first commit

script/antibFinalGeneExpr.r

+# Script to load Agilent data, visualise with limma and analyse using maSigPro
+
+##load libraries
+library(Biobase)
+library(limma)
+library(VennDiagram)
+# Input for the gene expression microarray data
+##read targets with the experimental design, map sample files to conditions
+targets <- readTargets("/home/user/allInfoDir/workspace/testdir_ssb/targets_1.txt") #without replicate information
+poolData <- readTargets("/home/user/allInfoDir/workspace/testdir_ssb/targets_pool.txt") #with replicate information
+##read in raw data as an ElistRaw object (limma)
+rawData <- read.maimages(targets,green.only = TRUE, source = "agilent.median", path = "/home/user/allInfoDir/workspace/testdir_ssb/ileumNjejunum") #from LIMMA for Agilent microarrays
+write.table (rawData, file = "/testdir_ssb/raw_data.txt", sep = "\t")
+
+###Quality Control
+#colors for controls
+spotTypes <- readSpotTypes("/home/user/allInfoDir/workspace/testdir_ssb/spotTypesAllGrouped.txt") #information on controls in the microarray for quality control
+spotTypes$SpotType
+rawData$genes$Status <- controlStatus(spotTypes, rawData$genes)
+##background correction,use normalize mexp method and add offset 1 (in case we want to take logs) and normalize (quantile normalization method)
+rawBgcorrect <- backgroundCorrect(rawData, method = "normexp", offset = 1)
+normal <- normalizeBetweenArrays(rawBgcorrect, method = "quantile") #makes it an Elist object
+normalExprsnData <- normal$E #the expression part of the Elist object
+#changes the filenames to condition names just to make the graphs readable
+colnames(normalExprsnData) <- poolData$Condition
+##MA plots for whole normal$E adapted from the limma function plotMA3by2()
+#gives .pdf files with 6 plots in each
+source("/home/user/allInfoDir/workspace/newFunctions/plotMA3by2Mod.r")
+plotMA3by2Mod(normal)
+
+##Fitting, ranking and merging biological replicates
+# substitute the probe names with gene names where ever possible, but retain the unmapped probes
+mapProbeGene <- read.delim("PnGn.txt", as.is = T)
+#use the information from agilent to map probe names to gene names
+normalNoControl <- normal[normal$genes$ControlType == 0,]
+replacedProbeName <- normalNoControl$genes$ProbeName
+position <- which(mapProbeGene$ProbeName %in% normalNoControl$genes$ProbeName)
+replacedProbeName <- replace(replacedProbeName, position, mapProbeGene$GeneName)
+normWithGeneName <- normalNoControl
+normWithGeneName$genes$ProbeName <- replacedProbeName
+rownames(normWithGeneName$E) <- replacedProbeName
+save(normWithGeneName, file="D:/workspace/testdir_ssb/output/NormalGnPn.RData")
+## average over probes that map to the same gene
+normGeneProbeAvg <- avereps(normWithGeneName, ID=normWithGeneName$gene$ProbeName)
+colnames(normGeneProbeAvg) <- poolData$Condition
+
+## Remove some probes (lower percentile and duplicate probes)
+#Get the highest of the dark spots to get a base value and then multiply by 1.1
+negative95 <- apply (normal$E [normal$gene$ControlType == -1,], 2, function(x) quantile(x, p = 0.95))
+cutoff <- matrix (1.1 * negative95, nrow(normGeneProbeAvg), ncol(normGeneProbeAvg), byrow = TRUE)
+#A spot is "expressed" if it is above the threshold in at least half of the conditions 
+isExpressed <- rowSums(normGeneProbeAvg$E > cutoff) >= dim(targets)[1]/2
+condition <- unique(targets$Condition)
+exprsdInAtleast1 <- list()
+for(i in 1:length(condition)) {
+  nCols <- grep(condition[i], colnames(normGeneProbeAvg$E))
+  isExpressed <- rowSums(normGeneProbeAvg$E[,nCols] > cutoff[,nCols])== length(nCols) 
+  perCondition <- normGeneProbeAvg$genes$ProbeName[isExpressed]
+  exprsdInAtleast1[[i]] <- perCondition
+}
+specificCols <- unique(unlist(exprsdInAtleast1))
+reduGeneProbeSpC <- normGeneProbeAvg[specificCols,]
+save(reduGeneProbeSpC, file= "/home/user/allInfoDir/workspace/testdir_ssb/output/reduGeneProbeSpC.RData")
+
+## PCA plots
+#Limit columns, change according to requirements
+#For expressed genes
+#ExprGenes <- avgGenes[match(i.t3.days,avgGenes$genes$ProbeName),]
+#For all the genes
+exprGenes <- normal[match(i.t3.days,normal$genes$ProbeName),]
+tissue <- "jejunum"
+onlyJejunum <- avgGenes[,grep("jejunum", colnames(avgGenes))]
+expr.mat <- as.matrix(onlyJejunum$E)
+rownames(expr.mat) <- onlyJejunum$gene$ProbeName
+#Performing PCA
+pca <- prcomp(t(expr.mat))
+source("/home/user/allInfoDir/workspace/newFunctions/colourPlotPdfPca.r")
+colourPlotPdfPca(pca, colnames(expr.mat), day, treatment)
+
+##Hierarchical
+d <- dist(t(expr.mat), method = "euclidean")
+d <- as.dist(1 - cor(expr.mat))
+hr <- hclust(d, method = "complete", members = NULL)
+plot(hr)
+dhr <- as.dendrogram(hr)
+pdf("Hierarchical_pool.pdf", height = 14, width = 14)
+  par( mar= c(3,10,1,10))
+  plot(dhr,type = c("rectangle"), center = FALSE, edge.root = FALSE, leaflab = c("perpendicular"), dLeaf = NULL,  horiz = TRUE)
+dev.off()
+
+################## Quadratic regression analysis ##############
+library(maSigPro)
+load("/home/user/allInfoDir/workspace/testdir_ssb/output/GnPnspC.RData") #data that has been normalised, low intensity is removed and gene names substituted,  where ever possible so has probe names too
+jData <- reduGnPnspC$E[,grep("jejunum", colnames(reduGnPnspC$E))]
+source("/home/user/allInfoDir/workspace/newFunctions/makeDesignMasigpro.r")
+expDesign <- makeDesignMasigpro(targets, colnames(reduGnPnspC))
+#standardise the data 
+library(Mfuzz)
+jDataEset <- new("ExpressionSet", expr=jData) 
+featurenames <- rownames(jData)
+featureNames(jDataEset)<-featurenames
+SjData <- standardise(jDataEset)
+# perform analysis with the wrapper function
+analysisWhole <- maSigPro(exprs(SjData), Q=0.05, vars="groups", expDesign, pdf=F, cluster.method="mfuzz")
+#original file
+load("/home/user/allInfoDir/workspace/testdir_ssb/output/maSigProJStd.RData")
+analysis <- analysisWhole
+
+listCtrl <- rownames(analysis$sig.genes$tmt1$sig.profiles)
+listTmt1 <- rownames(analysis$sig.genes$tmt2vstmt1$sig.profiles)
+listTmt2 <- rownames(analysis$sig.genes$tmt3vstmt1$sig.profiles)
+
+genesTmt1 <- listTmt1[grep("A_72_",listTmt1, invert=T)]
+genesTmt2 <- listTmt2[grep("A_72_",listTmt2, invert=T)]
+genesTmt1 <- gsub("[[:punct:]]", "\\.",genesTmt1)
+genesTmt2 <- gsub("[[:punct:]]", "\\.",genesTmt2)
+
+tmt1Ntmt2 <- intersect(genesTmt1, genesTmt2)
+onlyTmt1 <- setdiff(genesTmt1, genesTmt2)
+onlyTmt2 <- setdiff(genesTmt2, genesTmt1)
+
+tmt1Ntmt2 <- scan(file= "/home/user/allInfoDir/workspace/testdir_ssb/masigpro/Jalone/jdue2tmt.txt", sep="\t", what="")
+onlyTmt1 <- scan(file= "/home/user/allInfoDir/workspace/testdir_ssb/masigpro/Jalone/jonlyt2.txt", sep="\t", what="")
+onlyTmt2 <- scan(file= "/home/user/allInfoDir/workspace/testdir_ssb/masigpro/Jalone/jonlyt3.txt", sep="\t", what="")
+
+########## TopGO script ###########
+library(topGO)
+# goids from biomart in convOtherDB
+# humanMart <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
+# goids = getBM(attributes=c('entrezgene','go_id'), filters='entrezgene', values=allEnt, mart=humanMart)
+# geneID2GO <- data.frame()
+# for(i in 1:length(allEnt)){ 
+#   tmp <- goids[goids$entrezgene %in% allEnt[i],]
+#   idTmp <- paste(tmp$go_id[grep("GO", tmp$go_id)], collapse=", ")
+#   oneRow <- data.frame(entrezgene=allEnt[i], go_id=idTmp, stringsAsFactors=F)
+#   if(oneRow$go_id != "")
+#   geneID2GO <- rbind(geneID2GO,oneRow)
+#   
+# }
+# 
+# write.table(geneID2GO, file="geneID2GOprop.txt", sep="\t", row.names=F, col.names=F, quote=F)
+geneID2GO <- readMappings(file="geneID2GOprop.txt")
+propGnId <- read.table(file=paste0("/home/user/allInfoDir/workspace/conversion/propGenesIds.txt"), sep="\t", as.is=T)
+allEntrez <- as.character(propGnId$Id)
+selectGenesList <- list(tmt1Ntmt2, onlyTmt1, onlyTmt2)
+fnameVector <- c("tmt1Ntmt2topGOBP", "onlyTmt1topGOBP", "onlyTmt2topGOBP")
+for(i in 1:length(selGenesList)) { 
+  fname=fnameVector[i]
+  selectGenes <- selectGenesList[[i]]
+  selectEntrezIds <- as.character(unique(propGnId$Id[propGnId$Gn %in% selectGenes]))
+  geneList <- factor(as.integer(allEntrez %in% selectEntrezIds))
+  names(geneList) <- allEntrez
+  GOdataBP <- new("topGOdata", ontology = "BP", allGenes = geneList, annot = annFUN.gene2GO, gene2GO = geneID2GO)
+#   save(GOdataBP, file=paste0("D:/workspace/topGO/GOdata", fname, ".RData"))
+  GOdata <- GOdataBP
+  annotatedGenes <- genesInTerm(GOdata) #terms for genes, opp to geneID2GO
+  goNames <- names(annotatedGenes)
+  test.stat <- new("classicCount", testStatistic = GOFisherTest, name = "Fisher test")
+  resultFisher <- getSigGroups(GOdata, test.stat)
+  pvalFis <- score(resultFisher)
+  pvalFis <- sort(pvalFis)
+  pval.01 <- sum(pvalFis < 0.01)
+  significantGO <- names(pvalFis[1:pval.01])   
+  source("/home/user/allInfoDir/workspace/newFunctions/makeTableTopGO.r")
+  makeTableTopGO(significantGO, annotatedGenes, fname)
+}
+  
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script/antibFinalIntegration.r

+########## Data Integration ###############
+# This script performs data preprocessing and data integration of host gene expression and microbiota
+# Load libraries
+library(RColorBrewer)
+library(igraph)
+library(mixOmics)
+# load and process microbiota data
+inputMicrob <- read.delim(file="/home/user/allInfoDir/workspace/microbiota/selectedMasigpro.txt", as.is=T)
+inputMicrob <- t(inputMicrob)
+# separate data based on conditions
+tmt1Ntmt2Microb4mixomics <- inputMicrob
+onlytmt1Microb4mixomics <- inputMicrob[grep("T3", rownames(inputMicrob), invert = T),]
+onlytmt2Microb4mixomics <- inputMicrob[grep("T2", rownames(inputMicrob), invert = T),]
+# load and process gene expression data
+load("/home/user/allInfoDir/workspace/testdir_ssb/output/GnspC.RData")
+inputExpression <- reduGn$E[,grep("jejunum", colnames(reduGn$E))]
+inputExpression <- t(as.matrix(inputExpression))
+tmt1Ntmt2 <- scan(file= paste0("/home/user/allInfoDir/workspace/testdir_ssb/masigpro/Jalone/jdue2tmt.txt"), what="")
+onlyTmt1 <- scan(file= paste0("/home/user/allInfoDir/workspace/testdir_ssb/masigpro/Jalone/jonlyt2.txt"), what="")
+onlyTmt2 <- scan(file= paste0("/home/user/allInfoDir/workspace/testdir_ssb/masigpro/Jalone/jonlyt3.txt"), what="")
+# separate the different groups of genes for the analysis, genes
+tmt1Ntmt2Exprsn <- inputExpression[,colnames(inputExpression) %in% tmt1Ntmt2]
+onlyTmt1Exprsn <- inputExpression[,colnames(inputExpression) %in% onlyTmt1]
+onlyTmt2Exprsn <- inputExpression[,colnames(inputExpression) %in% onlyTmt2]
+# separate the different groups of genes for the analysis, conditions
+tmt1Ntmt2Exprsn4mixomics <- tmt1Ntmt2Exprsn
+onlyTmt1Exprsn4mixomics <- onlyTmt1Exprsn[grep("T3", rownames(onlyTmt1Exprsn), invert = T),]
+onlyTmt2Exprsn4mixomics <- onlyTmt2Exprsn[grep("T2", rownames(onlyTmt2Exprsn), invert = T),]
+## mixomics anlaysis
+# calculate similarities
+ncomp = 8 #number of components is usually number of conditions - 1
+tmt1Ntmt2Spars <- spls(X = tmt1Ntmt2Microb4mixomics, Y = tmt1Ntmt2Exprsn4mixomics, ncomp=ncomp)
+ncomp = 5
+onlyTmt1Spars <- spls(X = onlytmt1Microb4mixomics, Y = onlyTmt1Exprsn4mixomics, ncomp=ncomp)
+onlyTmt2Spars <- spls(X = onlytmt2Microb4mixomics, Y = onlyTmt2Exprsn4mixomics, ncomp=ncomp)
+# build networks
+color.edge <- colorRampPalette(c("darkgreen", "green", "yellow", "red", "darkred")) # make a color palette for the network
+ncomp = 8
+netResultTmt1Ntmt2 <- network(tmt1Ntmt2Spars, comp = 1:ncomp, keep.var = TRUE, shape.node = c("rectangle", "rectangle"), color.node = c("white", "pink"), color.edge = color.edge(10), alpha = 3, threshold = 0.8)
+ncomp = 5
+netResultOnlyTmt1 <- network(onlyTmt1Spars, comp = 1:ncomp, keep.var = TRUE, shape.node = c("rectangle", "rectangle"), color.node = c("white", "pink"), color.edge = color.edge(10), alpha = 3, threshold = 0.8)
+netResultOnlyTmt2 <- network(onlyTmt2Spars, comp = 1:ncomp, keep.var = TRUE, shape.node = c("rectangle", "rectangle"), color.node = c("white", "pink"), color.edge = color.edge(10), alpha = 3, threshold = 0.8)

script/antibFinalMicrobiota.r

+############# Microbiota data ####################
+# This script reads in relative contribution data of microbiota, filters it and performs regression analysis using masigpro
+#Read in relative contribution data and filter out the groups with low contribution at the level 3 (genera)
+#Process data with proper row and column names
+allMicrob <- read.delim(file="/home/user/allInfoDir/workspace/microbiota/withTmt.txt", as.is=T)
+colNames <- colnames(allMicrob)
+colNames <- sub("(\\.[1234567890]+)(T[1234567890]+)", "\\2\\1", colNames, perl=T)
+colnames(allMicrob) <- colNames
+# Rearrange the columns
+dLevels <- c("J8", "J55", "J176")
+dT <- c("T1", "T2", "T3")
+allMicrob <- allMicrob[,unlist(lapply(dT, grep, colnames(allMicrob)))]
+allMicrob <- allMicrob[,unlist(lapply(dLevels, grep, colnames(allMicrob)))]
+write.table(palmicrobT, file="D:/workspace/microbiota/microbProper.txt")
+palmicrobT <- read.table(file="/home/user/allInfoDir/workspace/microbiota/microbProper.txt", as.is=T)
+#check the values for variance
+allVar <- list()
+variance <- NULL
+palmicrob <- allMicrob
+condition <- colnames(allMicrob)
+condition <- sub("\\.[1234567890]+", "", condition)
+condition <- unique(condition)
+exLeast1 <- list()
+for(i in 1:length(condition)) {
+  nCols <- grep(condition[i], colnames(allMicrob))
+  for(j in 1:dim(allMicrob)[1])
+    variance[j] <- as.numeric(var(t(allMicrob[,nCols][j,])))
+  allVar[[i]] <- variance
+}
+lapply(allVar, function(x) max(x))
+#shorten microb names
+shorten <- scan(file="D:/workspace/microbiota/MgroupsShort.txt", what="", sep="\n")
+shorten <- sub("et rel.", "", shorten)
+rownames(allMicrob) <- shorten
+#check cutoff n filter
+# highContri <- rowSums(almicrob > cutoff)>=4
+source("/home/user/allInfoDir/workspace/newFunctions/filterPerCondition.r")
+specificCols <- filterPerCondition(allMicrob, 0.1, condition) 
+filteredMicrob <- allMicrob[specificCols,]
+passFilt <- rownames(filteredMicrob)
+write(passFilt, file="D:/workspace/microbiota/passFilt.txt")
+passFilt <- scan(file="D:/workspace/microbiota/passFilt.txt", what="", sep="\t")
+######## Masigpro
+library(maSigPro)
+#remove the three samples from animals rejected in gene expression quality control
+mpoolData <- colnames(filteredMicrob)
+remTmp <- c ("J8T2.8", "J176T2.8", "J176T3.4")
+rnames <- mpoolData[!mpoolData %in% remTmp]
+filteredMicrob <- subset(filteredMicrob, select= -c(J8T2.8, J176T2.8, J176T3.4)) #NA value in output check
+expDesign <- read.table(file="/home/user/allInfoDir/workspace/testdir_ssb/masigpro/edesignJ.txt")
+rownames(expDesign) <- colnames(filteredMicrob)
+firstStep <- p.vector(data=filteredMicrob, design=expDesign)
+# write(rownames(firstStep$SELEC), file=paste0(wd,"/microbiota/masigMicrob1st46.txt"), sep="\t")
+selectedMicrob <- firstStep$SELEC
+write.table(selectedMicrob, file="/home/user/allInfoDir/workspace/microbiota/selectedMasigpro.txt", sep = "\t", quote = F)
+########################################
\ No newline at end of file

script/colourPlotPdfPca.r

+colourPlotPdfPca <- function(pca, cnames.expr.mat, day, treatment){ 
+  pca <- pca
+  sum.pca <- summary(pca)
+  cnames.expr.mat <- cnames.expr.mat
+  day <- day
+  treatment <- treatment
+  colors <- cnames.expr.mat
+  names(colors) <- cnames.expr.mat
+  colors[grep(paste0("_", day[1], "_"), colors)] <- "red"
+  colors[grep(paste0("_", day[2], "_"), colors)] <- "green"
+  colors[grep(paste0("_", day[3], "_"), colors)] <- "blue"
+  treatmentsAll <- cnames.expr.mat
+  names(treatmentsAll) <- cnames.expr.mat
+  treatmentsAll[grep("_T1", treatmentsAll)] <- 1
+  treatmentsAll[grep("_T2", treatmentsAll)] <- 2
+  treatmentsAll[grep("_T3", treatmentsAll)] <- 3
+  
+  #Put the plots in a .pdf
+  pdf("normal_pca.pdf", height=10, width=10) 
+    plot(pca$x , main="PCA", pch=17, col=colors)
+    text(pca$x, pos=1, treatmentsAll)
+    mtext(paste("first two components explain ",format(100 * sum.pca$importance["Cumulative Proportion",2],digits=2),"% of variance", sep=""))
+  dev.off()
+
+}
\ No newline at end of file

script/contrastAcrossDays.r

+contrastAcrossDays <- function(tissue, day, treatment, f) { 
+  tissue <- tissue
+  day <- day
+  treatment <- treatment
+  f <- f
+  req.contrast <- NULL
+  contrast.names.tissue <- NULL
+  contrast.names <- NULL
+  for (i in 1:2) {
+    req.tissue <- levels(f)[grep (tissue[i], levels(f))]
+    for (j in 1:3){
+      req.contrast <- NULL
+      req.treatment <- req.tissue[grep (treatment[j], req.tissue)]
+      for (k in 1:3){
+        req.contrast <- append(req.contrast, req.treatment[grep (day[k], req.treatment)])
+        if (k == 3)
+          contrast.names.tissue[[j]] <- c(paste(req.contrast[k-2],"-", req.contrast[k-1], sep= ""), paste(req.contrast[k-1],"-", req.contrast[k], sep= ""))
+      }
+    }
+    contrast.names[[i]] <- contrast.names.tissue
+    names(contrast.names[[i]]) <- treatment
+  }
+  names(contrast.names) <- tissue
+  return(contrast.names)
+}
\ No newline at end of file

script/filterPerCondition.r

+filterPerCondition <- function(dataDF, threshold, condition){ 
+  dataDF <- dataDF #a dataframe 
+  threshold <- threshold # numeric
+  condition <- condition # should be found in the colnames of dataDF
+  cutoff <- matrix (threshold, nrow(dataDF), ncol(dataDF), byrow = TRUE)
+  contribLeast1 <- list()
+  for(i in 1:length(condition)) {
+    nCols <- grep(condition[i], colnames(dataDF))
+    highContrib <- rowSums(dataDF[,nCols] > cutoff[,nCols]) == length(nCols) 
+    perCondition <- rownames(dataDF)[highContrib]
+    contribLeast1[[i]] <- perCondition
+    cat(condition[i], "\n", length(perCondition), "\n") #jus for debugging n compilin info
+  }
+  specificCols <- unique(unlist(contribLeast1))
+  return(specificCols)
+}
\ No newline at end of file

script/makeDesignMasigpro.r

+makeDesignMasigpro <- function(targets, cnames.reduGnPnspC){ 
+  targets <- targets
+  cnames.reduGnPnscp <- cnames.reduGnPnspC
+  dLevels <- c("jejunum_8", "jejunum_55", "jejunum_176")
+  dT <- c("T1", "T2", "T3")
+  targets <- targets[unlist(lapply(dT, grep, targets$Condition)),]
+  targets <- targets[unlist(lapply(dLevels, grep, targets$Condition)),]
+  jnoPool <- unique(targets$Condition)[grep("jejunum", unique(targets$Condition))]
+  jpNames <-  cnames.reduGnPnspC[grep("jejunum", cnames.reduGnPnspC)]
+  
+  #generate the columns necessary for the matrix
+  names(jnoPool) <- c(1:9)
+  
+  repl <- NULL
+  tpt <- NULL
+  tmt1 <- rep(0, 33)
+  tmt2 <- rep(0, 33)
+  tmt3 <- rep(0, 33)
+    
+  for(i in 1:length(jnoPool)) { 
+    fornames <- as.character(unlist(strsplit(jnoPool[i], split = "_|-")))
+    pos <- grep(jnoPool[i], jpNames)
+    repl <- append(repl,as.integer(rep(names(jnoPool[i]), each=length(pos))))
+    tpt <- append(tpt, as.integer(rep(fornames[2], each=length(pos))))
+    if(fornames[3] == "T1")
+      tmt1[pos] <- as.integer(rep(1, each=length(pos)))
+    if(fornames[3] == "T2")  
+      tmt2[pos] <- as.integer(rep(1, each=length(pos)))
+    if(fornames[3] == "T3")
+      tmt3[pos] <- as.integer(rep(1, each=length(pos)))
+  }
+  
+  jexpDesign <- as.matrix(cbind(tpt, repl, tmt1, tmt2, tmt3))
+  rownames(jexpDesign) <- jpNames
+  return(jexpDesign)
+}
\ No newline at end of file

script/makeTableTopGO.r

+makeTableTopGO <- function(significantGO, annotatedGenes, fname){ 
+  significantGO <- significantGO
+  annotatedGenes <- annotatedGenes
+  nRow <- length(significantGO)
+  enrichTable <- as.data.frame(matrix(rep(0, 6*nRow), nrow=nRow))
+  colNames=c("RowNum", "GO.ID", "GO.Term","p.value", "GeneInList", "GeneInGO")
+  colnames(enrichTable) <- colNames                             
+  for(j in 1:nRow) {
+    genesTerm <- annotatedGenes[[significantGO[j]]]
+    tGenes <- intersect(selectEntrezIds, genesTerm)
+    enrichTable$RowNum[j] <-j
+    enrichTable$GO.ID[j] <-significantGO[j]
+    enrichTable$GO.Term[j] <- GOTERM[[significantGO[j]]]@Term
+    enrichTable$p.value[j] <- unname(pvalFis[j])
+    enrichTable$GeneInList[j] <- length(tGenes)
+    enrichTable$GeneInGO[j] <- length(genesTerm)
+  }
+  write.table(enrichTable, file=paste0(fname, ".txt"), row.names=F, quote=F, sep="\t")
+}
\ No newline at end of file

script/plotMA3by2Mod.r

+plotMA3by2Mod <- function(normal) { 
+  x <- class(normal)
+  if(x[[1]] == "EList"){ 
+    MA <- normal
+    MA$E <- as.matrix(MA$E)
+    narrays <- ncol(MA$E)
+    npages <- ceiling(narrays/6)
+    main <- colnames(MA)
+    width <- 6.5
+    height <- 10
+    xlim <- c(3, 20)
+    ylim <- c(-15, 15)
+    prefix <- 'MA for Elist'
+    ext <- "pdf"
+    xlab <- "A"
+    ylab <- "M"
+    status <- MA$genes$Status
+    for (ipage in 1:npages) {
+      i1 <- ipage * 6 - 5
+      i2 <- min(ipage * 6, narrays)
+      pname = paste(prefix, "-", i1, "-", i2, ".", ext, sep = "")
+      pdf(pname , height = height, width= width)
+      par(mfrow = c(3, 2))
+      for (i in i1:i2) {
+        plotMA(MA, array = i, xlim = xlim, ylim = ylim, legend = (i%%6 == 1), zero.weights = TRUE, main = main[i])
+      }
+      dev.off()
+    }
+  
+  } else { print("This function was modified only for Elist objects")}
+ 
+}
\ No newline at end of file