cellchat最新完整版本

 

getwd()


dir.create("~/silicosis/fibroblast_20230930_universal_special/cellchat_fibroblast_macrohpages",recursive = TRUE)

setwd("~/silicosis/fibroblast_20230930_universal_special/cellchat_fibroblast_macrohpages")



dir.create("~/silicosis/fibroblast_20230930_universal_special/cellchat_fibroblast_macrohpages")

setwd("~/silicosis/fibroblast_20230930_universal_special/cellchat_fibroblast_macrohpages")





library(CellChat)

#####1 macrophage----
load("~/silicosis/silicosis_cluster_merge.rds")
library(Seurat)
library(ggplot2)
macrophage=subset(All.merge,idents=c('IM','AM1','AM2','AM3'))
#macrophage$cell.type=factor(macrophage$cell.type,levels = c('IM','AM1','AM2','AM3'))
macrophage$group=ifelse(grepl(pattern = 'NS',x = macrophage$stim),'NS','SiO2')
DimPlot(macrophage,label = TRUE)
macrophage$cell_type=Idents(macrophage)

#####2 fibroblast-----
dir.create("~/silicosis/fibroblast_myofibroblast/")
load("~/silicosis/fibroblast_myofibroblast/subset_data_fibroblast_myofibroblast.rds")


DimPlot(subset_data,label = TRUE,repel = TRUE)
DimPlot(subset_data,label = TRUE,split.by = 'group',repel = TRUE,label.size = 8)
head([email protected])

FeaturePlot(subset_data,features = c("Scgb1a1",'Epcam','Spp1','Inmt'))


ggplot([email protected], 
       aes(x=group, fill=Idents(subset_data))) + geom_bar(position = "fill")


#######3 fibro-macrophages---------
subset_data2=merge(subset_data,macrophage)

#DimPlot(subset_data2,label = TRUE)

subset_data=subset_data2
subset_data=subset_data2[,grep(pattern = "Inflammatory fibroblast",x = Idents(subset_data2),invert = TRUE) ]

subset_data[["percent.mt"]] <- PercentageFeatureSet(subset_data, pattern = "^mt-")
VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

library(dplyr)
subset_data = subset_data %>%
  Seurat::NormalizeData(verbose = FALSE) %>%  
  FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
  ScaleData(verbose = FALSE) %>%
  RunPCA(npcs = 50, verbose = FALSE)
ElbowPlot(subset_data, ndims = 50)
VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)


## 466	510
#[email protected]$stim <- c(rep("Exp", length(grep("1$",colnames(subset_data)))),rep("Con", length(grep("2$",colnames(subset_data)))))
#table(subset_data$stim)

library('harmony')

subset_data <- subset_data %>% RunHarmony("stim", plot_convergence = TRUE)
harmony_embeddings <- Embeddings(subset_data, 'harmony') 
#######################cluster
dims = 1:30
subset_data <- subset_data %>% 
  RunUMAP(reduction = "harmony", dims = dims) %>% 
  RunTSNE(reduction = "harmony", dims = dims) %>% 
  FindNeighbors(reduction = "harmony", dims = dims)

subset_data=FindClusters(subset_data,resolution =0.3)
DimPlot(subset_data,label = TRUE,repel = TRUE)
DimPlot(subset_data,label = TRUE,repel = TRUE,reduction = 'tsne')

head([email protected])

#########cellchat-------

#library(dplyr)
library(cowplot)
library(Seurat)
library(harmony)
library(ggplot2)
require(Matrix)
require(magrittr)
library(openxlsx)
#BiocManager::install('CellChat',force = TRUE)
#devtools::install_github("jinworks/CellChat",dependencies = TRUE)
library(CellChat)


getwd()
table(Idents(subset_data))
table(subset_data$group)
#subset_data$stim=subset_data$group
print(getwd())
DimPlot(subset_data,label =TRUE,group.by = 'cell.type',reduction = 'tsne')

table(Idents(subset_data))
Idents(subset_data)=subset_data$cell_type

path=getwd()

#head([email protected])
#Idents(subset_data)
#table(subset_data$stim)
##	CellChat
dir.create(paste(path, "CellChat", sep = "/"))
setwd(paste(path, "CellChat", sep = "/"))
getwd()
#setwd("../")
dev.off()




for(stim in unique(subset_data$stim)){
  #stim="NS_56"
  #stim="SiO2_7","NS_7"
  print(stim)
  path="~/silicosis/fibroblast_20230930_universal_special/cellchat_fibroblast_macrohpages"
  dir.create(paste(path, "cellchat", stim, sep = "/"),recursive = TRUE)
  setwd(     paste(path, "cellchat", stim, sep = "/")  )
  getwd()
  
  
  data.input = subset_data$RNA@data[, subset_data$stim==stim]
  head(data.input)
  
  meta = data.frame(labels = Idents(subset_data)[subset_data$stim==stim], row.names = colnames(subset_data)[subset_data$stim==stim])
  head(meta)
  
  meta$labels = droplevels(meta$labels, exclude = setdiff(levels(meta$labels),unique(meta$labels)))
  
  cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")
  CellChatDB <- CellChatDB.mouse
  cellchat@DB <- CellChatDB
  cellchat <- subsetData(cellchat)
  #future::plan("multiprocess", workers = 4) 
  cellchat <- identifyOverExpressedGenes(cellchat)
  cellchat <- identifyOverExpressedInteractions(cellchat)
  cellchat <- projectData(cellchat, PPI.mouse)
  cellchat <- computeCommunProb(cellchat)  #不可有有为0的细胞数的组
  cellchat <- filterCommunication(cellchat, min.cells = 0) #设置最低细胞数 通常为10
  cellchat <- computeCommunProbPathway(cellchat)
  cellchat <- aggregateNet(cellchat)
  df.net <- subsetCommunication(cellchat)
  write.xlsx(df.net,file='0.Cell-Cell_Communications_At_L-R.xlsx', rowNames=F, colNames=T)
  df.net <- subsetCommunication(cellchat, slot.name = "netP")
  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
  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()
  
  mat
  #devtools::install_github("igraph/rigraph")
  
  # Error in i_set_edge_attr(x, attr(value, "name"), index = value, value = attr(value,  : 
  #                                                                                Length of new attribute value must be 1 or 9, the number of target edges, not 2
  pdf("1.NumberOfInteractions_Split.pdf")
  for (i in 1:nrow(mat)) {
    #i=1
    mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
    mat2
    
    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
  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()
  
  #install.packages('igraph',version='1.3.5')
  
  pdf("2.InteractionWeights_Split--.pdf")
  library(igraph)
  for (i in 1:nrow(mat)) {
    # i=4
    print(i)
    mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
    mat2[i, ] <- mat[i, ]
    mat2
    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
  getwd()
  
  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(1,5)
  print(getwd())
  
  
  pdf("3.Sig_Pathway_Hierarchy_Plot_.pdf")
  for(i in pathways){
    #i=1
    print(i)
    p = netVisual_aggregate(cellchat, signaling = i,vertex.receiver = vertex.receiver, 
                            vertex.label.cex = 0.4,layout = 'hierarchy')
    title(main = paste0(i,' signaling'))
    print(p)
  }
  dev.off()
  getwd()
  # a=1
  # a
# getwd()
# setwd("./cellchat/SiO2_7/")
#   load("./cellchat/SiO2_7/cellchat_SiO2_7_.RData")
#   pathways = cellchat@netP$pathways
#  
   library(CellChat)
    pdf("3.Sig_Pathway_Circle_Plot_.pdf")
  for(i in pathways){
    print(i)
    p = netVisual_aggregate(cellchat,show.legend = TRUE, 
                            signaling = i,signaling.name = paste(i,"sigaling"),
                            ,pt.title=10,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_",stim,"_.RData"))
}



# for(i in unique(subset_data$stim)){
#   setwd( paste(path, "cellchat", i, sep = "/")  )
#   load("cellchat.RData")
#   pdf("4.Sig_Pathway_L-R_pair_bubbleplot--.pdf", width=25, height=20)
#   p = netVisual_bubble(cellchat, remove.isolate = FALSE)
#   print(p)
#   dev.off()
# }