#高级版的多个单细胞亚群的多个标记基因可视化
rm(list = ls())
library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
load(file = 'basic.sce.pbmc.Rdata')

DimPlot(pbmc, 
        reduction = 'umap', 
        label = TRUE, pt.size = 0.5) + NoLegend()
sce=pbmc

features= c('IL7R', 'CCR7','CD14', 'LYZ', 'IL7R', 'S100A4',"MS4A1", "CD8A",'FOXP3',
            'FCGR3A', 'MS4A7', 'GNLY', 'NKG7',
            'FCER1A', 'CST3','PPBP')
DotPlot(sce, 
        features = unique(features)) + RotatedAxis()

if (file.exists('sce.markers.all_10_celltype.Rdata')) {
  load('sce.markers.all_10_celltype.Rdata')
}else {
  sce.markers <- FindAllMarkers(object = sce, only.pos = TRUE,
                                min.pct = 0.25, 
                                thresh.use = 0.25)
  save(sce.markers,file = 'sce.markers.all_10_celltype.Rdata')

}
DT::datatable(sce.markers) 

library(dplyr) 
#不同seurat版本的avg_logFC 不一样 
top5 <- sce.markers %>% group_by(cluster) %>% top_n(5, avg_log2FC)
DoHeatmap(sce,
          top5$gene,
          size=3)

    image.png

    p <- DotPlot(sce, 
             features = unique(top5$gene) ,
             assay='RNA' )  + coord_flip()

p+ theme(axis.text.x = element_text(angle = 45, 
                                    vjust = 0.5, hjust=0.5))

    image.png

    head(top5)
top5=top5[!duplicated(top5$gene),]
select_genes_all=split(top5$gene,top5$cluster)
select_genes_all
DotPlot(object = sce, 
        features=select_genes_all, 
        assay = "RNA") + theme(axis.text.x = element_text(angle = 45, 
                                                          vjust = 0.5, hjust=0.5))

    image.png
    图 高级版本的多个单细胞亚群的多个标记基因可视化