ggplot画热图 合并细胞组合细胞 单细胞基因整体表达量 合并多个细胞整体表达量条形热图 合并热图

input

library(reshape2)
  my_av=melt(av,value.name = "value")
  head(my_av)
  colnames(my_av)=c('gene','cell_type','value')
  ggplot(my_av[round(runif(100,1,nrow(my_av))),],
         aes(x=gene,y=value,fill = cell_type),color=cell_type)+
    geom_boxplot()
  
  mydata=my_av[round(runif(100,1,nrow(my_av))),]
  head(mydata)
  p1=ggplot(mydata,aes(x=cell_type,y=gene,fill=value))
  p2 <- p1+geom_raster()+scale_fill_gradient2(low="#003366", high="#990033", mid="white")
  p2
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pheatmap::pheatmap(av[round(runif(100,1,nrow(av))),])
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DoHeatmap(sce.all,features = cg)
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``go
整体表达量，绘制细胞类型之间的平均值相关性热图 -------------------------------------------------------

先展示一下Marker基因在细胞类型之间的表达情况

dev.off()
FeaturePlot(sce.all,‘Ccl5’)+DimPlot(sce.all,label = T,repel = T)
ggsave(‘Lum-and-umap-celltype.pdf’,width = 5,height = 8)
av <-AverageExpression(sce.all,
group.by = “celltype”,
assays = “RNA”)
av=av[[1]]
head(av)
write.csv(av,file = ‘AverageExpression-celltype.csv’)
cg=names(tail(sort(apply(av, 1, sd)),1000))
pheatmap::pheatmap(cor(av[cg,]),display_numbers = T)
pheatmap::pheatmap(cor(av[cg,]),display_numbers = T,
file = ‘AverageExpression-celltype.pdf’)
dev.off()

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