今天小雲想學習利用linkET包來繪制mantel test相關性熱圖，代碼如下：

01

安裝需要的R包

install.packages(“ggplot2”)install.packages(“tidyverse”)install.packages(“devtools”)devtools::install_github("Hy4m/linkET", force = TRUE)

02

載入需要的R包

library(devtools)library(ggplot2)library(tidyverse)library(linkET)

03

代碼展示

#讀取數據varechem<-read.csv(file="varechem.csv",row.names=1)

varespec<-read.csv(file="varespec.csv",row.names=1)

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# mantel testmantel <- mantel_test(varespec, varechem, # 傳入需要檢測的兩個矩陣 mantel_fun = 'mantel', spec_select = list(Spec01 = 1:7, Spec02 = 8:18, Spec03 = 19:37, Spec04 = 38:44)) %>% mutate(r = cut(r, breaks = c(-Inf, 0.2, 0.4, Inf), labels = c("< 0.2", "0.2 - 0.4", ">= 0.4")), p = cut(p, breaks = c(-Inf, 0.01, 0.05, Inf), labels = c("< 0.01", "0.01 - 0.05", ">= 0.05")))#計算環境因子相關性系數：cor2 <- correlate(varechem)

#繪制相關性熱圖p1<- qcorrplot(cor2,grid_col = "grey50",grid_size = 0.2,type = "upper",diag = FALSE) +geom_square() +scale_fill_gradientn(colours = c("#610214", "#d05646", "#f5f4f4", "#569cc7", "#0b3b71"),limits = c(-1, 1))

#添加顯著性標簽：p2 <- p1+geom_mark(size = 4,only_mark = T,sig_level = c(0.05, 0.01, 0.001),sig_thres = 0.05,colour = 'white')

#在相關性熱圖上添加mantel連線：p3<- p2+geom_couple(data = mantel,aes(colour = p, size = r),curvature = nice_curvature())+scale_size_manual(values = c(0.5, 1.2, 2)) + #連線粗細scale_colour_manual(values = c("#d85c01", "#29d300", "#A2A2A288")) + #連線配色#修改圖例：guides(size = guide_legend(title = "Mantel r",override.aes = list(colour = "grey35"),order = 2),colour = guide_legend(title = "Mantel p",override.aes = list(size = 3),order = 1),fill = guide_colorbar(title = "Pearson r", order = 3))

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小雲感覺效果還不錯，該包與ggplot2語法兼容，在繪圖的同時可以學習相關的ggplot2的繪圖知識，值得推薦，今天小雲的分享就到這裡瞭，有需要的可以借鑒學習，下期再見。

選分析方向可以從最新發表文獻入手，當然最便捷的方法當然還是持續關註小雲的生信思路推薦，想定制創新性思路歡迎直接call小雲，雲生信團隊竭誠為您的科研助力！