gpnmb+ gpnmb-的AT2细胞在空转上的映射 mapping----3.2.2seurat版本

#因为seurat包3.2.2版本做单细胞和空转的映射mapping比较好，所以此处用3.2.2版本

#此处先把3.2.2的seurat版本安装在G盘，再加载
#设置包的路径
.libPaths()
.libPaths(c("G:/R_big_packages/","D:/Win10 System/Documents/R/win-library/4.1","C:/Program Files/R/R-4.1.0/library"))
#.libPaths("G:/R_big_packages/")
.libPaths()

'''
require(devtools)
install_version("Seurat", version = "3.2.2",
                repos = "http://cran.us.r-project.org")

'''#首先安装下面的包再安装seurat
#install.packages('https://cran.r-project.org/src/contrib/Archive/spatstat/spatstat_1.64-1.tar.gz', repos=NULL,type="source")


#Older versions of Seurat
#Old versions of Seurat, from Seurat v2.0.1 and up, are hosted in CRAN’s archive. To install an old version of Seurat, run:
  
  # Enter commands in R (or R studio, if installed)
  # Install the remotes package 
 # install.packages('remotes')
# Replace '2.3.0' with your desired version
#remotes::install_version(package = 'Seurat', version = package_version('3.2.2'))
  
#安装完成
library(Seurat)
session_info()














library(dplyr)
library(cowplot)
library(ggplot2)
library(patchwork)
library(openxlsx)




#load(file="G:/silicosis/sicosis/unknown/ST_mapping_1216/All.merge.SCT.rds")

#制作All.merge.SCT文件
table(All.merge.SCT$cell.type)
file = "G:/silicosis/sicosis/YSQ/2022-8-2_EPITHELIAL-4"##	改成想存放文件的路径
dir.create(file)
setwd(file)
getwd()
path=getwd()
getwd()
load("G:/silicosis/sicosis/silicosis-1122-merge/silicosis_cluster_merge.rds")## 17226 25002

label = as.character(Idents(All.merge))
label
table(label)
#Igha+ AT2  cell 
#字体容易出错
label[which(label %in% c("AT2 cell-1", "AT2 cell-2", "Igha+ AT2  cell"))] = "AT2 cell"
table(label)
label[1:100]

label[which(label %in% "AT2 cell" & All.merge$RNA@counts["Gpnmb", ] > 0)] = "Gpnmb+ AT2 cell"
table(label)
label[which(label %in% "AT2 cell" & All.merge$RNA@counts["Gpnmb", ] == 0)] = "Gpnmb- AT2 cell"
levels(label)
table(label)
table(Idents(All.merge))
label
levels(Idents(All.merge))
label = factor(label, levels = c(levels(Idents(All.merge))[1:17], "Gpnmb+ AT2 cell", "Gpnmb- AT2 cell"))
table(label)
Idents(All.merge)=label
table(Idents(All.merge))


All.merge.SCT <- CreateSeuratObject(counts = All.merge@assays$RNA@counts, project = "silicosis")	#17226	26758
All.merge.SCT = SCTransform(All.merge.SCT, verbose = FALSE) %>% RunPCA(verbose = FALSE)  #默认3000个HVG

#非常重要的步骤！！！#All.merge.SCT$cell.type = label
table(Idents(All.merge))
table(All.merge.SCT)
All.merge.SCT$cell.type=Idents(All.merge)

DotPlot(All.merge,features = "Gpnmb") 
DotPlot(All.merge,features = "S100a8")
DotPlot(All.merge.SCT,features = "Gpnmb",group.by = "cell.type")
getwd()


cell.type = All.merge.SCT$cell.type
#制作完成

#load(file="G:/silicosis/sicosis/unknown/ST_mapping_1216/All.merge.SCT.rds")









library(Seurat)
library(dplyr)
library(cowplot)
library(ggplot2)
library(RColorBrewer)
library(patchwork)
SpatialColors <- colorRampPalette(colors = rev(x = brewer.pal(n = 11, name = "Spectral")))





#加载sio2_56_sct
load("G:/silicosis/需求/矽肺-数据分析结果-0119--/矽肺-数据分析结果-0119/sio2_56_sct.rds")


anterset.sio2_56_sct <- FindTransferAnchors(reference = All.merge.SCT,
                                            query = sio2_56_sct,
                                            normalization.method="SCT")
predictions.assay.sio2_56_sct <- TransferData(anchorset = anterset.sio2_56_sct,
                                              refdata = All.merge.SCT$cell.type,
                                              prediction.assay = TRUE,
                                              dims = 1:30)
sio2_56_sct[["predictions"]] <- predictions.assay.sio2_56_sct
DefaultAssay(sio2_56_sct) <- "predictions"

rowSums(sio2_56_sct$predictions@data)
dim(sio2_56_sct$predictions@data)
sio2_56_sct$predictions@data = sio2_56_sct$predictions@data[c(levels(cell.type), "max"), ]

rowSums(sio2_56_sct$predictions@data)
pre.mat = sio2_56_sct$predictions@data[-dim(sio2_56_sct$predictions@data)[1],]
pre.mat.sum = data.frame(cell.type = rownames(pre.mat),score = rowSums(pre.mat))
pdf("SiO2_56_spot_cell_type_prediction_split.pdf")
for(i in levels(cell.type)){
  p <- SpatialPlot(sio2_56_sct, features=i, pt.size.factor=1.6)+scale_fill_gradientn(limits=c(0,1),colours=SpatialColors(n=100))+labs(title = "SiO2_56")
  print(p)
}
dev.off() 

num = nrow(sio2_56_sct$predictions@data)
max_index = apply(sio2_56_sct$predictions@data[-num,],2,which.max)
max_cell_type = rownames(sio2_56_sct$predictions@data)[max_index]
max_cell_type = factor(max_cell_type, levels=levels(cell.type))
table(max_cell_type)
spot.mat = data.frame(table(max_cell_type))
colnames(spot.mat) = c("cell.type", "spot.num")
spot.num.score = merge(spot.mat, pre.mat.sum, by.x="cell.type", sort=F)
write.xlsx(spot.num.score, "SiO2_56_cellType_spotNum_score.xlsx", col.names=T, row.names=F)
sio2_56_sct$spot.cell.type = max_cell_type

pdf("SiO2_56_spot_cell_type_prediction_all.pdf", width = 10)
SpatialPlot(sio2_56_sct, group.by="spot.cell.type")+labs(title = "SiO2_56")+guides(fill = guide_legend(override.aes = list(size = 5)))
dev.off() 

pdf("SiO2_56_max_spot_cellType.pdf")
for (i in levels(cell.type)){
  idx = which(max_cell_type == i)
  cell = colnames(sio2_56_sct)[idx]
  if(length(idx)!=0){
    p = SpatialPlot(sio2_56_sct, cells.highlight=cell)+theme(legend.position="None")+labs(title=i)
    print(p)
  }
}
dev.off()


#################sio2_7
#load("/data/home/longmin/code_test/silicosis_scRNAseq/unknown/ST_mapping_1012/sio2_7_sct.rds")
load("G:/silicosis/需求/矽肺-数据分析结果-0119-yll/矽肺-数据分析结果-0119/sio2_7_sct.rds")
anterset.sio2_7_sct <- FindTransferAnchors(reference = All.merge.SCT,query = sio2_7_sct,normalization.method="SCT")
predictions.assay.sio2_7_sct <- TransferData(anchorset = anterset.sio2_7_sct, refdata = All.merge.SCT$cell.type, prediction.assay = TRUE, dims = 1:30)
sio2_7_sct[["predictions"]] <- predictions.assay.sio2_7_sct
DefaultAssay(sio2_7_sct) <- "predictions"
# save(sio2_7_sct,file="sio2_7_sct.prediction.rds")
#load("G:\\silicosis\\sicosis\\YSQ\\2022-8-2_EPITHELIAL\\spatialMapping\\Gpnmb\\sio2_7_sct.prediction.rds")
rowSums(sio2_7_sct$predictions@data)
dim(sio2_7_sct$predictions@data)
sio2_7_sct$predictions@data = sio2_7_sct$predictions@data[c(levels(cell.type), "max"), ]
rowSums(sio2_7_sct$predictions@data)
pre.mat = sio2_7_sct$predictions@data[-dim(sio2_7_sct$predictions@data)[1],]
pre.mat.sum = data.frame(cell.type = rownames(pre.mat),score = rowSums(pre.mat))
pdf("SiO2_7_spot_cell_type_prediction_split.pdf")
for(i in levels(cell.type)){
  p <- SpatialPlot(sio2_7_sct, features=i, pt.size.factor=1.6)+scale_fill_gradientn(limits=c(0,1),colours=SpatialColors(n=100))+labs(title = "SiO2_7")
  print(p)
}
dev.off() 

num = nrow(sio2_7_sct$predictions@data)
max_index = apply(sio2_7_sct$predictions@data[-num,],2,which.max)
max_cell_type = rownames(sio2_7_sct$predictions@data)[max_index]
max_cell_type = factor(max_cell_type, levels=levels(cell.type))
table(max_cell_type)
spot.mat = data.frame(table(max_cell_type))
colnames(spot.mat) = c("cell.type", "spot.num")
spot.num.score = merge(spot.mat, pre.mat.sum, by.x="cell.type", sort=F)

write.xlsx(spot.num.score, "SiO2_7_cellType_spotNum_score.xlsx", col.names=T, row.names=F)
sio2_7_sct$spot.cell.type = max_cell_type

pdf("SiO2_7_spot_cell_type_prediction_all.pdf")
SpatialPlot(sio2_7_sct, group.by="spot.cell.type")+labs(title = "SiO2_7")+guides(fill = guide_legend(override.aes = list(size = 5)))
dev.off() 

pdf("SiO2_7_max_spot_cellType.pdf")
for (i in levels(cell.type)){
  idx = which(max_cell_type == i)
  cell = colnames(sio2_7_sct)[idx]
  if(length(idx)!=0){
    p = SpatialPlot(sio2_7_sct, cells.highlight=cell)+theme(legend.position="None")+labs(title=i)
    print(p)
  }
}
dev.off()

#################NS_56
#load("/data/home/longmin/code_test/silicosis_scRNAseq/unknown/ST_mapping_1012/NS_56_sct.rds")
#################NS_56
load("G:/silicosis/需求/矽肺-数据分析结果-0119-yll/矽肺-数据分析结果-0119/NS_56_sct.rds")

anterset.NS_56_sct <- FindTransferAnchors(reference = All.merge.SCT,query = NS_56_sct,normalization.method="SCT")
predictions.assay.NS_56_sct <- TransferData(anchorset = anterset.NS_56_sct, refdata = All.merge.SCT$cell.type, prediction.assay = TRUE, dims = 1:30)
NS_56_sct[["predictions"]] <- predictions.assay.NS_56_sct
DefaultAssay(NS_56_sct) <- "predictions"
#save(NS_56_sct,file="NS_56_sct.prediction.rds")
rowSums(NS_56_sct$predictions@data)
dim(NS_56_sct$predictions@data)
NS_56_sct$predictions@data = NS_56_sct$predictions@data[c(levels(cell.type), "max"), ]
rowSums(NS_56_sct$predictions@data)
pre.mat = NS_56_sct$predictions@data[-dim(NS_56_sct$predictions@data)[1],]
pre.mat.sum = data.frame(cell.type = rownames(pre.mat),score = rowSums(pre.mat))

pdf("NS_56_spot_cell_type_prediction_split.pdf")
for(i in levels(cell.type)){
  p <- SpatialPlot(NS_56_sct, features=i, pt.size.factor=1.6)+scale_fill_gradientn(limits=c(0,1),colours=SpatialColors(n=100))+labs(title = "NS_56")
  print(p)
}
dev.off() 

num = nrow(NS_56_sct$predictions@data)
max_index = apply(NS_56_sct$predictions@data[-num,],2,which.max)
max_cell_type = rownames(NS_56_sct$predictions@data)[max_index]
max_cell_type = factor(max_cell_type, levels=levels(cell.type))
table(max_cell_type)
spot.mat = data.frame(table(max_cell_type))
colnames(spot.mat) = c("cell.type", "spot.num")
spot.num.score = merge(spot.mat, pre.mat.sum, by.x="cell.type", sort=F)
write.xlsx(spot.num.score, "NS_56_cellType_spotNum_score.xlsx", col.names=T, row.names=F)
NS_56_sct$spot.cell.type = max_cell_type

pdf("NS_56_spot_cell_type_prediction_all.pdf")
SpatialPlot(NS_56_sct, group.by="spot.cell.type")+labs(title = "NS_56")+guides(fill = guide_legend(override.aes = list(size = 5)))
dev.off() 
pdf("NS_56_max_spot_cellType.pdf")
for (i in levels(cell.type)){
  idx = which(max_cell_type == i)
  cell = colnames(NS_56_sct)[idx]
  if(length(idx)!=0){
    p = SpatialPlot(NS_56_sct, cells.highlight=cell)+theme(legend.position="None")+labs(title=i)
    print(p)
  }
}
dev.off()


#################NS_7
#load("/data/home/longmin/code_test/silicosis_scRNAseq/unknown/ST_mapping_1012/NS_7_sct.rds")

load("G:/silicosis/需求/矽肺-数据分析结果-0119-yll/矽肺-数据分析结果-0119/NS_7_sct.rds")
anterset.NS_7_sct <- FindTransferAnchors(reference = All.merge.SCT,query = NS_7_sct,normalization.method="SCT")
predictions.assay.NS_7_sct <- TransferData(anchorset = anterset.NS_7_sct, refdata = All.merge.SCT$cell.type, prediction.assay = TRUE, dims = 1:30)
NS_7_sct[["predictions"]] <- predictions.assay.NS_7_sct
DefaultAssay(NS_7_sct) <- "predictions"
save(NS_7_sct,file="NS_7_sct.prediction.rds")
rowSums(NS_7_sct$predictions@data)
dim(NS_7_sct$predictions@data)
NS_7_sct$predictions@data = NS_7_sct$predictions@data[c(levels(cell.type), "max"), ]
rowSums(NS_7_sct$predictions@data)
pre.mat = NS_7_sct$predictions@data[-dim(NS_7_sct$predictions@data)[1],]
pre.mat.sum = data.frame(cell.type = rownames(pre.mat),score = rowSums(pre.mat))

pdf("NS_7_spot_cell_type_prediction_split.pdf")
for(i in levels(cell.type)){
  p <- SpatialPlot(NS_7_sct, features=i, pt.size.factor=1.6)+scale_fill_gradientn(limits=c(0,1),colours=SpatialColors(n=100))+labs(title = "NS_7")
  print(p)
}
dev.off() 

num = nrow(NS_7_sct$predictions@data)
max_index = apply(NS_7_sct$predictions@data[-num,],2,which.max)
max_cell_type = rownames(NS_7_sct$predictions@data)[max_index]
max_cell_type = factor(max_cell_type, levels=levels(cell.type))
table(max_cell_type)
spot.mat = data.frame(table(max_cell_type))
colnames(spot.mat) = c("cell.type", "spot.num")
spot.num.score = merge(spot.mat, pre.mat.sum, by.x="cell.type", sort=F)
write.xlsx(spot.num.score, "NS_7_cellType_spotNum_score.xlsx", col.names=T, row.names=F)
NS_7_sct$spot.cell.type = max_cell_type

pdf("NS_7_spot_cell_type_prediction_all.pdf")
SpatialPlot(NS_7_sct, group.by="spot.cell.type")+labs(title = "NS_7")+guides(fill = guide_legend(override.aes = list(size = 5)))
dev.off() 

pdf("NS_7_max_spot_cellType.pdf")
for (i in levels(cell.type)){
  idx = which(max_cell_type == i)
  cell = colnames(NS_7_sct)[idx]
  if(length(idx)!=0){
    p = SpatialPlot(NS_7_sct, cells.highlight=cell)+theme(legend.position="None")+labs(title=i)
    print(p)
  }
}
dev.off()






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