## correlation heatmap ### 应用场景 可以同时展示多个变量之间的相关关系，比如，同时展示10个基因直接的相关系数 ### correlation plot ```R library(ggcorrplot) # 准备数据 # 构建相关表达矩阵 get_lower_tri<-function(cormat){ cormat[upper.tri(cormat)] <- NA return(cormat) } # Get upper triangle of the correlation matrix get_upper_tri <- function(cormat){ cormat[lower.tri(cormat)]<- NA return(cormat) } # Reorder the correlation matrix reorder_cormat <- function(cormat){ # Use correlation between variables as distance dd <- as.dist((1-cormat)/2) hc <- hclust(dd) cormat <-cormat[hc$order, hc$order] } # 利用前面两节中差异表达数据，选取10个基因的数据做展示 rt_cor <- t(rt_ht[20:30, ]) cormat <- round(cor(rt_cor), 2) ``` > cormat数据结构如下  ```R svg(file = 'heatmap9.svg', width = 10, height = 6) ggcorrplot(cormat) dev.off() # 可以改变下颜色 svg(file = 'heatmap10.svg', width = 10, height = 6) ggcorrplot(cormat, hc.order = TRUE, outline.col = "white", colors = c("#6D9EC1", "white", "#E46726")) dev.off() # 可以按照相关系数排个序 svg(file = 'heatmap11.svg', width = 10, height = 6) ggcorrplot(cormat, hc.order = TRUE, outline.col = "white") dev.off() # 可以展示upper或者lower # 设置type参数 svg(file = 'heatmap12.svg', width = 10, height = 6) ggcorrplot(cormat, hc.order = TRUE, outline.col = "white", type = "lower") dev.off() # 可以加上相关系数的值 # lab = TRUE svg(file = 'heatmap12.svg', width = 10, height = 6) ggcorrplot(cormat, hc.order = TRUE, type = "lower", lab = TRUE) dev.off() # 也可以换种展示方式 # method = "circle" svg(file = 'heatmap14.svg', width = 10, height = 6) ggcorrplot(cormat, hc.order = TRUE, type = "lower", method = "circle") dev.off() ```