034 矩阵相关性热图

目的

可以较为直观比较多个因子与组内的所有因子之间的相关性。

image.png

代码

setwd("D:/生信代码复现/矩阵相关性分析") #💚💚💚💚工作目录（需修改）

# 加载所需库
library(Hmisc)
library(dplyr)
library(linkET)
library(ggplot2)

# 读取数据
df <- read.csv(file.choose(), header = TRUE, row.names = 1)

# 提取目标因子和环境因子
target_factors <- c("AGB", "BGB")   #💚💚💚💚这个改成你想要比较的参数，可以多个
env_factors <- setdiff(colnames(df), target_factors)

# 分离环境因子数据
env <- df[, env_factors]

# 计算相关性矩阵和 P 值矩阵
df_rcorr <- rcorr(as.matrix(df), type = "pearson")
r <- df_rcorr$r  # 相关系数矩阵
p <- df_rcorr$P  # P 值矩阵

# 初始化结果数据框
results <- data.frame(P = character(), R = numeric(), Y = character(), X = character(), sig = character(), type = character(), stringsAsFactors = FALSE)

# 遍历每个目标因子和环境因子的组合，计算相关性
for (y in target_factors) {
  for (x in env_factors) {
    r_value <- r[x, y]
    p_value <- p[x, y]
    
    # 确定显著性水平和相关性方向
    significance <- ifelse(p_value < 0.05, "< 0.05", ">= 0.05")
    sig <- ifelse(r_value > 0, ">0", "<0")
    type <- ifelse(r_value > 0, "实线", "虚线")
    
    # 添加结果到数据框
    results <- rbind(results, data.frame(P = significance, R = round(r_value, 2), Y = y, X = x, sig = sig, type = type))
  }
}

# 查看结果
print(results)

# 设置颜色映射
cols <- c(">= 0.05" = "grey", "< 0.05" = "#1B9E77", "< 0.01" = "#D95F02")

# 绘制相关性图，带显著性标记
qcorrplot(correlate(env), type = "lower", diag = FALSE) +
  geom_square() +
  geom_text(aes(label = ifelse(p < 0.001, "***",
                               ifelse(p < 0.01, "**",
                                      ifelse(p < 0.05, "*", "")))),
            size = 5, color = "black") +  # 根据 p 值设置星号
  geom_couple(aes(
    colour = P,          # 使用显著性 P 值来设置线的颜色
    size = abs(R),       # 根据相关系数 R 的绝对值调整线条粗细
    linetype = type,     # 根据 type 列选择实线或虚线
    from = Y, to = X     # from 和 to 指定因变量和环境因子
  ), data = results, curvature = 0.15) +
  scale_fill_gradientn(colours = RColorBrewer::brewer.pal(3, "RdBu")) +
  scale_size_continuous(range = c(0.5, 2)) +  # 设置线条粗细范围
  scale_colour_manual(values = cols) +
  scale_linetype_manual(values = c("实线" = "solid", "虚线" = "dashed")) +  # 设置线型映射
  guides(
    size = guide_legend(title = "Correlation Strength",
                        override.aes = list(colour = "grey35"), order = 2),
    colour = guide_legend(title = "P value",
                          override.aes = list(size = 3), order = 1),
    fill = guide_colorbar(title = "Pearson's r", order = 3),
    linetype = guide_legend(title = "Line Type", order = 4)
  ) +
  theme_minimal() +
  theme(panel.grid = element_blank(),  # 移除网格
        axis.text.x = element_text(angle = 45, hjust = 1)) +  # x轴标签旋转
  labs(title = "Environmental Factors Correlation with Biomass",
       x = "Environmental Factor (X)", y = "Target Factor (Y)")

复现出来的图片

Rplot.png

代码

remove(list = ls()) ##清空当前环境
setwd("D:/生信代码复现/相关性分析/复现文件") ##设置路径



library(dplyr)
library(linkET)
library(ggplot2)

speciese <- read.csv(file.choose(),header = T,row.names = NULL)
env <- read.csv(file.choose(),header = T,row.names = NULL)

mantel01 <- mantel_test(speciese, env,
                        spec_select = list(Y1 = 1:7,
                                           Y2 = 8:18,
                                           Y3 = 19:37,
                                           Y4 = 38:44
                                           )) %>% 
  mutate(rd = cut(r, breaks = c(-Inf, 0.2, 0.4, Inf),
                  labels = c("< 0.2", "0.2 - 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")))
qcorrplot(correlate(env), 
          type = "lower",
          diag = FALSE,
          ) +
  geom_square() +
  geom_couple(aes(colour = pd, size = rd),data = mantel01, curvature = 0.1,
              node.colour = c("blue", "blue"),
              node.fill = c("grey", "grey"),
              node.size = c(3.5, 2.5),
              ) +
  scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"),
                       limits = c(-1, 1),
                       breaks = seq(-1,1,0.5)) +
  geom_mark(size=2.5,
            only_mark=T,
            sig_level=c(0.05,0.01,0.001),
            sig_thres=0.05)+
  scale_size_manual(values = c(0.5, 1, 1.5, 2)) +
  scale_colour_manual(values = color_pal(3)) +
  guides(size = guide_legend(title = "Mantel's r",
                             override.aes = list(colour = "grey35"), 
                             order = 2),
         colour = guide_legend(title = "Mantel's P", 
                               override.aes = list(size = 1.5), 
                               order = 1),
         fill = guide_colorbar(title = "Pearson's r", order = 3))

ggsave("Mantel test.tiff",width = 8,height = 6)

复现的图片

image.png