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dataPreprocessing_CCTSIproject_Final.R
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dataPreprocessing_CCTSIproject_Final.R
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#Data preprocessing and creating the predictor matrix
rm(list=ls())
#Install required packages
#install.packages("tidyverse")
library(tidyverse)
#install.packages("readxl")
library(readxl)
#install.packages("rJava")
#install.packages("xlsx")
library(xlsx)
#install.packages("ggplot2")
library(ggplot2)
#Glioma -- CSU
glioma.CSU = read_excel("/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Glioma_CSU.xlsx",
sheet = 1)
glioma.CSU = as.data.frame(glioma.CSU)
dim(glioma.CSU) #234 x 25
colnames(glioma.CSU)
#Find samples by processors -- XY and DN
which.glioma.CSU.XY = which(glioma.CSU$Processor == "XY")
length(unique(glioma.CSU$PatientID[which.glioma.CSU.XY])) #19: Samples 1-19
which.glioma.CSU.DN = which(glioma.CSU$Processor == "DN")
length(unique(glioma.CSU$PatientID[which.glioma.CSU.DN])) #20: Samples 20-39
#Find subjects with less than 3 slices
which.nas.glioCSU = which(glioma.CSU$`# of slices` < 3)
pIDs.noSlices.lessthan3.GC = unique(glioma.CSU$PatientID[which.nas.glioCSU])
pIDs.noSlices.lessthan3.GC #4 subjects -- 329102 (2) 203038 (1) 302105 (1) 280649 (1)
glioma.CSU$`# of slices`[which.nas.glioCSU]
#Find subjects with NOSE muscle as normal tissue
which.nose.glioCSU = which(glioma.CSU$Normal_Nose == 1)
pIDs.nose.GC = unique(glioma.CSU$PatientID[which.nose.glioCSU])
pIDs.nose.GC #NULL
glioma.CSU$Normal_Nose[which.nose.glioCSU] #NULL
#Find which variables have NA's
nas.GlioCSU = apply(glioma.CSU, 2, function(x) sum(is.na(x)))
colnames(glioma.CSU)[which(nas.GlioCSU > 0)]
#"Series#" "Img#" "DiseaseStatus" "CenterX" "CenterY"
#"Center_SI" "Area_cm2" "Mean_SI" "SD_SI"
#Find variability within the scanner covariates
colnames(glioma.CSU)[17:23]
for(i in 17:23){
print(colnames(glioma.CSU)[i])
print(length(unique(glioma.CSU[, i])))
}
#Glioma -- Outside
glioma.Outside = read_excel("/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Glioma_Outside.xlsx",
sheet = 1)
glioma.Outside = as.data.frame(glioma.Outside)
dim(glioma.Outside) #120 x 26
colnames(glioma.Outside)
#Find subjects with less than 3 slices
which.nas.glioOut = which(glioma.Outside$`# slices` < 3)
pIDs.noSlices.lessthan3.GO = unique(glioma.Outside$PatientID[which.nas.glioOut])
pIDs.noSlices.lessthan3.GO #3 subjects: 341925(2) 335115(1) 331176(2)
glioma.Outside$`# slices`[which.nas.glioOut]
#Find subjects with NOSE muscle as normal tissue
which.nose.glioOut = which(glioma.Outside$Normal_Nose == 1)
pIDs.nose.GO = unique(glioma.Outside$PatientID[which.nose.glioOut])
pIDs.nose.GO #NULL
glioma.Outside$Normal_Nose[which.nose.glioOut] #NULL
#Find samples by processors -- XY and DN
which.glioma.Out.XY = which(glioma.Outside$Processor == "XY")
length(unique(glioma.Outside$PatientID[which.glioma.Out.XY])) #11: Samples 1-11
which.glioma.Out.DN = which(glioma.Outside$Processor == "DN")
length(unique(glioma.Outside$PatientID[which.glioma.Out.DN])) #9: Samples 12-20
#Find which variables have NA's
nas.GlioOut = apply(glioma.Outside, 2, function(x) sum(is.na(x)))
colnames(glioma.Outside)[which(nas.GlioOut > 0)]
#"Series#" "Image#" "DiseaseStatus" "CenterX" "CenterY"
#"Center_SI" "Area_cm2" "Mean_SI" "SD_SI" "FOVRecon"
na.go = Reduce(intersect, list(which(!is.na(glioma.Outside$`Series#`)), which(is.na(glioma.Outside$`FOVRecon`))))
length(na.go) #46
##Write the sample info on dogs with NA's within "FOVRecon"
# write.table(unique(glioma.Outside[na.go, c(2:5, 24)]), "gliomaOutside_FOVReconNAs.csv",
# sep = ",",
# row.names = F,
# col.names = T)
#Find variability within the scanner covariates
colnames(glioma.Outside)[18:24]
for(i in 18:24){
print(colnames(glioma.Outside)[i])
print(length(unique(glioma.Outside[, i])))
}
#Meningioma -- CSU
meningioma.CSU = read_excel("/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Meningioma_CSU.xlsx",
sheet = 1)
meningioma.CSU = as.data.frame(meningioma.CSU)
dim(meningioma.CSU) #636 x 25
colnames(meningioma.CSU)
#Find subjects with less than 3 slices
which.nas.meninCSU = which(meningioma.CSU$`# of slices` < 3)
pIDs.noSlices.lessthan3.MC = unique(meningioma.CSU$PatientID[which.nas.meninCSU])
pIDs.noSlices.lessthan3.MC #4 subjects: 34 (1) 55 (1) 78 (1) 104 (2)
meningioma.CSU$`# of slices`[which.nas.meninCSU]
#Find subjects with NOSE muscle as normal tissue
which.nose.meninCSU = which(meningioma.CSU$Normal_Nose == 1)
pIDs.nose.MC = unique(meningioma.CSU$PatientID[which.nose.meninCSU])
pIDs.nose.MC #3 subjects: 22 34 45
meningioma.CSU$Normal_Nose[which.nose.meninCSU]
#Find samples by processors -- XY and DN
which.menin.CSU.XY = which(meningioma.CSU$Processor == "XY")
length(unique(meningioma.CSU$PatientID[which.menin.CSU.XY])) #52: Samples 1-52
which.menin.CSU.DN = which(meningioma.CSU$Processor == "DN")
length(unique(meningioma.CSU$PatientID[which.menin.CSU.DN])) #54: Samples 53-106
#Find which variables have NA's
nas.meninCSU = apply(meningioma.CSU, 2, function(x) sum(is.na(x)))
colnames(meningioma.CSU)[which(nas.meninCSU > 0)]
#[1] "Series#" "Image#" "DiseaseStatus" "CenterX" "CenterY"
#[6] "Center_SI" "Area_cm2" "Mean_SI" "SD_SI"
#Find variability within the scanner covariates
colnames(meningioma.CSU)[17:23]
for(i in 17:23){
print(colnames(meningioma.CSU)[i])
print(length(unique(meningioma.CSU[, i])))
}
#Meningioma -- Outside
meningioma.Outside_1 = read_excel("/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Meningioma_Outside.xlsx",
sheet = 1)
meningioma.Outside_1 = as.data.frame(meningioma.Outside_1)
dim(meningioma.Outside_1) #480 x 26
colnames(meningioma.Outside_1)
#Find subjects with less than 3 slices
which.nas.meninOut = which(meningioma.Outside_1$`# of slices` < 3)
pIDs.noSlices.lessthan3.MO = unique(meningioma.Outside_1$PatientID[which.nas.meninOut])
pIDs.noSlices.lessthan3.MO #2 subjects: 40 (2) 72 (2)
meningioma.Outside_1$`# of slices`[which.nas.meninOut]
#Find subjects with NOSE muscle as normal tissue
which.nose.meninOut = which(meningioma.Outside_1$Normal_Nose == 1)
length(which.nose.meninOut) #24
pIDs.nose.MO = unique(meningioma.Outside_1$PatientID[which.nose.meninOut])
pIDs.nose.MO #21 44 52 57
meningioma.Outside_1$Normal_Nose[which.nose.meninOut]
#Find samples by processors -- XY and DN
which.menin.Out.XY = which(meningioma.Outside_1$Processor == "XY")
length(unique(meningioma.Outside_1$PatientID[which.menin.Out.XY])) #40: Samples 1-40
which.menin.Out.DN = which(meningioma.Outside_1$Processor == "DN")
length(unique(meningioma.Outside_1$PatientID[which.menin.Out.DN])) #40: Samples 41-80
#Find which variables have NA's
nas.meninOut = apply(meningioma.Outside_1, 2, function(x) sum(is.na(x)))
colnames(meningioma.Outside_1)[which(nas.meninOut > 0)]
#[1] "Series#" "Image#" "DiseaseStatus" "CenterX" "CenterY"
#[6] "Center_SI" "Area_cm2" "Mean_SI" "SD_SI" "FreqPhase1"
#[11] "FreqPhase2" "FOVRecon"
na.mo = Reduce(intersect, list(which(!is.na(meningioma.Outside_1$`Series#`)), which(is.na(meningioma.Outside_1$`FOVRecon`))))
na.mo.1 = Reduce(union, list(na.mo, which(is.na(meningioma.Outside_1$FreqPhase1))))
length(na.mo.1) #220
# #Write the sample info on dogs with NA's within "FOVRecon"
# write.table(unique(meningioma.Outside_1[na.mo.1, c(2:5, 22:24)]), "meningiomaOutside_FOVReconFreqPhaseNAs.csv",
# sep = ",",
# row.names = F,
# col.names = T)
sum(is.na(meningioma.Outside_1$FreqPhase1)) #6
sum(is.na(meningioma.Outside_1$FreqPhase2)) #6
sum(is.na(meningioma.Outside_1$FOVRecon)) #222
#Find variability within the scanner covariates
colnames(meningioma.Outside_1)[18:24]
for(i in 18:24){
print(colnames(meningioma.Outside_1)[i])
print(length(unique(meningioma.Outside_1[, i])))
}
#Find out which sample has FreqPhase1-FreqPhase2 data missing
omit1 = which(is.na(meningioma.Outside_1$FreqPhase1))
omit2 = which(is.na(meningioma.Outside_1$FreqPhase2))
sum(omit1-omit2)
length(unique(meningioma.Outside_1$PatientID[omit1])) #1
meningioma.Outside = meningioma.Outside_1[-omit1,]
dim(meningioma.Outside) #474 x 26
sum(is.na(meningioma.Outside$FreqPhase1))
# install.packages("xlsx")
# library(xlsx)
write.xlsx(meningioma.Outside, "/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Meningioma_Outside.xlsx",
row.names = F, sheetName = "Final_v2", append = T)
#Find subjects with less than 3 slices
which.nas.meninOut = which(meningioma.Outside$`# of slices` < 3)
pIDs.noSlices.lessthan3.MO = unique(meningioma.Outside$PatientID[which.nas.meninOut])
pIDs.noSlices.lessthan3.MO #2 subjects: 40 (2) 72 (2)
meningioma.Outside$`# of slices`[which.nas.meninOut]
#Find subjects with NOSE muscle as normal tissue
which.nose.meninOut = which(meningioma.Outside$Normal_Nose == 1)
length(which.nose.meninOut) #24
pIDs.nose.MO = unique(meningioma.Outside$PatientID[which.nose.meninOut])
pIDs.nose.MO #21 44 52 57
meningioma.Outside$Normal_Nose[which.nose.meninOut]
#Collect all the outside sites
outlabs = Reduce(union, list(glioma.Outside$OutsideSite, meningioma.Outside$OutsideSite))
sort(outlabs)
write.csv2(sort(outlabs), file = "alloutlabs.csv")
#Check outside labs
#WestVet vsCada West vet Specialists
unique(glioma.Outside[(glioma.Outside$OutsideSite == "Cada West Vet Specialists"), 2:5])
#13
unique(meningioma.Outside[(meningioma.Outside$OutsideSite == "WestVet"), 2:5])
#54 58 67
#Compute the ratio of mean_SI(D)/mean_SI(N)
#Glioma_CSU
#glioma.CSU = read_excel("/Users/Nandyd/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles/Glioma_CSU.xlsx",
# sheet = 1)
glioma.CSU = as.data.frame(glioma.CSU)
colnames(glioma.CSU)
colnames(glioma.CSU)[c(1,14,15,16)] #[1] "Processor" "Sex" "Breed" "BreedType"
for(l in setdiff(1:ncol(glioma.CSU), c(1,14,15,16))){
#print(class(glioma.CSU[,l]))
glioma.CSU[,l] = as.numeric(glioma.CSU[,l])
}
#Test if disease/normal are consecutively stored
test1 = rep(NA, nrow(glioma.CSU)/2)
for(i in 1:(nrow(glioma.CSU)/2)){
test1[i] = glioma.CSU$DiseaseStatus[(i-1)*2+1]*glioma.CSU$DiseaseStatus[2*i]
}
sum(test1, na.rm = T)
n_glioCSU = nrow(glioma.CSU)
dnRatio.meanSI.glioCSU = rep(0, n_glioCSU/2)
dnRatio.sdSI.glioCSU = rep(0, n_glioCSU/2)
dnRatio.cvSI.glioCSU = rep(0, n_glioCSU/2)
dnRatio.centerSI.glioCSU = rep(0, n_glioCSU/2)
for(i in 1:(n_glioCSU/2)){
if(is.na(glioma.CSU$Mean_SI[(i-1)*2 + 1])){
dnRatio.meanSI.glioCSU[i] = NA
dnRatio.sdSI.glioCSU[i] = NA
dnRatio.cvSI.glioCSU[i] = NA
dnRatio.centerSI.glioCSU[i] = NA
} else {
dnRatio.meanSI.glioCSU[i] = glioma.CSU$Mean_SI[(i-1)*2 + 1]/glioma.CSU$Mean_SI[2*i]
dnRatio.sdSI.glioCSU[i] = glioma.CSU$SD_SI[(i-1)*2 + 1]/glioma.CSU$SD_SI[2*i]
cv_d = glioma.CSU$SD_SI[(i-1)*2 + 1]/glioma.CSU$Mean_SI[(i-1)*2 + 1]
cv_n = glioma.CSU$SD_SI[2*i]/glioma.CSU$Mean_SI[2*i]
dnRatio.cvSI.glioCSU[i] = cv_d/cv_n
dnRatio.centerSI.glioCSU[i] = glioma.CSU$Center_SI[(i-1)*2 + 1]/glioma.CSU$Center_SI[2*i]
}
}
#Glioma_Outside
glioma.Outside = read_excel("/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Glioma_Outside.xlsx",
sheet = 1)
glioma.Outside = as.data.frame(glioma.Outside)
colnames(glioma.Outside)
colnames(glioma.Outside)[c(1,4,15,16,17)] #[1] "Processor" "OutsideSite" "Sex" "Breed" "BreedType"
for(l in setdiff(1:ncol(glioma.Outside), c(1,4,15,16,17))){
glioma.Outside[,l] = as.numeric(glioma.Outside[,l])
}
#Test if disease/normal are consecutively stored
test2 = rep(NA, nrow(glioma.Outside)/2)
for(i in 1:(nrow(glioma.Outside)/2)){
test2[i] = glioma.Outside$DiseaseStatus[(i-1)*2+1]*glioma.Outside$DiseaseStatus[2*i]
}
sum(test2, na.rm = T)
n_glioOut = nrow(glioma.Outside)
dnRatio.meanSI.glioOut = rep(0, n_glioOut/2)
dnRatio.sdSI.glioOut = rep(0, n_glioOut/2)
dnRatio.cvSI.glioOut = rep(0, n_glioOut/2)
dnRatio.centerSI.glioOut = rep(0, n_glioOut/2)
for(i in 1:(n_glioOut/2)){
if(is.na(glioma.Outside$Mean_SI[(i-1)*2 + 1])){
dnRatio.meanSI.glioOut[i] = NA
dnRatio.sdSI.glioOut[i] = NA
dnRatio.cvSI.glioOut[i] = NA
dnRatio.centerSI.glioOut[i] = NA
} else {
dnRatio.meanSI.glioOut[i] = glioma.Outside$Mean_SI[(i-1)*2 + 1]/glioma.Outside$Mean_SI[2*i]
dnRatio.sdSI.glioOut[i] = glioma.Outside$SD_SI[(i-1)*2 + 1]/glioma.Outside$SD_SI[2*i]
cv_d = glioma.Outside$SD_SI[(i-1)*2 + 1]/glioma.Outside$Mean_SI[(i-1)*2 + 1]
cv_n = glioma.Outside$SD_SI[2*i]/glioma.Outside$Mean_SI[2*i]
dnRatio.cvSI.glioOut[i] = cv_d/cv_n
dnRatio.centerSI.glioOut[i] = glioma.Outside$Center_SI[(i-1)*2 + 1]/glioma.Outside$Center_SI[2*i]
}
}
#Meningioma_CSU
meningioma.CSU = read_excel("/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Meningioma_CSU.xlsx",
sheet = 1)
meningioma.CSU = as.data.frame(meningioma.CSU)
colnames(meningioma.CSU)
colnames(meningioma.CSU)[c(1,14,15,16)] #[1] "Processor" "Sex" "Breed" "BreedType"
for(l in setdiff(1:ncol(meningioma.CSU), c(1,14,15,16))){
#print(class(glioma.CSU[,l]))
meningioma.CSU[,l] = as.numeric(meningioma.CSU[,l])
}
#Test if disease/normal are consecutively stored
test3 = rep(NA, nrow(meningioma.CSU)/2)
for(i in 1:(nrow(meningioma.CSU)/2)){
test3[i] = meningioma.CSU$DiseaseStatus[(i-1)*2+1]*meningioma.CSU$DiseaseStatus[2*i]
}
sum(test3, na.rm = T)
n_meninCSU = nrow(meningioma.CSU)
dnRatio.meanSI.meninCSU = rep(0, n_meninCSU/2)
dnRatio.sdSI.meninCSU = rep(0, n_meninCSU/2)
dnRatio.cvSI.meninCSU = rep(0, n_meninCSU/2)
dnRatio.centerSI.meninCSU = rep(0, n_meninCSU/2)
for(i in 1:(n_meninCSU/2)){
if(is.na(meningioma.CSU$Mean_SI[(i-1)*2 + 1])){
dnRatio.meanSI.meninCSU[i] = NA
dnRatio.sdSI.meninCSU[i] = NA
dnRatio.cvSI.meninCSU[i] = NA
dnRatio.centerSI.meninCSU[i] = NA
} else {
dnRatio.meanSI.meninCSU[i] = meningioma.CSU$Mean_SI[(i-1)*2 + 1]/meningioma.CSU$Mean_SI[2*i]
dnRatio.sdSI.meninCSU[i] = meningioma.CSU$SD_SI[(i-1)*2 + 1]/meningioma.CSU$SD_SI[2*i]
cv_d = meningioma.CSU$SD_SI[(i-1)*2 + 1]/meningioma.CSU$Mean_SI[(i-1)*2 + 1]
cv_n = meningioma.CSU$SD_SI[2*i]/meningioma.CSU$Mean_SI[2*i]
dnRatio.cvSI.meninCSU[i] = cv_d/cv_n
dnRatio.centerSI.meninCSU[i] = meningioma.CSU$Center_SI[(i-1)*2 + 1]/meningioma.CSU$Center_SI[2*i]
}
}
#Meningioma_Outside
#meningioma.Outside = read_excel("/Users/dnandy/OneDrive - The University of Colorado Denver/CCTSI_TM-Pilot_2020-21/DataFiles/Final_DataFiles_Manuscript/Meningioma_Outside.xlsx",
# sheet = 1)
meningioma.Outside = as.data.frame(meningioma.Outside)
colnames(meningioma.Outside)
colnames(meningioma.Outside)[c(1,4,15,16,17)] #[1] "Processor" "OutsideSite" "Sex" "Breed" "BreedType"
for(l in setdiff(1:ncol(meningioma.Outside), c(1,4,15,16,17))){
#print(class(glioma.CSU[,l]))
meningioma.Outside[,l] = as.numeric(meningioma.Outside[,l])
}
n_meninOut = nrow(meningioma.Outside)
dnRatio.meanSI.meninOut = rep(0, n_meninOut/2)
dnRatio.sdSI.meninOut = rep(0, n_meninOut/2)
dnRatio.cvSI.meninOut = rep(0, n_meninOut/2)
dnRatio.centerSI.meninOut = rep(0, n_meninOut/2)
for(i in 1:(n_meninOut/2)){
if(is.na(meningioma.Outside$Mean_SI[(i-1)*2 + 1])){
dnRatio.meanSI.meninOut[i] = NA
dnRatio.sdSI.meninOut[i] = NA
dnRatio.cvSI.meninOut[i] = NA
dnRatio.centerSI.meninOut[i] = NA
} else {
dnRatio.meanSI.meninOut[i] = meningioma.Outside$Mean_SI[(i-1)*2 + 1]/meningioma.Outside$Mean_SI[2*i]
dnRatio.sdSI.meninOut[i] = meningioma.Outside$SD_SI[(i-1)*2 + 1]/meningioma.Outside$SD_SI[2*i]
cv_d = meningioma.Outside$SD_SI[(i-1)*2 + 1]/meningioma.Outside$Mean_SI[(i-1)*2 + 1]
cv_n = meningioma.Outside$SD_SI[2*i]/meningioma.Outside$Mean_SI[2*i]
dnRatio.cvSI.meninOut[i] = cv_d/cv_n
dnRatio.centerSI.meninOut[i] = meningioma.Outside$Center_SI[(i-1)*2 + 1]/meningioma.Outside$Center_SI[2*i]
}
}
#Take average of (i) adjusted means, (ii) adjusted sds, (iii)adjusted CVs, and (iv) adjusted center-SIs
#across the slices (1/2/3) for each subject WITH weights 1/3, 2/3, and 1 respectively
#WITHOUT weighting w.r.t. ROI areas
#Glioma CSU
l1 = length(dnRatio.meanSI.glioCSU) #117
mean.dnRatio.meanSI.glioCSU = mean.dnRatio.sdSI.glioCSU =
mean.dnRatio.cvSI.glioCSU = mean.dnRatio.centerSI.glioCSU = rep(0, l1/3)
wtmean.dnRatio.meanSI.glioCSU = wtmean.dnRatio.sdSI.glioCSU =
wtmean.dnRatio.cvSI.glioCSU = wtmean.dnRatio.centerSI.glioCSU = rep(0, l1/3)
for(i in 1:(l1/3)){
nas = sum(is.na(dnRatio.meanSI.glioCSU[((i-1)*3 + 1):(3*i)]))
wt = 1-(nas/3)
#weighted (by # of slices) means
wtmean.dnRatio.meanSI.glioCSU[i] = mean(dnRatio.meanSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.sdSI.glioCSU[i] = mean(dnRatio.sdSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.cvSI.glioCSU[i] = mean(dnRatio.cvSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.centerSI.glioCSU[i] = mean(dnRatio.centerSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
mean.dnRatio.meanSI.glioCSU[i] = mean(dnRatio.meanSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.sdSI.glioCSU[i] = mean(dnRatio.sdSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.cvSI.glioCSU[i] = mean(dnRatio.cvSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.centerSI.glioCSU[i] = mean(dnRatio.centerSI.glioCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
}
#Glioma Outside
l2 = length(dnRatio.meanSI.glioOut) #60
mean.dnRatio.meanSI.glioOut = mean.dnRatio.sdSI.glioOut =
mean.dnRatio.cvSI.glioOut = mean.dnRatio.centerSI.glioOut = rep(0, l2/3)
wtmean.dnRatio.meanSI.glioOut = wtmean.dnRatio.sdSI.glioOut =
wtmean.dnRatio.cvSI.glioOut = wtmean.dnRatio.centerSI.glioOut = rep(0, l2/3)
for(i in 1:(l2/3)){
nas = sum(is.na(dnRatio.meanSI.glioOut[((i-1)*3 + 1):(3*i)]))
wt = 1-(nas/3)
#weighted (by # of slices) means
wtmean.dnRatio.meanSI.glioOut[i] = mean(dnRatio.meanSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.sdSI.glioOut[i] = mean(dnRatio.sdSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.cvSI.glioOut[i] = mean(dnRatio.cvSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.centerSI.glioOut[i] = mean(dnRatio.centerSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
mean.dnRatio.meanSI.glioOut[i] = mean(dnRatio.meanSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.sdSI.glioOut[i] = mean(dnRatio.sdSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.cvSI.glioOut[i] = mean(dnRatio.cvSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.centerSI.glioOut[i] = mean(dnRatio.centerSI.glioOut[((i-1)*3 + 1):(3*i)], na.rm = T)
}
#Meningioma CSU
l3 = length(dnRatio.meanSI.meninCSU) #318
mean.dnRatio.meanSI.meninCSU = mean.dnRatio.sdSI.meninCSU =
mean.dnRatio.cvSI.meninCSU = mean.dnRatio.centerSI.meninCSU = rep(0, l3/3)
wtmean.dnRatio.meanSI.meninCSU = wtmean.dnRatio.sdSI.meninCSU =
wtmean.dnRatio.cvSI.meninCSU = wtmean.dnRatio.centerSI.meninCSU = rep(0, l3/3)
for(i in 1:(l3/3)){
nas = sum(is.na(dnRatio.meanSI.meninCSU[((i-1)*3 + 1):(3*i)]))
wt = 1-(nas/3)
#weighted (by # of slices) means
wtmean.dnRatio.meanSI.meninCSU[i] = mean(dnRatio.meanSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.sdSI.meninCSU[i] = mean(dnRatio.sdSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.cvSI.meninCSU[i] = mean(dnRatio.cvSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.centerSI.meninCSU[i] = mean(dnRatio.centerSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
mean.dnRatio.meanSI.meninCSU[i] = mean(dnRatio.meanSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.sdSI.meninCSU[i] = mean(dnRatio.sdSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.cvSI.meninCSU[i] = mean(dnRatio.cvSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.centerSI.meninCSU[i] = mean(dnRatio.centerSI.meninCSU[((i-1)*3 + 1):(3*i)], na.rm = T)
}
#Meningioma Outside
l4 = length(dnRatio.meanSI.meninOut) #237
mean.dnRatio.meanSI.meninOut = mean.dnRatio.sdSI.meninOut =
mean.dnRatio.cvSI.meninOut = mean.dnRatio.centerSI.meninOut = rep(0, l4/3)
wtmean.dnRatio.meanSI.meninOut = wtmean.dnRatio.sdSI.meninOut =
wtmean.dnRatio.cvSI.meninOut = wtmean.dnRatio.centerSI.meninOut = rep(0, l4/3)
for(i in 1:(l4/3)){
nas = sum(is.na(dnRatio.meanSI.meninOut[((i-1)*3 + 1):(3*i)]))
wt = 1-(nas/3)
#weighted (by # of slices) means
wtmean.dnRatio.meanSI.meninOut[i] = mean(dnRatio.meanSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.sdSI.meninOut[i] = mean(dnRatio.sdSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.cvSI.meninOut[i] = mean(dnRatio.cvSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
wtmean.dnRatio.centerSI.meninOut[i] = mean(dnRatio.centerSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T) * wt
mean.dnRatio.meanSI.meninOut[i] = mean(dnRatio.meanSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.sdSI.meninOut[i] = mean(dnRatio.sdSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.cvSI.meninOut[i] = mean(dnRatio.cvSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T)
mean.dnRatio.centerSI.meninOut[i] = mean(dnRatio.centerSI.meninOut[((i-1)*3 + 1):(3*i)], na.rm = T)
}
# #Boxplots -- adjusted SDs
# #Glioma -- CSU
# mean.dnRatio.sdSI.glioCSU.XY = mean.dnRatio.sdSI.glioCSU[1:19]
# mean.dnRatio.sdSI.glioCSU.DN = mean.dnRatio.sdSI.glioCSU[20:39]
# #Glioma -- Outside
# mean.dnRatio.sdSI.glioOut.XY = mean.dnRatio.sdSI.glioOut[1:11]
# mean.dnRatio.sdSI.glioOut.DN = mean.dnRatio.sdSI.glioOut[12:20]
#
# #Meningioma -- CSU
# mean.dnRatio.sdSI.meninCSU.XY = mean.dnRatio.sdSI.meninCSU[1:52]
# mean.dnRatio.sdSI.meninCSU.DN = mean.dnRatio.sdSI.meninCSU[53:106]
# #Meningioma -- Outside
# mean.dnRatio.sdSI.meninOut.XY = mean.dnRatio.sdSI.meninOut[1:40]
# mean.dnRatio.sdSI.meninOut.DN = mean.dnRatio.sdSI.meninOut[41:79]
#
# png("DNXY_boxplot_final_sdSI.png",
# width = 12, height = 8, units = "in", res = 300)
# boxplot(mean.dnRatio.sdSI.glioCSU.XY,
# mean.dnRatio.sdSI.glioCSU.DN,
# mean.dnRatio.sdSI.glioOut.XY,
# mean.dnRatio.sdSI.glioOut.DN,
# mean.dnRatio.sdSI.meninCSU.XY,
# mean.dnRatio.sdSI.meninCSU.DN,
# mean.dnRatio.sdSI.meninOut.XY,
# mean.dnRatio.sdSI.meninOut.DN,
# col=c(2,2,3,3,4,4,5,5),
# #notch = T,
# names = c("GC-XY","GC-DN","GO-XY","GO-DN",
# "MC-XY","MC-DN","MO-XY","MO-DN"),
# ylab = "μ(adj-SD(SI))",
# xlab="Sub-populations",
# main = "Processors (DN & XY): μ(adj-SD(SI))\n(across four sub-populations)"
# )
# dev.off()
#
#
# #Density plots -- adjusted cent(SI)s
# #Glioma -- CSU
# mean.dnRatio.centSI.glioCSU.XY = mean.dnRatio.centerSI.glioCSU[1:19]
# mean.dnRatio.centSI.glioCSU.DN = mean.dnRatio.centerSI.glioCSU[20:39]
# #Glioma -- Outside
# mean.dnRatio.centSI.glioOut.XY = mean.dnRatio.centerSI.glioOut[1:11]
# mean.dnRatio.centSI.glioOut.DN = mean.dnRatio.centerSI.glioOut[12:20]
#
# #Meningioma -- CSU
# mean.dnRatio.centSI.meninCSU.XY = mean.dnRatio.centerSI.meninCSU[1:52]
# mean.dnRatio.centSI.meninCSU.DN = mean.dnRatio.centerSI.meninCSU[53:106]
# #Meningioma -- Outside
# mean.dnRatio.centSI.meninOut.XY = mean.dnRatio.centerSI.meninOut[1:40]
# mean.dnRatio.centSI.meninOut.DN = mean.dnRatio.centerSI.meninOut[41:79]
#
# png("DNXY_boxplot_final_centSI.png",
# width = 12, height = 8, units = "in", res = 300)
# boxplot(mean.dnRatio.centSI.glioCSU.XY,
# mean.dnRatio.centSI.glioCSU.DN,
# mean.dnRatio.centSI.glioOut.XY,
# mean.dnRatio.centSI.glioOut.DN,
# mean.dnRatio.centSI.meninCSU.XY,
# mean.dnRatio.centSI.meninCSU.DN,
# mean.dnRatio.centSI.meninOut.XY,
# mean.dnRatio.centSI.meninOut.DN,
# col=c(2,2,3,3,4,4,5,5),
# #notch = T,
# names = c("GC-XY","GC-DN","GO-XY","GO-DN",
# "MC-XY","MC-DN","MO-XY","MO-DN"),
# ylab = "μ(adj-cent(SI))",
# xlab="Sub-populations",
# main = "Processors (DN & XY): μ(adj-cent(SI))\n(across four sub-populations)"
# )
# dev.off()
#Results: Baseline classification of diseases
#Apply logistic regression
#Meningioma + Glioma: all CSU cases
menin.CSU.image3 = data.frame(adjmeanSI = mean.dnRatio.meanSI.meninCSU,
adjsdSI = mean.dnRatio.sdSI.meninCSU,
adjcenterSI = mean.dnRatio.centerSI.meninCSU)
dim(menin.CSU.image3) #106 x 3
glio.CSU.image3 = data.frame(adjmeanSI = mean.dnRatio.meanSI.glioCSU,
adjsdSI = mean.dnRatio.sdSI.glioCSU,
adjcenterSI = mean.dnRatio.centerSI.glioCSU)
dim(glio.CSU.image3) #39 x 3
meninglio.CSU.image3 = rbind(menin.CSU.image3, glio.CSU.image3)
#Meningioma = 1, Glioma = 0
meninglio.CSU.image3$dis.lab = c(rep(1, nrow(menin.CSU.image3)), rep(0, nrow(glio.CSU.image3)))
#Meningioma + Glioma: all Outside cases
menin.Out.image3 = data.frame(adjmeanSI = mean.dnRatio.meanSI.meninOut,
adjsdSI = mean.dnRatio.sdSI.meninOut,
adjcenterSI = mean.dnRatio.centerSI.meninOut)
dim(menin.Out.image3) #79 x 3
glio.Out.image3 = data.frame(adjmeanSI = mean.dnRatio.meanSI.glioOut,
adjsdSI = mean.dnRatio.sdSI.glioOut,
adjcenterSI = mean.dnRatio.centerSI.glioOut)
dim(glio.Out.image3) #20 x 3
meninglio.Out.image3 = rbind(menin.Out.image3, glio.Out.image3)
#Meningioma = 1, Glioma = 0
meninglio.Out.image3$dis.lab = c(rep(1, nrow(menin.Out.image3)), rep(0, nrow(glio.Out.image3)))
library(knitr)
library(tidyverse)
library(ggplot2)
library(rstatix)
library(ggpubr)
library(mice)
library(lattice)
library(reshape2)
library(DataExplorer)
library(caret)
library(corrplot)
library(RColorBrewer)
#CSU
head(meninglio.CSU.image3)
str(meninglio.CSU.image3)
summary(meninglio.CSU.image3)
introduce(meninglio.CSU.image3)
table(meninglio.CSU.image3$dis.lab)
# png("meninglioCSU_corr_final_3img_dislab.png",
# width = 12, height = 8, units = "in", res = 300)
# cor.meninglioCSU.3imgDis = cor(meninglio.CSU.image3)
# corrplot.mixed(cor.meninglioCSU.3imgDis,
# order="AOE")
#plot_correlation(meninglio.CSU.image3,
# title = "Inter-correlations among 3 image variables and tumor-types\n Site: CSU | 1 = Meningioma, 0 = Glioma",
# #xlab = "Variables",
# ggtheme = theme(plot.title = element_text(hjust=0.5, face = "bold")))
#dev.off()
# png("meninglioCSU_hist_final_3img.png",
# width = 12, height = 6, units = "in", res = 300)
# plot_histogram(meninglio.CSU.image3,
# title = "Histograms of the 3 MRI Variables [CSU]",
# ggtheme = theme(plot.title = element_text(hjust=0.5, face = "bold")))
# dev.off()
#Outside sites
head(meninglio.Out.image3)
str(meninglio.Out.image3)
summary(meninglio.Out.image3)
introduce(meninglio.Out.image3)
table(meninglio.Out.image3$dis.lab)
# png("meninglioOut_corr_final_3img_dislab.png",
# width = 12, height = 8, units = "in", res = 300)
# cor.meninglioOut.3imgDis = cor(meninglio.Out.image3)
# corrplot.mixed(cor.meninglioOut.3imgDis,
# order="AOE")
# # plot_correlation(meninglio.Out.image3,
# # title = "Correlations among the 3 MRI Variables & Disease Labels [Outside]\n[1 = Meningioma, 0 = Glioma]",
# # ggtheme = theme(plot.title = element_text(hjust=0.5, face = "bold")))
# dev.off()
# png("Out_hist_3meanadjMRIvars.png",
# width = 12, height = 8, units = "in", res = 300)
# plot_histogram(meninglio.Out.image3,
# title = "Histograms of the 3 MRI Variables [Outside]",
# ggtheme = theme(plot.title = element_text(hjust=0.5, face = "bold")))
# dev.off()
#install.packages("AID")
#library(AID)
#Box-Cox transform the adjsdSI variable
#CSU
meninglio.CSU.image3.1 = meninglio.CSU.image3
colnames(meninglio.CSU.image3.1)
# meninglio.CSU.image3.1[, 2] = log(meninglio.CSU.image3$adjsdSI)
# colnames(meninglio.CSU.image3.1)[2] = "log.adjsdSI"
# plot_correlation(meninglio.CSU.image3.1)
# plot_histogram(meninglio.CSU.image3.1)
# shapiro.test(meninglio.CSU.image3.1$log.adjsdSI) #pval = 0.6469
# shapiro.test(meninglio.CSU.image3.1$adjmeanSI) #pval = 0.1651
# shapiro.test(meninglio.CSU.image3.1$adjcenterSI) #pval = 0.2808
#Outside sites
#tran.adjsdSI.Out = boxcoxnc(meninglio.Out.image3$adjsdSI, method = "sw")
meninglio.Out.image3.1 = meninglio.Out.image3
colnames(meninglio.Out.image3.1)
# meninglio.Out.image3.1[, 2] = tran.adjsdSI.Out$tf.data
# colnames(meninglio.Out.image3.1)[2] = "boxcox.adjsdSI"
# plot_correlation(meninglio.Out.image3.1)
# plot_histogram(meninglio.Out.image3.1)
# shapiro.test(meninglio.Out.image3.1$boxcox.adjsdSI) #pval = 0.9962
# shapiro.test(meninglio.Out.image3.1$adjmeanSI) #pval = 0.907
# shapiro.test(meninglio.Out.image3.1$adjcenterSI) #pval = 0.1203
#Add biological covariates: Sex, Age, BreedType
#Add technical covariate: Processor
#Add scanner covariates: TR, TE, NEX, Thick, FreqPhase1, FreqPhase2
######################### CSU ##############################
# CSU -- Meningioma
#############################################################
l.meninCSU = nrow(menin.CSU.image3)
agemos.meninCSU = sex.meninCSU = breedType.meninCSU =
processor.meninCSU =
TR.meninCSU = TE.meninCSU = NEX.meninCSU = thick.meninCSU =
freq1.meninCSU = freq2.meninCSU = rep(NA, l.meninCSU)
for(i in 1:l.meninCSU){
agemos.meninCSU[i] = meningioma.CSU$Age_month[(i-1)*6+1]
sex.meninCSU[i] = meningioma.CSU$Sex[(i-1)*6+1]
breedType.meninCSU[i] = meningioma.CSU$BreedType[(i-1)*6+1]
processor.meninCSU[i] = meningioma.CSU$Processor[(i-1)*6+1]
TR.meninCSU[i] = meningioma.CSU$TR[(i-1)*6+1]
TE.meninCSU[i] = meningioma.CSU$TE[(i-1)*6+1]
NEX.meninCSU[i] = meningioma.CSU$NEX[(i-1)*6+1]
thick.meninCSU[i] = meningioma.CSU$Thick[(i-1)*6+1]
freq1.meninCSU[i] = meningioma.CSU$FreqPhase1[(i-1)*6+1]
freq2.meninCSU[i] = meningioma.CSU$FreqPhase2[(i-1)*6+1]
}
#Age
round(summary(agemos.meninCSU), 4)
round(c(median(agemos.meninCSU), mad(agemos.meninCSU)), 4)
#Sex
table(sex.meninCSU) #Just F: 3, Just M: 3, F SP: 51; M CAS: 49
f.sex.meninCSU = which(sex.meninCSU == "F SP" | sex.meninCSU == "FEMALE SP" | sex.meninCSU == "FEMALE")
sex.meninCSU[f.sex.meninCSU] = "F"
m.sex.meninCSU = which(sex.meninCSU == "M CAS" | sex.meninCSU == "MALE CAS" | sex.meninCSU == "MALE")
sex.meninCSU[m.sex.meninCSU] = "M"
table(sex.meninCSU) #F: 54, M: 52
#Breedtype
table(breedType.meninCSU) #Brachy: 15; Non-brachy: 91
#Processor
table(processor.meninCSU) #DN: 54, XY: 52
#TR
round(summary(TR.meninCSU), 4)
round(c(median(TR.meninCSU), mad(TR.meninCSU)), 4)
#TE
round(summary(TE.meninCSU), 4)
round(c(median(TE.meninCSU), mad(TE.meninCSU)), 4)
#NEX
round(summary(NEX.meninCSU), 4)
round(c(median(NEX.meninCSU), mad(NEX.meninCSU)), 4)
#Thick
round(summary(thick.meninCSU), 4)
round(c(median(thick.meninCSU), mad(thick.meninCSU)), 4)
#Frequency Phases 1/2
plot(freq1.meninCSU, freq2.meninCSU)
round(summary(freq1.meninCSU), 4)
round(c(median(freq1.meninCSU), mad(freq1.meninCSU)), 4)
length(unique(freq1.meninCSU)) #4
round(summary(freq2.meninCSU), 4)
round(c(median(freq2.meninCSU), mad(freq2.meninCSU)), 4)
length(unique(freq2.meninCSU)) #2
#MR Image Variables
summary(menin.CSU.image3)
round(apply(menin.CSU.image3, 2, median), 4)
round(apply(menin.CSU.image3, 2, mad), 4)
######################### CSU ##############################
# CSU -- Glioma
#############################################################
l.glioCSU = nrow(glio.CSU.image3)
agemos.glioCSU = sex.glioCSU = breedType.glioCSU =
processor.glioCSU =
TR.glioCSU = TE.glioCSU = NEX.glioCSU = thick.glioCSU =
freq1.glioCSU = freq2.glioCSU = rep(NA, l.glioCSU)
for(i in 1:l.glioCSU){
agemos.glioCSU[i] = glioma.CSU$Age_month[(i-1)*6+1]
sex.glioCSU[i] = glioma.CSU$Sex[(i-1)*6+1]
breedType.glioCSU[i] = glioma.CSU$BreedType[(i-1)*6+1]
processor.glioCSU[i] = glioma.CSU$Processor[(i-1)*6+1]
TR.glioCSU[i] = glioma.CSU$TR[(i-1)*6+1]
TE.glioCSU[i] = glioma.CSU$TE[(i-1)*6+1]
NEX.glioCSU[i] = glioma.CSU$NEX[(i-1)*6+1]
thick.glioCSU[i] = glioma.CSU$Thick[(i-1)*6+1]
freq1.glioCSU[i] = glioma.CSU$FreqPhase1[(i-1)*6+1]
freq2.glioCSU[i] = glioma.CSU$FreqPhase2[(i-1)*6+1]
}
#Age
round(summary(agemos.glioCSU), 4)
round(c(median(agemos.glioCSU), mad(agemos.glioCSU)), 4)
#Sex
table(sex.glioCSU) #Just F: 1, Just M: 1, F SP: 11; M CAS: 9
f.sex.glioCSU = which(sex.glioCSU == "F SP" | sex.glioCSU == "FEMALE SP" | sex.glioCSU == "FEMALE")
sex.glioCSU[f.sex.glioCSU] = "F"
m.sex.glioCSU = which(sex.glioCSU == "M CAS" | sex.glioCSU == "MALE CAS" | sex.glioCSU == "MALE")
sex.glioCSU[m.sex.glioCSU] = "M"
table(sex.glioCSU) #F: 21, M: 18
#Breedtype
table(breedType.glioCSU) #Brachy: 15; Non-brachy: 24
#Processor
table(processor.glioCSU) #DN: 20, XY: 19
#TR
round(summary(TR.glioCSU), 4)
round(c(median(TR.glioCSU), mad(TR.glioCSU)), 4)
#TE
round(summary(TE.glioCSU), 4)
round(c(median(TE.glioCSU), mad(TE.glioCSU)), 4)
#NEX
round(summary(NEX.glioCSU), 4)
round(c(median(NEX.glioCSU), mad(NEX.glioCSU)), 4)
#Thick
round(summary(thick.glioCSU), 4)
round(c(median(thick.glioCSU), mad(thick.glioCSU)), 4)
#Frequency Phases 1/2
plot(freq1.glioCSU, freq2.glioCSU)
round(summary(freq1.glioCSU), 4)
round(c(median(freq1.glioCSU), mad(freq1.glioCSU)), 4)
length(unique(freq1.glioCSU)) #3
round(summary(freq2.glioCSU), 4)
round(c(median(freq2.glioCSU), mad(freq2.glioCSU)), 4)
length(unique(freq2.glioCSU)) #3
#MR Image Variables
summary(glio.CSU.image3)
round(apply(glio.CSU.image3, 2, median), 4)
round(apply(glio.CSU.image3, 2, mad), 4)
#Combine Meningiom/Glioma for CSU
agemos.meninglio.CSU = c(agemos.meninCSU, agemos.glioCSU)
hist(agemos.meninglio.CSU) #Pretty normally distributed
shapiro.test(agemos.meninglio.CSU) # p = 0.1814
sex.meninglio.CSU = c(sex.meninCSU, sex.glioCSU)
table(sex.meninglio.CSU) #F: 75, M: 70
sex.meninglio.CSU = ifelse(sex.meninglio.CSU == "F", 0, 1)
table(sex.meninglio.CSU)#0 (F): 75, 1 (M): 70
breedType.meninglio.CSU = c(breedType.meninCSU, breedType.glioCSU)
table(breedType.meninglio.CSU) #Brachy: 30, Non-brachy: 115
breedType.meninglio.CSU = ifelse(breedType.meninglio.CSU == "Non-brachycephalic", 0, 1)
table(breedType.meninglio.CSU) # 1 (Brachycephalic): 30, 0 (Non-brachycephalic): 115
processor.meninglio.CSU = c(processor.meninCSU, processor.glioCSU)
table(processor.meninglio.CSU) #DN: 74, XY: 71
processor.meninglio.CSU = ifelse(processor.meninglio.CSU == "XY", 0, 1)
table(processor.meninglio.CSU) # 1 (DN): 74, 0 (XY): 71
TR.meninglio.CSU = c(TR.meninCSU, TR.glioCSU)
hist(TR.meninglio.CSU)
shapiro.test(TR.meninglio.CSU) # p = 2.651E-05
TE.meninglio.CSU = c(TE.meninCSU, TE.glioCSU)
hist(TE.meninglio.CSU)
shapiro.test(TE.meninglio.CSU) # p = 2.929e-07
NEX.meninglio.CSU = c(NEX.meninCSU, NEX.glioCSU)
hist(NEX.meninglio.CSU)
shapiro.test(NEX.meninglio.CSU) # p = 1.37E-09
thick.meninglio.CSU = c(thick.meninCSU, thick.glioCSU)
hist(thick.meninglio.CSU)
shapiro.test(thick.meninglio.CSU) # p = 5.242E-12
freq1.meninglio.CSU = c(freq1.meninCSU, freq1.glioCSU)
hist(freq1.meninglio.CSU)
shapiro.test(freq1.meninglio.CSU) # p = 5.409E-12
freq2.meninglio.CSU = c(freq2.meninCSU, freq2.glioCSU)
hist(freq2.meninglio.CSU)
shapiro.test(freq2.meninglio.CSU) # p < 2.2E-16
#Add biological covariates: Sex, Age, Breed-type
#Add technical covariates: Processor
#Add scanner covariates: TR, TE, NEX, Thick, FreqPhase1, FreqPhase2
######################### Outside ##############################
# Outside -- Meningioma
#################################################################
l.meninOut = nrow(menin.Out.image3)
agemos.meninOut = sex.meninOut = breedType.meninOut =
processor.meninOut =
TR.meninOut = TE.meninOut = NEX.meninOut = thick.meninOut =
freq1.meninOut = freq2.meninOut = rep(NA, l.meninOut)
for(i in 1:l.meninOut){
agemos.meninOut[i] = meningioma.Outside$Age_month[(i-1)*6+1]
sex.meninOut[i] = meningioma.Outside$Sex[(i-1)*6+1]
breedType.meninOut[i] = meningioma.Outside$BreedType[(i-1)*6+1]
processor.meninOut[i] = meningioma.Outside$Processor[(i-1)*6+1]
TR.meninOut[i] = meningioma.Outside$TR[(i-1)*6+1]
TE.meninOut[i] = meningioma.Outside$TE[(i-1)*6+1]
NEX.meninOut[i] = meningioma.Outside$NEX[(i-1)*6+1]
thick.meninOut[i] = meningioma.Outside$Thick[(i-1)*6+1]
freq1.meninOut[i] = meningioma.Outside$FreqPhase1[(i-1)*6+1]
freq2.meninOut[i] = meningioma.Outside$FreqPhase2[(i-1)*6+1]
}
#Age
round(summary(agemos.meninOut), 4)
round(c(median(agemos.meninOut), mad(agemos.meninOut)), 4)
#Sex
table(sex.meninOut) #Just F: 1, Just M: 3, F SP: 20; M CAS: 18
f.sex.meninOut = which(sex.meninOut == "F SP" | sex.meninOut == "FEMALE SP" | sex.meninOut == "FEMALE")
sex.meninOut[f.sex.meninOut] = "F"
m.sex.meninOut = which(sex.meninOut == "M CAS" | sex.meninOut == "MALE CAS" | sex.meninOut == "MALE")
sex.meninOut[m.sex.meninOut] = "M"
table(sex.meninOut) #F: 40, M: 39
#Breedtype
table(breedType.meninOut) #Brachy: 7; Non-brachy: 72
#Processor
table(processor.meninOut) #DN: 39, XY: 40
#TR
round(summary(TR.meninOut), 4)
round(c(median(TR.meninOut), mad(TR.meninOut)), 4)
#TE
round(summary(TE.meninOut), 4)
round(c(median(TE.meninOut), mad(TE.meninOut)), 4)
#NEX
round(summary(NEX.meninOut), 4)
round(c(median(NEX.meninOut), mad(NEX.meninOut)), 4)
#Thick
round(summary(thick.meninOut), 4)
round(c(median(thick.meninOut), mad(thick.meninOut)), 4)
#Frequency Phases 1/2
plot(freq1.meninOut, freq2.meninOut)
summary(freq1.meninOut)
round(c(median(freq1.meninOut), mad(freq1.meninOut)), 4)
length(unique(freq1.meninOut)) #8
summary(freq2.meninOut)
round(c(median(freq2.meninOut), mad(freq2.meninOut)), 4)
length(unique(freq2.meninOut)) #19
#MR Image Variables
summary(menin.Out.image3)
round(apply(menin.Out.image3, 2, median), 4)
round(apply(menin.Out.image3, 2, mad), 4)
######################### Outside ##############################
# Outside -- Glioma
#################################################################
l.glioOut = nrow(glio.Out.image3)
agemos.glioOut = sex.glioOut = breedType.glioOut =
processor.glioOut =
TR.glioOut = TE.glioOut = NEX.glioOut = thick.glioOut =
freq1.glioOut = freq2.glioOut = rep(NA, l.glioOut)
for(i in 1:l.glioOut){
agemos.glioOut[i] = glioma.Outside$Age_month[(i-1)*6+1]
sex.glioOut[i] = glioma.Outside$Sex[(i-1)*6+1]
processor.glioOut[i] = glioma.Outside$Processor[(i-1)*6+1]
breedType.glioOut[i] = glioma.Outside$BreedType[(i-1)*6+1]
TR.glioOut[i] = glioma.Outside$TR[(i-1)*6+1]
TE.glioOut[i] = glioma.Outside$TE[(i-1)*6+1]
NEX.glioOut[i] = glioma.Outside$NEX[(i-1)*6+1]
thick.glioOut[i] = glioma.Outside$Thick[(i-1)*6+1]
freq1.glioOut[i] = glioma.Outside$FreqPhase1[(i-1)*6+1]
freq2.glioOut[i] = glioma.Outside$FreqPhase2[(i-1)*6+1]
}
#Age
round(summary(agemos.glioOut), 4)
round(c(median(agemos.glioOut), mad(agemos.glioOut)), 4)
#Sex
table(sex.glioOut) #Just F: 0, Just M: 1; MALE: 1; FEMALE SP: 7; F SP: 7; M CAS: 3; MALE CAS: 3
f.sex.glioOut = which(sex.glioOut == "F SP" | sex.glioOut == "FEMALE SP" | sex.glioOut == "FEMALE")
sex.glioOut[f.sex.glioOut] = "F"
m.sex.glioOut = which(sex.glioOut == "M CAS" | sex.glioOut == "MALE CAS" | sex.glioOut == "MALE")
sex.glioOut[m.sex.glioOut] = "M"
table(sex.glioOut) #F: 14, M: 6
#Breedtype
table(breedType.glioOut) #Brachy: 10; Non-brachy: 10
#Processor
table(processor.glioOut) #DN: 9, XY: 11