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dataProcess_CalbicansKO.R
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dataProcess_CalbicansKO.R
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## R script to process, analyze, and visualize genetic knockouts in C. albicans experiment
# load libraries
rm(list = ls())
library(amap)
library(gplots)
library("extrafont")
#font_import(recursive = FALSE)
#loadfonts(device = "pdf")
# load and process index for data - see example index file
setwd('/path/to/data/index/')
geneIndex = read.table('geneIndex.csv', sep=",")
lowerIndex = as.matrix(geneIndex[lower.tri(geneIndex, diag=TRUE)])
upperIndex = as.matrix(geneIndex[upper.tri(geneIndex, diag=TRUE)])
upperIndex_i = upperIndex
# load index to gene name mapping
geneNames <- read.table('geneNames.csv', sep=",")
# reverse the string order of the name for upper genes
strReverse <- function(x)
sapply(lapply(strsplit(x, NULL), rev), paste, collapse="")
upperIndex = strReverse(upperIndex)
# get index of aligned gene combinations
sortedLower = sort(lowerIndex, index.return=TRUE)
sortedUpper = sort(upperIndex, index.return=TRUE)
# load and process data: should be a folder with .csv files of
# OD measurements for each condition; see example file
setwd('/path/to/OD/data/')
conditions <- list.files()
# define variables
noReps <- 8 # user defined
noGenes <- nrow(geneNames)
noCond <- length(conditions)
noStrains <- length(lowerIndex)
# initialize matrices
allData <- matrix(data=NA, nrow=noStrains, ncol=noReps*noCond)
names <- matrix(data=NA,nrow=1,ncol=noReps*noCond)
avgData <- matrix(data=NA, nrow=noStrains, ncol=noCond)
avgLower <- matrix(data=NA, nrow=noStrains, ncol=noCond)
avgUpper <- matrix(data=NA, nrow=noStrains, ncol=noCond)
# assign data to appropratiate matrices
# colums will be conditions, rows will be strains
for (i in 1:noCond){
# pull out data for current condition
currentData <- read.table(conditions[i], sep=",")
# separate replicates
controls <- currentData[,(noGenes+1):ncol(currentData)]
knockouts <- currentData[,1:noGenes]
# save only rows from file that contain data
knockouts <- knockouts[complete.cases(knockouts),]
controls <- controls[complete.cases(controls),]
# initialize temporary matrices to store current data
allCurrentData <- matrix(data=NA, nrow=noStrains, ncol=noReps)
currentNames <- matrix(data=NA, nrow=1, ncol=noReps)
for (j in 1:(noReps/2)){
# pull out replicate data
repData <- knockouts[(j*noGenes-(noGenes-1)):(j*noGenes),]
# pull out control data and take average
ctrlData <- controls[(j*2-1):(j*2),]
ctrlAvg <- mean(as.matrix(ctrlData), na.rm=TRUE)
# pull out data for forward and reverse KO cases
lower <- as.matrix(repData[lower.tri(repData, diag = TRUE)])
upper <- as.matrix(repData[upper.tri(repData, diag = TRUE)])
# average strain matches
orderedLower <- as.matrix(lower[sortedLower$ix])
orderedUpper <- as.matrix(upper[sortedUpper$ix])
allCurrentData[,(j*2-1)] <- orderedLower/ctrlAvg
allCurrentData[,(j*2)] <- orderedUpper/ctrlAvg
# remove .csv from file names for conditions
currentNames[(j*2-1):(j*2)] <- gsub(".csv", "", conditions[i])
}
# fill final matrices and build average data matrices
allData[,((i*noReps-(noReps-1)):(i*noReps))] <- allCurrentData
avgData[,i] <- rowMeans(allCurrentData, na.rm=TRUE)
names[,((i*noReps-(noReps-1)):(i*noReps))] <- currentNames
avgLower[,i] <- rowMeans(orderedLower/ctrlAvg, na.rm=TRUE)
avgUpper[,i] <- rowMeans(orderedUpper/ctrlAvg, na.rm=TRUE)
}
# name rows and columns
geneList <- sortedLower$x
geneList_i <- geneList
singles <- diag(as.matrix(geneIndex))
singlesIndex <- match(singles, geneList)
geneList <- paste0(paste0('-', geneList),'-')
for (i in 1:nrow(geneNames)){
geneList <- gsub(paste0('^-', geneNames[i,1]), geneNames[i,2], geneList)
geneList <- gsub(paste0(geneNames[i,1], '-$'), geneNames[i,2], geneList)
}
geneList <- gsub('-', '', geneList)
# rename columns and rows
avgData <- as.matrix(avgData)
avgLower <- as.matrix(avgLower)
avgUpper <- as.matrix(avgUpper)
rownames(allData)<-geneList
colnames(allData)<-names
rownames(avgData)<-geneList
colnames(avgData)<-gsub(".csv", "", conditions)
rownames(avgLower)<-sortedLower$x
colnames(avgLower)<-gsub(".csv", "", conditions)
rownames(avgUpper)<-upperIndex_i[sortedUpper$ix]
colnames(avgUpper)<-gsub(".csv", "", conditions)
# remove empty columns (for conditions that had fewer reps)
avgData_i <- avgData
allData <- allData[,complete.cases(t(allData))]
avgData <- avgData[,complete.cases(t(avgData))]
completeConditions <- conditions[complete.cases(t(avgData_i))]
# save data to csv files
setwd('/path/to/save/data/')
write.csv(allData, "allData.csv")
write.csv(avgData, "avgData.csv")
# make heatmaps of "raw" data in plate shape
triangleHM <- function(triangleData, triangleType, colors, extension){
tDataCol <- ncol(triangleData)
for (i in 1:tDataCol){
triangleGeneIndex <- geneIndex
rownames(triangleGeneIndex) <- geneNames[,2]
colnames(triangleGeneIndex) <- geneNames[,2]
# reverse the gene index place holder for half triangle before sorting
if (triangleType=="half"){
}
# sort the data
sortRow <- sort(rownames(triangleGeneIndex), index.return=TRUE)
sortCol <- sort(colnames(triangleGeneIndex), index.return=TRUE)
triangleGeneIndex <- triangleGeneIndex[sortRow$ix, sortCol$ix]
# replace upper trinagle with NaN
if (triangleType=="half"){
triangleGeneIndex[upper.tri(triangleGeneIndex)]<-NaN
}
triangleGeneIndex <- as.matrix(triangleGeneIndex)
# replace letters with data values
tDataCol <-ncol(triangleData)
for (j in 1:nrow(triangleData)){
triangleGeneIndex <- gsub(paste0(paste0('^', rownames(triangleData)[j]),'$'),triangleData[j,i],triangleGeneIndex)
}
if (triangleType=="half"){
tDataCol <-ncol(triangleData)
for (j in 1:nrow(triangleData)){
triangleGeneIndex <- gsub(paste0(paste0('^', rownames(avgUpper)[j]),'$'),triangleData[j,i],triangleGeneIndex)
}
}
# convert from character to double for heatmap
triangleGeneIndex <- apply(triangleGeneIndex, c(1,2), as.numeric)
# generate pdf of heatmap (no clustering)
pdf(paste0(paste0(paste0(colnames(triangleData)[i], extension), triangleType), '.pdf'),
width = 7, height = 7, family="Arial", useDingbats=FALSE)
heatmap.2(triangleGeneIndex, Rowv=FALSE, Colv=FALSE, trace="none",
dendrogram="none", keysize=1.1, key.title='', key.xlab='',
main=colnames(triangleData)[i], col=my_palette, breaks=colors,
density.info='none', na.color='gray80')
dev.off()
}
}
avgData <- as.matrix(avgData)
# triangle HM of OD data, half
tData <- avgData
rownames(tData) <- geneList_i
# remove outliers if they exist
# in our experiment, tpo3yor1 and yor1tpo3 were experimental outliers
outlier <- c(which(geneList_i=='jg'),
which(geneList_i=='gj'))
avgLower[outlier,]<-NaN
avgUpper[outlier,]<-NaN
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 48)
colors = unique(c(seq(min(tData, na.rm=TRUE),1,length=25),seq(1,max(tData, na.rm=TRUE),length=25)))
# triangle HM of OD data, full
tData <- rbind(avgLower, avgUpper)
triangleHM(tData, "full", colors, "_OD")
# track single gene KO data
singlesData <- allData[singlesIndex,]
rownames(singlesData) <- singles
singlesAvgData <- as.matrix(avgData[singlesIndex,])
rownames(singlesAvgData) <- singles
# pull out avg data ratio matrix to repopulate with expected growth rates
expectedGrowth <- allData
rownames(expectedGrowth) <- geneList_i
for (r in 1:nrow(expectedGrowth)){
for (c in 1:ncol(expectedGrowth)){
strain <- rownames(expectedGrowth)[r]
splitStrain <- strsplit(strain, "")
val <- matrix(data=NA,nrow=length(splitStrain[[1]]),ncol=1)
for (l in 1:length(splitStrain[[1]])){
letter <- splitStrain[[1]][l]
valIndex <- match(letter, rownames(singlesData))
val[l] <- singlesData[valIndex,c]
}
expectedVal <- prod(val)
expectedGrowth[r,c] <- expectedVal
}
}
# rename rows for expected data matrix
rowsExpectedGrowth <- paste0(paste0('-', rownames(expectedGrowth)), '-')
for (i in 1:nrow(geneNames)){
rowsExpectedGrowth <- gsub(paste0('^-', geneNames[i,1]),geneNames[i,2],rowsExpectedGrowth)
rowsExpectedGrowth <- gsub(paste0(geneNames[i,1], '-$'),geneNames[i,2],rowsExpectedGrowth)
}
rownames(expectedGrowth) <- gsub('-', '', rowsExpectedGrowth)
# update strain names for singlesData
rowSD <- paste0(paste0('-', rownames(singlesData)), '-')
rowSDavg <- paste0(paste0('-', rownames(singlesAvgData)), '-')
for (i in 1:nrow(geneNames)){
rowSD <- gsub(paste0('^-', geneNames[i,1]),geneNames[i,2], rowSD)
rowSD <- gsub(paste0(geneNames[i,1], '-$'),geneNames[i,2], rowSD)
rowSDavg <- gsub(paste0('^-', geneNames[i,1]),geneNames[i,2], rowSDavg)
rowSDavg <- gsub(paste0(geneNames[i,1], '-$'),geneNames[i,2], rowSDavg)
}
rownames(singlesData)<-gsub('-', '', rowSD)
rownames(singlesAvgData)<-gsub('-', '', rowSDavg)
# remove outliers if they exist
# in our experiment, tpo3yor1 and yor1tpo3 were experimental outliers
outlier <- c(which(rownames(allData)=='tpo3yor1'),
which(rownames(allData)=='yor1tpo3'))
allData <- allData[-outlier,]
avgData <- avgData[-outlier,]
avgUpper <- avgUpper[-outlier,]
avgLower <- avgLower[-outlier,]
expectedGrowth <- expectedGrowth[-outlier,]
## compare expected growth numbers to actual growth values (p-value based)
# initialize matrices
avgData <- as.matrix(avgData)
avgUpper <- as.matrix(avgUpper)
avgLower <- as.matrix(avgLower)
pVal <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgData), colnames(avgData)))
pVal_low <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgData), colnames(avgData)))
pVal_up <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgData), colnames(avgData)))
eps <- matrix(data=NA,nrow=nrow(allData),ncol=ncol(allData),
dimnames=list(rownames(allData), colnames(allData)))
pVal_eps <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgData), colnames(avgData)))
epsAvg <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgData), colnames(avgData)))
epsUpper <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgUpper), colnames(avgUpper)))
epsLower <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgLower), colnames(avgLower)))
pVal_epsRecip <- matrix(data=NA,nrow=nrow(allData),ncol=length(completeConditions),
dimnames=list(rownames(avgData), colnames(avgData)))
# calculate p-values
for (i in 1:length(completeConditions)){
currentCond <- gsub(".csv", "", completeConditions[i])
currentExpected <- as.matrix(expectedGrowth[,which(colnames(expectedGrowth) %in% currentCond)])
currentActual <- as.matrix(allData[,which(colnames(allData) %in% currentCond)])
for (j in 1:nrow(currentExpected)){
p <- t.test(currentExpected[j,seq(1,ncol(currentExpected),2)],currentActual[j,])
pLow <- t.test(currentExpected[j,seq(1,ncol(currentExpected),2)],currentActual[j,seq(1,ncol(currentExpected),2)])
pUp <- t.test(currentExpected[j,seq(1,ncol(currentExpected),2)],currentActual[j,seq(2,ncol(currentExpected),2)])
pVal[j,i] <- p$p.value
pVal_low[j,i] <- pLow$p.value
pVal_up[j,i] <- pUp$p.value
eps_current <- currentActual[j,]-currentExpected[j,]
eps[j,(i*noReps-(noReps-1)):(i*noReps)] <- eps_current
p_eps <- t.test(eps[j,],mu=0)
pVal_eps[j,i] <- p_eps$p.value
epsAvg[j,i] <- mean(eps_current)
epsLower[j,i] <- mean(eps_current[seq(1,ncol(currentExpected),2)])
epsUpper[j,i] <- mean(eps_current[seq(2,ncol(currentExpected),2)])
pRecip <- t.test(eps_current[seq(1,ncol(currentExpected),2)], eps_current[seq(2,ncol(currentExpected),2)])
pVal_epsRecip[j,i] <- pRecip$p.value
}
}
# eps triangle heatmap "full"
epsAvgT <- rbind(epsLower, epsUpper)
for (i in 1:nrow(geneNames)){
rownames(epsAvgT) <- gsub(paste0('^', geneNames[i,2]), geneNames[i,1], rownames(epsAvgT))
rownames(epsAvgT) <- gsub(paste0(geneNames[i,2],'$'), geneNames[i,1], rownames(epsAvgT))
}
colors = unique(seq(-1,1,length=49))
triangleHM(epsAvgT, "full", colors, "_EPS")
pValwSingles <- pVal
# delete single strains from pVal, pVal_eps, epsAvg, eps
rownames(epsLower)<-rownames(eps)
rownames(epsUpper)<-rownames(eps)
pVal <- pVal[-match(rownames(singlesData),rownames(pVal)),]
eps <- eps[-match(rownames(singlesData),rownames(eps)),]
pVal_eps <- pVal_eps[-match(rownames(singlesData),rownames(pVal_eps)),]
epsAvg <- epsAvg[-match(rownames(singlesData),rownames(epsAvg)),]
pVal_low <- pVal_low[-match(rownames(singlesData),rownames(pVal_low)),]
pVal_up <- pVal_up[-match(rownames(singlesData),rownames(pVal_up)),]
pVal_epsRecip <- pVal_epsRecip[-match(rownames(singlesData),rownames(pVal_epsRecip)),]
epsLower <- epsLower[-match(rownames(singlesData),rownames(epsLower)),]
epsUpper <- epsUpper[-match(rownames(singlesData),rownames(epsUpper)),]
# multiple hypothesis correction
pVal <- as.matrix(pVal)
pVal_low <- as.matrix(pVal_low)
pVal_up <- as.matrix(pVal_up)
pVal_epsRecip <- as.matrix((pVal_epsRecip))
pVal_eps <- as.matrix(pVal_eps)
pValAdj <- matrix(data=NA, nrow=nrow(pVal), ncol=ncol(pVal),
dimnames=list(rownames(pVal), colnames(pVal)))
pValAdj_low <- matrix(data=NA, nrow=nrow(pVal_low), ncol=ncol(pVal_low),
dimnames=list(rownames(pVal_low), colnames(pVal_low)))
pValAdj_up <- matrix(data=NA, nrow=nrow(pVal_up), ncol=ncol(pVal_up),
dimnames=list(rownames(pVal_up), colnames(pVal_up)))
pValAdj_recip <- matrix(data=NA, nrow=nrow(pVal_epsRecip), ncol=ncol(pVal_epsRecip),
dimnames=list(rownames(pVal_epsRecip), colnames(pVal_epsRecip)))
pValAdj_eps <- matrix(data=NA, nrow=nrow(pVal_eps), ncol=ncol(pVal_eps),
dimnames=list(rownames(pVal_eps), colnames(pVal_eps)))
for (i in 1:ncol(pVal)){
pValAdj[,i] <- p.adjust(pVal[,i], method="BH")
}
for (i in 1:ncol(pVal_low)){
pValAdj_low[,i] <- p.adjust(pVal_low[,i], method="BH")
}
for (i in 1:ncol(pVal_up)){
pValAdj_up[,i] <- p.adjust(pVal_up[,i], method="BH")
}
for (i in 1:ncol(pVal_eps)){
pValAdj_eps[,i] <- p.adjust(pVal_eps[,i], method="BH")
}
for (i in 1:ncol(pVal_epsRecip)){
pValAdj_recip[,i] <- p.adjust(pVal_epsRecip[,i], method="BH")
}
# Look at some pVal stats for reciprocal knockouts
bin = as.matrix(pValAdj_recip[,colnames(pValAdj_recip)=="NT"]>0.05)
print(paste("recipNT", sum(bin==TRUE)/(nrow(bin))*100))
print(paste("recipAll", sum(as.matrix(pValAdj_recip>0.05)==TRUE)/(nrow(pValAdj_recip)*ncol(pValAdj_recip))*100))
# postive and negative interaction lists (based on eps)
binary_eps <- matrix(data=0,nrow=nrow(pValAdj_eps),ncol=ncol(pValAdj_eps))
binary_eps[which(pValAdj_eps<0.05 & epsAvg<0)] <- -1
binary_eps[which(pValAdj_eps<0.05 & epsAvg>0)] <- 1
rownames(binary_eps)<-rownames(pValAdj_eps)
colnames(binary_eps)<-colnames(pValAdj_eps)
# postive and negative interaction lists (based on old p-value calc)
binary <- matrix(data=0,nrow=nrow(pValAdj),ncol=ncol(pValAdj))
binary[which(pValAdj<0.05 & epsAvg<0)] <- -1
binary[which(pValAdj<0.05 & epsAvg>0)] <- 1
rownames(binary)<-rownames(pValAdj)
colnames(binary)<-colnames(pValAdj)
binary_low <- matrix(data=0,nrow=nrow(pValAdj_low),ncol=ncol(pValAdj_low))
binary_low[which(pValAdj_low<0.05 & epsLower<0)] <- -1
binary_low[which(pValAdj_low<0.05 & epsLower>0)] <- 1
rownames(binary_low)<-rownames(pValAdj_low)
colnames(binary_low)<-colnames(pValAdj_low)
binary_up <- matrix(data=0,nrow=nrow(pValAdj_up),ncol=ncol(pValAdj_up))
binary_up[which(pValAdj_up<0.05 & epsUpper<0)] <- -1
binary_up[which(pValAdj_up<0.05 & epsUpper>0)] <- 1
rownames(binary_up)<-rownames(pValAdj_up)
colnames(binary_up)<-colnames(pValAdj_up)
# Look at some pVal stats for reciprocal knockouts
binary_comp <- (binary_up == binary_low) & (binary_up !=0) & (binary_low !=0)
print(sum(binary_comp==TRUE)/(nrow(binary_comp)*ncol(binary_comp))*100)
print(sum(binary_comp[,colnames(binary_comp)=="NT"]==TRUE)/(nrow(binary_comp))*100)
binary_comp_print <- binary_comp
binary_comp_print[binary_comp_print==TRUE] <- 1
binary_comp_print[binary_comp_print==FALSE] <- 0
# write FC and pValue data to spreadsheets
write.csv(epsAvg, "epsAvg.csv")
write.csv(epsLower, "eps_low.csv")
write.csv(epsUpper, "eps_up.csv")
write.csv(eps, "eps.csv")
write.csv(pVal_eps, "pVal_eps.csv")
write.csv(pVal, "pVal.csv")
write.csv(pVal_low, "pVal_low.csv")
write.csv(pVal_up, "pVal_up.csv")
write.csv(pValAdj, "pValAdj.csv")
write.csv(pValAdj_low, "pValAdj_low.csv")
write.csv(pValAdj_up, "pValAdj_up.csv")
write.csv(pValAdj_eps, "pValAdj_eps.csv")
write.csv(pValAdj_recip, "pValAdj_recip.csv")
write.csv(binary_eps, "binary_eps.csv")
write.csv(binary, "binary.csv")
write.csv(binary_low, "binary_low.csv")
write.csv(binary_up, "binary_up.csv")
write.csv(expectedGrowth, "expected.csv")
write.csv(binary_comp_print, "binary_comp_print.csv")
write.csv(pVal_epsRecip, "pVal_epsRecip.csv")
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 48)
# heatmap of average eps values
colors = unique(seq(-1,1,length=49))
epsAvg <- as.matrix(epsAvg)
pdf(paste0('epsAvg', '.pdf'), width = 5, height = 10, family="Arial", useDingbats=FALSE)
par(mar=c(5, 4, 4, 2))
heatmap.2(epsAvg,scale="none",trace="none",cexRow=0.6,cexCol=0.8,keysize=.3,
key.title='', col=my_palette, breaks=colors, symkey=FALSE,
key.xlab='', density.info='none',
lmat=rbind(rbind(c(0,3),c(2,1)),c(0,4)), lhei=c(2,4,.85), lwid=c(2,4))
dev.off()
# heatmap of average data matrix
colors = unique(c(seq(min(avgData, na.rm=TRUE),1,length=25),seq(1,max(avgData, na.rm=TRUE),length=25)))
pdf(paste0('avgData', '.pdf'), width = 5, height = 10, family="Arial", useDingbats=FALSE)
par(mar=c(5, 4, 4, 2))
heatmap.2(avgData,scale="none",trace="none",cexRow=0.6,cexCol=0.8,keysize=.3,
key.title='', col=my_palette, breaks=colors, symkey=FALSE,
key.xlab='', density.info='none',
lmat=rbind(rbind(c(0,3),c(2,1)),c(0,4)), lhei=c(2,4,.85), lwid=c(2,4))
dev.off()