如何在R中执行脚本的循环和迭代？

我正在运行一个R脚本来分析一些生物学数据。以下提供了代码段数据和脚本的示例。该数据文件有很多列（但我想重点关注第5列-Gene）。我有100多个这样的数据文件（将所有文件中的第5个Gene列视为感兴趣的列）。目前，我正在R中分别运行每个文件，并且过程很繁琐。我想同时为所有数据文件运行一个R脚本，并根据文件名将所有数据保存在不同的文件夹中。是否可以在脚本中一次循环或迭代并分析所有数据文件。请协助我。

谢谢，

图菲克

格式化的问题和修订的数据框

插入：要读取的文件名

M3.1_IPA.txt
M8.1_IPA.txt
M8.2_IPA.txt
M8.3_IPA.txt

1.加载基因集进行分析

Data_file <- read.delim(file = "./M3.1_IPA.txt", stringsAsFactors = FALSE, check.names = FALSE, row.names = NULL)

dput(head(Data_file, 5))
structure(list(row.names = c("M3.1_ALPP", "M3.1_ALS2CR14", "M3.1_ANKRD30B", 
"M3.1_ARL16", "M3.1_BCYRN1"), X = 1:5, Module = c("M3.1", "M3.1", 
"M3.1", "M3.1", "M3.1"), Title = c("Cell Cycle", "Cell Cycle", 
"Cell Cycle", "Cell Cycle", "Cell Cycle"), Gene = c("ALPP", "ALS2CR14", 
"ANKRD30B", "ARL16", "BCYRN1"), Probes = c("ILMN_1693789", "ILMN_1787314", 
"ILMN_1730678", "ILMN_2188119", "ILMN_1678757"), Module_gene = c("M3.1_ALPP", 
"M3.1_ALS2CR14", "M3.1_ANKRD30B", "M3.1_ARL16", "M3.1_BCYRN1"
)), row.names = c(NA, 5L), class = "data.frame")

Module_10_1 <- Data_file[,5]
Module_10_1_geneLists <- list(Module_10_1_geneListName=Module_10_1)

2. 选择要使用的主题数据库（即，TSS附近的生物和距离）

   data(motifAnnotations_hgnc)
   motifRankings <- importRankings("./hg19-tss-centered-10kb-7species.mc9nr.feather")
   

3. 计算浓缩

   motifs_AUC <- calcAUC(Module_10_1_geneLists, motifRankings, nCores=1)
   Type = "Enrichment"
   Module = "M10_1"
   auc <- getAUC(motifs_AUC)
   
   ##Export the plots##
   pdf(paste0(Type, "_", Module, "_AUC_Histogram_Plot.pdf"), height = 5, width = 10)
       hist(auc, main="Module_10_1", xlab="AUC histogram",
         breaks=100, col="#ff000050", border="darkred")
       nes3 <- (3*sd(auc)) + mean(auc)
       abline(v=nes3, col="red")
   dev.off()
   

4. 选择重要的图案和/或对TF进行注释

   motifEnrichmentTable <- addMotifAnnotation(motifs_AUC, nesThreshold=3,
   
   motifAnnot=motifAnnotations_hgnc)
   
   ##Export the Motif enrichment analysis results###
   write.csv(motifEnrichmentTable, file ="./motifEnrichmentTable.csv", row.names = F, sep = ",")
   

5. 鉴定每个母题中具有最佳富集的基因

   motifEnrichmentTable_wGenes_v1 <- addSignificantGenes(motifEnrichmentTable, 
                rankings=motifRankings, 
                geneSets=Module_10_1_geneLists)
   
   ##Export the Motif enrichment analysis results##
   write.csv(motifEnrichmentTable_wGenes_v1, 
             file ="./motifEnrichmentTable_wGenes_v1.csv", 
             row.names = F, sep = ",")
   

6. 绘制一些图案

   geneSetName <- "Module_10_1_Genelist"
   selectedMotifs <- c("cisbp__M6275", 
   
   sample(motifEnrichmentTable$motif, 2))
   par(mfrow=c(2,2))
   
   Type_v1 <- "Few_motifs"
   ##Export the plots
   pdf(paste0(Type_v1, "_", Module, "_Sig_Genes_Plot.pdf"), height = 5, width = 5)
   getSignificantGenes(Module_10_1_geneLists, 
                       motifRankings,
                        signifRankingNames=selectedMotifs,
                        plotCurve=TRUE, maxRank=5000, genesFormat="none",
                        method="aprox")
   dev.off()
   

7. RcisTarget的最终输出是data.table并导出到html

   motifEnrichmentTable_wGenes_v1_wLogo <- addLogo(motifEnrichmentTable_wGenes_v1)
   resultsSubset <- motifEnrichmentTable_wGenes_v1_wLogo[1:147,]
   Type_v2 <- "Motifs_Table"
   
   library(DT)
   ## export the data table to html file format###
   dtable <- datatable(resultsSubset[,-c("enrichedGenes", "TF_lowConf"), with=FALSE], 
                   escape = FALSE, # To show the logo
                   filter="top", options=list(pageLength=100))
   html_test <- "dtable.html"
   saveWidget(dtable, html_test)
   

8. 建立网络并导出为html格式

   signifMotifNames <- motifEnrichmentTable$motif[1:3]
   incidenceMatrix <- getSignificantGenes(Module_10_1_geneLists, 
                                       motifRankings,
                                       signifRankingNames=signifMotifNames,
                                       plotCurve=TRUE, maxRank=5000, 
                                       genesFormat="incidMatrix",
                                       method="aprox")$incidMatrix
   
   library(reshape2)
   
   edges <- melt(incidenceMatrix)
   edges <- edges[which(edges[,3]==1),1:2]
   colnames(edges) <- c("from","to")
   
   library(visNetwork)
   motifs <- unique(as.character(edges[,1]))
   genes <- unique(as.character(edges[,2]))
   nodes <- data.frame(id=c(motifs, genes),   
                       label=c(motifs, genes),    
                       title=c(motifs, genes), # tooltip 
                       shape=c(rep("diamond", length(motifs)), rep("elypse", length(genes))),
                       color=c(rep("purple", length(motifs)), rep("skyblue", length(genes))))
   Network <- visNetwork(nodes, edges) %>% visOptions(highlightNearest = TRUE, 
                                        nodesIdSelection = TRUE)
   ##Export the network##
   visSave(Network, file ="Network.html")