inmt grem1 spatial am1 am3 空转cellchat

load("~/silicosis/spatial/cellchat2_spatial/2024-01-09 18_51_33.849761figures/trims_0.8cellchat.rds") #setwd("~/silicosis/spatial/cellchat2_spatial/2024-01-09 18_51_33.849761figures/") # DefaultAssay(d.all)="SCT" # [email protected] %>%head() # Idents(d.all) # brain=d.all # brain <- RunPCA(brain, assay = "SCT", verbose = FALSE) # brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30) # brain <- FindClusters(brain, verbose = FALSE) # brain <- RunUMAP(brain, reduction = "pca", dims = 1:30) # SpatialDimPlot(brain, label = TRUE, label.size = 3) getwd() # setwd("./cellchat2_spatial/2024-01-09 18_51_33.849761figures/") #输出单细胞类图表------ library(CellChat) { print(getwd()) df.net <- subsetCommunication(cellchat) head(df.net) openxlsx:: write.xlsx(df.net,file='0.Cell-Cell_Communications_At_L-R.xlsx', rowNames=F, colNames=T) df.net <- subsetCommunication(cellchat, slot.name = "netP") openxlsx:: write.xlsx(df.net,file='0.Cell-Cell_Communications_At_Pathway.xlsx', rowNames=F, colNames=T) groupSize <- as.numeric(table(cellchat@idents)) ## NumberOfInteractions mat <- cellchat@net$count openxlsx:: write.xlsx(mat, file='1.NumberOfInteractions.xlsx', rowNames=T, colNames=T) pdf("1.NumberOfInteractions.pdf") netVisual_circle(mat, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") dev.off() pdf("1.NumberOfInteractions_Split.pdf") for (i in 1:nrow(mat)) { mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat)) mat2[i, ] <- mat[i, ] p = netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i]) print(p) } dev.off() ## InteractionWeights mat <- cellchat@net$weight openxlsx:: write.xlsx(mat, file='2.InteractionWeights.xlsx', rowNames=T, colNames=T) pdf("2.InteractionWeights.pdf") netVisual_circle(mat, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength") dev.off() pdf("2.InteractionWeights_Split.pdf") for (i in 1:nrow(mat)) { mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat)) mat2[i, ] <- mat[i, ] p = netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i]) print(p) } dev.off() ## cellchat@netP$pathways: the signaling pathways showing significant communications pathways = cellchat@netP$pathways ## the left portion shows autocrine and paracrine signaling to certain cell groups of interest (i.e, the defined vertex.receiver) ## the right portion shows autocrine and paracrine signaling to the remaining cell groups in the dataset vertex.receiver = seq(1,2) pdf("3.Sig_Pathway_Hierarchy_Plot.pdf") for(i in pathways){ print(i) p = netVisual_aggregate(cellchat, signaling = i,vertex.receiver = vertex.receiver, layout='hierarchy' #, vertex.label.cex = 0.4 ) title(main = paste0(i,' signaling')) print(p) } dev.off() pdf("3.Sig_Pathway_Circle_Plot.pdf") for(i in pathways){ print(i) p = netVisual_aggregate(cellchat, signaling = i,vertex.receiver = vertex.receiver, layout = "circle") title(main = paste0(i,' signaling')) print(p) } dev.off() pdf("4.Sig_Pathway_L-R_pair_Contribution.pdf") for(i in pathways){ print(i) p = netAnalysis_contribution(cellchat, signaling = i, title = paste0(i, " signaling pathway", " Contribution of each L-R pair")) print(p) } dev.off() pdf("4.Sig_Pathway_L-R_pair_bubbleplot.pdf", width=25, height=20) p = netVisual_bubble(cellchat, remove.isolate = FALSE) print(p) dev.off() cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP") pdf("5.Signaling_Roles_Of_Cell_Groups_Heatmap.pdf") for(i in pathways){ print(i) p = netAnalysis_signalingRole_network(cellchat, signaling = i, width = 8, height = 2.5, font.size = 10) print(p) } dev.off() pdf("4.Sig_Pathway_L-R_pair_bubbleplot.pdf", width=25, height=20) p = netVisual_bubble(cellchat, remove.isolate = FALSE) print(p) dev.off() pdf("5.Signaling_Roles_Of_Cell_Groups_2D.pdf") p = netAnalysis_signalingRole_scatter(cellchat) print(p) dev.off() pdf("5.signals_Contribution_Of_Cell_Groups_Heatmap.pdf", width=10) ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing", font.size = 5) ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming", font.size = 5) print(ht1 + ht2) dev.off() save(cellchat, file = paste0("cellchat_","_.RData")) pdf('6_heatmap_counts_weight.pdf') p1=netVisual_heatmap(cellchat, measure = "count", color.heatmap = 'Blues') p2=netVisual_heatmap(cellchat, measure = "weight", color.heatmap = "Blues") print(p1) print(p2) dev.off() head(cellchat@netP) pdf('7_chord_signalling_cellgroups.pdf') for (i in cellchat@netP$pathways) { p= netVisual_chord_cell(cellchat,signaling = i) print(p) } dev.off() pdf('8_chord_LR_cellgroups.pdf') for (i in cellchat@netP$pathways) { p= netVisual_chord_gene(cellchat,sources.use = 1,targets.use = (3:4),slot.name = "net" ) print(p) } for (i in cellchat@netP$pathways) { p= netVisual_chord_gene(cellchat,sources.use = 1,targets.use = (3:4),slot.name = "netP" ) print(p) } dev.off() library(NMF) library(ggalluvial) library(CellChat) selectK(cellchat,pattern = "outgoing") .libPaths() npatterns=3 pdf('9_patterns_outcoming3.pdf' ) par(mfrow=c(1,2)) cellchat=identifyCommunicationPatterns(cellchat,pattern = "outgoing",k=npatterns, heatmap.show = TRUE,height = 10) dev.off() pdf("9_river_outgoning_pattern3.pdf",width = 9) p=netAnalysis_river(cellchat,pattern = "outgoing") print(p) dev.off() pdf("9_dotplot_outgoing_pattern3.pdf" ) p=netAnalysis_dot(cellchat,pattern = 'outgoing') print(p) dev.off() getwd() #selectK(cellchat,pattern = "incoming") pdf('9_patterns_incoming 3.pdf' ) par(mfrow=c(1,2)) # cellchat=identifyCommunicationPatterns(cellchat,pattern = "incoming",k=npatterns,height = 9) cellchat=identifyCommunicationPatterns(cellchat,pattern = "incoming",k=3,height = 15) dev.off() pdf('9_patterns_incoming 3.pdf' ) par(mfrow=c(1,2)) # cellchat=identifyCommunicationPatterns(cellchat,pattern = "incoming",k=npatterns,height = 9) cellchat=identifyCommunicationPatterns(cellchat,pattern = "incoming",k=3,height = 20) dev.off() pdf("9_river_incoming_pattern3.pdf",width = 9) p=netAnalysis_river(cellchat,pattern = "incoming") print(p) dev.off() pdf("9_dotplot_incoming_pattern3.pdf" ) p=netAnalysis_dot(cellchat,pattern = 'incoming') print(p) dev.off() npatterns=2 pdf('patterns_outcoming2.pdf' ) par(mfrow=c(1,2)) cellchat=identifyCommunicationPatterns(cellchat,pattern = "outgoing",k=npatterns, heatmap.show = TRUE,height = 9) dev.off() pdf("9_river_outgoning_pattern2.pdf",width = 9) p=netAnalysis_river(cellchat,pattern = "outgoing") print(p) dev.off() pdf("9_dotplot_outgoing_pattern2.pdf" ) p=netAnalysis_dot(cellchat,pattern = 'outgoing') print(p) dev.off() getwd() #selectK(cellchat,pattern = "incoming") pdf('patterns_incoming2.pdf' ) par(mfrow=c(1,2)) cellchat=identifyCommunicationPatterns(cellchat,pattern = "incoming",k=npatterns,height = 9) cellchat=identifyCommunicationPatterns(cellchat,pattern = "incoming",k=3,height = 9) dev.off() pdf("9_river_incoming_pattern2.pdf",width = 9) p=netAnalysis_river(cellchat,pattern = "incoming") print(p) dev.off() pdf("9_dotplot_incoming_pattern2.pdf" ) p=netAnalysis_dot(cellchat,pattern = 'incoming') print(p) dev.off() } 13#可视化--- ptm = Sys.time() groupSize <- as.numeric(table(cellchat@idents)) par(mfrow = c(1,2), xpd=TRUE) netVisual_circle(cellchat@net$count, vertex.weight = rowSums(cellchat@net$count), weight.scale = T, label.edge= F, title.name = "Number of interactions") netVisual_circle(cellchat@net$weight, vertex.weight = rowSums(cellchat@net$weight), weight.scale = T, label.edge= F, title.name = "Interaction weights/strength") color.heatmap <- colorRampPalette(c("lightpink", "red", "darkred"))(9) netVisual_heatmap(cellchat, measure = "count", color.heatmap = c('white','blue')) netVisual_heatmap(cellchat, measure = "count", color.heatmap = 'Blues') netVisual_heatmap(cellchat, measure = "weight", color.heatmap = "Blues") cellchat@netP$pathways pathways.show <- c("SPP1") # Circle plot par(mfrow=c(1,1)) 14#空间可视化 # Spatial plot library(ggplot2) # Assuming ggplot2 is used for plotting # Define the slice names to iterate over slice.names <- c("SiO2_7", "SiO2_56", "NS_56", "NS_7") # Initiate a PDF file for output pdf("cellchat_aggregate_spatial_plots.pdf") # Loop through each slice name and generate the plot for (slice.name in slice.names) { p <- netVisual_aggregate(cellchat, signaling = pathways.show, slice.use = slice.name, layout = "spatial", edge.width.max = 2, vertex.size.max = 1, alpha.image = 0.2, vertex.label.cex = 0) print(p) # Print the plot to the PDF } # Close the PDF file dev.off() # Setting `vertex.label.cex = 0` to hide the labels on the spatial plot execution.time = Sys.time() - ptm print(as.numeric(execution.time, units = "secs")) #> [1] 1.171192 # Compute the network centrality scores cellchat <- netAnalysis_computeCentrality(cellchat, 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 groups par(mfrow=c(1,1)) netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 8, height = 2.5, font.size = 10) par(mfrow=c(1,1)) netVisual_aggregate(cellchat, signaling = pathways.show, slice.use = c("SiO2_56"), layout = "spatial", edge.width.max = 2, alpha.image = 0.2, vertex.weight = "incoming", vertex.size.max = 6, vertex.label.cex = 0) netAnalysis_contribution(cellchat, signaling = pathways.show) # Take an input of a few genes spatialFeaturePlot(cellchat, features = c("Tgfb1"), slice.use = "SiO2_7", point.size = 0.8, color.heatmap = "Reds", direction = 1) print(getwd()) #request 2 .libPaths(c( "/home/data/t040413/R/x86_64-pc-linux-gnu-library/4.2", "/home/data/t040413/R/yll/usr/local/lib/R/site-library", "/home/data/refdir/Rlib/", "/usr/local/lib/R/library")) # SiO2_7, NS_7, SiO2_56, NS_56 setwd('/home/data/t040413/silicosis/spatial/') dir.create("~/silicosis/spatial/cellchat2_spatial/am1_am3_inmt_grem1") setwd("~/silicosis/spatial/cellchat2_spatial/am1_am3_inmt_grem1");print(getwd()) library(Seurat) library(dplyr) library(CellChat) load("~/silicosis/spatial/cellchat2_spatial/2024-01-09 18_51_33.849761figures/trims_0.8cellchat.rds") table(cellchat@idents) lrs=cellchat@LR$LRsig # Take an input of a ligand-receptor pair spatialFeaturePlot(cellchat, pairLR.use = "TGFB1_TGFBR1_TGFBR2", slice.use = "SiO2_7", point.size = 0.5, do.binary = FALSE, cutoff = 0.05, enriched.only = F, color.heatmap = c('blue','white','red'), direction = 1) # Take an input of a ligand-receptor pair and show expression in binary spatialFeaturePlot(cellchat, pairLR.use = "BMP2_BMPR1A_ACVR2A", slice.use = "SiO2_7", point.size = 1.5, do.binary = TRUE, cutoff = 0.05, enriched.only = F, color.heatmap = "Reds", direction = 1) # Take an input of a ligand-receptor pair and show expression in binary spatialFeaturePlot(cellchat, pairLR.use = "BMP2_BMPR1A_ACVR2A", slice.use = "SiO2_7", point.size = 1.5, do.binary = TRUE, cutoff = 0.05, enriched.only = F, color.heatmap = "Reds", direction = 1) load("~/silicosis/spatial/cellchat2_spatial/2024-