DoHeatmap的优化+ComplexHeatmap绘制带特定基因的单细胞热图

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单细胞绘图系列：

• Seurat绘图函数总结
• 使用ggplot2优化Seurat绘图
• scRNAseq灵活的点图绘制：FlexDotPlot
• 富集分析结果雷达图

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1. 使用DoHeatmap绘制Seurat自带热图

library(Seurat)
library(dplyr)
pbmc <- readRDS("pbmc.rds") #导入注释好的演示数据集
pbmc.markers <- FindAllMarkers(pbmc,only.pos = T,min.pct = 0.1,logfc.threshold = 0.25)
top10 <- pbmc.markers%>%group_by(cluster)%>%top_n(n=10,wt=avg_log2FC)
DoHeatmap(pbmc,features = top10$gene)+NoLegend()
##DoHeatmap输入的的是scale.data矩阵

稍作优化（调整热图颜色+调整细胞类型标签颜色，最好与UMAP图一致）

DoHeatmap(pbmc,   
          features = as.character(unique(top10$gene)),   
          group.by = "cell_type",  
          assay = "RNA",  
          group.colors = c("#C77CFF","#7CAE00","#00BFC4","#F8766D","#AB82FF","#90EE90","#00CD00","#008B8B"))+  #设置组别颜色
  scale_fill_gradientn(colors = c("navy","white","firebrick3"))

scale_fill_gradientn()系列函数用法见：ggplot2点图

更改横轴顺序（根据实际需要）

pbmc$cell_type <- factor(x = pbmc$cell_type, levels = c('Naive CD4 T','Memory CD4 T','CD8 T','CD14+ Mono','FCGR3A+ Mono','B','NK','DC','Platelet'))  
DoHeatmap(pbmc,   
          features = as.character(unique(top10$gene)),   
          group.by = "cell_type",  
          assay = "RNA",  
          group.colors = c("#C77CFF","#7CAE00","#00BFC4","#F8766D","#AB82FF","#90EE90","#00CD00","#008B8B","#FFA500"))+ #设置组别颜色
  scale_fill_gradientn(colors = c("navy","white","firebrick3"))#设置热图颜色  

2. 使用ComplexHeatmap绘制带特定基因的热图

ComplexHeatmap
优点：功能非常强大，支持一张热图中分组分别聚类（control之间聚类，treatment之间聚类）
缺点：参数基本上只适用于这一个包
参考：https://jokergoo.github.io/ComplexHeatmap-reference/book/

library(ComplexHeatmap)
##提取标准化表达矩阵
#⚠️提取scale.data矩阵的时候一定要注意，做ScaleData()的时候一定是scale了所有的基因，而不是默认的2000个基因 
mat <- GetAssayData(pbmc,slot = 'scale.data')
##获得基因和细胞聚类信息
gene_features <- top10
cluster_info <- sort(pbmc$cell_type)
##筛选矩阵
mat <- as.matrix(mat[top10$gene,names(cluster_info)])
##输入想在图上展示出来的marker基因，获得基因在热图中的位置信息 
gene <- c('CD3E','CD8B','S100A9','CD14','LYZ',"CD79B","GNLY","GZMB","PF4")
gene_pos <- which(rownames(mat)%in%gene)
row_anno <- rowAnnotation(gene=anno_mark(at=gene_pos,labels = gene))
##画个热图看看
Heatmap(mat,
        cluster_rows = FALSE,
        cluster_columns = FALSE,
        show_column_names = FALSE,
        show_row_names = FALSE,
        column_split = cluster_info,
        right_annotation = row_anno)

做一下美化，调整一下颜色

##设置列标签的颜色，最好和umap/tsne细胞群的颜色一一对应 
col <- c('plum','coral1','bisque','gold2','hotpink3','lightsalmon3','rosybrown2','lightcyan2','thistle3')
names(col) <- levels(cluster_info)
top_anno <- HeatmapAnnotation(cluster=anno_block(gp=gpar(fill=col),
                                                 labels = levels(cluster_info),
                                                 labels_gp = gpar(cex=0.5,col='white')))
##给热图改个好看的颜色
library(circlize)
col_fun = colorRamp2(c(-2, 1, 4), c("#377EB8", "white", "#E41A1C"))
#col_fun2 = colorRamp2(c(-2, 1, 4), c("#92b7d1", "white", "#d71e22"))
Heatmap(mat,
        cluster_rows = FALSE,
        cluster_columns = FALSE,
        show_column_names = FALSE,
        show_row_names = FALSE,
        column_split = cluster_info,
        top_annotation = top_anno, #在热图边上增加注释
        column_title = NULL,
        right_annotation = row_anno,
        heatmap_legend_param = list(
          title='Expression',
          title_position='leftcenter-rot'),
        col = col_fun)

完成～