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dataPreprocessing.R
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#train_frame: The raw training input data
#test_Frame: The raw testing data
#dictionary: The data dictionary
#MinVisit: The minumum observation in months for column imputation
#colImputeThreshold: The minimum fraction of missing data per column
#rowImputeThreshold: The minimum fraction of missing data per patient
#Returns the Adjusted Frames
#Train Adjusted
#Test Adjusted
dataTADPOLEPreprocesing <- function(train_frame,test_Frame,dictionary,MinVisit=36,colImputeThreshold=0.25,rowImputeThreshold=0.25,includeID=TRUE)
{
library("FRESA.CAD")
SetIDSColumns <- c("RID","D1","D2","SITE","COLPROT","ORIGPROT")
DatesColumns <- c("VISCODE","EXAMDATE_bl","EXAMDATE","Years_bl","Month_bl","Month","M")
Diagnosis <- c("DX","DX_bl","DXCHANGE")
BasicColumns <- c("AGE","PTGENDER","PTEDUCAT","PTETHCAT","PTRACCAT","PTMARRY")
QuestionsColumnsSet1 <- c("CDRSB","ADAS11","ADAS13","MMSE","RAVLT_learning","RAVLT_immediate","FAQ","MOCA","EcogPtMem","EcogPtLang","EcogPtVisspat","EcogPtPlan","EcogPtOrgan","EcogPtDivatt","EcogPtTotal","EcogSPMem","EcogSPLang","EcogSPVisspat","EcogSPPlan","EcogSPOrgan","EcogSPDivatt","EcogSPTotal")
QuestionsColumnsSet2 <- c("CDRSB_bl","ADAS11_bl","ADAS13_bl","MMSE_bl","RAVLT_learning_bl","RAVLT_immediate_bl","FAQ_bl","MOCA_bl","EcogPtMem_bl","EcogPtLang_bl","EcogPtVisspat_bl","EcogPtPlan_bl","EcogPtOrgan_bl","EcogPtDivatt_bl","EcogPtTotal_bl","EcogSPMem_bl","EcogSPLang_bl","EcogSPVisspat_bl","EcogSPPlan_bl","EcogSPOrgan_bl","EcogSPDivatt_bl","EcogSPTotal_bl")
BaseFeaturesColumnsData <- c("APOE4","FDG","PIB","AV45","Ventricles","Hippocampus","WholeBrain","Entorhinal","Fusiform","MidTemp","ICV","Ventricles_bl","Hippocampus_bl","WholeBrain_bl","Entorhinal_bl","Fusiform_bl","MidTemp_bl","ICV_bl","FDG_bl","PIB_bl","AV45_bl","ABETA_UPENNBIOMK9_04_19_17","TAU_UPENNBIOMK9_04_19_17","PTAU_UPENNBIOMK9_04_19_17")
train_frame$RID <- as.numeric(train_frame$RID)
train_frame$AGE <- as.numeric(train_frame$AGE)
train_frame$PTGENDER <- 1*(train_frame$PTGENDER=="Male")
test_Frame$RID <- as.numeric(test_Frame$RID)
test_Frame$AGE <- as.numeric(test_Frame$AGE)
test_Frame$PTGENDER <- 1*(test_Frame$PTGENDER=="Male")
combinedBasicCol <- c(SetIDSColumns,DatesColumns,Diagnosis,BasicColumns)
notQuantitative <- c(1:length(combinedBasicCol))
volumeCOlumns <- subset(dictionary,UNITS=="mm3")$FLDNAME
volumeCOlumns <- volumeCOlumns[order(dictionary[dictionary$FLDNAME %in% volumeCOlumns,"TEXT"])]
AreaCOlumns <- subset(dictionary,UNITS=="mm2")$FLDNAME
AreaCOlumns <- AreaCOlumns[order(dictionary[dictionary$FLDNAME %in% AreaCOlumns,"TEXT"])]
ThicknessCOlumns <- subset(dictionary,UNITS=="mm")$FLDNAME
ThicknessCOlumns <- ThicknessCOlumns[order(dictionary[dictionary$FLDNAME %in% ThicknessCOlumns,"TEXT"])]
Rightlocations <- dictionary[regexpr('Right', dictionary$TEXT) > 0,"FLDNAME"]
Leftlocations <- dictionary[regexpr('Left', dictionary$TEXT) > 0,"FLDNAME"]
VolumeRight <- volumeCOlumns[volumeCOlumns %in% Rightlocations]
VolumeLeft <- volumeCOlumns[volumeCOlumns %in% Leftlocations]
AreaRight <- AreaCOlumns[AreaCOlumns %in% Rightlocations]
AreaLeft <- AreaCOlumns[AreaCOlumns %in% Leftlocations]
ThicknessRight <- ThicknessCOlumns[ThicknessCOlumns %in% Rightlocations]
ThicknessLeft <- ThicknessCOlumns[ThicknessCOlumns %in% Leftlocations]
otherVolumes <- volumeCOlumns[!(volumeCOlumns %in% c(Rightlocations,Leftlocations))]
print(dictionary[dictionary$FLDNAME %in% ThicknessRight[1:5],"TEXT"])
print(dictionary[dictionary$FLDNAME %in% ThicknessLeft[1:5],"TEXT"])
train_frame_Transformed <- train_frame[,c(SetIDSColumns,DatesColumns,Diagnosis,BasicColumns,QuestionsColumnsSet1,QuestionsColumnsSet2,BaseFeaturesColumnsData,otherVolumes,VolumeRight,VolumeLeft,AreaRight,AreaLeft,ThicknessRight,ThicknessLeft)]
testcolnames <- colnames(test_Frame)
traincolnames <- colnames(train_frame_Transformed)
print(c(length(testcolnames),length(traincolnames)))
trainNotInTest <- !(traincolnames %in% testcolnames)
if (sum(trainNotInTest)>0)
{
print(traincolnames[trainNotInTest])
missingTestColumns <- NULL
for (selcolumns in traincolnames[trainNotInTest])
{
missingTestColumns <- cbind(missingTestColumns,rep(NA,nrow(test_Frame)))
}
colnames(missingTestColumns) <- traincolnames[trainNotInTest]
test_Frame_Transformed <- cbind(test_Frame,missingTestColumns)
}
else
{
test_Frame_Transformed <- test_Frame
}
Used_Train_columns <- colnames(train_frame_Transformed)
test_Frame_Transformed <- test_Frame_Transformed[,Used_Train_columns]
##Train
colnumeric <- c((length(combinedBasicCol)+1):ncol(train_frame_Transformed))
train_frame_Transformed[,colnumeric] <- sapply(train_frame_Transformed[,colnumeric],as.numeric)
train_frame_Transformed[,otherVolumes] <- (train_frame_Transformed[,otherVolumes])^(1/3)
train_frame_Transformed[,VolumeRight] <- (train_frame_Transformed[,VolumeRight])^(1/3)
train_frame_Transformed[,VolumeLeft] <- (train_frame_Transformed[,VolumeLeft])^(1/3)
train_frame_Transformed[,AreaRight] <- (train_frame_Transformed[,AreaRight])^1/2
train_frame_Transformed[,AreaLeft] <- (train_frame_Transformed[,AreaLeft])^1/2
train_frame_Transformed$nICV <- train_frame_Transformed$ICV^(1/3)
ICV <- train_frame_Transformed$nICV
train_frame_Transformed[,otherVolumes] <- (train_frame_Transformed[,otherVolumes])/ICV
train_frame_Transformed[,VolumeRight] <- (train_frame_Transformed[,VolumeRight])/ICV
train_frame_Transformed[,VolumeLeft] <- (train_frame_Transformed[,VolumeLeft])/ICV
train_frame_Transformed[,AreaRight] <- (train_frame_Transformed[,AreaRight])/ICV
train_frame_Transformed[,AreaLeft] <- (train_frame_Transformed[,AreaLeft])/ICV
train_frame_Transformed[,ThicknessRight] <- (train_frame_Transformed[,ThicknessRight])/ICV
train_frame_Transformed[,ThicknessLeft] <- (train_frame_Transformed[,ThicknessLeft])/ICV
MeanVolume <- (train_frame_Transformed[,VolumeRight] + train_frame_Transformed[,VolumeLeft])/2
colnames(MeanVolume) <- paste("Mean",colnames(MeanVolume),sep="_")
DifVolume <- abs(train_frame_Transformed[,VolumeRight] - train_frame_Transformed[,VolumeLeft])
colnames(DifVolume) <- paste("Dif",colnames(DifVolume),sep="_")
train_frame_Transformed[,VolumeRight] <-NULL
train_frame_Transformed[,VolumeLeft] <- NULL
MeanArea <- (train_frame_Transformed[,AreaRight] + train_frame_Transformed[,AreaLeft])/2
colnames(MeanArea) <- paste("Mean",colnames(MeanArea),sep="_")
DifArea <- abs(train_frame_Transformed[,AreaRight] - train_frame_Transformed[,AreaLeft])
colnames(DifArea) <- paste("Dif",colnames(DifArea),sep="_")
train_frame_Transformed[,AreaRight] <- NULL
train_frame_Transformed[,AreaLeft] <- NULL
MeanThickness <- (train_frame_Transformed[,ThicknessRight] + train_frame_Transformed[,ThicknessLeft])/2
colnames(MeanThickness) <- paste("Mean",colnames(MeanThickness),sep="_")
DifThickness <- abs(train_frame_Transformed[,ThicknessRight] - train_frame_Transformed[,ThicknessLeft])
colnames(DifThickness) <- paste("Dif",colnames(DifThickness),sep="_")
train_frame_Transformed[,ThicknessRight] <- NULL
train_frame_Transformed[,ThicknessLeft] <- NULL
train_frame_Transformed <- cbind(train_frame_Transformed,MeanVolume,DifVolume,MeanArea,DifArea,MeanThickness,DifThickness)
train_frame_Transformed$MeanVolumes <- apply(MeanVolume,1,mean,na.rm=TRUE)
train_frame_Transformed$StdVolumes <- apply(MeanVolume,1,sd,na.rm=TRUE)
train_frame_Transformed$COVOlumens <- train_frame_Transformed$StdVolumes/train_frame_Transformed$MeanVolumes
train_frame_Transformed$MeanArea <- apply(MeanArea,1,mean,na.rm=TRUE)
train_frame_Transformed$StdArea <- apply(MeanArea,1,sd,na.rm=TRUE)
train_frame_Transformed$COArea <- train_frame_Transformed$StdArea/train_frame_Transformed$MeanArea
train_frame_Transformed$MeanThickness <- apply(MeanThickness,1,mean,na.rm=TRUE)
train_frame_Transformed$StdThickness <- apply(MeanThickness,1,sd,na.rm=TRUE)
train_frame_Transformed$COMeanThickness <- train_frame_Transformed$StdThickness/train_frame_Transformed$MeanThickness
## Testing
test_Frame_Transformed[,colnumeric] <- sapply(test_Frame_Transformed[,colnumeric],as.numeric)
test_Frame_Transformed[,otherVolumes] <- (test_Frame_Transformed[,otherVolumes])^(1/3)
test_Frame_Transformed[,VolumeRight] <- (test_Frame_Transformed[,VolumeRight])^(1/3)
test_Frame_Transformed[,VolumeLeft] <- (test_Frame_Transformed[,VolumeLeft])^(1/3)
test_Frame_Transformed[,AreaRight] <- (test_Frame_Transformed[,AreaRight])^1/2
test_Frame_Transformed[,AreaLeft] <- (test_Frame_Transformed[,AreaLeft])^1/2
test_Frame_Transformed$nICV <- test_Frame_Transformed$ICV^(1/3)
ICV <- test_Frame_Transformed$nICV
test_Frame_Transformed[,otherVolumes] <- (test_Frame_Transformed[,otherVolumes])/ICV
test_Frame_Transformed[,VolumeRight] <- (test_Frame_Transformed[,VolumeRight])/ICV
test_Frame_Transformed[,VolumeLeft] <- (test_Frame_Transformed[,VolumeLeft])/ICV
test_Frame_Transformed[,AreaRight] <- (test_Frame_Transformed[,AreaRight])/ICV
test_Frame_Transformed[,AreaLeft] <- (test_Frame_Transformed[,AreaLeft])/ICV
test_Frame_Transformed[,ThicknessRight] <- (test_Frame_Transformed[,ThicknessRight])/ICV
test_Frame_Transformed[,ThicknessLeft] <- (test_Frame_Transformed[,ThicknessLeft])/ICV
MeanVolume <- (test_Frame_Transformed[,VolumeRight] + test_Frame_Transformed[,VolumeLeft])/2
colnames(MeanVolume) <- paste("Mean",colnames(MeanVolume),sep="_")
DifVolume <- abs(test_Frame_Transformed[,VolumeRight] - test_Frame_Transformed[,VolumeLeft])
colnames(DifVolume) <- paste("Dif",colnames(DifVolume),sep="_")
test_Frame_Transformed[,VolumeRight] <-NULL
test_Frame_Transformed[,VolumeLeft] <- NULL
MeanArea <- (test_Frame_Transformed[,AreaRight] + test_Frame_Transformed[,AreaLeft])/2
colnames(MeanArea) <- paste("Mean",colnames(MeanArea),sep="_")
DifArea <- abs(test_Frame_Transformed[,AreaRight] - test_Frame_Transformed[,AreaLeft])
colnames(DifArea) <- paste("Dif",colnames(DifArea),sep="_")
test_Frame_Transformed[,AreaRight] <- NULL
test_Frame_Transformed[,AreaLeft] <- NULL
MeanThickness <- (test_Frame_Transformed[,ThicknessRight] + test_Frame_Transformed[,ThicknessLeft])/2
colnames(MeanThickness) <- paste("Mean",colnames(MeanThickness),sep="_")
DifThickness <- abs(test_Frame_Transformed[,ThicknessRight] - test_Frame_Transformed[,ThicknessLeft])
colnames(DifThickness) <- paste("Dif",colnames(DifThickness),sep="_")
test_Frame_Transformed[,ThicknessRight] <- NULL
test_Frame_Transformed[,ThicknessLeft] <- NULL
test_Frame_Transformed <- cbind(test_Frame_Transformed,MeanVolume,DifVolume,MeanArea,DifArea,MeanThickness,DifThickness)
test_Frame_Transformed$MeanVolumes <- apply(MeanVolume,1,mean,na.rm=TRUE)
test_Frame_Transformed$StdVolumes <- apply(MeanVolume,1,sd,na.rm=TRUE)
test_Frame_Transformed$COVOlumens <- test_Frame_Transformed$StdVolumes/test_Frame_Transformed$MeanVolumes
test_Frame_Transformed$MeanArea <- apply(MeanArea,1,mean,na.rm=TRUE)
test_Frame_Transformed$StdArea <- apply(MeanArea,1,sd,na.rm=TRUE)
test_Frame_Transformed$COArea <- test_Frame_Transformed$StdArea/test_Frame_Transformed$MeanArea
test_Frame_Transformed$MeanThickness <- apply(MeanThickness,1,mean,na.rm=TRUE)
test_Frame_Transformed$StdThickness <- apply(MeanThickness,1,sd,na.rm=TRUE)
test_Frame_Transformed$COMeanThickness <- test_Frame_Transformed$StdThickness/test_Frame_Transformed$MeanThickness
test_Frame_Transformed$nICV <- test_Frame_Transformed$ICV^(1/3)
## Train Imputation
checkmissing <- colImputeThreshold*nrow(train_frame_Transformed)
table(train_frame_Transformed$VISCODE)
sum(is.na(train_frame_Transformed$DX))
shortvisits <- subset(train_frame_Transformed,M <= MinVisit)
tokeep = !(colnames(shortvisits) %in% c("DX","nICV"))
missingData <- c(rep(FALSE,length(notQuantitative)),
apply(is.na(shortvisits[,-notQuantitative]),2,sum) > checkmissing)
missingData <- missingData & tokeep
print(sum(!missingData))
train_frame_Transformed_red <- train_frame_Transformed[,!missingData]
print(colnames(train_frame_Transformed_red))
checkRowmissing <- rowImputeThreshold*ncol(train_frame_Transformed_red)
mssingRowData <- apply(is.na(train_frame_Transformed_red),1,sum) >= checkRowmissing
print(sum(!mssingRowData))
train_frame_Transformed_red <- train_frame_Transformed_red[!mssingRowData,]
# colnames(train_frame_Transformed_red)
theincluded <- unique(c("RID","AGE","PTGENDER",colnames(train_frame_Transformed_red)[-notQuantitative]))
colnotincluded <- !(colnames(train_frame_Transformed_red) %in% theincluded)
TadpoleOnlyFeatures <- train_frame_Transformed_red[,theincluded]
TadpoleTrain_Imputed <- train_frame_Transformed_red
allAdustedZrank <- TadpoleTrain_Imputed
TadpoleTrain_Imputed <- cbind(train_frame_Transformed_red[,colnotincluded],nearestNeighborImpute(TadpoleOnlyFeatures,catgoricCol=c("RID","PTGENDER")))
fnames <- colnames(TadpoleTrain_Imputed)
fnames <- str_replace_all(fnames," ","_")
fnames <- str_replace_all(fnames,"/","_")
fnames <- str_replace_all(fnames,":","_")
fnames <- str_replace_all(fnames,"//.","_")
colnames(TadpoleTrain_Imputed) <- fnames
table(TadpoleTrain_Imputed$PTGENDER)
# TadpoleTrain_Imputed$PTGENDER <- 1*(TadpoleTrain_Imputed$PTGENDER=="Male")
# table(TadpoleTrain_Imputed$PTGENDER)
TadpoleTrain_Imputed$AGE <- TadpoleTrain_Imputed$AGE + TadpoleTrain_Imputed$Years_bl
cognitiveNormal <- subset(TadpoleTrain_Imputed,DX=="NL" & VISCODE=="bl")
predictors <- colnames(cognitiveNormal)[-notQuantitative]
predictors <- cbind(predictors,predictors)
allAdusted <- featureAdjustment(predictors, baseModel="1+AGE+nICV",data=TadpoleTrain_Imputed,referenceframe=cognitiveNormal,strata="PTGENDER", type = "LM", pvalue = 0.001)
adjustedContol <- subset(allAdusted,DX=="NL" & VISCODE=="bl")
trainAdustedZrank <- rankInverseNormalDataFrame(predictors,
allAdusted,
adjustedContol,
strata="PTGENDER")
## Testing imputation
newrawnames <- paste(train_frame_Transformed_red$RID,train_frame_Transformed_red$VISCODE,sep="_")
rownames(train_frame_Transformed_red) <- newrawnames
test_Frame_Transformed_red <- test_Frame_Transformed[,!missingData]
print(nrow(test_Frame_Transformed_red))
newrawnames <- paste(test_Frame_Transformed_red$RID,test_Frame_Transformed_red$VISCODE,sep="_")
testIDS <- unique(test_Frame_Transformed_red$RID)
print(c(length(newrawnames),length(unique(newrawnames))))
rownames(test_Frame_Transformed_red) <- newrawnames
trainIDInTest <- train_frame_Transformed_red[train_frame_Transformed_red$RID %in% testIDS,]
rownames(trainIDInTest) <- paste(trainIDInTest$RID,trainIDInTest$VISCODE,sep="_")
trainObsnotinTest <- !(rownames(trainIDInTest) %in% rownames(test_Frame_Transformed_red))
traininnotest <- trainIDInTest[trainObsnotinTest,]
test_Frame_Transformed_red <- rbind(test_Frame_Transformed_red,traininnotest)
print(nrow(test_Frame_Transformed_red))
test_Frame_Transformed_red <- test_Frame_Transformed_red[order(test_Frame_Transformed_red$EXAMDATE),]
test_Frame_Transformed_red <- test_Frame_Transformed_red[order(as.integer(test_Frame_Transformed_red$RID)),]
# checkRowmissing <- 3 #at least 3 features
# mssingRowData <- apply(is.na(test_Frame_Transformed_red),1,sum) > checkRowmissing
# print(sum(!mssingRowData))
# test_Frame_Transformed_red <- test_Frame_Transformed_red[!mssingRowData,]
test_Frame_Transformed_red$RID <- as.numeric(test_Frame_Transformed_red$RID)
test_Frame_Transformed_red$AGE <- as.numeric(test_Frame_Transformed_red$AGE)
# test_Frame_Transformed_red$PTGENDER <- 1*(test_Frame_Transformed_red$PTGENDER=="Male")
train_frame_Transformed_red$RID <- as.numeric(train_frame_Transformed_red$RID)
train_frame_Transformed_red$AGE <- as.numeric(train_frame_Transformed_red$AGE)
# train_frame_Transformed_red$PTGENDER <- 1*(train_frame_Transformed_red$PTGENDER=="Male")
# print(colnames(test_Frame_Transformed_red))
theincluded <- unique(c("AGE","PTGENDER",colnames(test_Frame_Transformed_red)[-notQuantitative]))
colcategorical <- c("PTGENDER")
if (includeID)
{
theincluded <- c("RID",theincluded)
colcategorical <- c("RID",colcategorical)
}
TadpoleOnlyFeatures <- test_Frame_Transformed_red[,theincluded]
TadpoleRefOnlyFeatures <- train_frame_Transformed_red[,theincluded]
print(ncol(TadpoleOnlyFeatures))
# print(colnames(TadpoleOnlyFeatures))
colnotincluded <- !(colnames(test_Frame_Transformed_red) %in% theincluded)
print(colnames(test_Frame_Transformed_red)[colnotincluded])
# print(summary(TadpoleOnlyFeatures))
# print(summary(TadpoleRefOnlyFeatures))
Tadpole_Imputed <- nearestNeighborImpute(TadpoleOnlyFeatures,TadpoleRefOnlyFeatures,catgoricCol=colcategorical,includeID)
print(c(nrow(Tadpole_Imputed),ncol(Tadpole_Imputed)))
Tadpole_Imputed <- cbind(test_Frame_Transformed_red[,colnotincluded],Tadpole_Imputed)
print(c(nrow(Tadpole_Imputed),ncol(Tadpole_Imputed)))
fnames <- colnames(Tadpole_Imputed)
fnames <- str_replace_all(fnames," ","_")
fnames <- str_replace_all(fnames,"/","_")
fnames <- str_replace_all(fnames,":","_")
fnames <- str_replace_all(fnames,"//.","_")
colnames(Tadpole_Imputed) <- fnames
print(table(Tadpole_Imputed$PTGENDER))
testAdusted <- featureAdjustment(predictors, baseModel="1+AGE+nICV",data=Tadpole_Imputed,referenceframe=cognitiveNormal,strata="PTGENDER", type = "LM", pvalue = 0.001)
print(c(nrow(testAdusted),ncol(testAdusted)))
testAdustedZrank <- rankInverseNormalDataFrame(predictors,
testAdusted,
adjustedContol,
strata="PTGENDER")
print(c(nrow(testAdustedZrank),ncol(testAdustedZrank)))
DataFrames <- list(AdjustedTrainFrame=trainAdustedZrank,
testingFrame = testAdustedZrank,
Train_Imputed = TadpoleTrain_Imputed,
Test_Imputed = Tadpole_Imputed
)
return (DataFrames)
}