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DataPreprocessingOnly.Rmd
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---
title: "TADPOLE Data Preprocessing"
output: html_notebook
editor_options:
chunk_output_type: console
---
# Tadpole Data Processing
Here we will remove columns with many NA
All volumes will be transformed by the cubic root
All areas will be transformed by the square root
Left and right features will be described by the mean and the absolute differences
```{r}
library("FRESA.CAD")
dataThreshold <- 0.35
```
## Data loading
```{r}
TADPOLE_D1_D2_Dict <- read.csv("C:/Users/jtame/Dropbox (Personal)/Documents/FRESACAD/TADPOLE/TADPOLE/TADPOLE_D1_D2_Dict.csv", na.strings=c("NA",-4,"-4.0",""," "))
TADPOLE_D1_D2 <- read.csv("C:/Users/jtame/Dropbox (Personal)/Documents/FRESACAD/TADPOLE/TADPOLE/TADPOLE_D1_D2.csv", na.strings=c("NA",-4,"-4.0",""," "))
TADPOLE_D3 <- read.csv("C:/Users/jtame/Dropbox (Personal)/Documents/FRESACAD/TADPOLE/TADPOLE/TADPOLE_D3.csv", na.strings=c("NA",-4,"-4.0",""," "))
```
## Basic Patient Data
```{r}
SetIDSColumns <- c("PTID","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")
```
## Get the ID of volumetric data
```{r}
volumeCOlumns <- subset(TADPOLE_D1_D2_Dict,UNITS=="mm3")$FLDNAME
volumeCOlumns <- volumeCOlumns[order(TADPOLE_D1_D2_Dict[TADPOLE_D1_D2_Dict$FLDNAME %in% volumeCOlumns,"TEXT"])]
AreaCOlumns <- subset(TADPOLE_D1_D2_Dict,UNITS=="mm2")$FLDNAME
AreaCOlumns <- AreaCOlumns[order(TADPOLE_D1_D2_Dict[TADPOLE_D1_D2_Dict$FLDNAME %in% AreaCOlumns,"TEXT"])]
ThicknessCOlumns <- subset(TADPOLE_D1_D2_Dict,UNITS=="mm")$FLDNAME
ThicknessCOlumns <- ThicknessCOlumns[order(TADPOLE_D1_D2_Dict[TADPOLE_D1_D2_Dict$FLDNAME %in% ThicknessCOlumns,"TEXT"])]
Rightlocations <- TADPOLE_D1_D2_Dict[regexpr('Right', TADPOLE_D1_D2_Dict$TEXT) > 0,"FLDNAME"]
Leftlocations <- TADPOLE_D1_D2_Dict[regexpr('Left', TADPOLE_D1_D2_Dict$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(TADPOLE_D1_D2_Dict[TADPOLE_D1_D2_Dict$FLDNAME %in% ThicknessRight[1:5],"TEXT"])
print(TADPOLE_D1_D2_Dict[TADPOLE_D1_D2_Dict$FLDNAME %in% ThicknessLeft[1:5],"TEXT"])
```
## Data Transfomrations
```{r}
TADPOLE_D1_D2_Transformed <- TADPOLE_D1_D2[,c(SetIDSColumns,DatesColumns,Diagnosis,BasicColumns,QuestionsColumnsSet1,QuestionsColumnsSet2,BaseFeaturesColumnsData,otherVolumes,VolumeRight,VolumeLeft,AreaRight,AreaLeft,ThicknessRight,ThicknessLeft)]
Used_D1_D2_columns <- colnames(TADPOLE_D1_D2_Transformed)
TADPOLE_D1_D2_Transformed[,23:ncol(TADPOLE_D1_D2_Transformed)] <- sapply(TADPOLE_D1_D2_Transformed[,23:ncol(TADPOLE_D1_D2_Transformed)],as.numeric)
TADPOLE_D1_D2_Transformed[,otherVolumes] <- (TADPOLE_D1_D2_Transformed[,otherVolumes])^(1/3)
TADPOLE_D1_D2_Transformed[,VolumeRight] <- (TADPOLE_D1_D2_Transformed[,VolumeRight])^(1/3)
TADPOLE_D1_D2_Transformed[,VolumeLeft] <- (TADPOLE_D1_D2_Transformed[,VolumeLeft])^(1/3)
TADPOLE_D1_D2_Transformed[,AreaRight] <- (TADPOLE_D1_D2_Transformed[,AreaRight])^1/2
TADPOLE_D1_D2_Transformed[,AreaLeft] <- (TADPOLE_D1_D2_Transformed[,AreaLeft])^1/2
ICV <- TADPOLE_D1_D2$ICV^(1/3)
TADPOLE_D1_D2_Transformed[,otherVolumes] <- (TADPOLE_D1_D2_Transformed[,otherVolumes])/ICV
TADPOLE_D1_D2_Transformed[,VolumeRight] <- (TADPOLE_D1_D2_Transformed[,VolumeRight])/ICV
TADPOLE_D1_D2_Transformed[,VolumeLeft] <- (TADPOLE_D1_D2_Transformed[,VolumeLeft])/ICV
TADPOLE_D1_D2_Transformed[,AreaRight] <- (TADPOLE_D1_D2_Transformed[,AreaRight])/ICV
TADPOLE_D1_D2_Transformed[,AreaLeft] <- (TADPOLE_D1_D2_Transformed[,AreaLeft])/ICV
TADPOLE_D1_D2_Transformed[,ThicknessRight] <- (TADPOLE_D1_D2_Transformed[,ThicknessRight])/ICV
TADPOLE_D1_D2_Transformed[,ThicknessLeft] <- (TADPOLE_D1_D2_Transformed[,ThicknessLeft])/ICV
MeanVolume <- (TADPOLE_D1_D2_Transformed[,VolumeRight] + TADPOLE_D1_D2_Transformed[,VolumeLeft])/2
colnames(MeanVolume) <- paste("Mean",colnames(MeanVolume),sep="_")
DifVolume <- abs(TADPOLE_D1_D2_Transformed[,VolumeRight] - TADPOLE_D1_D2_Transformed[,VolumeLeft])
colnames(DifVolume) <- paste("Dif",colnames(DifVolume),sep="_")
TADPOLE_D1_D2_Transformed[,VolumeRight] <-NULL
TADPOLE_D1_D2_Transformed[,VolumeLeft] <- NULL
MeanArea <- (TADPOLE_D1_D2_Transformed[,AreaRight] + TADPOLE_D1_D2_Transformed[,AreaLeft])/2
colnames(MeanArea) <- paste("Mean",colnames(MeanArea),sep="_")
DifArea <- abs(TADPOLE_D1_D2_Transformed[,AreaRight] - TADPOLE_D1_D2_Transformed[,AreaLeft])
colnames(DifArea) <- paste("Dif",colnames(DifArea),sep="_")
TADPOLE_D1_D2_Transformed[,AreaRight] <- NULL
TADPOLE_D1_D2_Transformed[,AreaLeft] <- NULL
MeanThickness <- (TADPOLE_D1_D2_Transformed[,ThicknessRight] + TADPOLE_D1_D2_Transformed[,ThicknessLeft])/2
colnames(MeanThickness) <- paste("Mean",colnames(MeanThickness),sep="_")
DifThickness <- abs(TADPOLE_D1_D2_Transformed[,ThicknessRight] - TADPOLE_D1_D2_Transformed[,ThicknessLeft])
colnames(DifThickness) <- paste("Dif",colnames(DifThickness),sep="_")
TADPOLE_D1_D2_Transformed[,ThicknessRight] <- NULL
TADPOLE_D1_D2_Transformed[,ThicknessLeft] <- NULL
TADPOLE_D1_D2_Transformed <- cbind(TADPOLE_D1_D2_Transformed,MeanVolume,DifVolume,MeanArea,DifArea,MeanThickness,DifThickness)
TADPOLE_D1_D2_Transformed$MeanVolumes <- apply(MeanVolume,1,mean,na.rm=TRUE)
TADPOLE_D1_D2_Transformed$StdVolumes <- apply(MeanVolume,1,sd,na.rm=TRUE)
TADPOLE_D1_D2_Transformed$COVOlumens <- TADPOLE_D1_D2_Transformed$StdVolumes/TADPOLE_D1_D2_Transformed$MeanVolumes
TADPOLE_D1_D2_Transformed$MeanArea <- apply(MeanArea,1,mean,na.rm=TRUE)
TADPOLE_D1_D2_Transformed$StdArea <- apply(MeanArea,1,sd,na.rm=TRUE)
TADPOLE_D1_D2_Transformed$COArea <- TADPOLE_D1_D2_Transformed$StdArea/TADPOLE_D1_D2_Transformed$MeanArea
TADPOLE_D1_D2_Transformed$MeanThickness <- apply(MeanThickness,1,mean,na.rm=TRUE)
TADPOLE_D1_D2_Transformed$StdThickness <- apply(MeanThickness,1,sd,na.rm=TRUE)
TADPOLE_D1_D2_Transformed$COMeanThickness <- TADPOLE_D1_D2_Transformed$StdThickness/TADPOLE_D1_D2_Transformed$MeanThickness
```
### Remove columns and subjects with more than 50% of missing data
```{r}
dataThreshold <- 0.25
dataThreshold2 <- 0.10
checkmissing <- dataThreshold*nrow(TADPOLE_D1_D2_Transformed)
table(TADPOLE_D1_D2_Transformed$VISCODE)
sum(is.na(TADPOLE_D1_D2_Transformed$DX))
missingData <- apply(is.na(subset(TADPOLE_D1_D2_Transformed,M <= 36)),2,sum) > checkmissing
sum(!missingData)
TADPOLE_D1_D2_Transformed_red <- TADPOLE_D1_D2_Transformed[,!missingData]
TADPOLE_Complete_Transformed <- TADPOLE_D1_D2_Transformed_red[complete.cases(TADPOLE_D1_D2_Transformed_red),]
checkColmissing <- dataThreshold2*ncol(TADPOLE_D1_D2_Transformed_red)
mssingRowData <- apply(is.na(TADPOLE_D1_D2_Transformed_red),1,sum) > checkColmissing
sum(!mssingRowData)
TADPOLE_D1_D2_Transformed_red <- TADPOLE_D1_D2_Transformed_red[!mssingRowData,]
colnames(TADPOLE_D1_D2_Transformed_red)
```
### Imputing Missing Data
```{r}
TADPOLE_D1_D2_Transformed_red$nICV <- TADPOLE_D1_D2_Transformed_red$ICV^(1/3)
TadpoleOnlyFeatures <- TADPOLE_D1_D2_Transformed_red[,-c(1:22)]
Tadpole_Imputed <- cbind(TADPOLE_D1_D2_Transformed_red[,c(1:22)],nearestNeighborImpute(TadpoleOnlyFeatures))
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
save(Tadpole_Imputed,file="Tadpole_Imputed.RDATA")
```
### Adjust for Age Gender and ICV
```{r}
load(file="Tadpole_Imputed.RDATA")
table(Tadpole_Imputed$DX)
table(Tadpole_Imputed$PTGENDER)
Tadpole_Imputed$PTGENDER <- 1*(Tadpole_Imputed$PTGENDER=="Male")
table(Tadpole_Imputed$PTGENDER)
Tadpole_Imputed$AGE <- Tadpole_Imputed$AGE + Tadpole_Imputed$Years_bl
cognitiveNormal <- subset(Tadpole_Imputed,DX=="NL" & VISCODE=="bl")
predictors <- colnames(cognitiveNormal)[-c(1:22)]
predictors <- cbind(predictors,predictors)
allAdusted <- featureAdjustment(predictors, baseModel="1+AGE+nICV",data=Tadpole_Imputed,referenceframe=cognitiveNormal,strata="PTGENDER", type = "LM", pvalue = 0.001)
models <- attr(allAdusted,"models")
save(allAdusted,file="allAdusted.RDATA")
load(file="allAdusted.RDATA")
allAdustedZrank <- rankInverseNormalDataFrame(predictors,
allAdusted,
subset(allAdusted,DX=="NL" & VISCODE=="bl"),
strata="PTGENDER")
save(allAdustedZrank,file="allAdustedZrank.RDATA")
load(file="allAdustedZrank.RDATA")
models <- TrainTadpoleClassModels(allAdustedZrank,predictors=c("AGE","PTGENDER",colnames(allAdustedZrank)[-c(1:22)]))
```
Creating the Full D3 Set
```{r}
```