scMayoMap自动注释-应用版

rm(list=ls())
load('./scRNA_after_sctype.Rdata')
pkgs <- c("ggplot2", "dplyr","tidyr","tibble","reshape2")
sapply(pkgs, require, character.only = TRUE)
library(scMayoMap)
scMayoMapDatabase1<-scMayoMapDatabase
table(scMayoMapDatabase1$tissue)

# adipose tissue                bladder                  blood 
# 659                    456                    767 
# bone            bone marrow                  brain 
# 587                   1059                   2902 
# breast                 embryo                    eye 
# 593                   1250                    356 
# gastrointestinal tract                  heart                 kidney 
# 1195                    518                   1856 
# liver                   lung          mammary gland 
# 986                   1681                    666 
# muscle                  other                  ovary 
# 797                    539                    543 
# pancreas               placenta               prostate 
# 1033                    294                    144 
# skin                 spleen                stomach 
# 659                    864                    203 
# testis                 thymus                  tooth 
# 587                     37                    132 
# uterus 
# 295 

Idents(sce)<-'RNA_snn_res.0.8'
seurat.markers <- FindAllMarkers(sce, method = 'MAST')
scMayoMap.obj <- scMayoMap(data = seurat.markers, database=scMayoMapDatabase, tissue = 'liver')
plt <- scMayoMap.plot(scMayoMap.object = scMayoMap.obj)

res_markers<-scMayoMap.obj$markers
gns <- scMayoMap.obj$markers$genes[scMayoMap.obj$markers$cluster==0 &scMayoMap.obj$markers$celltype=='B cell']
gns <- strsplit(gns, ',')[[1]]
DotPlot(sce, features = gns)
res <- scMayoMap.obj$res

    sce
  
    celltype=data.frame(ClusterID=0:21 ,
                        celltype= 0:21) 
    #定义细胞亚群        
    celltype[celltype$ClusterID %in% c( 9,11,16,17,20 ),2]='B cell'
    celltype[celltype$ClusterID %in% c(  1,5,6,8,10,13 ),2]='Kupffer cell'
    celltype[celltype$ClusterID %in% c( 0,3,4 ),2]='T cell'  
    celltype[celltype$ClusterID %in% c(  7  ),2]='Monocyte'
    celltype[celltype$ClusterID %in% c( 2,18,19 ),2]='Neutrophil'   
    celltype[celltype$ClusterID %in% c( 21 ),2]='Epithelial cell'
    celltype[celltype$ClusterID %in% c( 12 ),2]='Endothelial cell'   
    celltype[celltype$ClusterID %in% c( 14,15 ),2]='Liver bud hepatic cell'
    
    head(celltype)
    celltype
    table(celltype$celltype)
    sce@meta.data$celltype = "NA"
    
    for(i in 1:nrow(celltype)){
      sce@meta.data[which(sce@meta.data$RNA_snn_res.0.8 == celltype$ClusterID[i]),'celltype'] <- celltype$celltype[i]}
table(sce$celltype)
DimPlot(sce,group.by = 'celltype',label = T)

library(gplots) 
balloonplot(table(sce$celltype,sce$customclassif))