R/getGSEAlm_Diseases.R
In kmezhoud/canceR: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC
Defines functions
getGSEAlm_Diseases
Documented in
getGSEAlm_Diseases
#' get GSEA linear modeling by studies (diseases)
#' @usage
#' getGSEAlm_Diseases()
#'
#' @return a dataframe with annotation (GO, BP)
#' @export
#' @examples
#' readRDS(paste(path.package("canceR"),"/extdata/rdata/ucec_tcga_pubGSEA1021.rds", sep=""))
#' \dontrun{
#' getGSEAlm_Diseases
#' }
#'@importFrom stats model.matrix
#'
getGSEAlm_Diseases <-function(){
##Remove "GenesDetails" objectf if exists
ifrm <- function(obj, env = ENV()) {
obj <- deparse(substitute(obj))
if(exists(obj, envir = env)) {
rm(list = obj, envir = env)
}
}
ifrm(ENV$GenesDetails, ENV)
####Create a new directory "Results/GSEAlm" for output.
Sys.chmod(getwd(), mode = "0777", use_umask = TRUE)
if(!file.exists("Results/")){
dir.create(file.path(paste(getwd(), "/Results", sep="")), showWarnings = FALSE)
dir.create(file.path(paste(getwd(), "/Results/GSEAlm", sep="")), showWarnings = FALSE)
} else if (!file.exists("Results/GSEAlm/")){
dir.create(file.path(paste(getwd(), "/Results/GSEAlm", sep="")), showWarnings = FALSE)
}
testCheckedCaseGenProf()
Lchecked_Studies <- ENV$lchecked_Studies_forCases
Lchecked_Cases <- length(ENV$curselectCases)
Lchecked_GenProf <- length(ENV$curselectGenProfs)
#dialog function to select the number of samples
ENV$ReturnDialogGeneClasses<- dialogGeneClassifier(Lchecked_Cases)
if(ENV$ReturnDialogGeneClasses[1] == "ID_CANCEL"){
stop()
} else{
DiseasesType <- 0
SamplingProfsData<-0
ProfData<-0
LengthGenProfs<-0
LengthCases=0
for (i in 1:Lchecked_Studies){
Si =ENV$checked_StudyIndex[i]
progressBar_ProfilesData <- tkProgressBar(title = ENV$Studies[Si], min = 0,
max = Lchecked_GenProf, width = 400)
#tkfocus(progressBar_ProfilesData)
LastLengthGenProfs = LengthGenProfs
LengthGenProfs = LengthGenProfs + ENV$LGenProfs[i]+1
LastLengthCases = LengthCases
LengthCases= LengthCases + ENV$LCases[i]+1
for (k in 1:Lchecked_Cases){
Sys.sleep(0.1)
setTkProgressBar(progressBar_ProfilesData, k, label=paste( round(k/Lchecked_GenProf*100, 0),
"% of Expression Set"))
if (ENV$curselectGenProfs[k] <= LengthGenProfs && ENV$curselectGenProfs[k]>LastLengthGenProfs){
GenProf<-ENV$GenProfsRefStudies[ENV$curselectGenProfs[k]]
Case<-ENV$CasesRefStudies[ENV$curselectCases[k]]
if(length(ENV$GeneList)>500){
ProfData <- getMegaProfData(ENV$GeneList,k )
} else{
ProfData<-getProfileData(ENV$cgds,ENV$GeneList, GenProf,Case)
}
ProfData <- t(ProfData)
# ##Convert data frame to numeric structure
# print("converting data frame of Profile data to numeric stucture...")
#
# cidx <- !(sapply(ProfData, is.numeric))
# ProfData[cidx] <- lapply(ProfData[cidx], as.numeric)
#
#for(p in 1:ncol(ProfData)){
# ProfData[,p] <- as.numeric(ProfData[,p])
#}
## for loop is faster than apply fonction
#rnames <- rownames(ProfData)
#ProfData <- as.data.frame(apply(ProfData,2 ,function(x) as.numeric(x)))
#rownames(ProfData) <- rnames
if(!exists(deparse(substitute(ENV$GenesDetails)))) {
ProfData <- t(t(ProfData))
## Display AssyData with Tcl Table
title=paste(ENV$StudyChoice[k],":",ENV$GenProfsStudies[ENV$curselectGenProfs][k])
getInTable(ProfData,title)
}
if(ncol(ProfData)<ENV$ReturnDialogGeneClasses[1]){
msgBigSampl <- paste(ENV$StudyRefCase[k], "has only", ncol(ProfData),"samples.","\nSelect at Max: ",ncol(ProfData), "samples")
tkmessageBox(message=msgBigSampl, icon="info")
break
close(progressBar_ProfilesData)
}
set.seed(1234)
SamplingProfData <- t(apply(ProfData, 1,function(x)sample(x,ENV$ReturnDialogGeneClasses[1])))
SamplingColnamesProfData <- sample(colnames(ProfData), ENV$ReturnDialogGeneClasses[1])
colnames(SamplingProfData) <- SamplingColnamesProfData
SamplingProfsData <- cbind(SamplingProfsData,SamplingProfData)
print(paste ("sampling data from study:", k))
### ONly for example brca_tcga73genes.RData
##ENV$StudyRefGenProf <- c("brca_tcga", "prad_tcga")
##Extracting Disease Type
DiseaseType<- as.matrix(rep(ENV$StudyRefGenProf[k],times=ENV$ReturnDialogGeneClasses[1]))
DiseasesType <- c(DiseasesType, DiseaseType)
}
}
close(progressBar_ProfilesData)
}
SamplingProfsData<- SamplingProfsData[,-1]
DiseasesType <-DiseasesType[-1]
DiseasesType <- as.data.frame(DiseasesType)
rownames(DiseasesType) <- colnames(SamplingProfsData)
ENV$DiseasesType <- DiseasesType
## create labelDescription for columns of phenoData.
## labeldescription is used by Biobase packages
## In our case labelDescription is Equal to column names
metaData <- data.frame(labelDescription= "DiseasesType", row.names="DiseasesType") ## Bioconductor’s Biobase package provides a class called AnnotatedDataFrame
##that conveniently stores and manipulates
##the phenotypic data and its metadata in a coordinated fashion.
phenoData<-new("AnnotatedDataFrame", data=DiseasesType, varMetadata=metaData)
##Assembling an ExpressionSet
eSetClassifier<-Biobase::ExpressionSet(assayData=SamplingProfsData, phenoData=phenoData, annotation="GO")
##translate Négative to positive value
if(min(Biobase::exprs(eSetClassifier), na.rm=TRUE)<0){
print("There are negative values. Translating values by adding the absolute of minimum value to all matrix")
Biobase::exprs(eSetClassifier) <- Biobase::exprs(eSetClassifier) +(abs(min(Biobase::exprs(eSetClassifier), na.rm=TRUE)))
}
ENV$eSetClassifier <- eSetClassifier
## create MSigDb for eSetClassifier
getMSigDB(ENV$eSetClassifier,1)
#run GSEAlm
#nperm determines the number of permutations used to estimate the null distribution
#of the enrichment score
dialogOptionGSEAlm(1, ENV$DiseasesType)
print("dialog Option of GSEAlm: OK")
## test if Covariables works together else get message box
# if (inherits(try(pVals <-GSEAlm::gsealmPerm(ENV$eSetClassifier,ENV$coVariables,ENV$mSigDB_forGeneList,nperm= ENV$permutVal, na.rm=TRUE), silent=TRUE),"try-error"))
# {
# msgBadCovariables <- paste("There is incompatible variables. Select an other Formula.")
# tkmessageBox(message=msgBadCovariables, icon="warning")
# close(progressBar_ProfilesData)
# stop(msgBadCovariables)
# } else{
# print("Computing of pVals using gsealmPerm function ...")
# pVals <-GSEAlm::gsealmPerm(ENV$eSetClassifier,ENV$coVariables,ENV$mSigDB_forGeneList,nperm= ENV$permutVal, na.rm=TRUE)
# }
print("Computing of pVals using gsealmPerm function ...")
pVals <- gsealmPerm(ENV$eSetClassifier,ENV$coVariables,ENV$mSigDB_forGeneList,nperm= ENV$permutVal, na.rm=TRUE)
#print(paste("pVals:", StudyRefCase[k]))
# we have to correct for multiple testing. In this case we use the method from Benjamini-Hochberg
pVals <- apply(pVals, 2, p.adjust, method = "BH", n = nrow(pVals))
pVal_Permut <- paste(names(table(pData(ENV$eSetClassifier)))[2])
colnames(pVals) <- c(paste("Down Regulated in ",pVal_Permut),paste("Up Regulated in ",pVal_Permut,"nperm:",ENV$permutVal,"pVal:",ENV$seuilpVal))
if(exists(deparse(substitute(GenesDetails)), envir = ENV)) {
assign(paste("pVals", gsub(".RData","",ENV$mSigDB_SubName),sep="_"),pVals, envir=ENV)
workspace <- getwd()
setwd("./Results/GSEAlm")
######################
Sys.chmod(getwd(), mode = "0777", use_umask = TRUE)
if(!file.exists(paste(ENV$StudyRefCase, collapse="_VS_"))){
dir.create(file.path(paste(getwd(),"/",paste(ENV$StudyRefCase, collapse="_VS_") , sep="")), showWarnings = FALSE)
}
setwd(paste("./",paste(ENV$StudyRefCase, collapse="_VS_"), sep=""))
#################
write.table(pVals, paste("pVals_", gsub(".RData","",ENV$mSigDB_SubName),".txt", sep=""), sep="\t")
#set a significance threshold
THRESHOLD<-ENV$seuilpVal
#gene sets that are downregulated in the BLC
downRegulated <- data.frame(sort(pVals[pVals[, 1]< THRESHOLD,1]))
names(downRegulated) <- paste("Down Regulated in ",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
#colnames(pVals) <- c(paste("Down Regulated\n",pVal_Permut),paste("Up Regulated\n",pVal_Permut))
assign(paste("downRegulated","GeneClass" ,gsub(".RData","",ENV$mSigDB_SubName), sep="_"),downRegulated, envir=ENV)
#save downRegulted table
write.table(downRegulated,"_GeneClass_",paste("downRegulated_", gsub(".RData","",mSigDB_SubName),".txt", sep=""), sep="\t")
#gene sets that are upregulated in basal breast cancer
upRegulated <- data.frame(sort(pVals[pVals[, 2] < THRESHOLD, 2]))
#colnames(upRegulated[1]) <- paste("Up Regulated\n", seuilpVal, pVal_permut, coVariables)
names(upRegulated) <- paste("Up Regulated in",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
assign(paste("upRegulated","GeneClass" ,gsub(".RData","",ENV$mSigDB_SubName),sep="_"),upRegulated, envir=ENV)
#save upRegulated table
write.table(upRegulated,"_GeneClass_" ,paste("upRegulated_", gsub(".RData","",mSigDB_SubName),".txt", sep=""), sep="\t")
setwd(workspace)
}else{
assign(paste("pVals", gsub(".RData","",ENV$mSigDB_SubName),ENV$StudyRefCase[k] ,sep="_"),pVals, envir=ENV)
workspace <- getwd()
setwd("./Results/GSEAlm")
################
Sys.chmod(getwd(), mode = "0777", use_umask = TRUE)
if(!file.exists(paste(ENV$StudyRefCase, collapse="_VS_"))){
dir.create(file.path(paste(getwd(),"/",paste(ENV$StudyRefCase, collapse="_VS_") , sep="")), showWarnings = FALSE)
}
setwd(paste("./",paste(ENV$StudyRefCase, collapse="_VS_"), sep=""))
################
write.table(pVals, paste("pVals_", gsub(".RData","",ENV$mSigDB_SubName),"_",ENV$StudyRefCase[k],".txt", sep=""), sep="\t")
#set a significance threshold
THRESHOLD<-ENV$seuilpVal
#gene sets that are downregulated in the BLC
downRegulated <- data.frame(sort(pVals[pVals[, 1]< THRESHOLD,1]))
names(downRegulated) <- paste("Down Regulated in ",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
#colnames(pVals) <- c(paste("Down Regulated\n",pVal_Permut),paste("Up Regulated\n",pVal_Permut))
assign(paste("downRegulated", gsub(".RData","",ENV$mSigDB_SubName), ENV$StudyRefCase[k],sep="_"),downRegulated, envir=ENV)
#save downRegulted table
write.table(downRegulated, paste("downRegulated_", gsub(".RData","",ENV$mSigDB_SubName),"_",ENV$StudyRefCase[k],".txt", sep=""), sep="\t")
#gene sets that are upregulated in basal breast cancer
upRegulated <- data.frame(sort(pVals[pVals[, 2] < THRESHOLD, 2]))
#colnames(upRegulated[1]) <- paste("Up Regulated\n", seuilpVal, pVal_permut, coVariables)
names(upRegulated) <- paste("Up Regulated in",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
assign(paste("upRegulated", gsub(".RData","",ENV$mSigDB_SubName), ENV$StudyRefCase[k],sep="_"),upRegulated, envir=ENV)
#save upRegulted table
write.table(upRegulated, paste("upRegulated_", gsub(".RData","",ENV$mSigDB_SubName),"_",ENV$tudyRefCase[k],".txt", sep=""), sep="\t")
setwd(workspace)
}
##Get results in Tcltk Table
downRegulatedTab <- rbind(colnames(downRegulated), downRegulated)
downRegulatedTab <- cbind(rownames(downRegulatedTab),downRegulatedTab)
downRegulatedTab[,1]<- as.character(downRegulatedTab[,1])
downRegulatedTab[1,1] <- "***** Down Regulated Gene Sets *****"
colnames(downRegulatedTab) <- NULL
upRegulatedTab <- rbind(colnames(upRegulated), upRegulated)
upRegulatedTab <- cbind(rownames(upRegulatedTab),upRegulatedTab)
upRegulatedTab[,1]<- as.character(upRegulatedTab[,1])
upRegulatedTab[1,1] <- "***** Up Regulated Gene Sets *****"
colnames(upRegulatedTab) <- NULL
UpDownRegulatedTable <- rbind.na(downRegulatedTab,upRegulatedTab)
getInTable(UpDownRegulatedTable, title=paste(ENV$StudyRefCase[1]," / ",ENV$StudyRefCase[2]))
}
}
kmezhoud/canceR documentation built on March 4, 2024, 12:34 a.m.
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Defines functions getGSEAlm_Diseases
Documented in getGSEAlm_Diseases
#' get GSEA linear modeling by studies (diseases)
#' @usage
#' getGSEAlm_Diseases()
#'
#' @return a dataframe with annotation (GO, BP)
#' @export
#' @examples
#' readRDS(paste(path.package("canceR"),"/extdata/rdata/ucec_tcga_pubGSEA1021.rds", sep=""))
#' \dontrun{
#' getGSEAlm_Diseases
#' }
#'@importFrom stats model.matrix
#'
getGSEAlm_Diseases <-function(){
##Remove "GenesDetails" objectf if exists
ifrm <- function(obj, env = ENV()) {
obj <- deparse(substitute(obj))
if(exists(obj, envir = env)) {
rm(list = obj, envir = env)
}
}
ifrm(ENV$GenesDetails, ENV)
####Create a new directory "Results/GSEAlm" for output.
Sys.chmod(getwd(), mode = "0777", use_umask = TRUE)
if(!file.exists("Results/")){
dir.create(file.path(paste(getwd(), "/Results", sep="")), showWarnings = FALSE)
dir.create(file.path(paste(getwd(), "/Results/GSEAlm", sep="")), showWarnings = FALSE)
} else if (!file.exists("Results/GSEAlm/")){
dir.create(file.path(paste(getwd(), "/Results/GSEAlm", sep="")), showWarnings = FALSE)
}
testCheckedCaseGenProf()
Lchecked_Studies <- ENV$lchecked_Studies_forCases
Lchecked_Cases <- length(ENV$curselectCases)
Lchecked_GenProf <- length(ENV$curselectGenProfs)
#dialog function to select the number of samples
ENV$ReturnDialogGeneClasses<- dialogGeneClassifier(Lchecked_Cases)
if(ENV$ReturnDialogGeneClasses[1] == "ID_CANCEL"){
stop()
} else{
DiseasesType <- 0
SamplingProfsData<-0
ProfData<-0
LengthGenProfs<-0
LengthCases=0
for (i in 1:Lchecked_Studies){
Si =ENV$checked_StudyIndex[i]
progressBar_ProfilesData <- tkProgressBar(title = ENV$Studies[Si], min = 0,
max = Lchecked_GenProf, width = 400)
#tkfocus(progressBar_ProfilesData)
LastLengthGenProfs = LengthGenProfs
LengthGenProfs = LengthGenProfs + ENV$LGenProfs[i]+1
LastLengthCases = LengthCases
LengthCases= LengthCases + ENV$LCases[i]+1
for (k in 1:Lchecked_Cases){
Sys.sleep(0.1)
setTkProgressBar(progressBar_ProfilesData, k, label=paste( round(k/Lchecked_GenProf*100, 0),
"% of Expression Set"))
if (ENV$curselectGenProfs[k] <= LengthGenProfs && ENV$curselectGenProfs[k]>LastLengthGenProfs){
GenProf<-ENV$GenProfsRefStudies[ENV$curselectGenProfs[k]]
Case<-ENV$CasesRefStudies[ENV$curselectCases[k]]
if(length(ENV$GeneList)>500){
ProfData <- getMegaProfData(ENV$GeneList,k )
} else{
ProfData<-getProfileData(ENV$cgds,ENV$GeneList, GenProf,Case)
}
ProfData <- t(ProfData)
# ##Convert data frame to numeric structure
# print("converting data frame of Profile data to numeric stucture...")
#
# cidx <- !(sapply(ProfData, is.numeric))
# ProfData[cidx] <- lapply(ProfData[cidx], as.numeric)
#
#for(p in 1:ncol(ProfData)){
# ProfData[,p] <- as.numeric(ProfData[,p])
#}
## for loop is faster than apply fonction
#rnames <- rownames(ProfData)
#ProfData <- as.data.frame(apply(ProfData,2 ,function(x) as.numeric(x)))
#rownames(ProfData) <- rnames
if(!exists(deparse(substitute(ENV$GenesDetails)))) {
ProfData <- t(t(ProfData))
## Display AssyData with Tcl Table
title=paste(ENV$StudyChoice[k],":",ENV$GenProfsStudies[ENV$curselectGenProfs][k])
getInTable(ProfData,title)
}
if(ncol(ProfData)<ENV$ReturnDialogGeneClasses[1]){
msgBigSampl <- paste(ENV$StudyRefCase[k], "has only", ncol(ProfData),"samples.","\nSelect at Max: ",ncol(ProfData), "samples")
tkmessageBox(message=msgBigSampl, icon="info")
break
close(progressBar_ProfilesData)
}
set.seed(1234)
SamplingProfData <- t(apply(ProfData, 1,function(x)sample(x,ENV$ReturnDialogGeneClasses[1])))
SamplingColnamesProfData <- sample(colnames(ProfData), ENV$ReturnDialogGeneClasses[1])
colnames(SamplingProfData) <- SamplingColnamesProfData
SamplingProfsData <- cbind(SamplingProfsData,SamplingProfData)
print(paste ("sampling data from study:", k))
### ONly for example brca_tcga73genes.RData
##ENV$StudyRefGenProf <- c("brca_tcga", "prad_tcga")
##Extracting Disease Type
DiseaseType<- as.matrix(rep(ENV$StudyRefGenProf[k],times=ENV$ReturnDialogGeneClasses[1]))
DiseasesType <- c(DiseasesType, DiseaseType)
}
}
close(progressBar_ProfilesData)
}
SamplingProfsData<- SamplingProfsData[,-1]
DiseasesType <-DiseasesType[-1]
DiseasesType <- as.data.frame(DiseasesType)
rownames(DiseasesType) <- colnames(SamplingProfsData)
ENV$DiseasesType <- DiseasesType
## create labelDescription for columns of phenoData.
## labeldescription is used by Biobase packages
## In our case labelDescription is Equal to column names
metaData <- data.frame(labelDescription= "DiseasesType", row.names="DiseasesType") ## Bioconductor’s Biobase package provides a class called AnnotatedDataFrame
##that conveniently stores and manipulates
##the phenotypic data and its metadata in a coordinated fashion.
phenoData<-new("AnnotatedDataFrame", data=DiseasesType, varMetadata=metaData)
##Assembling an ExpressionSet
eSetClassifier<-Biobase::ExpressionSet(assayData=SamplingProfsData, phenoData=phenoData, annotation="GO")
##translate Négative to positive value
if(min(Biobase::exprs(eSetClassifier), na.rm=TRUE)<0){
print("There are negative values. Translating values by adding the absolute of minimum value to all matrix")
Biobase::exprs(eSetClassifier) <- Biobase::exprs(eSetClassifier) +(abs(min(Biobase::exprs(eSetClassifier), na.rm=TRUE)))
}
ENV$eSetClassifier <- eSetClassifier
## create MSigDb for eSetClassifier
getMSigDB(ENV$eSetClassifier,1)
#run GSEAlm
#nperm determines the number of permutations used to estimate the null distribution
#of the enrichment score
dialogOptionGSEAlm(1, ENV$DiseasesType)
print("dialog Option of GSEAlm: OK")
## test if Covariables works together else get message box
# if (inherits(try(pVals <-GSEAlm::gsealmPerm(ENV$eSetClassifier,ENV$coVariables,ENV$mSigDB_forGeneList,nperm= ENV$permutVal, na.rm=TRUE), silent=TRUE),"try-error"))
# {
# msgBadCovariables <- paste("There is incompatible variables. Select an other Formula.")
# tkmessageBox(message=msgBadCovariables, icon="warning")
# close(progressBar_ProfilesData)
# stop(msgBadCovariables)
# } else{
# print("Computing of pVals using gsealmPerm function ...")
# pVals <-GSEAlm::gsealmPerm(ENV$eSetClassifier,ENV$coVariables,ENV$mSigDB_forGeneList,nperm= ENV$permutVal, na.rm=TRUE)
# }
print("Computing of pVals using gsealmPerm function ...")
pVals <- gsealmPerm(ENV$eSetClassifier,ENV$coVariables,ENV$mSigDB_forGeneList,nperm= ENV$permutVal, na.rm=TRUE)
#print(paste("pVals:", StudyRefCase[k]))
# we have to correct for multiple testing. In this case we use the method from Benjamini-Hochberg
pVals <- apply(pVals, 2, p.adjust, method = "BH", n = nrow(pVals))
pVal_Permut <- paste(names(table(pData(ENV$eSetClassifier)))[2])
colnames(pVals) <- c(paste("Down Regulated in ",pVal_Permut),paste("Up Regulated in ",pVal_Permut,"nperm:",ENV$permutVal,"pVal:",ENV$seuilpVal))
if(exists(deparse(substitute(GenesDetails)), envir = ENV)) {
assign(paste("pVals", gsub(".RData","",ENV$mSigDB_SubName),sep="_"),pVals, envir=ENV)
workspace <- getwd()
setwd("./Results/GSEAlm")
######################
Sys.chmod(getwd(), mode = "0777", use_umask = TRUE)
if(!file.exists(paste(ENV$StudyRefCase, collapse="_VS_"))){
dir.create(file.path(paste(getwd(),"/",paste(ENV$StudyRefCase, collapse="_VS_") , sep="")), showWarnings = FALSE)
}
setwd(paste("./",paste(ENV$StudyRefCase, collapse="_VS_"), sep=""))
#################
write.table(pVals, paste("pVals_", gsub(".RData","",ENV$mSigDB_SubName),".txt", sep=""), sep="\t")
#set a significance threshold
THRESHOLD<-ENV$seuilpVal
#gene sets that are downregulated in the BLC
downRegulated <- data.frame(sort(pVals[pVals[, 1]< THRESHOLD,1]))
names(downRegulated) <- paste("Down Regulated in ",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
#colnames(pVals) <- c(paste("Down Regulated\n",pVal_Permut),paste("Up Regulated\n",pVal_Permut))
assign(paste("downRegulated","GeneClass" ,gsub(".RData","",ENV$mSigDB_SubName), sep="_"),downRegulated, envir=ENV)
#save downRegulted table
write.table(downRegulated,"_GeneClass_",paste("downRegulated_", gsub(".RData","",mSigDB_SubName),".txt", sep=""), sep="\t")
#gene sets that are upregulated in basal breast cancer
upRegulated <- data.frame(sort(pVals[pVals[, 2] < THRESHOLD, 2]))
#colnames(upRegulated[1]) <- paste("Up Regulated\n", seuilpVal, pVal_permut, coVariables)
names(upRegulated) <- paste("Up Regulated in",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
assign(paste("upRegulated","GeneClass" ,gsub(".RData","",ENV$mSigDB_SubName),sep="_"),upRegulated, envir=ENV)
#save upRegulated table
write.table(upRegulated,"_GeneClass_" ,paste("upRegulated_", gsub(".RData","",mSigDB_SubName),".txt", sep=""), sep="\t")
setwd(workspace)
}else{
assign(paste("pVals", gsub(".RData","",ENV$mSigDB_SubName),ENV$StudyRefCase[k] ,sep="_"),pVals, envir=ENV)
workspace <- getwd()
setwd("./Results/GSEAlm")
################
Sys.chmod(getwd(), mode = "0777", use_umask = TRUE)
if(!file.exists(paste(ENV$StudyRefCase, collapse="_VS_"))){
dir.create(file.path(paste(getwd(),"/",paste(ENV$StudyRefCase, collapse="_VS_") , sep="")), showWarnings = FALSE)
}
setwd(paste("./",paste(ENV$StudyRefCase, collapse="_VS_"), sep=""))
################
write.table(pVals, paste("pVals_", gsub(".RData","",ENV$mSigDB_SubName),"_",ENV$StudyRefCase[k],".txt", sep=""), sep="\t")
#set a significance threshold
THRESHOLD<-ENV$seuilpVal
#gene sets that are downregulated in the BLC
downRegulated <- data.frame(sort(pVals[pVals[, 1]< THRESHOLD,1]))
names(downRegulated) <- paste("Down Regulated in ",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
#colnames(pVals) <- c(paste("Down Regulated\n",pVal_Permut),paste("Up Regulated\n",pVal_Permut))
assign(paste("downRegulated", gsub(".RData","",ENV$mSigDB_SubName), ENV$StudyRefCase[k],sep="_"),downRegulated, envir=ENV)
#save downRegulted table
write.table(downRegulated, paste("downRegulated_", gsub(".RData","",ENV$mSigDB_SubName),"_",ENV$StudyRefCase[k],".txt", sep=""), sep="\t")
#gene sets that are upregulated in basal breast cancer
upRegulated <- data.frame(sort(pVals[pVals[, 2] < THRESHOLD, 2]))
#colnames(upRegulated[1]) <- paste("Up Regulated\n", seuilpVal, pVal_permut, coVariables)
names(upRegulated) <- paste("Up Regulated in",names(table(pData(ENV$eSetClassifier)))[2],"\tpVal<",THRESHOLD)
assign(paste("upRegulated", gsub(".RData","",ENV$mSigDB_SubName), ENV$StudyRefCase[k],sep="_"),upRegulated, envir=ENV)
#save upRegulted table
write.table(upRegulated, paste("upRegulated_", gsub(".RData","",ENV$mSigDB_SubName),"_",ENV$tudyRefCase[k],".txt", sep=""), sep="\t")
setwd(workspace)
}
##Get results in Tcltk Table
downRegulatedTab <- rbind(colnames(downRegulated), downRegulated)
downRegulatedTab <- cbind(rownames(downRegulatedTab),downRegulatedTab)
downRegulatedTab[,1]<- as.character(downRegulatedTab[,1])
downRegulatedTab[1,1] <- "***** Down Regulated Gene Sets *****"
colnames(downRegulatedTab) <- NULL
upRegulatedTab <- rbind(colnames(upRegulated), upRegulated)
upRegulatedTab <- cbind(rownames(upRegulatedTab),upRegulatedTab)
upRegulatedTab[,1]<- as.character(upRegulatedTab[,1])
upRegulatedTab[1,1] <- "***** Up Regulated Gene Sets *****"
colnames(upRegulatedTab) <- NULL
UpDownRegulatedTable <- rbind.na(downRegulatedTab,upRegulatedTab)
getInTable(UpDownRegulatedTable, title=paste(ENV$StudyRefCase[1]," / ",ENV$StudyRefCase[2]))
}
}
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