printFlush <- print
library(impute)
probeAnnotation21kdatMethUsed <- read.csv(file.path("dnamage","probeAnnotation21kdatMethUsed.csv"))
probeAnnotation27k <- read.csv(file.path("dnamage","datMiniAnnotation27k.csv"))
datClock <- read.csv(file.path("dnamage","AdditionalFile3.csv"))
source(file.path("dnamage", "NORMALIZATION.R"))
missing.sites <- setdiff(probeAnnotation21kdatMethUsed$Name, dat1$ProbeID)
if (length(missing.sites) > 0) {
vals <- with(probeAnnotation21kdatMethUsed, {
goldstandard2[match(missing.sites, Name)]
})
missing.data <- matrix(vals,nrow=length(vals),ncol=ncol(dat1)-1)
missing.data <- data.frame(ProbeID=missing.sites, missing.data)
colnames(missing.data) <- colnames(dat1)
dat1 <- rbind(dat1, missing.data)
}
nSamples <- ncol(dat1)-1
normalizeData <- TRUE
trafo= function(x,adult.age=20) {
x=(x+1)/(1+adult.age);
ifelse(x<=1,log(x),x-1);
}
anti.trafo= function(x,adult.age=20) {
ifelse(x<0,
(1+adult.age)*exp(x)-1,
(1+adult.age)*x+adult.age)
}
XchromosomalCpGs <- as.character(probeAnnotation27k$Name[probeAnnotation27k$Chr=="X"])
selectXchromosome <- is.element(dat1[,1], XchromosomalCpGs )
selectXchromosome[is.na(selectXchromosome)] <- FALSE
meanXchromosome <- rep(NA, dim(dat1)[[2]]-1)
meanXchromosome= as.numeric(apply( as.matrix(dat1[selectXchromosome,-1]),2,mean,na.rm=TRUE))
dat1 <- dat1[match(probeAnnotation21kdatMethUsed$Name,
dat1$ProbeID),]
set.seed(1)
source(file.path("dnamage", "StepwiseAnalysis.txt"))
horvath.example <- datout
rm(anti.trafo,
betaEst2,
blc2,
BMIQ,
BMIQcalibration,
CalibrateUnitInterval,
CheckBMIQ,
Comment,
dat1,
datClock,
datMethClock,
datMethClock0,
datMethUsedNormalized,
datout,
dimnames1,
fastImputation,
lab1,
maxMethBySample,
meanMethBySample,
meanXchromosome,
minMethBySample,
noMissingPerSample,
normalizeData,
nSamples,
predictedAge,
predictedGender,
printFlush,
probeAnnotation21kdatMethUsed,
probeAnnotation27k,
restSamples,
selectCpGsClock,
selectXchromosome,
trafo,
XchromosomalCpGs)
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