（reference：Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder）
读入数据
setwd("/Users/ks_ts/Documents/公众号文章/2022-11-22Complexheatmap热图聚类注释/")Enrich_df<- read.csv("./Enrich_df.csv", header = T, row.names = 1)library(dendextend) library(ComplexHeatmap)library(tibble)library(ggplot2)head(Enrich_df)
行聚类信息
Enrich_df1 <- as.data.frame(t(Enrich_df))#转置group_info <- as.data.frame(rownames(Enrich_df1))#做一个分组信息数据dend1 = cluster_between_groups(Enrich_df, group_info$`rownames(Enrich_df1)`)#聚类dend1 = color_branches(dend1, k = 4, col = c("#F5A089",'#CBA53C','#88CFC8','#66A5CA'))#设置聚类数，颜色 dend1 = dend1 %>% set("branches_lwd", 2) #聚类树的宽度dend1
查看聚类情况
HM <- Heatmap(as.matrix(Enrich_df1),cluster_rows = dend1, cluster_columns = F)#做一个热图看看效果HM
画热图
#颜色设置,需要是原始行名顺序group_info$cluser = cutree(dend1, k = 4)group_info$color = ifelse(group_info$cluser=='1', "#F5A089",ifelse(group_info$cluser=='2','#CBA53C',ifelse(group_info$cluser=='3','#88CFC8','#66A5CA')))col_cluster = group_info$colornames(col_cluster) <- group_info$`rownames(Enrich_df1)`#-----作图Heatmap(as.matrix(Enrich_df1),cluster_rows = dend1,#行聚类用前面设置好的、这样颜色也对应设置cluster_columns = F,show_column_names = T,show_row_names = T,row_names_side = 'left',column_title = NULL,heatmap_legend_param = list(title=' ',labels=c('Present','Absent'),direction = "horizontal",labels_gp = gpar(fontsize = 10),border='black'), #legend设置col = c('#F2F2F0','#5A8FCA'),rect_gp = gpar(col = "grey", lwd = 1),row_names_gp = gpar(col=col_cluster, fontsize=10),#设置文字颜色column_names_gp = gpar(fontsize = 10))
不过学习重在举一反三，我突然想到，有一点内容是可以用到和拓展的，那就是对于行列的注释，之前我们一直使用图形颜色注释，看起来费劲，那么是否可以直接将文字注释为不用的颜色呢？显然可以：
row_info = rowAnnotation(foo = anno_text(rownames(Enrich_df1),location = 0,just = "left",gp = gpar(col=col_cluster, fontsize=10)))Heatmap(as.matrix(Enrich_df1),cluster_rows = T,cluster_columns = F,show_column_names = T,show_row_names = F,column_title = NULL,heatmap_legend_param = list(title=' ',labels=c('Present','Absent'),labels_gp = gpar(fontsize = 10),border='black'),col = c('#F2F2F0','#5A8FCA'),rect_gp = gpar(col = "grey", lwd = 1),row_names_gp = gpar(col=col_cluster, fontsize=10),column_names_gp = gpar(fontsize = 10))+row_info
这样就完成注释了，不同的颜色代表不同组！ggplot2绘制
到这里，估计还有小伙伴想问ggplot2做怎么弄，就简单演示一下吧：ggplot2同样能够完成热图绘制，只不过没有那么灵活，需要将宽数据转化为长数据：
library(tidyr)library(dplyr)# 转换为长格式df_long <- Enrich_df1 %>%rownames_to_column("pathway") %>%pivot_longer(cols = -pathway, names_to = "gene", values_to = "value")#将value转化为因子，固定x轴基因顺序df_long$value <- as.factor(df_long$value)df_long$gene <- factor(df_long$gene,levels=colnames(Enrich_df1))#plotggplot(data=df_long,aes(x=gene,y=pathway))+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=90,hjust=1, vjust=0.5,colour = 'black', size = 10),axis.text.y = element_text(colour = 'black', size = 10),plot.margin=unit(c(0.4,0.4,0.4,0.4),units=,"cm"))+scale_fill_manual(values = c('#F2F2F0','#5A8FCA'))
绘制聚类：
library(ggtree)##行聚类phr <- hclust(dist(Enrich_df1),method = "ward.D2")#plot聚类树p_cluster <- ggtree(phr, branch.length ="none")p_cluster
组合：
#将value转化为因子，固定x轴基因顺序df_long$value <- as.factor(df_long$value)df_long$gene <- factor(df_long$gene,levels=colnames(Enrich_df1))#plotp = ggplot(data=df_long,aes(x=gene,y=pathway))+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=90,hjust=1, vjust=0.5,colour = 'black', size = 10),axis.text.y = element_text(colour = 'black', size = 10),plot.margin=unit(c(0.4,0.4,0.4,0.4),units=,"cm"))+scale_fill_manual(values = c("0" = '#F2F2F0', "1" = '#5A8FCA'),labels = c("0" = "Absent", "1" = "Present"), # 修改legend标签name = "" # 修改legend标题)library(aplot)p %>% insert_left(p_cluster, width =0.2)
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