#20230909尝试整合mPFC rAAV与V1 RV数据 install.packages("ape") RV_Infected<-RV_infected_mock_intergreted[,RV_infected_mock_intergreted$sample%in%"RV_infected"] RV_MOCK<-RV_infected_mock_intergreted[,RV_infected_mock_intergreted$sample%in%"MOCK"] rAAV_Infected<-all.Adult[,all.Adult$BC_label%in%"Barcoded"] rAAV_MOCK<-all.Adult[,all.Adult$BC_label%in%"Unbarcoded"] VIRUS_LIST<-list(RV_Infected,RV_MOCK,rAAV_Infected,rAAV_MOCK) virus_sample_name<- c('RV_infected', 'RV_MOCK',"rAAV_infected","rAAV_MOCK") for (i in 1:length(VIRUS_LIST)){ VIRUS_LIST[[i]]@meta.data$sample <- virus_sample_name[[i]]} p <- list() for (i in 1:4){ p[[i]] <- DimPlot(VIRUS_LIST[[i]], reduction = 'tsne') + labs(title = virus_sample_name[i]) + guides(colour=guide_legend(ncol=2, override.aes = list(size=2))) } plot_grid(plotlist = p, ncol =2) #整合 features <- SelectIntegrationFeatures(object.list = VIRUS_LIST) adult.anchors <- FindIntegrationAnchors(object.list = VIRUS_LIST, anchor.features = features) adult.inte <- IntegrateData(anchorset = adult.anchors) adult.inte <- ScaleData(adult.inte, verbose = FALSE) adult.inte <- RunPCA(adult.inte, npcs = 10, verbose = FALSE) adult.inte <- FindNeighbors(adult.inte, reduction = "pca", dims = 1:20) adult.inte <- FindClusters(adult.inte, resolution = 1) adult.inte <- RunUMAP(adult.inte, reduction = "pca", dims = 1:10) adult.inte <- RunTSNE(adult.inte, reduction = "pca", dims = 1:10) adult.inte$seurat_clusters VlnPlot(adult.inte, features = c("Snap25",'Gad1','Gad2',"Sox6", "Pvalb","Sst","Prox1","Vip", "Aldoc", "Slc1a3", "Aqp4", "Olig2", "Olig1","Pdgfra", "C1ql1","Fcrls", "Trem2", "Slc17a7",'Calb1','Cux2','Rorb','Bdnf', 'Ptn',"Col23a1", 'Tshz2','Cbln2','Grp',"Syt6", 'Pou3f1','Etv1','Adamts2','Dlk1','Npr3'), stack = TRUE, flip = TRUE, assay = 'RNA') + NoLegend() level_maintype<-c( "Pvalb","Sst","Vip/Lamp5","Astro","Oligo","Microglia","L2/3IT","L4/5IT","L6IT","L5NP","L6CT","L5ET") Idents(adult.inte) <- factor(Idents(adult.inte),levels=level_maintype) levels(Idents(adult.inte)) Idents(adult.inte) <- 'seurat_clusters' Idents(adult.inte) <- 'Maintype' adult.inte<- BuildClusterTree(object = adult.inte, dims=1:10) phy <- Tool(object = adult.inte, slot = 'BuildClusterTree') plot(phy) adult.inte$Maintype <- as.character(adult.inte$seurat_clusters) adult.inte$Maintype[which(adult.inte$Maintype %in% c(8))] <- "Pvalb" adult.inte$Maintype[which(adult.inte$Maintype %in% c(9))] <- "Sst" adult.inte$Maintype[which(adult.inte$Maintype %in% c(13,27))] <- "Vip/Lamp5" adult.inte$Maintype[which(adult.inte$Maintype %in% c(2,14))] <- "L2/3IT"#cux2 adult.inte$Maintype[which(adult.inte$Maintype %in% c(0,6))] <- "L4/5IT" adult.inte$Maintype[which(adult.inte$Maintype %in% c(3,4,23,25,19))] <- "L6IT" adult.inte$Maintype[which(adult.inte$Maintype %in% c(1))] <- "L6CT" adult.inte$Maintype[which(adult.inte$Maintype %in% c(11))] <- "L5ET" adult.inte$Maintype[which(adult.inte$Maintype %in% c(16))] <- "L5NP" adult.inte$Maintype[which(adult.inte$Maintype %in% c(10,15,21))] <- "Microglia" adult.inte$Maintype[which(adult.inte$Maintype %in% c(5,17,24,7))] <- "Astro" adult.inte$Maintype[which(adult.inte$Maintype %in% c(12,18,20,22,26))] <- "Oligo" adult.inte$Maintype[which(adult.inte$Maintype %in% c(7))] <- "7" adult.inte$Maintype[which(adult.inte$Maintype %in% c(19))] <- "19" Idents(adult.inte) <- 'Maintype' VlnPlot(adult.inte, features = c('Gad1',"Sox6","Pvalb","Sst","Prox1","Vip","Lamp5","Slc17a7","Col23a1", "Rorb","Foxp2","Bcl6","Ctss","Slc1a3","Mog"),group.by = "Maintype",stack = TRUE, flip = TRUE, fill.by="ident", assay = 'RNA') + NoLegend() DimPlot(adult.inte, reduction = 'umap', label = T, ncol = 2) DimPlot(adult.inte, split.by = 'sample', reduction = 'tsne', label = T, ncol = 2) + theme(axis.line = element_blank(), axis.ticks = element_blank(), axis.text = element_blank(), plot.title = element_text(size = 30)) + labs(x='', y='') saveRDS(adult.inte,"H:/Project1_RV Receptor Projection/FIG1.皮层单细胞RV rAAV感染数据分析/rAAV_RV.inte2.RDS") table(SLQ_IPC$predicted.id) SLQ_IPC$`ACB-I` SLQ_IPC$`BLA-I`