热图7：ggplot2/ComplexHeatmap做离散型热图

很久之前我们出过热图系列，一共有6篇文章，反响还是可以，但是最近发现很多新关注的小伙伴没有翻看的习惯，居然不知道，所以今天推文全部列出来。此外，这篇文章的内容是补充之前的不足，因为没有涉及到离散型热图的做法，这里我们以一篇Cell文章为引子，展示下ggplot2/ComplexHeatmap做离散型热图，这样热图系列就完善了。

我们不可能将所有文章出现的热图复现一遍，请从这短小的几篇介绍中发挥想象，深入学习，通过变化可以展示更多的图形。

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图片来源

image.png

(Reference：Peng Y R , et al. Molecular Classification and Comparative Taxonomics of Foveal and Peripheral Cells in Primate Retina. 2018. Cell)

构建数据

作图数据和之前gene表达量一样，只不过将数值换成了因子：

image.png

方法1：ggplot2

加载数据并转化为ggplot长数据：

setwd('F:/生物信息学/离散型热图')
A <- read.csv("gene_dis.csv", header = T)
library(tidyr)
dft <-gather(A, disease, value, 2:9)
dft

library(forcats)
dft$gene <- as.factor(dft$gene)
dft$gene <- fct_inorder(dft$gene)
library(ggplot2)

作图：

ggplot(data=dft,aes(x=disease,y=gene))+
  geom_tile(aes(fill=value),color="grey")+
  theme_minimal()+
  theme(panel.border = element_rect(fill=NA,color="black", size=1, linetype="solid"),
        panel.grid = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title = element_blank(),
        axis.text.x = element_text(angle=45,hjust=1, colour = 'black', size = 12),
        axis.text.y = element_text(colour = 'black', size = 12),
        plot.margin=unit(c(0.4,0.4,0.4,0.4),units=,"cm"))+
  scale_fill_manual(values = c('white','black'))+
  labs(fill="Disease\nassociation")

image.png

方法2：ComplexHeatmap

管它什么类型热图，只要是热图，ComplexHeatmap就能搞定。读入数据作图，效果和ggplot2一样。

library(ComplexHeatmap)
B <- read.csv("gene_dis.csv", header = T, row.names = 1)


Heatmap(B,
        cluster_rows = F,
        cluster_columns = F,
        show_column_names = T,
        show_row_names = T,
        row_names_side =  'left',
        column_title = NULL,
        heatmap_legend_param = list(
          title='Disease\nassociation'),
        col = c('white','black'),
        border = 'black',
        rect_gp = gpar(col = "grey", lwd = 1),
        row_names_gp = gpar(fontsize = 10),
        column_names_gp = gpar(fontsize = 10))

image.png

我们还可以为热图添加注释，黑白配上一点彩色，感觉还挺有艺术感。

disease <- c("disease1","disease2","disease3","disease4","disease5","disease6","disease7","disease8")
group <- c("Male_spc","Male_spc","Male_spc","Female_spc","Female_spc","Female_spc","MF","MF")
Group <- data.frame(disease, group)#创建数据框

top_anno=HeatmapAnnotation(df=Group,
                           border = T,
                           show_annotation_name = F,
                           col = list(group=c('Male_spc'='#006699',
                                              'Female_spc'='#993333',
                                              'MF'='#33CCCC')))

Heatmap(B,
        cluster_rows = F,
        cluster_columns = F,
        show_column_names = F,
        show_row_names = T,
        row_names_side =  'left',
        column_title = NULL,
        heatmap_legend_param = list(
          title='Disease\nassociation'),
        col = c('white','black'),
        border = 'black',
        rect_gp = gpar(col = "grey", lwd = 1),
        row_names_gp = gpar(fontsize = 10),
        column_names_gp = gpar(fontsize = 10),
        top_annotation = top_anno)

image.png

这就是热图得全部内容了，还是那句话，自己要学会探索学习，不能一味得依靠别人解决错误。通过一个小例子，也要学会拓展，才能创造！

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