ggheatmap--绘制更加个性化的热图！

作图丫

发布于 2022-03-29 13:57:00

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发布于 2022-03-29 13:57:00

文章被收录于专栏：作图丫

导语

GUIDE ╲

作为目前最常见的一种可视化手段，热图因其丰富的色彩变化和生动饱满的信息表达被广泛应用于各种大数据分析场景。

背景介绍

相信大家对热图一定都不陌生了，在数据的可视化分析中，是经常使用热图去做展示的，基于R语言的一些热图绘制R包，如pheatmap,complexheatmap等等，都是常用的工具，今天小编给大家介绍的是一个基于ggplot2的热图绘制工具--ggheatmap，基于ggplot2可以使我们在热图的绘制过程中更加个性化！

R包安装

BiocManager::install("ggheatmap")
library(ggheatmap)

可视化介绍

基本用法

首先对数据进行预处理

library(tidyverse)
library(Jerry)
data(arms_data)
#得到AMP相关性矩阵
arms_data_dist_amp = arms_data %>% 
  select(starts_with("Amp")) %>%
  cor() %>%
  as.matrix() 
#得到Del相关性矩阵
arms_data_dist_del = arms_data %>% 
  select(starts_with("Del")) %>%
  cor() %>%
  as.matrix()

获取相关矩阵的下三角或上三角

#设置上三角为NA
arms_data_dist_amp %>%
  get_tri(diag = TRUE, upper.tri = TRUE) 
  ##           Amp_ACA   Amp_NEC   Amp_CIN   Amp_EBV    Amp_GS Amp_MSI
## Amp_ACA        NA        NA        NA        NA        NA      NA
## Amp_NEC 0.8971477        NA        NA        NA        NA      NA
## Amp_CIN 0.7313157 0.6677643        NA        NA        NA      NA
## Amp_EBV 0.6380635 0.6225627 0.7994932        NA        NA      NA
## Amp_GS  0.6683191 0.6172034 0.7019668 0.6668315        NA      NA
## Amp_MSI 0.4838802 0.5112640 0.5480767 0.5583757 0.8330379      NA

arms_data_long_amp = arms_data_dist_amp %>%
  get_tri(diag = TRUE, upper.tri = TRUE) %>%
  reshape2::melt(na.rm = TRUE) %>%
  mutate(
    Var1 = mapply(function(x) x[2], str_split(Var1, "_") ),
    Var2 = mapply(function(x) x[2], str_split(Var2, "_") )
  ) 
  head(arms_data_long_amp

##   Var1 Var2     value
## 2  NEC  ACA 0.8971477
## 3  CIN  ACA 0.7313157
## 4  EBV  ACA 0.6380635
## 5   GS  ACA 0.6683191
## 6  MSI  ACA 0.4838802
## 9  CIN  NEC 0.6677643

使用ggheatmap进行可视化

arms_data_long_amp = arms_data_long_amp %>%
  mutate(Var1 = factor(Var1, levels = c("ACA","NEC","CIN","EBV","GS","MSI"),
                       labels = c("MANEC-Aca","MANEC-Nec","STAD-CIN","STAD-EBV","STAD-GS","STAD-MSI")
                       ),
         Var2 = factor(Var2, levels = c("ACA","NEC","CIN","EBV","GS","MSI"),
                       labels = c("MANEC-Aca","MANEC-Nec","STAD-CIN","STAD-EBV","STAD-GS","STAD-MSI"))
         )
# Create a ggheatmap
ggheatmap <- ggplot(arms_data_long_amp, aes(Var2, Var1, fill = value))+
  
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "blue", high = "red", mid = "grey100", 
                       midpoint = 0.5, limit = c(0, 0.9), space = "Lab", 
                       name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 10, hjust = 1),
        axis.text.y = element_text(
                                   size = 10)
        )+
  coord_fixed() +
  geom_rect(xmin = 0.5, xmax = 3.5, ymin = 0.5, ymax = 3.5, col = "black", alpha = 0) 

ggheatmap

绘制复杂一些的热图

##生成一个矩阵用来绘图
set.seed(123)
df <- matrix(runif(225,0,10),ncol = 15)
colnames(df) <- paste("sample",1:15,sep = "")
rownames(df) <- sapply(1:15, function(x)paste(sample(LETTERS,3,replace = F),collapse = ""))
df[1:4,1:4]

##对数据做一些注释和分组
row_metaData <- data.frame(exprtype=sample(c("Up","Down"),15,replace = T),
                           genetype=sample(c("Metabolism","Immune","None"),15,replace = T))
rownames(row_metaData) <- rownames(df)
col_metaData <- data.frame(tissue=sample(c("Normal","Tumor"),15,replace = T),
                           risklevel=sample(c("High","Low"),15,replace = T))
rownames(col_metaData) <- colnames(df)
##定义各个板块的颜色，整合为col
exprcol <- c("#EE0000FF","#008B45FF" )
names(exprcol) <- c("Up","Down")
genecol <- c("#EE7E30","#5D9AD3","#D0DFE6FF")
names(genecol) <- c("Metabolism","Immune","None")
tissuecol <- c("#98D352","#FF7F0E")
names(tissuecol) <- c("Normal","Tumor")
riskcol <- c("#EEA236FF","#46B8DAFF")
names(riskcol) <- c("High","Low")
col <- list(exprtype=exprcol,genetype=genecol,tissue=tissuecol,risklevel=riskcol)
text_rows <- sample(rownames(df),3)

p<- ggheatmap(df,cluster_rows = T,cluster_cols = T,scale = "row",
              text_show_rows = text_rows,
              cluster_num = c(3,3),
              ##聚类树的颜色
              tree_color_rows = c("#008B45FF","#631879FF","#008280FF"),
              tree_color_cols = c("#1F77B4FF","#FF7F0EFF","#2CA02CFF"),
              ##行和列的注释信息
              annotation_rows = row_metaData,
              annotation_cols = col_metaData,
              annotation_color = col
)
p

展示热图的组成

ggheatmap_plotlist(p)

ggheatmap_theme()

ggheatmap_theme() 是ggheatmap包特有的函数，能设置ggheatmap除聚类树外各组件绘制元素的样式。

p%>%
  ggheatmap_theme(1:5,
                  theme =list(
                    ##设置列名垂直，字体
                    theme(axis.text.x = element_text(angle = 90,face = "bold",size = 10),
                          axis.text.y = element_text(colour = "red",face = "bold")),
                    theme(legend.title = element_text(face = "bold")),
                    theme(legend.title = element_text(face = "bold")),
                    theme(legend.title = element_text(face = "bold")),
                    theme(legend.title = element_text(face = "bold"))
                  ))

改变马赛克形状

ggheatmap(df,cluster_rows = T,cluster_cols = T,scale = "row",
          text_show_rows = text_rows,
          ##边框颜色
          border = "grey",
          ##形状
          shape = "circle",
          cluster_num = c(3,3),
          tree_color_rows = c("#008B45FF","#631879FF","#008280FF"),
          tree_color_cols = c("#1F77B4FF","#FF7F0EFF","#2CA02CFF"),
          annotation_rows = row_metaData,
          annotation_cols = col_metaData,
          annotation_color = col
)%>%
  ggheatmap_theme(1,theme =list(theme(axis.text.x = element_text(angle = 90,face = "bold",size = 10),
                                      axis.text.y = element_text(colour = "red",face = "bold"))))

布局改变

ggheatmap(df,cluster_rows = T,cluster_cols = T,scale = "row",
          text_show_rows = text_rows,
          border = "grey",
          cluster_num = c(3,3),
          tree_color_rows = c("#008B45FF","#631879FF","#008280FF"),
          tree_color_cols = c("#1F77B4FF","#FF7F0EFF","#2CA02CFF"),
          annotation_rows = row_metaData,
          annotation_cols = col_metaData,
          annotation_color = col,
          ##下面参数可以设置各个元素的布局位置
          text_position_rows = "left",
          text_position_cols = "top",
          tree_position_rows = "right",
          tree_position_cols = "bottom",
          annotation_position_rows = "right",
          annotation_position_cols = "bottom"
          
)%>%
  ggheatmap_theme(1,theme =list(theme(axis.text.x = element_text(angle = 90,face = "bold",size = 10),
                                      axis.text.y = element_text(colour = "red",face = "bold"))))