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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