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)