wxj platte

library(Seurat)
library(dplyr)
library(cowplot)
library(ggplot2)
library(harmony)
library(patchwork)
library(openxlsx)
#library('harmony')
rm(list=ls())file = "D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/0823/wxj_4_Platelet_"## 改成想存放文件的路径dir.create(file)
setwd(file)
getwd()#从sepsis_cluster_merge.rds获得选定的细胞群==load("D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/0805/cluster_merge/sepsis_cluster_merge.rds")##   改路径
as.matrix(table(Idents(All.merge), All.merge$stim))subset_data.count = All.merge@assays$RNA@counts[, which(All.merge$cell.type == "Platelet")]##15499  115
dim(subset_data.count)subset_data = CreateSeuratObject(counts = subset_data.count, project = "Platelet")
subset_data[["percent.mt"]] <- PercentageFeatureSet(subset_data, pattern = "^mt-")
nFeature_RNA=200
VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
dim(subset_data)subset_data = subset_data %>%Seurat::NormalizeData(verbose = FALSE) %>%  FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%ScaleData(verbose = FALSE) %>%RunPCA(npcs = 50, verbose = FALSE)
ElbowPlot(subset_data, ndims = 50)
VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## 466  510
subset_data@meta.data$stim <- c(rep("Exp", length(grep("1$",colnames(subset_data)))),rep("Con", length(grep("2$",colnames(subset_data)))))
table(subset_data$stim)##########################run harmony
#BiocManager::install('harmony')
library('harmony')
subset_data <- subset_data %>% RunHarmony("stim", plot_convergence = TRUE)
harmony_embeddings <- Embeddings(subset_data, 'harmony')
#######################cluster
dims = 1:30
subset_data <- subset_data %>% RunUMAP(reduction = "harmony", dims = dims) %>% RunTSNE(reduction = "harmony", dims = dims) %>% FindNeighbors(reduction = "harmony", dims = dims)
#save(subset_data, file="sepsis_Platelet.rds")
load("sepsis_Platelet.rds")load("D:\\Win10 System\\Documents\\WeChat Files\\wxid_f27yna03e0v622\\FileStorage\\File\\2023-01\\.RData")DotPlot(subset_data,features = "Fyn",split.by = "stim")
FeaturePlot(subset_data,features = "Fyn",split.by = "stim")
DimPlot(subset_data,features = "Fyn",split.by = "stim")DotPlot(subset_data,features = c("Fyn","Xrk8",'Lyn',  'Lck' ,'Syk', 'Txk' ,  'Plscr1', 'Ano6' ),split.by = "stim")DotPlot(subset_data,features = c("Fyn","Xrk8",'Lyn',  'Lck' ,'Syk', 'Txk' ,  'Plscr1', 'Ano6' ),group.by = "stim")+ggtitle("platelet")DotPlot(All.merge,features = c("Fyn","Xrk8",'Lyn',  'Lck' ,'Syk', 'Txk' , 'Plscr1', 'Ano6' ),group.by = "stim")+ggtitle("Endothelial cells")table(subset_data$stim)
print("platelet")
table(subset_endo$stim)DotPlot(subset_endo,features = c("Fyn", 'Plscr1', 'Ano6' ),group.by = "stim")+ggtitle("Endothelial cells")############################2023-1-31
load("D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/0805/cluster_merge/sepsis_cluster_merge.rds")##   改路径
as.matrix(table(Idents(All.merge), All.merge$stim))DotPlot(All.merge,features = c("Fyn"),group.by = "cell.type",split.by = "stim")+ggtitle("All cells")markers=c("Fyn",  "Plscr1","Ano6",  "Prkch",
"Lck", "Txk","Cd300a","Cd300lb","Cd300lf","Havcr2","Mfge8")#Kxr8, Rage 两个基因在单细胞里不存在
grep(pattern = "xr",rownames(All.merge),value = T)DotPlot(All.merge,features = markers,group.by = "cell.type",split.by = "stim")+RotatedAxis()+ggtitle("All cells")
# 把markers输出为pdf文件
getwd()
file = "D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/0823/wxj_4_Platelet_"##   改成想存放文件的路径dir.create(file)
setwd(file)
getwd()for (eachgene in markers) {DotPlot(All.merge,features = eachgene,group.by = "cell.type",split.by = "stim")+ggtitle("All cells")ggplot2::ggsave(filename = paste0(eachgene,".pdf"),width = 7,height = 9)}###############################33不需要运行
r = 0.2
if (TRUE)
{load(paste(file, "sepsis_Platelet.rds", sep="/"))dir.create(paste(file,r,sep="/"))setwd(paste(file,r,sep="/"))subset_data <- FindClusters(subset_data, resolution = r)save(subset_data, file=paste0("sepsis_Platelet_r", r, ".rds"))subset_data <- ScaleData(subset_data, verbose = FALSE, features = rownames(subset_data))pdf("1_Platelet_cluster_TSNE.pdf")p = DimPlot(subset_data, reduction = "tsne", label = TRUE, pt.size = 1,label.size = 6)print(p)dev.off()pdf("1_Platelet_cluster_UMAP.pdf")p = DimPlot(subset_data, reduction = "umap", label = TRUE, pt.size = 1,label.size = 6)print(p)dev.off()stat = cbind(table(Idents(subset_data), subset_data$stim), All = rowSums(table(Idents(subset_data), subset_data$stim)))stat = rbind(stat, All = colSums(stat))stat<-as.data.frame(stat)statwrite.xlsx(stat, "2_Platelet_cluster_stat.xlsx", col.names=T, row.names=T)######################## find markermarkers <- FindAllMarkers(subset_data,  min.pct = 0.25, logfc.threshold = 0.25, only.pos=T)write.xlsx(markers,"3_Platelet_cluster_markers.xlsx", col.names=T,row.names=F)
}r = 0.2
load(paste(file, "sepsis_Platelet.rds", sep="/"))
dir.create(paste(file,r,sep="/"))
setwd(paste(file,r,sep="/"))
subset_data <- FindClusters(subset_data, resolution = r)
save(subset_data, file=paste0("sepsis_Platelet_r", r, ".rds"))
subset_data <- ScaleData(subset_data, verbose = FALSE, features = rownames(subset_data))
pdf("1_Platelet_cluster_TSNE.pdf")
p = DimPlot(subset_data, reduction = "tsne", label = TRUE, pt.size = 1,label.size = 6)
print(p)
dev.off()
pdf("1_Platelet_cluster_UMAP.pdf")
p = DimPlot(subset_data, reduction = "umap", label = TRUE, pt.size = 1,label.size = 6)
print(p)
dev.off()
stat = cbind(table(Idents(subset_data), subset_data$stim), All = rowSums(table(Idents(subset_data), subset_data$stim)))
stat = rbind(stat, All = colSums(stat))
write.xlsx(stat, "2_Platelet_cluster_stat.xlsx", col.names=T, row.names=T)
######################## find marker
markers <- FindAllMarkers(subset_data,  min.pct = 0.25, logfc.threshold = 0.25, only.pos=T)
write.xlsx(markers,"3_Platelet_cluster_markers.xlsx", col.names=T,row.names=F)

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