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gpnmb+ gpnmb-AT2 cell idling mapping Epithelial cell idling mapping
2022-08-03 08:34:00 【youngleeyoung】
这里是引用
library(Seurat)
library(dplyr)
library(cowplot)
library(ggplot2)
library(harmony)
library(patchwork)
library(openxlsx)
file = "G:/silicosis/sicosis/YSQ/2022-8-2_EPITHELIAL"## 改成想存放文件的路径
dir.create(file)
setwd(file)
getwd()
path=getwd()
load("G:\\silicosis\\sicosis\\YSQ\\3-23_EPITHELIAL\\silicosi_AT2.rds")
DotPlot(subset_data,features = "Gpnmb")
DotPlot(subset_data,features = "Gpnmb",group.by = "stim")
label = as.character(Idents(subset_data))
label
levels(label)
table(label)
label[which(label %in% c("AT2 cell-1", "AT2 cell-2", "Igha+ AT2 cell"))] = "AT2 cell"
label[which(label %in% "AT2 cell" & subset_data$RNA@counts["Gpnmb", ] > 0)] = "Gpnmb+ AT2 cell"
label[which(label %in% "AT2 cell" & subset_data$RNA@counts["Gpnmb", ] == 0)] = "Gpnmb- AT2 cell"
#label = factor(label, levels = c(levels(Idents(subset_data))[1:17], "Gpnmb+ AT2 cell", "Gpnmb- AT2 cell"))
table(label)
#label = factor(label, levels = c("Gpnmb+ AT2 cell", "Gpnmb- AT2 cell"))
subset_data$my_group=label
Idents(subset_data)=subset_data$my_group
Idents(subset_data)
markers_for_gpnmb_postive_and_gpnmb_negtive=FindMarkers(subset_data,ident.1 ="Gpnmb+ AT2 cell",
ident.2 = "Gpnmb- AT2 cell")
head(markers_for_gpnmb_postive_and_gpnmb_negtive)
getwd()
write.xlsx(markers_for_gpnmb_postive_and_gpnmb_negtive,file = "differential_markers_for_gpnmb_postive_and_gpnmb_negtive.xlsx",
col.names=T, row.names=T)
#save(subset_data,file = "subsetdata_for_gpnmb+_.rds")
load("G:\\silicosis\\sicosis\\YSQ\\2022-8-2_EPITHELIAL\\spatialMapping\\Gpnmb\\subsetdata_for_gpnmb+_.rds")
Idents(subset_data)=subset_data$stim
#2
##gpnmb阳性 And negative in idling on map
load("G:/silicosis/sicosis/silicosis-1122-merge/silicosis_cluster_merge.rds")## 17226 25002
#阳性的AT2细胞在四个组别空转上的分布;
{
getwd()
dir.create(paste(path, "spatialMapping", "Gpnmb", sep = "/"),recursive = TRUE)
setwd(paste(path, "spatialMapping", "Gpnmb", sep = "/"))
getwd()
label = as.character(Idents(All.merge))
label
#Igha+ AT2 cell
#Font error prone
label[which(label %in% c("AT2 cell-1", "AT2 cell-2", "Igha+ AT2 cell"))] = "AT2 cell"
label[which(label %in% "AT2 cell" & All.merge$RNA@counts["Gpnmb", ] > 0)] = "Gpnmb+ AT2 cell"
label[which(label %in% "AT2 cell" & All.merge$RNA@counts["Gpnmb", ] == 0)] = "Gpnmb- AT2 cell"
levels(label)
table(label)
label = factor(label, levels = c(levels(Idents(All.merge))[1:17], "Gpnmb+ AT2 cell", "Gpnmb- AT2 cell"))
levels(label)
levels(Idents(All.merge))
table(label)
All.merge.SCT <- CreateSeuratObject(counts = All.merge@assays$RNA@counts, project = "silicosis") #17226 26758
All.merge.SCT = SCTransform(All.merge.SCT, verbose = FALSE) %>% RunPCA(verbose = FALSE) #默认3000个HVG
All.merge.SCT$cell.type = label
table(All.merge.SCT$cell.type)
dim(All.merge.SCT@assays$SCT@scale.data)
cell.type = All.merge.SCT$cell.type
library(Seurat)
library(dplyr)
library(cowplot)
library(ggplot2)
library(RColorBrewer)
library(patchwork)
SpatialColors <- colorRampPalette(colors = rev(x = brewer.pal(n = 11, name = "Spectral")))
#################sio2_56
#load("/data/home/longmin/code_test/silicosis_scRNAseq/unknown/ST_mapping_1012/sio2_56_sct.rds")
getwd()
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_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$cell.type, prediction.assay = TRUE, dims = 1:30)
sio2_56_sct[["predictions"]] <- predictions.assay.sio2_56_sct
DefaultAssay(sio2_56_sct) <- "predictions"
save(sio2_56_sct,file="sio2_56_sct.prediction.rds")
rowSums(sio2_56_sct$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$predictions@data)[max_index]
max_cell_type = factor(max_cell_type, levels=levels(cell.type))
table(max_cell_type)
spot.mat = data.frame(table(max_cell_type))
colnames(spot.mat) = c("cell.type", "spot.num")
spot.num.score = merge(spot.mat, pre.mat.sum, by.x="cell.type", sort=F)
library(openxlsx)
write.xlsx(spot.num.score, "SiO2_7_cellType_spotNum_score.xlsx", col.names=T, row.names=F)
sio2_7_sct$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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