row.names = 1, check.names = FALSE) %>%
  t() %>% as.data.frame()

# 使用pearson方法计算环境数据和物种数据之间的相关系数和p-value，并进行多重比较法的FDR校正
pp <- corr.test(env, genus, method = "pearson", adjust = "fdr")
cor <- pp$r # 获取相关系数矩阵
pvalue <- pp$p # 获取p-value矩阵

# 将相关系数矩阵转换为长格式，并添加p-value和显著性符号列
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"))

#将相关系数矩阵转换为宽格式，行名为环境变量，列名为物种，值为相关系数
rvalue <- df %>% 
  select(1, 2, 3) %>% 
  pivot_wider(names_from = "genus", values_from = r) %>% 
  column_to_rownames(var = "env")

# 将显著性符号矩阵转换为宽格式，行名为环境变量，列名为物种，值为显著性符号
pvalue <- df %>% 
  select(1, 2, 5) %>% 
  pivot_wider(names_from = "genus", values_from = p_signif) %>% 
  column_to_rownames(var = "env")

mycol <- scico(100, palette = "vik")

# 绘制热图，显示相关系数，行列聚类，无边框，显示p-value作为数字，设置数字字体大小和颜色
# 设置主标题为空格，设置单元格宽度和高度，使用自定义颜色映射
pheatmap(rvalue, scale = "none", cluster_row = TRUE, cluster_col = TRUE, border = NA,
         display_numbers = pvalue, fontsize_number = 12, number_color = "white",
         main = " ",
         cellwidth = 21, cellheight = 20, color = mycol)