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)