跟着Cell学单细胞转录组分析(七):细胞亚群分析及细胞互作

library(Seurat)immune <- subset(scedata, celltype=="Immune")immune <- ScaleData(immune, vars.to.regress = c("nCount_RNA", "percent.mt"), verbose = FALSE)immune <- FindVariableFeatures(immune, nfeatures = 4000)immune <- RunPCA(immune, npcs = 50, verbose = FALSE)immune <- FindNeighbors(immune, reduction = "pca", dims = 1:50)immune <- FindClusters(immune,                         resolution = seq(from = 0.1,                                          to = 1.0,                                          by = 0.2))immune <- RunUMAP(immune, reduction = "pca", dims = 1:50)library(clustree)clustree(immune)Idents(immune) <- "RNA_snn_res.0.5"immune$seurat_clusters <- immune@active.identDimPlot(immune, label = T,pt.size = 1)

immune_cellmarker <- c("CD3D",'CD3E','CD2',"CD4","CD8A",#T cell                       'CD79A','MZB1','MS4A1','CD79B',#B cell                       'FOXP3',"IL32",'TNFRSF18','TNFRSF4',#Treg                       'IL17A','IL17F','CD40LG',#Th17                       'S100A8','CXCL8','SOD2','NAMPT',#Neutrophil                       'SEPP1','C1QA','APOE','CD14','RNASE1',#Macrophage                       'TPSAB1','TPSB2','CPA3','HPGDS',#Mast                       'HLA-DRA','HLA-DPB1','CST3','HLA-DPA1',#mDC                       'PTGDS','SOX4','GZMB','IRF7',#pDC                       'IGHA1','IGHG1',"IGHG2",#Plasma                       'KLRF1','KLRD1','XCL2','XCL1'#NK                       )library(ggplot2)DotPlot(immune, features = immune_cellmarker)+  theme_bw()+  theme(panel.grid = element_blank(), axis.text.x=element_text(hjust = 1,vjust=0.5,angle=90))+  labs(x=NULL,y=NULL)+guides(size=guide_legend(order=3))+  scale_color_gradientn(values = seq(0,1,0.2),colours = c('#330066','#336699','#66CC66','#FFCC33'))

immune <- subset(immune, idents = c("1","8","9"), invert = TRUE)new.cluster.ids <- c("0"="Macrophage",                      "2"="T cell",                      "3"="Macrophage",                      "4"="mDC",                      "5"="Neutrophil",                      "6"="Macrophage",                      "7"="Macrophage",                      "10"="Mast")immune <- RenameIdents(immune, new.cluster.ids)                        immune$celltype <- immune@active.identDimPlot(immune, label = T,pt.size = 1,group.by = "celltype")

library(devtools)devtools::install_github("ShellyCoder/cellcall")library(cellcall)GM_immune <- subset(immune, group=="GM")test <- CreateObject_fromSeurat(Seurat.object= GM_immune, #seurat对象                                slot="counts",                                 cell_type="celltype", #细胞类型                                data_source="UMI",                                scale.factor = 10^6,                                 Org = "Homo sapiens") #物种信息mt <- TransCommuProfile(object = test,                        pValueCor = 0.05,                        CorValue = 0.1,                        topTargetCor=1,                        p.adjust = 0.05,                        use.type="median",                        probs = 0.9,                        method="mean",                        IS_core = TRUE,                        Org = 'Homo sapiens')

#有多少细胞类型就设置多少个颜色cell_color <- data.frame(color=c("#FF34B3","#BC8F8F","#20B2AA","#00F5FF","#FFA500"), stringsAsFactors = FALSE)rownames(cell_color) <- c("Macrophage","T cell","mDC","Neutrophil","Mast")#绘制互作图ViewInterCircos(object = mt, font = 2, cellColor = cell_color,                 lrColor = c("#F16B6F", "#84B1ED"),                arr.type = "big.arrow",arr.length = 0.04,                trackhight1 = 0.05, slot="expr_l_r_log2_scale",                linkcolor.from.sender = TRUE,                linkcolor = NULL, gap.degree = 0.5, #细胞类型多的话设置小点，不然图太大画不出来                trackhight2 = 0.032, track.margin2 = c(0.01,0.12), DIY = FALSE)

#可视化互作受配体关系viewPheatmap(object = mt, slot="expr_l_r_log2_scale", show_rownames = T,             show_colnames = T,treeheight_row=0, treeheight_col=10,             cluster_rows = T,cluster_cols = F,fontsize = 12,angle_col = "45",               main="score")