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library(corrplot)par(omi = c(0.3, 0.3, 0.3, 0.3),    cex = 1.2,    family = 'Arial') # windows系統(tǒng)可能需要安裝其他字體包M <- cor(mtcars) #以mtcars這個(gè)已有的數(shù)據(jù)為例，計(jì)算相關(guān)系數(shù)矩陣

corrplot(M, method = "pie", type = 'upper') #使用corrplot包繪制相關(guān)系數(shù)熱點(diǎn)圖，符號(hào)使用圓形的，只顯示右上部分

library(dplyr)library(corrplot)# 準(zhǔn)備數(shù)據(jù)，這一部分的數(shù)據(jù)在實(shí)際應(yīng)用中應(yīng)該是自己計(jì)算各個(gè)組別與具體某幾個(gè)基因的相關(guān)性包括p值和r值去表征相關(guān)性
set.seed(20190420)n <- ncol(mtcars)grp <- c('Group01', 'Group02', 'Group03') # 分組名稱sp <- c(rep(0.0008, 6), rep(0.007, 2), rep(0.03, 3), rep(0.13, 22)) # P值gx <- c(-4.5, -2.5, 1) # 分組的X坐標(biāo)gy <- c(n-1, n-5, 2.5) # 分組的Y坐標(biāo)df <- data.frame(  grp = rep(grp, each = n), # 分組名稱，每個(gè)重復(fù)n次  gx = rep(gx, each = n), # 組X坐標(biāo)，每個(gè)重復(fù)n次  gy = rep(gy, each = n), # 組Y坐標(biāo)，每個(gè)重復(fù)n次  x = rep(0:(n - 1) - 0.5, 3), # 變量連接點(diǎn)X坐標(biāo)  y = rep(n:1, 3), # 變量連接點(diǎn)Y坐標(biāo)  p = sample(sp), # 對(duì)人工生成p值進(jìn)行隨機(jī)抽樣  r = sample(c(rep(0.8, 4), rep(0.31, 7), rep(0.12, 22)))   # 對(duì)人工生成r值進(jìn)行隨機(jī)抽樣) #以上數(shù)據(jù)中的P值和r值本質(zhì)是根據(jù)具體的實(shí)際實(shí)驗(yàn)當(dāng)中的數(shù)據(jù)進(jìn)行計(jì)算得到的具有意義的數(shù)值，所以在以后的具體實(shí)戰(zhàn)中，只需要修改p值和r即可，gx，gy，x，y代表的是連接線的線段的初始點(diǎn)和終點(diǎn)不用調(diào)整

# 這一部分代碼是按照原圖圖例說明處理線條寬度和顏色映射df <- df %>% #此處使用的是dplyr包中的管道符將df傳遞給下一個(gè)分析函數(shù)作為輸入值。
  mutate( #此處使用的plyr包的mutate函數(shù)在原有數(shù)據(jù)框的基礎(chǔ)上，對(duì)已有數(shù)據(jù)進(jìn)行匯總，并且添加至新的列，以便于下游繪制不同p值和不同r值得線條顏色和線條粗細(xì)使用。    lcol = ifelse(p <= 0.001, '#1B9E77', NA), #使用ifelse函數(shù)根據(jù)p值和r值對(duì)線條的粗細(xì)和顏色進(jìn)行因子化處理，并且添加至新的列便于下游分析使用。    # p值小于0.001時(shí)，顏色為綠色，下面依次類推    lcol = ifelse(p > 0.001 & p <= 0.01, '#88419D', lcol),    lcol = ifelse(p > 0.01 & p <= 0.05, '#A6D854', lcol),    lcol = ifelse(p > 0.05, '#B3B3B3', lcol),    lwd = ifelse(r >= 0.5, 14, NA),    # r >= 0.5 時(shí)，線性寬度為14，下面依次類推    lwd = ifelse(r >= 0.25 & r < 0.5, 7, lwd),    lwd = ifelse(r < 0.25, 1, lwd)  )

segments(df$gx, df$gy, df$x, df$y, lty = 'solid', lwd = df$lwd,          col = df$lcol, xpd = TRUE) # 使用segments函數(shù)繪制連接線，df$gx, df$gy表示線段的起點(diǎn)，df$x, df$y表示線段的終點(diǎn)。points(df$gx, df$gy, pch = 24, col = 'blue', bg = 'blue', cex = 3, xpd = TRUE) # 組標(biāo)記點(diǎn)，繪制每個(gè)組的標(biāo)記點(diǎn)。df$gx, df$
gy表示組標(biāo)記點(diǎn)的坐標(biāo)，pch表示標(biāo)記點(diǎn)的符號(hào)類型，24表示三角形，text(df$gx - 0.5, df$gy, labels = df$grp, adj = c(1, 0.5), cex = 1.5, xpd = TRUE)# 表示每個(gè)組的名稱，首先限定名稱的顯示位置，然后就是顯示內(nèi)容。

labels01 <- c('<= 0.001','0.001 < x <= 0.01','0.01 < x <= 0.05','> 0.05')labels02 <- c('>= 0.5', '0.25 - 0.5', '< 0.25')labels_x <- rep(-6, 4)labels_y <- seq(4.6, 2.6, length.out = 4)text(-6.5, 5.2, 'P-value', adj = c(0, 0.5), cex = 1.2, font = 2, xpd = TRUE)text(labels_x, labels_y, labels01, adj = c(0, 0.5), cex = 1.2, xpd = TRUE)points(labels_x - 0.5, labels_y, pch = 20, col = c('#1B9E77', '#88419D','#A6D854', '#B3B3B3'),       cex = 3, xpd = TRUE)lines_x <- c(-6.5, -3, 0.5)lines_y <- rep(1.2, 3)text(-6.5, 1.9, "Mantel's r", adj = c(0, 0.5), cex = 1.2, font = 2, xpd = TRUE)text(lines_x + 1.5, lines_y, labels02, adj = c(0, 0.5), cex = 1.2, xpd = TRUE)segments(lines_x, lines_y, lines_x + 1, lines_y, lwd = c(14, 7, 2.5), lty = 'solid',          col = '#B3B3B3', xpd = TRUE)
## 圖例框框，這一部分就是繪制圖注信息外面框，本質(zhì)就是一條線段一條線段的拼接起來的，嚴(yán)格按照坐標(biāo)信息標(biāo)記即可。segments(-6.9, 5.6, -2.8, 5.6, lty = 'solid', lwd = 1.2,          col = 'grey50', xpd = TRUE)segments(-2.8, 5.6, -2.8, 1.8, lty = 'solid', lwd = 1.2,          col = 'grey50', xpd = TRUE)segments(-2.8, 1.8, 3.6, 1.8, lty = 'solid', lwd = 1.2,          col = 'grey50', xpd = TRUE)segments(3.6, 1.8, 3.6, 0.7, lty = 'solid', lwd = 1.2,          col = 'grey50', xpd = TRUE)segments(3.6, 0.7, -6.9, 0.7, lty = 'solid', lwd = 1.2,          col = 'grey50', xpd = TRUE)segments(-6.9, 0.7, -6.9, 5.6, lty = 'solid', lwd = 1.2,          col = 'grey50', xpd = TRUE)