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❝2、10X-Visum空间转录组(2)---下游分析数据基本处理-降维聚类-可视化
3、10X-Visum空间转录组(3)---细胞注释|基因集评分
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# cellchat version：2.1.2# Seurat version：4.3.0#本次演示数据为10X visum空转#部分内容系个人理解，如果有偏差，欢迎指正交流！#安装R包devtools::install_github("jinworks/CellChat")library(Seurat)library(CellChat)library(patchwork)
setwd("/home/tq_ziv/data_analysis/10X空间转录组/spatial_cellchat/")load("~/data_analysis/10X空间转录组/Spatial_integrated_5_8.RData")#我们是整合样本，所以提取一个样本做演示一下单样本的空转互作分析即可names(Spatial_integrated)# [1] "Early1" "Early2" "Mid1" "Mid2" "Old1" "Old2"Early1_sp <- subset(Spatial_integrated, orig.ident=="Early1")Early1_sp[-which(names(Early1_sp) %in% "Early1")] <- NULL
data.input = GetAssayData(Early1_sp, slot = "data", assay = "SCT")meta = Early1_sp@meta.data[,c("orig.ident","clusterLevel")]#取注释的celltypecolnames(meta) <- c("group","labels")spatial.locs = GetTissueCoordinates(Early1_sp, scale = NULL, cols = c("imagerow", "imagecol")) #空间坐标scalefactors = jsonlite::fromJSON(txt = file.path("/home/tq_ziv/data_analysis/10X空间转录组/sptial/Early_R01_S1_spatial/", 'scalefactors_json.json'))spot.size = 65 # the theoretical spot size (um) in 10X Visiumconversion.factor = spot.size/scalefactors$spot_diameter_fullresspatial.factors = data.frame(ratio = conversion.factor, tol = spot.size/2)#构建为一个dataframe
cellchat_E1 <- createCellChat(object = data.input, meta = meta, group.by = "labels",datatype = "spatial", coordinates = spatial.locs, spatial.factors = spatial.factors)#我的是小鼠数据，用小鼠数据库CellChatDB <- CellChatDB.mousecellchat_E1 <- subsetData(cellchat_E1)#目的是选择在CellChatDB.use中的信号基因future::plan("multisession", workers = 5) #可以设置多线程cellchat_E1 <- identifyOverExpressedGenes(cellchat_E1)cellchat_E1 <- identifyOverExpressedInteractions(cellchat_E1, variable.both = F)#推断通讯网络cellchat_E1 <- computeCommunProb(cellchat_E1, type = "triMean", trim = 0.1,distance.use = TRUE, interaction.range = 250,scale.distance = 0.01,contact.dependent = TRUE, contact.range = 100)cellchat_E1 <- filterCommunication(cellchat_E1, min.cells = 10)cellchat_E1 <- computeCommunProbPathway(cellchat_E1)#互作网络整合,可以设置soure和target，我们这里默认了，就是全部的cellchat_E1 <- aggregateNet(cellchat_E1)
#figure1-互作数目groupSize <- as.numeric(table(cellchat_E1@idents))par(mfrow = c(1,2), xpd=TRUE)netVisual_circle(cellchat_E1@net$count, vertex.weight = rowSums(cellchat_E1@net$count), weight.scale = T, label.edge= F, title.name = "Number of interactions")netVisual_circle(cellchat_E1@net$weight, vertex.weight = rowSums(cellchat_E1@net$weight), weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")
#figure2-heatmap展示netVisual_heatmap(cellchat_E1, measure = "count", color.heatmap = "Blues")
#展示互作关系#figure3-通路水平-弦图pathways.show <- c("IGF")# Circle plotpar(mfrow=c(1,1), xpd = TRUE) # `xpd = TRUE` should be added to show the titlenetVisual_aggregate(cellchat_E1, signaling = pathways.show, layout = "circle")
#figure4-Spatial plot-空间展示par(mfrow=c(1,1))netVisual_aggregate(cellchat_E1, signaling = pathways.show,layout = "spatial", edge.width.max = 2,vertex.size.max = 1, alpha.image = 0.2, vertex.label.cex = 3.5)
#figure5-信号# Compute the network centrality scorescellchat_E1 <- netAnalysis_computeCentrality(cellchat_E1, slot.name = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathways# Visualize the computed centrality scores using heatmap, allowing ready identification of major signaling roles of cell groupspar(mfrow=c(1,1))netAnalysis_signalingRole_network(cellchat_E1, signaling = pathways.show, width = 8, height = 2.5, font.size = 10)
#figure8-可以直接输入受配体对spatialFeaturePlot(cellchat_E1, pairLR.use = "VEGFA_VEGFR1",point.size = 0.5, do.binary = FALSE, cutoff = 0.05,enriched.only = F, color.heatmap = "Blues", direction = 1)
