seurat包分析多组对比单细胞数据
library(Seurat) #import data #C_data T_data 为要分析的data.frame Control<-CreateSeuratObject(counts =C_data,min.cells = 5, min.features = 10,project = "control") Treat<-CreateSeuratObject(counts =T_data,min.cells = 5, min.features = 10,project = "treat") #将多个数据合成一个list T_C<-list(Treat,Control) names(T_C)<-c("T","C") #分别对每个找Variable Features for (i in 1:length(T_C)){ T_C[[i]]<-FindVariableFeatures(T_C[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE) } #找到交集的feature T_C<- FindIntegrationAnchors(object.list = T_C, dims = 1:20) #整合数据 T_C <- IntegrateData(anchorset = T_C, dims = 1:20) DefaultAssay(T_C) <- "integrated" # Run the standard workflow for visualization and clustering T_C <- ScaleData(T_C, verbose = FALSE) T_C <- RunPCA(T_C, npcs = 30, verbose = FALSE) # t-SNE and Clustering T_C <- RunUMAP(T_C, reduction = "pca", dims = 1:20) T_C <- FindNeighbors(T_C, reduction = "pca", dims = 1:20) T_C <- FindClusters(T_C, resolution = 0.5) # Visualization p1 <- DimPlot(T_C, reduction = "umap", group.by = "stim") p2 <- DimPlot(T_C, reduction = "umap", label = TRUE) plot_grid(p1, p2)
