###Source Code for Pathways Analysis

########################################################
#######################################################
#########CONSTRUCTING THE NECESSARY DEG FILES##########
######################################################
######################################################
####Do this for as many DEG files as you need########


##Split the Data into Two Groups of interest (Normal and Tumor)
NormalCOAD <- COADgenomicMatrix[,grep(".11", colnames(COADgenomicMatrix))] 
TumorCOAD <- COADgenomicMatrix[,grep(".01", colnames(COADgenomicMatrix))] 
colnames(NormalCOAD)
NormalCOAD <- NormalCOAD[,-32]
NormalCOAD <- NormalCOAD[,-28]


##Set up design matrix
NormalDesign <- as.data.frame(colnames(NormalCOAD))
NormalDesign$Pheno <- "Normal"
NormalDesign$Color <- "Purple"
colnames(NormalDesign) <- c("SampleID", "Pheno", "Color")
TumorDesign <- as.data.frame(colnames(TumorCOAD))
TumorDesign$Pheno <- "Tumor"
TumorDesign$Color <- "Green"
colnames(TumorDesign) <- c("SampleID", "Pheno", "Color")
Design <- rbind(TumorDesign, NormalDesign)
Samples <- as.matrix(Design$SampleID)
Pheno <- as.matrix(Design$Pheno)
Color <- as.matrix(Design$Color)
COADdesign <- model.matrix(~ 0+factor(c(Pheno)))
colnames(COADdesign) <- c("Normal", "Tumor")

#Remove rows that have all 0s
COADgenomicMatrix <- round(COADgenomicMatrix)
COADRSEM.zero <- COADgenomicMatrix[!apply(COADgenomicMatrix, 1, function(row) all(row == 0)), ]
COADRSEM.50zero <- COADRSEM.zero[which(as.numeric(rowSums(COADRSEM.zero != 0)) > ncol(COADRSEM.zero)/2),]
COADRSEM.80zero <- COADRSEM.zero[which(as.numeric(rowSums(COADRSEM.zero != 0)) > (ncol(COADRSEM.zero))*0.2),]
COADRSEM.80zero <- COADRSEM.zero[rowSums(COADRSEM.zero==0)/ncol(COADRSEM.zero) < 0.8, ]
sapply(COADRSEM.50zero, class)

##Load Required Packages
source("http://bioconductor.org/biocLite.R")
biocLite(pkgs = c("Rsubread","limma","edgeR"))
library(Rsubread)
library(limma)
library(edgeR)
options(digits=2)

##Format RSEM Table to include only wanted patients
COADRSEMNvT <- COADgenomicMatrix[,Samples]
View(COADRSEMNvT)
sapply(COADRSEMNvT, class) 
class(COADRSEMNvT)
COADRSEMNvT <- as.matrix(COADRSEMNvT)
isexpr <- rowSums(cpm(COADRSEMNvT) > 10) >= 2
COADRSEMNvT <- COADRSEMNvT[isexpr,]

##Scale Normalization
y <- DGEList(counts = COADRSEMNvT, lib.size = colSums(COADRSEMNvT))
y <- calcNormFactors(y)

##Voom Normalization of RSEM values
COADvoomData <- voom(y,COADdesign,plot=TRUE)
plotMDS(voomData,xlim=c(-2.5,2.5))

##DEGListGeneration
fit <- lmFit(COADvoomData, COADdesign)
contrast.matrix <- makeContrasts(Tumor-Normal, levels=COADdesign)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
ColonTCGATvNDEG <- topTable(fit2, adjust="fdr", number=nrow(COADvoomData))
ColonTCGATvNDEG$Score <- (-log(ColonTCGATvNDEG$adj.P.Val)*ColonTCGATvNDEG$logFC)
ColonTCGATvNDEG$ABScore <- abs(ColonTCGATvNDEG$Score)
write.csv(ColonTCGATvNDEG, "ColonTCGATvNDEG.csv")



################################################################
###############################################################
#############PATHWAY SCORING ALGORITHM########################
##############################################################
##############################################################
#########Complete for as many conditions as you have##########

AllPathwaysKEGGPS <- read.delim("C:/Users/sp36462/Downloads/PanCancerMetabolism/AllPathwaysKEGGPS.txt")
AllPathwaysKEGGPS <- data.frame(lapply(AllPathwaysKEGGPS, as.character), stringsAsFactors=FALSE)


#############################ABSOLUTE VALUE SCORES###################################
ABSPathwayScore <- list()
ColonABSScoreSum <- function(x){
  for(i in 1:ncol(AllPathwaysKEGGPS)) { 
    y <- x[rownames(x) %in% as.character(AllPathwaysKEGGPS[,i]),]
    ABSPathwayScore <- as.vector(sum(y[,8])/sqrt(length(Samples)))
    print(ABSPathwayScore)
    plot(ABSPathwayScore)
  }
  
}

ColonABSScoreSum(ColonTCGATvNDEG)

######################POSITIVE/NEGATIVE SCORES################################
PathwayScore <- list()
ColonScoreSum <- function(x){
  for(i in 1:ncol(AllPathwaysKEGGPS)) { 
    y <- x[rownames(x) %in% as.character(AllPathwaysKEGGPS[,i]),]
    PathwayScore <- as.vector(sum(y[,7])/sqrt(length(Samples)))
    print(PathwayScore)
    plot(PathwayScore)
  }
  
}

ColonScoreSum(ColonTCGATvNDEG)




#########################################################
#########################################################
###########BOOTSTRAPPING CODE###########################
#######################################################
######################################################


###Construct a single file where each column is a list of pathway/absolute pathway scores##

###Colon
set.seed(1234)
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(6),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval1 = sum(z >= RemainingABSScores[1,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(4),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval2 = sum(z >= RemainingABSScores[2,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(24),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval3 = sum(z >= RemainingABSScores[3,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(32),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval4 = sum(z >= RemainingABSScores[4,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(6),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval5 = sum(z >= RemainingABSScores[5,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(10),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval6 = sum(z >= RemainingABSScores[6,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(20),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval7 = sum(z >= RemainingABSScores[7,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(12),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval8 = sum(z >= RemainingABSScores[8,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(4),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval9 = sum(z >= RemainingABSScores[9,2])/100000

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(6),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval10 = sum(z >= RemainingABSScores[10,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(5),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval11 = sum(z >= RemainingABSScores[11,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(65),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval12 = sum(z >= RemainingABSScores[12,2])/100000

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(33),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval13 = sum(z >= RemainingABSScores[13,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(13),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval14 = sum(z >= RemainingABSScores[14,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(33),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval15 = sum(z >= RemainingABSScores[15,2])/100000

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(14),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval16 = sum(z >= RemainingABSScores[16,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(75),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval17 = sum(z >= RemainingABSScores[17,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(71),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval18 = sum(z >= RemainingABSScores[18,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(68),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval19 = sum(z >= RemainingABSScores[19,2])/100000



## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(18),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval20 = sum(z >= RemainingABSScores[20,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(24),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval21 = sum(z >= RemainingABSScores[21,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(9),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval22 = sum(z >= RemainingABSScores[22,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(7),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval23 = sum(z >= RemainingABSScores[23,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(18),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval24 = sum(z >= RemainingABSScores[24,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(11),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval25 = sum(z >= RemainingABSScores[25,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(4),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval26 = sum(z >= RemainingABSScores[26,2])/100000


##Methionine Cycle
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample 47 (Methionine Cycle) genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(12),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval27 = sum(z >= RemainingABSScores[27,2])/100000

##(filename="Colon28.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(12),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval28 = sum(z >= RemainingABSScores[28,2])/100000

##(filename="Colon29.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(49),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval29 = sum(z >= RemainingABSScores[29,2])/100000

##(filename="Colon30.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(30),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval30 = sum(z >= RemainingABSScores[30,2])/100000

##(filename="Colon31.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(18),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval31 = sum(z >= RemainingABSScores[31,2])/100000

##(filename="Colon32.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(26),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval32 = sum(z >= RemainingABSScores[32,2])/100000

##(filename="Colon33.##")
##Polyamine Cycle
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample 13 (Folate Cycle) genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(9),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval33 = sum(z >= RemainingABSScores[33,2])/100000

##(filename="ColonPolyamine.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(29),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval34 = sum(z >= RemainingABSScores[34,2])/100000

##(filename="Colon35.##")

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(7),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval35 = sum(z >= RemainingABSScores[35,2])/100000

##(filename="Colon36.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(13),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}


pval36 = sum(z >= RemainingABSScores[36,2])/100000

##(filename="Colon37.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(3),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval37 = sum(z >= RemainingABSScores[37,2])/100000

##(filename="Colon38.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(1),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval38 = sum(z >= RemainingABSScores[38,2])/100000

##(filename="Colon39.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(48),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval39 = sum(z >= RemainingABSScores[39,2])/100000

##(filename="Colon40.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(18),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval40 = sum(z >= RemainingABSScores[40,2])/100000

##(filename="Colon41.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(15),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval41 = sum(z >= RemainingABSScores[41,2])/100000

##(filename="Colon42.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(89),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval42 = sum(z >= RemainingABSScores[42,2])/100000

##(filename="Colon43.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(60),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval43 = sum(z >= RemainingABSScores[43,2])/100000

##(filename="Colon44.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(8),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval44 = sum(z >= RemainingABSScores[44,2])/100000

##(filename="Colon45.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(11),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval45 = sum(z >= RemainingABSScores[45,2])/100000

##(filename="Colon46.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(15),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval46 = sum(z >= RemainingABSScores[46,2])/100000

##(filename="Colon47.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(14),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval47 = sum(z >= RemainingABSScores[47,2])/100000

##(filename="Colon48.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(14),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval48 = sum(z >= RemainingABSScores[48,2])/100000

##(filename="Colon49.##")

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(2),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval49 = sum(z >= RemainingABSScores[49,2])/100000

##(filename="Colon50.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(36),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval50 = sum(z >= RemainingABSScores[50,2])/100000

##(filename="Colon51.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(33),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval51 = sum(z >= RemainingABSScores[51,2])/100000

##(filename="Colon52.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(31),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval52 = sum(z >= RemainingABSScores[52,2])/100000

##(filename="Colon53.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(27),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval53 = sum(z >= RemainingABSScores[53,2])/100000

##(filename="Colon54.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(36),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval54 = sum(z >= RemainingABSScores[54,2])/100000

##(filename="Colon55.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(48),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval55 = sum(z >= RemainingABSScores[55,2])/100000

##(filename="Colon56.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(28),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval56 = sum(z >= RemainingABSScores[56,2])/100000

##(filename="Colon57.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(32),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval57 = sum(z >= RemainingABSScores[57,2])/100000

##(filename="Colon58.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(28),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval58 = sum(z >= RemainingABSScores[58,2])/100000

##(filename="Colon59.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(71),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval59 = sum(z >= RemainingABSScores[59,2])/100000

##(filename="Colon60.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(133),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval60 = sum(z >= RemainingABSScores[60,2])/100000

##(filename="Colon61.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(17),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval61 = sum(z >= RemainingABSScores[61,2])/100000

##(filename="Colon62.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(10),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval62 = sum(z >= RemainingABSScores[62,2])/100000

##(filename="Colon63.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(20),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval63 = sum(z >= RemainingABSScores[63,2])/100000

##(filename="Colon64.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(17),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval64 = sum(z >= RemainingABSScores[64,2])/100000

##(filename="Colon65.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(58),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval65 = sum(z >= RemainingABSScores[65,2])/100000

##(filename="Colon66.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(59),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval66 = sum(z >= RemainingABSScores[66,2])/100000

##(filename="Colon67.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(95),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval67 = sum(z >= RemainingABSScores[67,2])/100000

##(filename="Colon68.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(45),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval68 = sum(z >= RemainingABSScores[68,2])/100000

##(filename="Colon69.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(47),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval69 = sum(z >= RemainingABSScores[69,2])/100000

##(filename="Colon70.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(62),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval70 = sum(z >= RemainingABSScores[70,2])/100000

##(filename="Colon71.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(29),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval71 = sum(z >= RemainingABSScores[71,2])/100000

##(filename="Colon72.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(25),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval72 = sum(z >= RemainingABSScores[72,2])/100000

##(filename="Colon73.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(23),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval73 = sum(z >= RemainingABSScores[73,2])/100000

##(filename="Colon74.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(175),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval74 = sum(z >= RemainingABSScores[74,2])/100000

##(filename="Colon75.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(105),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval75 = sum(z >= RemainingABSScores[75,2])/100000

##(filename="Colon76.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(35),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval76 = sum(z >= RemainingABSScores[76,2])/100000

##(filename="Colon77.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(40),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval77 = sum(z >= RemainingABSScores[77,2])/100000

##(filename="Colon78.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(45),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval78 = sum(z >= RemainingABSScores[78,2])/100000

##(filename="Colon79.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(48),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval79 = sum(z >= RemainingABSScores[79,2])/100000

##(filename="Colon80.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(4),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval80 = sum(z >= RemainingABSScores[80,2])/100000

##(filename="Colon81.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(59),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval81 = sum(z >= RemainingABSScores[81,2])/100000

##(filename="Colon82.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(21),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval82 = sum(z >= RemainingABSScores[82,2])/100000

##(filename="Colon83.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(49),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval83 = sum(z >= RemainingABSScores[83,2])/100000

##(filename="Colon84.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(24),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval84 = sum(z >= RemainingABSScores[84,2])/100000

##(filename="Colon85.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(35),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval85 = sum(z >= RemainingABSScores[85,2])/100000

##(filename="Colon86.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(17),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval86 = sum(z >= RemainingABSScores[86,2])/100000

##(filename="Colon87.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(40),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval87 = sum(z >= RemainingABSScores[87,2])/100000

##(filename="Colon88.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(5),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval88 = sum(z >= RemainingABSScores[88,2])/100000

##(filename="Colon89.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(31),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval89 = sum(z >= RemainingABSScores[89,2])/100000

##(filename="Colon90.##")


z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(11),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval90 = sum(z >= RemainingABSScores[90,2])/100000

##(filename="Colon92.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(17),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval91 = sum(z >= RemainingABSScores[91,2])/100000

##(filename="Colon93.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(3),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval92 = sum(z >= RemainingABSScores[92,2])/100000

##(filename="Colon94.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(54),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval93 = sum(z >= RemainingABSScores[93,2])/100000

##(filename="Colon95.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(49),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval94 = sum(z >= RemainingABSScores[94,2])/100000

##(filename="Colon96.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(31),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval95 = sum(z >= RemainingABSScores[95,2])/100000

##(filename="Colon97.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(31),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval96 = sum(z >= RemainingABSScores[96,2])/100000

##(filename="Colon98.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(54),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval97 = sum(z >= RemainingABSScores[97,2])/100000

##(filename="Colon99.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(19),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval98 = sum(z >= RemainingABSScores[98,2])/100000

##(filename="Colon100.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(25),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval99 = sum(z >= RemainingABSScores[99,2])/100000

##(filename="Colon011.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(46),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval100 = sum(z >= RemainingABSScores[100,2])/100000

##(filename="Colonhsa.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(18),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval101 = sum(z >= RemainingABSScores[101,2])/100000

##(filename="Colon103.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(4),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval102 = sum(z >= RemainingABSScores[102,2])/100000

##(filename="Colon104.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(4),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval103 = sum(z >= RemainingABSScores[103,2])/100000

##(filename="Colon105.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(6),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval104 = sum(z >= RemainingABSScores[104,2])/100000

##(filename="Colon106.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(18),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval105 = sum(z >= RemainingABSScores[105,2])/100000

##(filename="Colon107.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(3),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval106 = sum(z >= RemainingABSScores[106,2])/100000

##(filename="Colon108.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(3),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval107 = sum(z >= RemainingABSScores[107,2])/100000

##(filename="Colon109.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(42),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval108 = sum(z >= RemainingABSScores[108,2])/100000

##(filename="Colon110.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(11),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval109 = sum(z >= RemainingABSScores[109,2])/100000

##(filename="Colon111.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(22),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval110 = sum(z >= RemainingABSScores[110,2])/100000

##(filename="Colon112.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(5),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval111 = sum(z >= RemainingABSScores[111,2])/100000

##(filename="Colon113.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(5),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval112 = sum(z >= RemainingABSScores[112,2])/100000

##(filename="Colon114.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(73),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval113 = sum(z >= RemainingABSScores[113,2])/100000

##(filename="Colon115.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(69),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval114 = sum(z >= RemainingABSScores[114,2])/100000

##(filename="Colon116.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.ABSScores <- sample(ColonTCGATvNDEG$ABSScore,
                           size=length(46),
                           replace = T
  )
  # Sum ABSScores
  z[i] <- sum(boot.ABSScores)/sqrt(103)
}

pval115 = sum(z >= RemainingABSScores[115,2])/100000




Pvals <- as.data.frame(cbind(pval1, pval2, pval3, pval4, pval5, pval6, pval7, pval8, pval9, pval10, pval11, pval12, pval13, pval14, pval15, pval16, pval17, pval18, pval19, pval20, pval21, pval22, pval23, pval24, pval25, pval26, pval27, pval28, pval29, pval30, pval31, pval32, pval33, pval34, pval35, pval36, pval37, pval38, pval39, pval40, pval41, pval42, pval43, pval44, pval45, pval46, pval47, pval48, pval49, pval50, pval51, pval52, pval53, pval54, pval55, pval56, pval57, pval58, pval59, pval60, pval61, pval62, pval63, pval64, pval65, pval66, pval67, pval68, pval69, pval70, pval71, pval72, pval73, pval74, pval75, pval76, pval77, pval78, pval79, pval80, pval81, pval82, pval83, pval84, pval85, pval86, pval87, pval88, pval89, pval90, pval91, pval92, pval93, pval94, pval95, pval96, pval97, pval98, pval99, pval100, pval101, pval102, pval103, pval104, pval105, pval106, pval107, pval108, pval109, pval110, pval111, pval112, pval113, pval114, pval115))
###CAN ADD MORE COLUMNS TO PVALS PRIOR TO EXPORT
##remove Glycolysis/Gluconeogenesis
write.csv(Pvals, "ColonABSPVal.csv")






###Construct a single file where each column is a list of  pathway scores##

###Colon
set.seed(1234)
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(6),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval1 = sum(z >= RemainingScores[1,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(4),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval2 = sum(z >= RemainingScores[2,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(24),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval3 = sum(z >= RemainingScores[3,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(32),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval4 = sum(z >= RemainingScores[4,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(6),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval5 = sum(z >= RemainingScores[5,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(10),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval6 = sum(z >= RemainingScores[6,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(20),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval7 = sum(z >= RemainingScores[7,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(12),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval8 = sum(z >= RemainingScores[8,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(4),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval9 = sum(z >= RemainingScores[9,2])/100000

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(6),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval10 = sum(z >= RemainingScores[10,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(5),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval11 = sum(z >= RemainingScores[11,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(65),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval12 = sum(z >= RemainingScores[12,2])/100000

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(33),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval13 = sum(z >= RemainingScores[13,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(13),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval14 = sum(z >= RemainingScores[14,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(33),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval15 = sum(z >= RemainingScores[15,2])/100000

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(14),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval16 = sum(z >= RemainingScores[16,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(75),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval17 = sum(z >= RemainingScores[17,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(71),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval18 = sum(z >= RemainingScores[18,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(68),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval19 = sum(z >= RemainingScores[19,2])/100000



## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(18),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval20 = sum(z >= RemainingScores[20,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(24),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval21 = sum(z >= RemainingScores[21,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(9),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval22 = sum(z >= RemainingScores[22,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(7),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval23 = sum(z >= RemainingScores[23,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(18),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval24 = sum(z >= RemainingScores[24,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(11),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval25 = sum(z >= RemainingScores[25,2])/100000


## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(4),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval26 = sum(z >= RemainingScores[26,2])/100000


##Methionine Cycle
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample 47 (Methionine Cycle) genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(12),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval27 = sum(z >= RemainingScores[27,2])/100000

##(filename="Colon28.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(12),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval28 = sum(z >= RemainingScores[28,2])/100000

##(filename="Colon29.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(49),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval29 = sum(z >= RemainingScores[29,2])/100000

##(filename="Colon30.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(30),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval30 = sum(z >= RemainingScores[30,2])/100000

##(filename="Colon31.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(18),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval31 = sum(z >= RemainingScores[31,2])/100000

##(filename="Colon32.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(26),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval32 = sum(z >= RemainingScores[32,2])/100000

##(filename="Colon33.##")
##Polyamine Cycle
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample 13 (Folate Cycle) genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(9),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval33 = sum(z >= RemainingScores[33,2])/100000

##(filename="ColonPolyamine.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(29),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval34 = sum(z >= RemainingScores[34,2])/100000

##(filename="Colon35.##")

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(7),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval35 = sum(z >= RemainingScores[35,2])/100000

##(filename="Colon36.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(13),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}


pval36 = sum(z >= RemainingScores[36,2])/100000

##(filename="Colon37.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(3),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval37 = sum(z >= RemainingScores[37,2])/100000

##(filename="Colon38.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(1),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval38 = sum(z >= RemainingScores[38,2])/100000

##(filename="Colon39.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(48),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval39 = sum(z >= RemainingScores[39,2])/100000

##(filename="Colon40.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(18),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval40 = sum(z >= RemainingScores[40,2])/100000

##(filename="Colon41.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(15),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval41 = sum(z >= RemainingScores[41,2])/100000

##(filename="Colon42.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(89),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval42 = sum(z >= RemainingScores[42,2])/100000

##(filename="Colon43.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(60),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval43 = sum(z >= RemainingScores[43,2])/100000

##(filename="Colon44.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(8),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval44 = sum(z >= RemainingScores[44,2])/100000

##(filename="Colon45.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(11),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval45 = sum(z >= RemainingScores[45,2])/100000

##(filename="Colon46.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(15),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval46 = sum(z >= RemainingScores[46,2])/100000

##(filename="Colon47.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(14),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval47 = sum(z >= RemainingScores[47,2])/100000

##(filename="Colon48.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(14),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval48 = sum(z >= RemainingScores[48,2])/100000

##(filename="Colon49.##")

## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(2),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval49 = sum(z >= RemainingScores[49,2])/100000

##(filename="Colon50.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(36),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval50 = sum(z >= RemainingScores[50,2])/100000

##(filename="Colon51.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(33),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval51 = sum(z >= RemainingScores[51,2])/100000

##(filename="Colon52.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(31),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval52 = sum(z >= RemainingScores[52,2])/100000

##(filename="Colon53.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(27),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval53 = sum(z >= RemainingScores[53,2])/100000

##(filename="Colon54.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(36),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval54 = sum(z >= RemainingScores[54,2])/100000

##(filename="Colon55.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(48),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval55 = sum(z >= RemainingScores[55,2])/100000

##(filename="Colon56.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(28),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval56 = sum(z >= RemainingScores[56,2])/100000

##(filename="Colon57.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(32),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval57 = sum(z >= RemainingScores[57,2])/100000

##(filename="Colon58.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(28),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval58 = sum(z >= RemainingScores[58,2])/100000

##(filename="Colon59.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(71),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval59 = sum(z >= RemainingScores[59,2])/100000

##(filename="Colon60.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(133),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval60 = sum(z >= RemainingScores[60,2])/100000

##(filename="Colon61.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(17),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval61 = sum(z >= RemainingScores[61,2])/100000

##(filename="Colon62.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(10),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval62 = sum(z >= RemainingScores[62,2])/100000

##(filename="Colon63.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(20),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval63 = sum(z >= RemainingScores[63,2])/100000

##(filename="Colon64.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(17),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval64 = sum(z >= RemainingScores[64,2])/100000

##(filename="Colon65.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(58),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval65 = sum(z >= RemainingScores[65,2])/100000

##(filename="Colon66.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(59),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval66 = sum(z >= RemainingScores[66,2])/100000

##(filename="Colon67.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(95),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval67 = sum(z >= RemainingScores[67,2])/100000

##(filename="Colon68.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(45),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval68 = sum(z >= RemainingScores[68,2])/100000

##(filename="Colon69.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(47),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval69 = sum(z >= RemainingScores[69,2])/100000

##(filename="Colon70.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(62),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval70 = sum(z >= RemainingScores[70,2])/100000

##(filename="Colon71.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(29),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval71 = sum(z >= RemainingScores[71,2])/100000

##(filename="Colon72.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(25),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval72 = sum(z >= RemainingScores[72,2])/100000

##(filename="Colon73.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(23),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval73 = sum(z >= RemainingScores[73,2])/100000

##(filename="Colon74.##")
## Resample DE genes 100000 times 
z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(175),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval74 = sum(z >= RemainingScores[74,2])/100000

##(filename="Colon75.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(105),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval75 = sum(z >= RemainingScores[75,2])/100000

##(filename="Colon76.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(35),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval76 = sum(z >= RemainingScores[76,2])/100000

##(filename="Colon77.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(40),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval77 = sum(z >= RemainingScores[77,2])/100000

##(filename="Colon78.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(45),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval78 = sum(z >= RemainingScores[78,2])/100000

##(filename="Colon79.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(48),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval79 = sum(z >= RemainingScores[79,2])/100000

##(filename="Colon80.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(4),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval80 = sum(z >= RemainingScores[80,2])/100000

##(filename="Colon81.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(59),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval81 = sum(z >= RemainingScores[81,2])/100000

##(filename="Colon82.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(21),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval82 = sum(z >= RemainingScores[82,2])/100000

##(filename="Colon83.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(49),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval83 = sum(z >= RemainingScores[83,2])/100000

##(filename="Colon84.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(24),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval84 = sum(z >= RemainingScores[84,2])/100000

##(filename="Colon85.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(35),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval85 = sum(z >= RemainingScores[85,2])/100000

##(filename="Colon86.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(17),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval86 = sum(z >= RemainingScores[86,2])/100000

##(filename="Colon87.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(40),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval87 = sum(z >= RemainingScores[87,2])/100000

##(filename="Colon88.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(5),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval88 = sum(z >= RemainingScores[88,2])/100000

##(filename="Colon89.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(31),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval89 = sum(z >= RemainingScores[89,2])/100000

##(filename="Colon90.##")


z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(11),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval90 = sum(z >= RemainingScores[90,2])/100000

##(filename="Colon92.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(17),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval91 = sum(z >= RemainingScores[91,2])/100000

##(filename="Colon93.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(3),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval92 = sum(z >= RemainingScores[92,2])/100000

##(filename="Colon94.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(54),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval93 = sum(z >= RemainingScores[93,2])/100000

##(filename="Colon95.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(49),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval94 = sum(z >= RemainingScores[94,2])/100000

##(filename="Colon96.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(31),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval95 = sum(z >= RemainingScores[95,2])/100000

##(filename="Colon97.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(31),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval96 = sum(z >= RemainingScores[96,2])/100000

##(filename="Colon98.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(54),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval97 = sum(z >= RemainingScores[97,2])/100000

##(filename="Colon99.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(19),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval98 = sum(z >= RemainingScores[98,2])/100000

##(filename="Colon100.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(25),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval99 = sum(z >= RemainingScores[99,2])/100000

##(filename="Colon011.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(46),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval100 = sum(z >= RemainingScores[100,2])/100000

##(filename="Colonhsa.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(18),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval101 = sum(z >= RemainingScores[101,2])/100000

##(filename="Colon103.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(4),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval102 = sum(z >= RemainingScores[102,2])/100000

##(filename="Colon104.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(4),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval103 = sum(z >= RemainingScores[103,2])/100000

##(filename="Colon105.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(6),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval104 = sum(z >= RemainingScores[104,2])/100000

##(filename="Colon106.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(18),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval105 = sum(z >= RemainingScores[105,2])/100000

##(filename="Colon107.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(3),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval106 = sum(z >= RemainingScores[106,2])/100000

##(filename="Colon108.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(3),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval107 = sum(z >= RemainingScores[107,2])/100000

##(filename="Colon109.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(42),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval108 = sum(z >= RemainingScores[108,2])/100000

##(filename="Colon110.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(11),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval109 = sum(z >= RemainingScores[109,2])/100000

##(filename="Colon111.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(22),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval110 = sum(z >= RemainingScores[110,2])/100000

##(filename="Colon112.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(5),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval111 = sum(z >= RemainingScores[111,2])/100000

##(filename="Colon113.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(5),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval112 = sum(z >= RemainingScores[112,2])/100000

##(filename="Colon114.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(73),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval113 = sum(z >= RemainingScores[113,2])/100000

##(filename="Colon115.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(69),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval114 = sum(z >= RemainingScores[114,2])/100000

##(filename="Colon116.##")

z <- vector()
for (i in 1:100000){
  # Sample genes from the pool of DEG
  boot.Scores <- sample(ColonTCGATvNDEG$Score,
                           size=length(46),
                           replace = T
  )
  # Sum Scores
  z[i] <- sum(boot.Scores)/sqrt(103)
}

pval115 = sum(z >= RemainingScores[115,2])/100000




Pvals <- as.data.frame(cbind(pval1, pval2, pval3, pval4, pval5, pval6, pval7, pval8, pval9, pval10, pval11, pval12, pval13, pval14, pval15, pval16, pval17, pval18, pval19, pval20, pval21, pval22, pval23, pval24, pval25, pval26, pval27, pval28, pval29, pval30, pval31, pval32, pval33, pval34, pval35, pval36, pval37, pval38, pval39, pval40, pval41, pval42, pval43, pval44, pval45, pval46, pval47, pval48, pval49, pval50, pval51, pval52, pval53, pval54, pval55, pval56, pval57, pval58, pval59, pval60, pval61, pval62, pval63, pval64, pval65, pval66, pval67, pval68, pval69, pval70, pval71, pval72, pval73, pval74, pval75, pval76, pval77, pval78, pval79, pval80, pval81, pval82, pval83, pval84, pval85, pval86, pval87, pval88, pval89, pval90, pval91, pval92, pval93, pval94, pval95, pval96, pval97, pval98, pval99, pval100, pval101, pval102, pval103, pval104, pval105, pval106, pval107, pval108, pval109, pval110, pval111, pval112, pval113, pval114, pval115))
###CAN ADD MORE COLUMNS TO PVALS PRIOR TO EXPORT
###Remove Glycolysis/Gluconeogenesis
write.csv(Pvals, "ColonPVal.csv")