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gene_expression.mod.r
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gene_expression.mod.r
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#! /usr/bin/env Rscript
arguments <- commandArgs(trailingOnly=TRUE)
print("Program path: /shares/NGS/MichalinaZ_mRNA/gene_expression.mod.r")
if(length(arguments) != 3) {stop("The CSV file, exp. analysis type (DESEQ2, EDGER) and filtering status (FILTERED, UNFILTERED) were not provided.")}
csvfile <- arguments[1]
variant <- gsub(sub(csvfile, pattern = "\\.csv$", replacement = ""), pattern = ".*/", replacement = "")
args <- seq(1,14)
suffix <- arguments[2]
Filters <- arguments[3]
if(Filters == "FILTERED" & suffix == "DESEQ2") {
Filters <- "pOverA(0.20,log2(100));function(x)(IQR(x)>0.25)" }
origdir <- '/workspace/lukasz/Michalina_Z/New_analyses/ALL_BAMS'
workdir <- paste(origdir, variant, suffix, Filters, sep = "/")
dir.create(workdir, recursive = TRUE)
setwd(workdir)
getwd()
inputdir <- "/workspace/lukasz/Michalina_Z/New_analyses/ALL_BAMS/BAMS"
gtffile <- "/workspace/lukasz/NGS-all-in-one/REFSEQS/GRCh38.primary_assembly.annotation.gtf.gz"
threads <- 60
stranded <- "no"
alpha <- 0.1
lfcThreshold <- 0
sample_col <- "SampleID"
grouping_var <- "ALL_SAMPLES"
group_values <- "NA"
ind.factor <- "condition"
ind.factor2 <- NA
csvfile <- read.csv(csvfile, header=TRUE)
csvfile$SampleID <- gsub(gsub(gsub(csvfile$SampleID, pattern = "^[^0-9]+", replacement = ""), pattern = "[^0-9]+$", replacement = ""), pattern = "i?_[0-9]+", replacement = "")
filenames <- grep(list.files(path = inputdir, full.names = TRUE), pattern = ".*sorted.bam$", value = TRUE)
filenames.ost <- NULL
for(i in csvfile[[sample_col]]) {filenames.ost <- append(filenames.ost, grep(filenames, pattern = paste0("[^0-9]", i, "[^0-9]"), value = TRUE))}
filenames <- filenames.ost
#inputdir <- readLines(file("stdin"))
save.image(file=paste0(suffix,".RData"))
if(length(args)<14) {
save.image(file=paste0(suffix,".RData"))
stop("Fourteen arguments must be supplied in the following order (Expression dir, suffix, mapping dir, gtffile, strand specificity, alpha, LFC, filtering functions, threads, CSV file, sample column, grouping column, group, factor columns)", call.=FALSE) }
library("parallel")
library("BiocParallel")
register(MulticoreParam(workers=threads))
#register(SerialParam())
if (grouping_var != "ALL_SAMPLES") {
groups <- unlist(strsplit(grouping_var,split=","))
group.values <- unlist(strsplit(group_values, split=","))
num=0
for (group in groups)
{
num=num+1
group.column <- unlist(strsplit(group.values[grep(x=group.values, pattern=group)],split=":"))[1]
group.value <- unlist(strsplit(group.values[grep(x=group.values, pattern=group)],split=":"))[2]
name <- paste("df.subset",num, sep=".")
assign(x=name,value=subset(csvfile, csvfile[[group.column]]==group.value))
}
if (length(groups) == 2) {
csvfile <- merge(x=df.subset.1, y=df.subset.2)
} else if (length(groups) == 1) {
csvfile <- df.subset.1
} else {
save.image(file=paste0(suffix,".RData"))
stop("The number of grouping variables cannot be higher than two.")}
}
if (length(args) == 14)
{ csvfile <- subset(csvfile,!is.na(csvfile[[ind.factor]]))
} else if (length(args) == 15)
{ csvfile <- subset(csvfile,!is.na(csvfile[[ind.factor]]) & !is.na(csvfile[[ind.factor2]]))}
if (length(csvfile[[sample_col]])<2) {
save.image(file=paste0(suffix,".RData"))
stop("There are not enough samples to perform further analyses.")}
library("Rsamtools")
bamfiles <- BamFileList(filenames)
library("GenomicFeatures")
csvfile[[sample_col]] <- as.factor(csvfile[[sample_col]])
# for(i in seq(from=14, to=length(args), by=1)){csvfile[[args[i]]] <- as.factor(csvfile[[args[i]]])}
if(grepl(x = names(bamfiles[1]),pattern = "*.bam$")) {
bamsamples <- gsub(x=names(bamfiles),pattern=".sorted.bam$",replacement="")
bamsamples <- gsub(gsub(gsub(bamsamples, pattern = "^[^0-9]+", replacement = ""), pattern = "[^0-9]+$", replacement = ""), pattern = "i?_[0-9]+", replacement = "")
bamsamples <- as.data.frame(bamsamples)}
names(bamsamples) <- c("names")
sampleTable <- merge(x=csvfile, y=bamsamples, by.x=sample_col, by.y="names")
rownames(sampleTable) <- sampleTable[[sample_col]]
if (nrow(sampleTable)<2) {
save.image(file=paste0(suffix,".RData"))
stop("At least two samples are necessary to perform the gene expression analysis.")
}
f.topGO <- function(geneList) {
library("topGO")
colMap <- function(x) {
.col <- rep(rev(heat.colors(length(unique(x)))), time = table(x))
return(.col[match(1:length(x), order(x))])
}
GOdata.BP <- new("topGOdata",
ontology = "BP",
allGenes = geneList,
geneSel = topDiffGenes,
annotationFun=annFUN.org, mapping = "org.Hs.eg.db", ID = "symbol",
nodeSize = 10)
if(length(GOdata.BP@graph@nodes) >= 50) {nodesn.BP <- 50} else {nodesn.BP <- length(GOdata.BP@graph@nodes)}
resultFisher.BP <- runTest(GOdata.BP, algorithm = "classic", statistic = "fisher")
resultFisher.BP
resultKS.BP <- runTest(GOdata.BP, algorithm = "classic", statistic = "ks")
resultKS.BP
resultKS.elim.BP <- runTest(GOdata.BP, algorithm = "elim", statistic = "ks")
resultKS.BP
allRes.BP.classicKS <- GenTable(GOdata.BP, classicFisher = resultFisher.BP,
classicKS = resultKS.BP, elimKS = resultKS.elim.BP,
orderBy = "classicKS", ranksOf = "classicFisher", topNodes = nodesn.BP)
allRes.BP.elimKS <- GenTable(GOdata.BP, classicFisher = resultFisher.BP,
classicKS = resultKS.BP, elimKS = resultKS.elim.BP,
orderBy = "elimKS", ranksOf = "classicFisher", topNodes = nodesn.BP)
allRes.BP.Fisher <- GenTable(GOdata.BP, classicFisher = resultFisher.BP,
classicKS = resultKS.BP, elimKS = resultKS.elim.BP,
orderBy = "classicFisher", ranksOf = "classicFisher", topNodes = nodesn.BP)
geneSig <- geneList[topDiffGenes(geneList)]
AnnotatedGenes.BP.classicKS <- sapply(allRes.BP.classicKS$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.BP, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.BP.classicKS[[x]][AnnotatedGenes.BP.classicKS[[x]] %in% names(geneSig)]}
GeneList.BP.classicKS <- sapply(allRes.BP.classicKS$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.BP.classicKS",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.BP.classicKS",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.BP.classicKS)
sink()
AnnotatedGenes.BP.elimKS <- sapply(allRes.BP.elimKS$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.BP, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.BP.elimKS[[x]][AnnotatedGenes.BP.elimKS[[x]] %in% names(geneSig)]}
GeneList.BP.elimKS <- sapply(allRes.BP.elimKS$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.BP.elimKS",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.BP.elimKS",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.BP.elimKS)
sink()
AnnotatedGenes.BP.Fisher <- sapply(allRes.BP.Fisher$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.BP, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.BP.Fisher[[x]][AnnotatedGenes.BP.Fisher[[x]] %in% names(geneSig)]}
GeneList.BP.Fisher <- sapply(allRes.BP.Fisher$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.BP.Fisher",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.BP.Fisher",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.BP.Fisher)
sink()
GOdata.CC <- new("topGOdata",
ontology = "CC",
allGenes = geneList,
geneSel = topDiffGenes,
annotationFun=annFUN.org, mapping = "org.Hs.eg.db", ID = "symbol",
nodeSize = 10)
if(length(GOdata.CC@graph@nodes) >= 50) {nodesn.CC <- 50} else {nodesn.CC <- length(GOdata.CC@graph@nodes)}
resultFisher.CC <- runTest(GOdata.CC, algorithm = "classic", statistic = "fisher")
resultFisher.CC
resultKS.CC <- runTest(GOdata.CC, algorithm = "classic", statistic = "ks")
resultKS.CC
resultKS.elim.CC <- runTest(GOdata.CC, algorithm = "elim", statistic = "ks")
resultKS.CC
allRes.CC.classicKS <- GenTable(GOdata.CC, classicFisher = resultFisher.CC,
classicKS = resultKS.CC, elimKS = resultKS.elim.CC,
orderBy = "classicKS", ranksOf = "classicFisher", topNodes = nodesn.CC)
allRes.CC.elimKS <- GenTable(GOdata.CC, classicFisher = resultFisher.CC,
classicKS = resultKS.CC, elimKS = resultKS.elim.CC,
orderBy = "elimKS", ranksOf = "classicFisher", topNodes = nodesn.CC)
allRes.CC.Fisher <- GenTable(GOdata.CC, classicFisher = resultFisher.CC,
classicKS = resultKS.CC, elimKS = resultKS.elim.CC,
orderBy = "classicFisher", ranksOf = "classicFisher", topNodes = nodesn.CC)
geneSig <- geneList[topDiffGenes(geneList)]
AnnotatedGenes.CC.classicKS <- sapply(allRes.CC.classicKS$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.CC, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.CC.classicKS[[x]][AnnotatedGenes.CC.classicKS[[x]] %in% names(geneSig)]}
GeneList.CC.classicKS <- sapply(allRes.CC.classicKS$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.CC.classicKS",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.CC.classicKS",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.CC.classicKS)
sink()
AnnotatedGenes.CC.elimKS <- sapply(allRes.CC.elimKS$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.CC, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.CC.elimKS[[x]][AnnotatedGenes.CC.elimKS[[x]] %in% names(geneSig)]}
GeneList.CC.elimKS <- sapply(allRes.CC.elimKS$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.CC.elimKS",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.CC.elimKS",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.CC.elimKS)
sink()
AnnotatedGenes.CC.Fisher <- sapply(allRes.CC.Fisher$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.CC, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.CC.Fisher[[x]][AnnotatedGenes.CC.Fisher[[x]] %in% names(geneSig)]}
GeneList.CC.Fisher <- sapply(allRes.CC.Fisher$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.CC.Fisher",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.CC.Fisher",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.CC.Fisher)
sink()
GOdata.MF <- new("topGOdata",
ontology = "MF",
allGenes = geneList,
geneSel = topDiffGenes,
annotationFun=annFUN.org, mapping = "org.Hs.eg.db", ID = "symbol",
nodeSize = 10)
if(length(GOdata.MF@graph@nodes) >= 50) {nodesn.MF <- 50} else {nodesn.MF <- length(GOdata.MF@graph@nodes)}
resultFisher.MF <- runTest(GOdata.MF, algorithm = "classic", statistic = "fisher")
resultFisher.MF
resultKS.MF <- runTest(GOdata.MF, algorithm = "classic", statistic = "ks")
resultKS.MF
resultKS.elim.MF <- runTest(GOdata.MF, algorithm = "elim", statistic = "ks")
resultKS.MF
allRes.MF.classicKS <- GenTable(GOdata.MF, classicFisher = resultFisher.MF,
classicKS = resultKS.MF, elimKS = resultKS.elim.MF,
orderBy = "classicKS", ranksOf = "classicFisher", topNodes = nodesn.MF)
allRes.MF.elimKS <- GenTable(GOdata.MF, classicFisher = resultFisher.MF,
classicKS = resultKS.MF, elimKS = resultKS.elim.MF,
orderBy = "elimKS", ranksOf = "classicFisher", topNodes = nodesn.MF)
allRes.MF.Fisher <- GenTable(GOdata.MF, classicFisher = resultFisher.MF,
classicKS = resultKS.MF, elimKS = resultKS.elim.MF,
orderBy = "classicFisher", ranksOf = "classicFisher", topNodes = nodesn.MF)
geneSig <- geneList[topDiffGenes(geneList)]
AnnotatedGenes.MF.classicKS <- sapply(allRes.MF.classicKS$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.MF, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.MF.classicKS[[x]][AnnotatedGenes.MF.classicKS[[x]] %in% names(geneSig)]}
GeneList.MF.classicKS <- sapply(allRes.MF.classicKS$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.MF.classicKS",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.MF.classicKS",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.MF.classicKS)
sink()
AnnotatedGenes.MF.elimKS <- sapply(allRes.MF.elimKS$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.MF, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.MF.elimKS[[x]][AnnotatedGenes.MF.elimKS[[x]] %in% names(geneSig)]}
GeneList.MF.elimKS <- sapply(allRes.MF.elimKS$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.MF.elimKS",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.MF.elimKS",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.MF.elimKS)
sink()
AnnotatedGenes.MF.Fisher <- sapply(allRes.MF.Fisher$GO.ID, function(x) { as.character(unlist(genesInTerm(object = GOdata.MF, whichGO = x)))})
genes_with_GO_term <- function(x) {AnnotatedGenes.MF.Fisher[[x]][AnnotatedGenes.MF.Fisher[[x]] %in% names(geneSig)]}
GeneList.MF.Fisher <- sapply(allRes.MF.Fisher$GO.ID,genes_with_GO_term)
if (length(args)==14) {
sink(paste("GO_top50-significant_genes.MF.Fisher",".(",ind.factor,").",suffix,".txt", sep = ""))
} else {
sink(paste("GO_top50-significant_genes.MF.Fisher",".(",ind.factor,"+",ind.factor2,").",suffix,".txt", sep = ""))
}
print(GeneList.MF.Fisher)
sink()
library(xlsx)
if (length(args)==14) {
write.xlsx2(allRes.BP.classicKS,row.names=FALSE,file=paste("GO-top_50.BP",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "classicKS")
write.xlsx2(allRes.BP.elimKS,row.names=FALSE,file=paste("GO-top_50.BP",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "elimKS", append = T)
write.xlsx2(allRes.BP.Fisher,row.names=FALSE,file=paste("GO-top_50.BP",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "Fisher", append = T)
write.xlsx2(allRes.CC.classicKS,row.names=FALSE,file=paste("GO-top_50.CC",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "classicKS")
write.xlsx2(allRes.CC.elimKS,row.names=FALSE,file=paste("GO-top_50.CC",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "elimKS", append = T)
write.xlsx2(allRes.CC.Fisher,row.names=FALSE,file=paste("GO-top_50.CC",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "Fisher", append = T)
write.xlsx2(allRes.MF.classicKS,row.names=FALSE,file=paste("GO-top_50.MF",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "classicKS")
write.xlsx2(allRes.MF.elimKS,row.names=FALSE,file=paste("GO-top_50.MF",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "elimKS", append = T)
write.xlsx2(allRes.MF.Fisher,row.names=FALSE,file=paste("GO-top_50.MF",".(",ind.factor,").",suffix,".xls", sep = ""), sheetName = "Fisher", append = T)
} else {
write.xlsx2(allRes.BP.classicKS,row.names=FALSE,file=paste("GO-top_50.BP",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "classicKS")
write.xlsx2(allRes.BP.elimKS,row.names=FALSE,file=paste("GO-top_50.BP",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "elimKS", append = T)
write.xlsx2(allRes.BP.Fisher,row.names=FALSE,file=paste("GO-top_50.BP",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "Fisher", append = T)
write.xlsx2(allRes.CC.classicKS,row.names=FALSE,file=paste("GO-top_50.CC",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "classicKS")
write.xlsx2(allRes.CC.elimKS,row.names=FALSE,file=paste("GO-top_50.CC",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "elimKS", append = T)
write.xlsx2(allRes.CC.Fisher,row.names=FALSE,file=paste("GO-top_50.CC",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "Fisher", append = T)
write.xlsx2(allRes.MF.classicKS,row.names=FALSE,file=paste("GO-top_50.MF",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "classicKS")
write.xlsx2(allRes.MF.elimKS,row.names=FALSE,file=paste("GO-top_50.MF",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "elimKS", append = T)
write.xlsx2(allRes.MF.Fisher,row.names=FALSE,file=paste("GO-top_50.MF",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = ""), sheetName = "Fisher", append = T)
}
if (length(args)==14) {
pdf(paste("GO-p-values_comparison",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("GO-p-values_comparison",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
pValue.classic <- score(resultKS.BP)
pValue.elim <- score(resultKS.elim.BP)[names(pValue.classic)]
gstat <- termStat(GOdata.BP, names(pValue.classic))
gSize <- gstat$Annotated / max(gstat$Annotated) * 4
gCol <- colMap(gstat$Significant)
plot(x=pValue.classic, y=pValue.elim, xlab = "p-value - KS test (classic)", ylab = "p-value - KS test (elim)", pch = 19, cex = gSize, col = gCol, title(main="Gene ontology analysis (Biological process)"))
pValue.classic <- score(resultKS.CC)
pValue.elim <- score(resultKS.elim.CC)[names(pValue.classic)]
gstat <- termStat(GOdata.CC, names(pValue.classic))
gSize <- gstat$Annotated / max(gstat$Annotated) * 4
gCol <- colMap(gstat$Significant)
plot(x=pValue.classic, y=pValue.elim, xlab = "p-value - KS test (classic)", ylab = "p-value - KS test (elim)", pch = 19, cex = gSize, col = gCol, title(main="Gene ontology analysis (Cellular component)"))
pValue.classic <- score(resultKS.MF)
pValue.elim <- score(resultKS.elim.MF)[names(pValue.classic)]
gstat <- termStat(GOdata.MF, names(pValue.classic))
gSize <- gstat$Annotated / max(gstat$Annotated) * 4
gCol <- colMap(gstat$Significant)
plot(x=pValue.classic, y=pValue.elim, xlab = "p-value - KS test (classic)", ylab = "p-value - KS test (elim)", pch = 19, cex = gSize, col = gCol, title(main="Gene ontology analysis (Molecular function)"))
dev.off()
if (length(args)==14) {
pdf(paste("GO-diagrams_top_10_test_KS_classic",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("GO-diagrams_top_10_test_KS_classic",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
tryCatch(expr={showSigOfNodes(GOdata.BP, score(resultKS.BP), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (biological processes)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (biological processes): ERROR")}})
tryCatch(expr={showSigOfNodes(GOdata.CC, score(resultKS.CC), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (cellular components)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (cellular components): ERROR")}})
tryCatch(expr={showSigOfNodes(GOdata.MF, score(resultKS.MF), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (molecular functions)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (molecular functions): ERROR")}})
dev.off()
if (length(args)==14) {
pdf(paste("GO-diagrams_top_10_test_KS_elim",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("GO-diagrams_top_10_test_KS_elim",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
tryCatch(expr={showSigOfNodes(GOdata.BP, score(resultKS.elim.BP), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (biological processes)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (biological processes): ERROR")}})
tryCatch(expr={showSigOfNodes(GOdata.CC, score(resultKS.elim.CC), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (cellular components)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (cellular components): ERROR")}})
tryCatch(expr={showSigOfNodes(GOdata.MF, score(resultKS.elim.MF), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (molecular functions)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (molecular functions): ERROR")}})
dev.off()
if (length(args)==14) {
pdf(paste("GO-diagrams_top_10_test_Fisher",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("GO-diagrams_top_10_test_Fisher",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
tryCatch(expr={showSigOfNodes(GOdata.BP, score(resultFisher.BP), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (biological processes)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (biological processes): ERROR")}})
tryCatch(expr={showSigOfNodes(GOdata.CC, score(resultFisher.CC), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (cellular components)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (cellular components): ERROR")}})
tryCatch(expr={showSigOfNodes(GOdata.MF, score(resultFisher.MF), firstSigNodes = 10, useInfo ='all'); title(main="Gene ontology (molecular functions)")},
error={function(e) {plot.new() + plot.window(xlim=c(-5,5), ylim=c(-5,5)); title(main="Gene ontology (molecular functions): ERROR")}})
dev.off()
}
if(grepl(suffix, pattern="DESEQ2")) {
if(! file.exists(paste0(suffix,".SE.transformed.filtered",".RData"))) {
if (! file.exists(paste0(suffix,".SE.transformed",".RData"))) {
if (length(args) == 14) { factors.formula <- as.formula(paste0("~",ind.factor)) } else { factors.formula <- as.formula(paste0("~",ind.factor,"+",ind.factor2)) }
txdb <- makeTxDbFromGFF(gtffile, format = "gtf", circ_seqs = character())
txdb
tbg <- transcriptsBy(txdb, by="gene")
save.image(file=paste0(suffix,".RData"))
library("GenomicAlignments")
if(stranded == "yes") {
se <- summarizeOverlaps(features=tbg, reads=bamfiles,
mode="Union",
singleEnd=TRUE,
ignore.strand=FALSE,
fragments=FALSE )
} else {
se <- summarizeOverlaps(features=tbg, reads=bamfiles,
mode="Union",
singleEnd=TRUE,
ignore.strand=TRUE,
fragments=FALSE )
}
se
dim(se)
assayNames(se)
head(assay(se), 3)
colSums(assay(se))
rowRanges(se)
str(metadata(rowRanges(se)))
colnames(se)
colData(se) <- DataFrame(sampleTable)
colData(se)
library("DESeq2")
ds <- DESeqDataSet(se, design = factors.formula)
if (length(ds[[sample_col]]) <= 30)
{
rld.e <- tryCatch(rld <- rlog(ds, blind = FALSE),error=function (e) { rlog(ds, blind = TRUE) })
if (exists("rld")) { rm(rld.e) } else { rld <- rld.e}
} else if (length(ds[[sample_col]]) > 30)
{
vsd <- vst(ds, blind = FALSE)
}
save.image(file=paste0(suffix,".SE.transformed",".RData"))
} else { load(file=paste0(suffix,".SE.transformed",".RData")) }
library("DESeq2")
if (exists("rld"))
{
library(genefilter)
if (Filters != "UNFILTERED")
{
ffunctions <- unlist(strsplit(Filters,split=";"))
for(i in seq(from=1, to=length(ffunctions),by=1)) { name <- paste("ff",i,sep=".")
assign(name,eval(parse(text=ffunctions[i])))}
ff.number <- length(grep(objects(),pattern="ff.[0-9]+"))
if (ff.number == 1)
{ selGenes <- genefilter(assay(rld),filterfun(ff.1))
} else if (ff.number == 2)
{ selGenes <- genefilter(assay(rld),filterfun(ff.1,ff.2))
} else if (ff.number == 3)
{ selGenes <- genefilter(assay(rld),filterfun(ff.1,ff.2,ff.3))
} else {
save.image(file=paste0(suffix,".SE.transformed",".RData"))
stop("The number of filtering functions is incorrect.") }
eset <- assay(rld[selGenes,])
rld[rownames(assay(rld)) %in% rownames(eset), ] -> rld
ds[rownames(assay(ds)) %in% rownames(eset), ] -> ds
nrow(assay(ds))
}
save.image(file=paste0(suffix,".SE.transformed.filtered",".RData"))
} else if(exists("vsd"))
{
library(genefilter)
if (Filters != "UNFILTERED")
{
ffunctions <- unlist(strsplit(Filters,split=";"))
for(i in seq(from=1, to=length(ffunctions),by=1)) { name <- paste("ff",i,sep=".")
assign(name,eval(parse(text=ffunctions[i])))}
ff.number <- length(grep(objects(),pattern="ff.[0-9]+"))
if (ff.number == 1)
{ selGenes <- genefilter(assay(vsd),filterfun(ff.1))
} else if (ff.number == 2)
{ selGenes <- genefilter(assay(vsd),filterfun(ff.1,ff.2))
} else if (ff.number == 3)
{ selGenes <- genefilter(assay(vsd),filterfun(ff.1,ff.2,ff.3))
} else {
save.image(file=paste0(suffix,".SE.transformed",".RData"))
stop("The number of filtering functions is incorrect.") }
eset <- assay(vsd[selGenes,])
vsd[rownames(assay(vsd)) %in% rownames(eset), ] -> vsd
ds[rownames(assay(ds)) %in% rownames(eset), ] -> ds
nrow(assay(ds))
}
save.image(file=paste0(suffix,".SE.transformed.filtered",".RData"))
}
} else { load(file=paste0(suffix,".SE.transformed.filtered",".RData")) }
library(DESeq2)
RUNID <- gsub(x=suffix, pattern="(^[^.]*)(.*)", replacement="\\1")
for (i in grep(ls(), pattern="(^rld$|^vsd$)", value=TRUE)) {
pdf(paste("Read counts histogram-",suffix,".pdf", sep = ""))
hist(counts(ds), col="gray", main=paste0("DESeq2 - histogram of cumulative read counts in the ", RUNID, " run."))
dev.off()
library(ggplot2)
library(pals)
if (length(args)==14) {
pdf(paste("PCA-plot.",i,".(",ind.factor,").",suffix,".pdf", sep = ""))
plot.pca <- plotPCA(get(i), intgroup = ind.factor, returnData=TRUE)
percentVar <- round(100 * attr(plot.pca, "percentVar"))
if(NROW(plot.pca$name)<=25) {
ggplot.1 <- ggplot(plot.pca, aes(x = PC1, y = PC2, color = name, shape = group)) +
geom_point(size =3) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
labs(color=sample_col, shape=ind.factor, title = "Principal component analysis (PCA) plot") +
coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
dev.off()
} else {
ggplot.1 <- ggplot(plot.pca, aes(x = PC1, y = PC2, color = group)) +
geom_point(size =3) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
labs(color=ind.factor, title = "Principal component analysis (PCA) plot") +
coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
dev.off()
}
} else {
pdf(paste("PCA-plot.",i,".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
plot.pca <- plotPCA(get(i), intgroup = c(ind.factor,ind.factor2), returnData=TRUE)
percentVar <- round(100 * attr(plot.pca, "percentVar"))
if(NROW(plot.pca$name)<=25) {
ggplot.1 <- ggplot(plot.pca, aes(x = PC1, y = PC2, color = name, shape = group)) +
geom_point(size =3) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
labs(color=sample_col, shape=paste(ind.factor,ind.factor2,sep=":"), title = "Principal component analysis (PCA) plot") +
coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
dev.off()
} else {
ggplot.1 <- ggplot(plot.pca, aes(x = PC1, y = PC2, color = group)) +
geom_point(size =3) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
labs(color=paste(ind.factor,ind.factor2,sep=":"), title = "Principal component analysis (PCA) plot") +
coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
dev.off()
}
}
library("genefilter")
#library(tidyverse)
library("AnnotationDbi")
library("org.Hs.eg.db")
topVarGenes <- head(order(rowVars(assay(get(i))), decreasing = TRUE), 20)
mat <- assay(get(i))[ topVarGenes, ]
mat <- mat - rowMeans(mat)
mat.ens <- mat
rownames(mat) <- gsub(rownames(mat),pattern="\\..*$", replacement="")
rownames(mat) <- as.vector(mapIds(org.Hs.eg.db,
keys=rownames(mat),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first"))
for(j in seq(from=1, to=nrow(mat))) {rownames(mat)[j] [is.na(rownames(mat)[j])] <- rownames(mat.ens)[j]}
library("pheatmap")
library("RColorBrewer")
if (length(args)==14) {
anno <- as.data.frame(colData(get(i))[, ind.factor],row.names = as.vector(sampleTable[[sample_col]]))
colnames(anno) <- ind.factor
pdf(paste("TopVarGenes.",i,".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
anno <- as.data.frame(colData(get(i))[, c(ind.factor,ind.factor2)],row.names = as.vector(sampleTable[[sample_col]]))
colnames(anno) <- c(ind.factor, ind.factor2)
pdf(paste("TopVarGenes.",i,".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
pheatmap(mat, annotation_col = anno)
dev.off()
sampleDists <- dist(t(assay(get(i))))
sampleDists
sampleDistMatrix <- as.matrix( sampleDists )
#colnames(sampleDistMatrix) <- NULL
if (length(args)==14) {
pdf(paste("Sample_distance.",i,".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("Sample_distance.",i,".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
pheatmap(sampleDistMatrix,
clustering_distance_rows = sampleDists,
clustering_distance_cols = sampleDists,
annotation_col = anno
)
dev.off()
}
library("DESeq2")
if (length(ds[[sample_col]])<2) {
save.image(file=paste0(suffix,".SE.transformed.filtered",".RData"))
stop('The analysis cannot be performed for less than two samples.')
} else if (exists("rld.e"))
{
res <- data.frame(
assay(rld),
avgLogExpr = ( assay(rld)[,2] + assay(rld)[,1] ) / 2,
rLogFC = assay(rld)[,2] - assay(rld)[,1], FC = 2^(assay(rld)[,2] - assay(rld)[,1]) )
res$Ensembl.ID <- gsub("\\..*","",rownames(res))
library("AnnotationDbi")
library("org.Hs.eg.db")
res$symbol <- mapIds(org.Hs.eg.db,
keys=res$Ensembl.ID,
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$Entrez <- mapIds(org.Hs.eg.db,
keys=res$Ensembl.ID,
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
} else
{
dds <- DESeq(ds)
pdf(paste("Read counts normalization-",suffix,".pdf", sep = ""))
par(mfrow=c(2,1))
boxplot(counts(dds)+1, col = "lightblue", las = 2, cex.names = 1, log="y")
title(main=paste0("DESeq2 - raw read counts in the ", RUNID, " run."), xlab=sample_col, ylab="Read counts in a log10 scale")
boxplot(counts(dds, normalized=TRUE)+1, col = "lightblue", las = 2, cex.names = 1, log="y")
title(main=paste0("DESeq2 - normalized read counts in the ", RUNID, " run."), xlab=sample_col, ylab="Read counts in a log10 scale")
dev.off()
res <- results(dds, alpha = alpha, lfcThreshold = lfcThreshold)
# library(tidyverse)
res$FC <- 2**(res$log2FoldChange)
res$Ensembl.ID <- gsub("\\..*","",rownames(res))
library("AnnotationDbi")
library("org.Hs.eg.db")
res$symbol <- mapIds(org.Hs.eg.db,
keys=res$Ensembl.ID,
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$Entrez <- mapIds(org.Hs.eg.db,
keys=res$Ensembl.ID,
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
geneList <- as.vector(res$padj)
names(geneList) <- as.vector(res$symbol)
geneList <- geneList[!is.na(geneList)]
topDiffGenes <- function(pvalue) {return(pvalue < 0.05) }
if(sum(topDiffGenes(geneList)) > 0)
{
f.topGO(geneList)
}
}
head(res)
#summary(res)
#mcols(res, use.names = TRUE)
if (! exists("rld.e")) {
library("apeglm")
if (length(args)==14) {
pdf(paste("MA-plot",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("MA-plot",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
plotMA(res, ylim = c(-5, 5))
dev.off()
}
if (length(args)==14) {
pdf(paste("P-value-histogram",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("P-value-histogram",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
if (! exists("rld.e")) {
hist(res$padj, col = "grey50", border = "white")
} else if (exists("rld.e"))
{
hist(res$FC, col = "grey50", border = "white")
}
dev.off()
if (! exists("rld.e")) {
resOrdered <- res[order(res$pvalue),]
head(resOrdered)
resOrderedDF <- as.data.frame(resOrdered)
if (length(args)==14) {
write.table(resOrderedDF, file = paste("DESeq2_analysis_results",".(",ind.factor,").",suffix,".csv", sep = ""), sep = ";", dec = ".", row.names = FALSE)
library("ReportingTools")
htmlRep <- HTMLReport(shortName="DESeq2 analysis report", title=paste("DESeq2_analysis_results",".(",ind.factor,").",suffix, sep = ""),
reportDirectory=paste("DESeq2_analysis_results",".(",ind.factor,").",suffix, sep = ""))
} else {
write.table(resOrderedDF, file = paste("DESeq2_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix,".csv", sep = ""), sep = ";", dec = ".", row.names = FALSE)
library("ReportingTools")
htmlRep <- HTMLReport(shortName="DESeq2 analysis report", title=paste("DESeq2_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix, sep = ""),
reportDirectory=paste("DESeq2_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix, sep = ""))
}
publish(resOrderedDF, htmlRep)
url <- finish(htmlRep)
browseURL(url, browser="firefox")
} else if (exists("rld.e"))
{
resOrdered <- res[order(res$FC),]
head(resOrdered)
if (length(args)==14) {
write.table(resOrdered, file = paste("DESeq2_analysis_results",".(",ind.factor,").",suffix,".csv", sep = ""), sep = ";", dec = ".", row.names = FALSE)
library("ReportingTools")
htmlRep <- HTMLReport(shortName="DESeq2 analysis report", title=paste("DESeq2_analysis_results",".(",ind.factor,").",suffix, sep = ""),
reportDirectory=paste("DESeq2_analysis_results",".(",ind.factor,").",suffix, sep = ""))
} else {
write.table(resOrdered, file = paste("DESeq2_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix,".csv", sep = ""), sep = ";", dec = ".", row.names = FALSE)
library("ReportingTools")
htmlRep <- HTMLReport(shortName="DESeq2 analysis report", title=paste("DESeq2_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix, sep = ""),
reportDirectory=paste("DESeq2_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix, sep = ""))
}
publish(resOrdered, htmlRep)
url <- finish(htmlRep)
browseURL(url, browser="firefox")
}
normalized.counts <- counts(dds, normalized=TRUE)
rownames(normalized.counts) <- gsub(rownames(normalized.counts),pattern="\\..*$", replacement="")
rownames(normalized.counts) <- as.vector(mapIds(org.Hs.eg.db,
keys=rownames(normalized.counts),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first"))
normalized.counts <- normalized.counts[!is.na(rownames(normalized.counts)),]
normalized.counts <- normalized.counts[order(rownames(normalized.counts)),]
normalized.counts <- cbind(normalized.counts, rownames(normalized.counts))
colnames(normalized.counts)[ncol(normalized.counts)] <- "Gene.name"
normalized.counts <- normalized.counts[,c(ncol(normalized.counts),1:ncol(normalized.counts)-1)]
normalized.counts <- as.data.frame(normalized.counts)
library(xlsx)
if (length(args)==14) {
write.xlsx2(normalized.counts,row.names=FALSE,file=paste("Normalized_counts",".(",ind.factor,").",suffix,".xls", sep = ""))
} else {
write.xlsx2(normalized.counts,row.names=FALSE,file=paste("Normalized_counts",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = "")) }
print("ColSums unfiltered:")
print(colSums(assay(se)))
print("ColSums filtered:")
print(colSums(counts(ds)))
final.RData.name <- paste0(suffix,".SE.transformed.filtered",".RData")
save.image(file = final.RData.name)
} else if(grepl(suffix, pattern="EDGER")) {
if (! file.exists(paste0(suffix,".SE.transformed",".RData"))) {
if (! file.exists(paste0(suffix,".SE.RData"))) {
library(Rsubread)
if(stranded == "yes") {
se <- featureCounts(files=filenames,annot.ext=gtffile,
isGTFAnnotationFile=TRUE,GTF.featureType="transcript",GTF.attrType="gene_id",
isPairedEnd = FALSE,
tmpDir=tempdir(),
nthreads=threads,
strandSpecific = 1 )
} else {
se <- featureCounts(files=filenames,annot.ext=gtffile,
isGTFAnnotationFile=TRUE,GTF.featureType="transcript",GTF.attrType="gene_id",
isPairedEnd = FALSE,
tmpDir=tempdir(),
nthreads=threads,
strandSpecific = 0 )
}
save.image(file=paste0(suffix,".RData"))
colnames(se$counts) <- bamsamples$names
library(edgeR)
edgeR.dge <- DGEList(counts=se$counts)
edgeR.dge <- calcNormFactors(edgeR.dge)
if (Filters != "UNFILTERED")
{
keep <- filterByExpr(edgeR.dge)
edgeR.counts.filtered <- edgeR.dge$counts[keep,]
edgeR.dge <- DGEList(counts=edgeR.counts.filtered)
edgeR.dge <- calcNormFactors(edgeR.dge)
}
save.image(file=paste0(suffix,".SE.RData")) } else { load(file=paste0(suffix,".SE.RData")) }
for(i in colnames(edgeR.dge$counts)) {temp <- edgeR.dge$counts[,i]/(edgeR.dge$samples$lib.size[rownames(edgeR.dge$samples) %in% i]*edgeR.dge$samples$norm.factors[rownames(edgeR.dge$samples) %in% i])*1e6; assign(value=temp,x=paste("col",i, sep="."))}
colList <- grep(objects(),pattern="^col\\..+$",value=TRUE)
edgeR.counts.norm <- sapply(mget(colList),cbind)
colnames(edgeR.counts.norm) <- colnames(edgeR.dge$counts)
rownames(edgeR.counts.norm) <- rownames(edgeR.dge$counts)
edgeR.counts.norm.df <- as.data.frame(edgeR.counts.norm, stringsAsFactors=FALSE)
rm(list=grep(objects(),pattern="^col\\..+$",value=TRUE))
sampleTable <- merge(x=edgeR.dge$samples, y=csvfile, by.x="row.names", by.y=sample_col)
colnames(sampleTable)[colnames(sampleTable) %in% "Row.names"] <- sample_col
if (length(args)==14) {
design <- model.matrix(~sampleTable[[ind.factor]]) } else {
design <- model.matrix(~sampleTable[[ind.factor]] + sampleTable[[ind.factor2]]) }
edgeR.dge <- estimateDisp(edgeR.dge, design)
rld <- cpm(edgeR.dge, log=TRUE) ## Shows normalized results in a log2 scale
#cpm(edgeR.dge, log=FALSE) ## Shows normalized results in a linear scale
save.image(file=paste0(suffix,".SE.transformed",".RData"))
} else {load(file=paste0(suffix,".SE.transformed",".RData"))}
library(edgeR)
RUNID <- gsub(x=suffix, pattern="(^[^.]*)(.*)", replacement="\\1")
pdf(paste("Read counts histogram-",suffix,".pdf", sep = ""))
hist(edgeR.dge$counts, col="gray", main=paste0("edgeR - histogram of cumulative read counts in the ", RUNID, " run."))
dev.off()
if (Filters != "UNFILTERED")
{
pdf(paste("Read counts normalization-",suffix,".pdf", sep = ""))
par(mfrow=c(2,1))
boxplot(edgeR.dge$counts+1, col = "lightgreen", las = 2, cex.names = 1, log="y")
title(main=paste0("edgeR - raw read counts in the ", RUNID, " run."), xlab=sample_col, ylab="Read counts in a log10 scale")
boxplot(edgeR.counts.norm.df+1, col = "lightgreen", las = 2, cex.names = 1, log="y")
title(main=paste0("edgeR - normalized read counts in the ", RUNID, " run."), xlab=sample_col, ylab="Read counts in a log10 scale")
dev.off()
} else {
pdf(paste("Read counts normalization-",suffix,".pdf", sep = ""))
par(mfrow=c(2,1))
boxplot(edgeR.dge$counts+1, col = "lightgreen", las = 2, cex.names = 1, log="y")
title(main=paste0("edgeR - raw read counts in the ", RUNID, " run."), xlab=sample_col, ylab="Read counts in a log10 scale")
boxplot(edgeR.counts.norm.df+1, col = "lightgreen", las = 2, cex.names = 1, log="y")
title(main=paste0("edgeR - normalized read counts in the ", RUNID, " run."), xlab=sample_col, ylab="Read counts in a log10 scale")
dev.off()
}
library(pals)
library(ggplot2)
plot.mds <- plotMDS(edgeR.dge)
dev.off()
unlink('Rplots.pdf')
plot.mds.df <- as.data.frame(plot.mds$cmdscale.out)
if (length(args)==14) {
pdf(paste("MDS-plot",".(",ind.factor,").",suffix,".pdf", sep = ""))
if(nrow(plot.mds.df)<=25) {
ggplot.1 <- ggplot(plot.mds.df, aes(x = V1, y = V2, color = sampleTable[[sample_col]], shape = sampleTable[[ind.factor]])) +
geom_point(size = 3) + coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
xlab("Leading logFC dim1") + ylab("Leading logFC dim2") +
labs(title = "Multidimensional scaling (MDS) plot", shape = ind.factor, color = sample_col) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
} else {
ggplot.1 <- ggplot(plot.mds.df, aes(x = V1, y = V2, color = sampleTable[[ind.factor]])) +
geom_point(size = 3) + coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
xlab("Leading logFC dim1") + ylab("Leading logFC dim2") +
labs(title = "Multidimensional scaling (MDS) plot", color = ind.factor) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
}
dev.off()
} else {
pdf(paste("MDS-plot",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
if(nrow(plot.mds.df)<=25) {
ggplot.1 <- ggplot(plot.mds.df, aes(x = V1, y = V2, color = sampleTable[[sample_col]], shape = interaction(sampleTable[[ind.factor]], sampleTable[[ind.factor2]]))) +
geom_point(size = 3) + coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
xlab("Leading logFC dim1") + ylab("Leading logFC dim2") +
labs(title = "Multidimensional scaling (MDS) plot", shape = paste(ind.factor, ind.factor2, sep = "+"), color = sample_col) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
} else {
ggplot.1 <- ggplot(plot.mds.df, aes(x = V1, y = V2, color = interaction(sampleTable[[ind.factor]], sampleTable[[ind.factor2]]))) +
geom_point(size = 3) + coord_fixed() + scale_color_manual(values = as.vector(cols25())) +
xlab("Leading logFC dim1") + ylab("Leading logFC dim2") +
labs(title = "Multidimensional scaling (MDS) plot", color = paste(ind.factor, ind.factor2, sep = "+")) +
theme(plot.title = element_text(hjust = 0.5))
print(ggplot.1)
}
dev.off()
}
library("genefilter")
#library(tidyverse)
library("AnnotationDbi")
library("org.Hs.eg.db")
topVarGenes <- head(order(rowVars(rld), decreasing = TRUE), 20)
mat <- rld[ topVarGenes, ]
mat <- mat - rowMeans(mat)
mat.ens <- mat
rownames(mat) <- gsub(rownames(mat),pattern="\\..*$", replacement="")
rownames(mat) <- as.vector(mapIds(org.Hs.eg.db,
keys=rownames(mat),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first"))
for(i in seq(from=1, to=nrow(mat))) {rownames(mat)[i] [is.na(rownames(mat)[i])] <- rownames(mat.ens)[i]}
library("pheatmap")
library("RColorBrewer")
if (length(args)==14) {
anno <- as.data.frame(sampleTable[, ind.factor],row.names = as.vector(sampleTable[[sample_col]]))
colnames(anno) <- ind.factor
pdf(paste("TopVarGenes.rld",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
anno <- as.data.frame(sampleTable[, c(ind.factor, ind.factor2)],row.names = as.vector(sampleTable[[sample_col]]))
colnames(anno) <- c(ind.factor, ind.factor2)
pdf(paste("TopVarGenes.rld",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
pheatmap(mat, annotation_col = anno)
dev.off()
sampleDists <- dist(t(rld))
sampleDists
sampleDistMatrix <- as.matrix(sampleDists)
#colnames(sampleDistMatrix) <- NULL
if (length(args)==14) {
pdf(paste("Sample_distance.rld",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("Sample_distance.rld",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
pheatmap(sampleDistMatrix,
clustering_distance_rows = sampleDists,
clustering_distance_cols = sampleDists,
annotation_col = anno
)
dev.off()
if (length(sampleTable[[sample_col]])<3) {
save.image(file=paste0(suffix,".SE.transformed",".RData"))
stop('The analysis cannot be performed for less than three samples.')}
fit <- glmQLFit(edgeR.dge, design)
qlf <- glmQLFTest(fit)
res <- topTags(qlf, n=nrow(qlf$table))
res$table$FC <- 2**res$table$logFC
res$table$Ensembl.ID <- gsub("\\..*","",rownames(res))
library("AnnotationDbi")
library("org.Hs.eg.db")
res$table$symbol <- mapIds(org.Hs.eg.db,
keys=res$table$Ensembl.ID,
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first")
res$table$Entrez <- mapIds(org.Hs.eg.db,
keys=res$table$Ensembl.ID,
column="ENTREZID",
keytype="ENSEMBL",
multiVals="first")
geneList <- as.vector(res$table$FDR)
names(geneList) <- as.vector(res$table$symbol)
geneList <- geneList[!is.na(geneList)]
topDiffGenes <- function(pvalue) {return(pvalue < 0.05) }
if(sum(topDiffGenes(geneList)) > 0)
{
f.topGO(geneList)
}
head(res$table)
if (length(args)==14) {
pdf(paste("P-value-histogram",".(",ind.factor,").",suffix,".pdf", sep = ""))
} else {
pdf(paste("P-value-histogram",".(",ind.factor,"+",ind.factor2,").",suffix,".pdf", sep = ""))
}
hist(res$table$FDR, col = "grey50", border = "white")
dev.off()
resOrderedDF <- res$table[order(res$table$FDR),]
head(resOrderedDF)
if (length(args)==14) {
write.table(resOrderedDF, file = paste("edgeR_analysis_results",".(",ind.factor,").",suffix,".csv", sep = ""), sep = ";", dec = ".", row.names = FALSE)
library("ReportingTools")
htmlRep <- HTMLReport(shortName="edgeR analysis report", title=paste("edgeR_analysis_results",".(",ind.factor,").",suffix, sep = ""),
reportDirectory=paste("edgeR_analysis_results",".(",ind.factor,").",suffix, sep = ""))
} else {
write.table(resOrderedDF, file = paste("edgeR_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix,".csv", sep = ""), sep = ";", dec = ".", row.names = FALSE)
library("ReportingTools")
htmlRep <- HTMLReport(shortName="edgeR analysis report", title=paste("edgeR_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix, sep = ""),
reportDirectory=paste("edgeR_analysis_results",".(",ind.factor,"+",ind.factor2,").",suffix, sep = ""))
}
publish(resOrderedDF, htmlRep)
url <- finish(htmlRep)
browseURL(url, browser="firefox")
normalized.counts <- cpm(edgeR.dge, log=FALSE)
rownames(normalized.counts) <- gsub(rownames(normalized.counts),pattern="\\..*$", replacement="")
rownames(normalized.counts) <- as.vector(mapIds(org.Hs.eg.db,
keys=rownames(normalized.counts),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first"))
normalized.counts <- normalized.counts[!is.na(rownames(normalized.counts)),]
normalized.counts <- normalized.counts[order(rownames(normalized.counts)),]
normalized.counts <- cbind(normalized.counts, rownames(normalized.counts))
colnames(normalized.counts)[ncol(normalized.counts)] <- "Gene.name"
normalized.counts <- normalized.counts[,c(ncol(normalized.counts),1:ncol(normalized.counts)-1)]
normalized.counts <- as.data.frame(normalized.counts)
library(xlsx)
if (length(args)==14) {
write.xlsx2(normalized.counts,row.names=FALSE,file=paste("Normalized_counts",".(",ind.factor,").",suffix,".xls", sep = ""))
} else {
write.xlsx2(normalized.counts,row.names=FALSE,file=paste("Normalized_counts",".(",ind.factor,"+",ind.factor2,").",suffix,".xls", sep = "")) }
print("ColSums unfiltered:")
print(colSums(se$counts))
print("ColSums filtered:")
print(colSums(edgeR.dge$counts))
final.RData.name <- paste0(suffix,".SE.transformed",".RData")
save.image(file = final.RData.name)
}
library("ReactomePA")
library("ggplot2")
res.df <- as.data.frame(res)
if (suffix == "DESEQ2") {
res.df.05 <- subset(res.df, padj < 0.05) } else if (suffix == "EDGER") {
res.df.05 <- subset(res.df, FDR < 0.05)}
for (exp.type in c("upregulated genes", "downregulated genes")) {
if (exp.type == "upregulated genes") {
res.df.05.FC1.5 <- subset(res.df.05, FC >= 3/2)
} else {
res.df.05.FC1.5 <- subset(res.df.05, FC <= 2/3)
}
geneList <- res.df.05.FC1.5$FC
names(geneList) <- res.df.05.FC1.5$Entrez
geneList <- geneList[!is.na(names(geneList))]
geneList <- sort(geneList, decreasing = TRUE)
geneList <- geneList[!duplicated(names(geneList))]
geneList.df <- as.data.frame(geneList)
colnames(geneList.df) <- "FC"
if (nrow(geneList.df) > 0) {
rownames(geneList.df) <- as.vector(mapIds(org.Hs.eg.db,
keys=rownames(geneList.df),
column="SYMBOL",