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devtools::install_github("jinworks/CellChat")library(CellChat)#version 2.1.2setwd("D:\\KS项目\\公众号文章\\cellchat受配体数据库更新及自定义")
#Step 1: 加载数据库文件interaction_input <- read.csv(file = './cpdb数据库文件/interaction_input.csv')complex_input <- read.csv(file = './cpdb数据库文件/complex_input.csv', row.names = 1)geneInfo <- read.csv(file = './cpdb数据库文件/gene_input.csv')#Step 2: 修改文件，以期让其适用于cellchat#gene information---需要有一列列名是“Symbol”geneInfo$Symbol <- geneInfo$hgnc_symbol#将基因名那一列命名为SymbolgeneInfo <- select(geneInfo, -c("ensembl"))geneInfo <- unique(geneInfo)#去除重复行#interaction_input---获取受配体信息（选择有用的，去除冗余的信息）# get the ligand information---添加一列ligand，并将其转化为symbol（大家熟悉的形式，cpdb这里写的是protein uniprot）idx_partnerA <- match(interaction_input$partner_a, geneInfo$uniprot)idx.use <- !is.na(idx_partnerA)interaction_input$ligand <- interaction_input$partner_ainteraction_input$ligand[idx.use] <- geneInfo$hgnc_symbol[idx_partnerA[idx.use]]# get the receptor information---添加一列receptoridx_partnerB <- match(interaction_input$partner_b, geneInfo$uniprot)idx.use <- !is.na(idx_partnerB)interaction_input$receptor <- interaction_input$partner_binteraction_input$receptor[idx.use] <- geneInfo$hgnc_symbol[idx_partnerB[idx.use]]# get other informationinteraction_input$interaction_name <- interaction_input$interactorsinteraction_input$interaction_name_2 <- interaction_input$interaction_nameinteraction_input$pathway_name <- interaction_input$classificationinteraction_input$pathway_name <- gsub(".*by ", "", interaction_input$pathway_name)interaction_input$annotation <- interaction_input$directionalityinteraction_input <- select(interaction_input, -c("partner_a","partner_b","protein_name_a","protein_name_b","interactors","classification","directionality"))##other_info---prepare complex_input filecomplexsubunits <- dplyr::select(complex_input, starts_with("uniprot"))for (i in 1:ncol(complexsubunits)) {idx_complex <- match(complex_input[,paste0("uniprot_",i)], geneInfo$uniprot)idx.use <- !is.na(idx_complex)complex_input[idx.use,paste0("uniprot_",i)] <- geneInfo$hgnc_symbol[idx_complex[idx.use]]}colnames(complex_input)[1:ncol(complexsubunits)] <- paste0("subunit_",seq_len(ncol(complexsubunits)))complex_input <- dplyr::select(complex_input, starts_with("subunit"))## prepare other informationother_info <- list(complex = complex_input)#Step 3: 更新数据库#这里为什么trim.pathway选择T，一定要让interactions有pathways注释，这是因为：#Warning: If no pathway information of each L-R pair is provided, all pathway-level analysis from CellChat cannot be used, such as computeCommunProb!#仅仅只有cpdb数据库db.new.cpdb <- updateCellChatDB(db = interaction_input, gene_info = geneInfo, other_info = other_info, trim.pathway = T,species_target = "human",merged = F)#将cpdb数据库和cellchat合并db.new.cpdb_ccdb <- updateCellChatDB(db = interaction_input, gene_info = geneInfo, other_info = other_info, trim.pathway = T,species_target = "human",merged = T)#保存文件，后续分析直接使用save(db.new.cpdb, file = 'db_new_cpdb.RData')save(db.new.cpdb_ccdb, file = 'db.new.cpdb_ccdb.RData')
CellChatDB <- CellChatDB.mouseclass(CellChatDB)#它是一个list，里面包含4个文件，即4个dataframe# [1] "list"interaction_input <- CellChatDB$interactioncomplex_input <- CellChatDB$complexcofactor_input <- CellChatDB$cofactorgeneInfo <- CellChatDB$geneInfowrite.csv(interaction_input, file = "interaction_input_CellChatDB.csv")write.csv(complex_input, file = "complex_input_CellChatDB.csv")write.csv(cofactor_input, file = "cofactor_input_CellChatDB.csv")write.csv(geneInfo, file = "geneInfo_input_CellChatDB.csv")#Excel中打开，就能具体查看到底有什么信息#然后直接修改即可#文件按照要求修改之后，读入对应的文件interaction_input <- read.csv(file = 'interaction_input_CellChatDB.csv', row.names = 1)complex_input <- read.csv(file = 'complex_input_CellChatDB.csv', row.names = 1)cofactor_input <- read.csv(file = 'cofactor_input_CellChatDB.csv', row.names = 1)geneInfo <- read.csv(file = ' geneInfo_input_CellChatDB.csv', row.names = 1)#cellchat数据库是list形式，所以将修改好的文件（4个dataframe）以list形式储存即可CellChatDB <- list()CellChatDB$interaction <- interaction_inputCellChatDB$complex <- complex_inputCellChatDB$cofactor <- cofactor_inputCellChatDB$geneInfo <- geneInfo
