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biologicalVariation_old.R
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biologicalVariation_old.R
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## Outlier detection procedure for biological variation data
## [1] Braga F, Panteghini M. Generation of data on within-subject biological variation in
#laboratory medicine. Critical Reviews in Clinical Laboratory Sciences. 2016
rm(list=ls())
library(WriteXLS)
library(ggplot2)
#library(outliers)
setwd("~/Dropbox/Abdurrahman BV/Veriler/")
tests = read.table("tests.txt", header = F, stringsAsFactors = F)
testName = "RBC"
print(testName)
data = read.table(paste0(testName,"_data.txt"), header = T, sep = "\t")
splitSubject = split(data, data$subject)
splitList = lapply(splitSubject, FUN = function(x){
x[,colSums(is.na(x))==0]
})
data = plyr::rbind.fill(lapply(splitList, as.data.frame))
dataFull = reshape(data, varying = names(data)[4:13], direction = "long", sep = "")
dataFull$'replicate.subject' = as.numeric(paste0(dataFull$replicate, dataFull$subject))
names(dataFull)[6] = "value"
dataFull = dataFull[complete.cases(dataFull),]
table(dataFull$value <=0)
dataFull[dataFull$value <=0,"value"] = NA
dataFull = dataFull[complete.cases(dataFull),]
head(dataFull)
dim(dataFull)
tbl = table(dataFull$subject)
naSubject = tbl[!(tbl%%2==0)]
naSubject2 = as.numeric(names(naSubject))
if(length(naSubject2) >0){
for(i in 1:length(naSubject2)){
nas3 = dataFull[dataFull$subject == naSubject2[i],]
tbl2 = table(nas3$time)
nas4 = tbl2[!(tbl2%%2==0)]
nas5 = as.numeric(names(nas4))
for(j in 1:length(nas5)){
dataFull[dataFull$subject == naSubject2[i] & dataFull$time == nas5[j],"value"] = NA
}
}
}
dataFull = dataFull[complete.cases(dataFull),]
head(dataFull)
dim(dataFull)
cat("\n------------------------------------\nStarting outlier analysis for", toupper(as.character(levels(dataFull$analyte))),"\n------------------------------------")
cat("\n------------------------------------","\nNormality Assumption","\n------------------------------------")
library(dplyr)
data_grp <- group_by(dataFull, subject)
plotData <- dplyr::summarise(data_grp, N = n(), Mean = mean(value), Min = min(value), Max = max(value))
plotData$subject <- as.factor(plotData$subject)
ggplot(plotData, aes(y = Mean, x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = Max, ymin = Min), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('RBC'~(x10^6 / mu~'L'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/log/rbc.jpg")
ggsave(path, width = 12, height = 9)
#####################################################################################
# 1. Normality Assumption #
#####################################################################################
## Outlier detection procedure for biological variation data
## [2] Braga F, Panteghini M. Generation of data on within-subject biological variation in
#laboratory medicine. Critical Reviews in Clinical Laboratory Sciences. 2016
library(nortest)
#data = dataWithoutWithinSubjectOutliers
#data = read.csv("Veriler_R_format.csv",header = T)
data = dataFull
#####################################################################################
# Step 1. NORMALITY and HOMOGENITY TESTS:-ON SET OF RESULTS FROM EACH INDIVIDUAL [2] #
#####################################################################################
# Step 2. NORMALITY and HOMOGENITY TESTS:-ON MEAN VALUES OF SUBJECTS [2] #
#####################################################################################
analyteValue = data$value
loganalytevalue = log(analyteValue)
logdata = data
logdata$value = loganalytevalue
replicate = as.factor(data$subject)
nreplicate = length(levels(replicate))
nameanalyte = toupper(as.character(levels(data$analyte)))
sw1 = tapply(analyteValue, replicate, shapiro.test)
unlist1 = data.frame(matrix(unlist(sw1), nrow=nreplicate, ncol=4, byrow=T))
unlist1$X2 = as.numeric(as.character(unlist1$X2))
pval1 = unlist1[,2]
naccept1 = table(pval1>0.05)[[2]]
nreject1 = table(pval1>0.05)[[1]]
names(naccept1) = names(nreject1) = NULL
if(nreject1 == 0){ # if data are normally distributed, then it uses original data, if not it applies a logarithmic transformation and uses this transformed-data for the rest of the calculations
cat("\n----------------------------------------------------------------------------------\nSTEP 1. NORMALITY TEST:-ON SET OF RESULTS FROM EACH INDIVIDUAL")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied")
cat("\n----------------------------------------------------------------------------------\nThe data is assumed to be normal on set of results from each individual for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nNumber of normally distributed subjectervations", naccept1,"\n----------------------------------------------------------------------------------")
cat("\nNumber of non-normally distributed subjectervations", nreject1,"\n----------------------------------------------------------------------------------")
meanval = tapply(analyteValue, replicate, mean)
sw2 = shapiro.test(meanval)
pval2 = sw2$p.value
if (pval2 >= 0.05){
cat("\n----------------------------------------------------------------------------------\nSTEP 2. NORMALITY TEST:-ON MEAN VALUES OF SUBJECTS")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied","\n----------------------------------------------------------------------------------")
cat("\nThe data is assumed to be normal on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nAnalyses can be continued by Linear Mixed Effect Models or ANOVA","\n----------------------------------------------------------------------------------")
} else {
ks1 = lillie.test(meanval)
pval3 = ks1$p.value
if (pval3 >= 0.05){
cat("\n----------------------------------------------------------------------------------\nSTEP 2. NORMALITY TEST:-ON MEAN VALUES OF SUBJECTS")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied","\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nKolmogorov-Smirnov's normality test is applied", "\n----------------------------------------------------------------------------------")
cat("\nThe data is assumed to be normal on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nAnalyses can be continued by Linear Mixed Effect Models or ANOVA","\n----------------------------------------------------------------------------------")
} else {
logmeanval = log(meanval)
sw3 = shapiro.test(logmeanval)
pval4 = sw3$p.value
if (pval4 >= 0.05){
cat("\n----------------------------------------------------------------------------------\nSTEP 2. NORMALITY TEST:-ON MEAN VALUES OF SUBJECTS")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied","\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nKolmogorov-Smirnov's normality test is applied", "\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated again on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nLogarithmic transformation is applied", "\n----------------------------------------------------------------------------------")
cat("\nThe data is assumed to be normal on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nAnalyses can be continued by Linear Mixed Effect Models or ANOVA","\n----------------------------------------------------------------------------------")
} else{
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied","\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nKolmogorov-Smirnov's normality test is applied", "\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated again on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nLogarithmic transformation is applied", "\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated again on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nThe data is not normally distributed for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nAnalyses should not be continued by Linear Mixed Effect Models or ANOVA","\n----------------------------------------------------------------------------------")
}
}
}
}else {
sw6 = tapply(loganalytevalue, replicate, shapiro.test)
unlist2 = data.frame(matrix(unlist(sw6), nrow=nreplicate, ncol=4, byrow=T))
unlist2$X2 = as.numeric(as.character(unlist2$X2))
pval6 = unlist2[,2]
naccept2 = table(pval6>0.05)[2]
nreject2 = table(pval6>0.05)[1]
names(naccept2) = names(nreject2) = NULL
if(naccept2 > nreject2){
meanval = tapply(analyteValue, replicate, mean)
logmeanval = log(meanval)
sw7 = shapiro.test(logmeanval)
pval7 = sw7$p.value
if (pval7 >= 0.05){
cat("\n----------------------------------------------------------------------------------\nSTEP 1. NORMALITY TEST:-ON SET OF RESULTS FROM EACH INDIVIDUAL")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied")
cat("\n----------------------------------------------------------------------------------\nNormality assumption is violated on set of results from each individual for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nNumber of normally distributed subjectervations", naccept1,"\n----------------------------------------------------------------------------------")
cat("\nNumber of non-normally distributed subjectervations", nreject1,"\n----------------------------------------------------------------------------------")
cat("\nLogarithmic transformation is applied", "\n----------------------------------------------------------------------------------")
cat("\nNumber of normally distributed subjectervations", naccept2,"\n----------------------------------------------------------------------------------")
cat("\nNumber of non-normally distributed subjectervations", nreject2,"\n----------------------------------------------------------------------------------")
cat("\nThe data is assumed to be normal on set of results from each individual for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\n----------------------------------------------------------------------------------\nSTEP 2. NORMALITY TEST:-ON MEAN VALUES OF SUBJECTS")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied","\n----------------------------------------------------------------------------------")
cat("\nThe data is assumed to be normal on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nAnalyses can be continued by Linear Mixed Effect Models or ANOVA","\n----------------------------------------------------------------------------------")
} else {
cat("\n----------------------------------------------------------------------------------\nSTEP 1. NORMALITY TEST:-ON SET OF RESULTS FROM EACH INDIVIDUAL")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied")
cat("\n----------------------------------------------------------------------------------\nNormality assumption is violated on set of results from each individual for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nNumber of normally distributed subjectervations", naccept1,"\n----------------------------------------------------------------------------------")
cat("\nNumber of non-normally distributed subjectervations", nreject1,"\n----------------------------------------------------------------------------------")
cat("\nLogarithmic transformation is applied", "\n----------------------------------------------------------------------------------")
cat("\nThe data is assumed to be normal on set of results from each individual for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\n----------------------------------------------------------------------------------\nSTEP 2. NORMALITY TEST:-ON MEAN VALUES OF SUBJECTS")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied","\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated again on mean values of subjects for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nThe data is not normally distributed for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nAnalyses should not be continued by Linear Mixed Effect Models or ANOVA","\n----------------------------------------------------------------------------------")
}
} else {
cat("\n----------------------------------------------------------------------------------\nSTEP 1. NORMALITY TEST:-ON SET OF RESULTS FROM EACH INDIVIDUAL")
cat("\n----------------------------------------------------------------------------------\nShapiro-Wilk's normality test is applied")
cat("\n----------------------------------------------------------------------------------\nNormality assumption is violated on set of results from each individual for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nNumber of normally distributed subjectervations", naccept1,"\n----------------------------------------------------------------------------------")
cat("\nNumber of non-normally distributed subjectervations", nreject1,"\n----------------------------------------------------------------------------------")
cat("\nLogarithmic transformation is applied", "\n----------------------------------------------------------------------------------")
cat("\nNormality assumption is violated again on set of results from each individual for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nThe data is not normally distributed for", nameanalyte,"\n----------------------------------------------------------------------------------")
cat("\nAnalyses should not be continued by Linear Mixed Effect Models or ANOVA","\n----------------------------------------------------------------------------------")
}
# #Between-subject variance homogeneity
# tapply(analyteValue, factor(replicate), var)
# betwSampBartRes = bartlett.test(analyteValue, factor(replicate))
#
# tapply(loganalytevalue, factor(replicate), var)
# betwSampBartLogRes = bartlett.test(loganalytevalue, factor(replicate))
#
# #Between-duplicate variance homogeneity
# bartletres = bartletlogres = matrix(NA,length(levels(replicate)),1)
# rownames(bartletres) = rownames(bartletlogres) = levels(replicate)
# colnames(bartletres) = colnames(bartletlogres) = "p.val"
}
# for (i in 1:length(levels(replicate))){
# x = data[data$subject == as.numeric(levels(replicate))[i],]
# logx = logdata[logdata$subject == as.numeric(levels(replicate))[i],]
# bart = bartlett.test(x$value, factor(x$replicate.subject))
# bart.log = bartlett.test(logx$value, factor(logx$replicate.subject))
# bartletres[i,1] = bart$p.value
# bartletlogres[i,1] = bart.log$p.value
# }
# bartletres
# bartletlogres = data.frame(row = 1:nrow(bartletlogres), bartletlogres)
#
# withinsubjectHomogenity = data.frame(matrix(NA,1,2))
# names(withinsubjectHomogenity) = c("Within subject homogenity", "Subjects")
# tbl = table(bartletlogres$p.val<0.05)
#
# if(TRUE %in% names(tbl)){
#
# withinsubjectHomogenity[1,1] = "Some subjects have heterogeneous variances"
#
# subjects = bartletlogres[bartletlogres$p.val<0.05,][,1]
#
# if(length(subjects) == 1){
#
# withinsubjectHomogenity[1,2] = subjects[[1]]
#
# }else{
#
# withinsubjectHomogenity[1,2] = paste(subjects, collapse = ",")
#
#
# }
#
# }else{
# withinsubjectHomogenity[1,1] = "homogeneous"
# withinsubjectHomogenity[1,2] = "No subject"
# }
#
# path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/withinsubjectHomogenity.xls")
# WriteXLS(withinsubjectHomogenity, path)
#
#
# bartletTest = data.frame(matrix(NA,1,5))
# names(bartletTest) = c("Test", "Statistic", "df", "p-value", "result")
#
# bartletTest[1,1] = "Bartlett"
# bartletTest[1,2] = betwSampBartLogRes$statistic[[1]]
# bartletTest[1,3] = betwSampBartLogRes$parameter[[1]]
# bartletTest[1,4] = betwSampBartLogRes$p.value[[1]]
# bartletTest[1,5] = if(bartletTest[1,4] > 0.05){"homogeneous"}else{"heterogeneous"}
#
# path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/betweenSubjectHomogenity.xls")
# WriteXLS(bartletTest, path)
#####################################################################################
# 2. Outlier Detection #
#####################################################################################
library(dplyr)
dataFull = dataFull
data_grp <- group_by(dataFull, subject)
plotData <- summarise(data_grp, N = n(), Mean = mean(value), Min = min(value), Max = max(value))
plotData$subject <- as.factor(plotData$subject)
plotPath = paste0(toupper(as.character(levels(dataFull$analyte))), " Values")
ggplot(plotData, aes(y = Mean, x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = Max, ymin = Min), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/log/Before outlier control.jpg")
ggsave(path, width = 12, height = 9)
ggplot(plotData, aes(y = exp(Mean), x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = exp(Max), ymin = exp(Min)), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/org/Before outlier control.jpg")
ggsave(path, width = 12, height = 9)
#####################################################################################
# Step 1. Look for outliers in the sets of duplicate results using the Cochran test #
# that examines the ratio of the maximum variance to the sum of the variances and #
# compares this to the appropriate critical values in statistical tables [1]. #
#####################################################################################
cat("\n------------------------------------","\nStep 1. Look for outliers in the sets of replicate results using the Cochran test","\n------------------------------------")
value = 1
criticalValue = 0
outlierDataList = list()
j = 0
outlierList = list()
while(value > criticalValue){
j=j+1
dataFull = dataFull[complete.cases(dataFull),]
dataList = list()
for(i in 1:length(levels(as.factor(dataFull$subject)))){
print(paste("Subject:", i))
data = dataFull[dataFull$subject == i,]
data = data[complete.cases(data),]
rep1 = data[data$replicate.subject == unique(data$replicate.subject)[1],"value"]
rep2 = data[data$replicate.subject == unique(data$replicate.subject)[2],"value"]
repData = cbind.data.frame(rep1,rep2)
repVar = apply(repData, 1, var)
subject = rep(i, length(rep1))
time = unique(data$time)
subject_time = paste0(subject, time)
reps = cbind.data.frame(subject, replicate.subject = data$replicate.subject, time = unique(data$time),subject_time, rep1, rep2, var = repVar)
dataList[[i]] = reps
}
dataNew = do.call(rbind.data.frame, dataList)
head(dataNew)
subjectNumber = nrow(dataNew)
df = 2
vars = dataNew$var
names(vars) = paste0(dataNew$subject, dataNew$time)
value <- max(vars)/sum(vars)
criticalValue = prospectr:::Cul(0.05, df, subjectNumber)
if(value > criticalValue){
outlierDataList[[dataNew[which.max(vars),"subject"]]] = dataNew[which.max(dataNew$var),]
outlierSubject = dataNew[which.max(vars),"subject"]
outlierTime = dataNew[which.max(vars),"time"]
dataFull[dataFull$subject==outlierSubject & dataFull$time ==outlierTime,][,"value"] = c(NA, NA)
outlierList[[j]] = dataNew[which.max(dataNew$var),]
dataFull = dataFull[complete.cases(dataFull),]
}
}
outlierData = do.call(rbind.data.frame, outlierList)
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/outliers/Step1_outliers.xls")
WriteXLS(outlierData, path)
cat("\n------------------------------------","\nCheck for the outliers in the 'outlierData' table above","\n------------------------------------")
cat("\n------------------------------------","\nStep 1 is succesfully completed. Moving on Step 2","\n------------------------------------")
## Plot
data_grp <- group_by(dataFull, subject)
plotData <- summarise(data_grp, N = n(), Mean = mean(value), Min = min(value), Max = max(value))
plotData$subject <- as.factor(plotData$subject)
plotPath = paste0(toupper(as.character(levels(dataFull$analyte))), " Values")
ggplot(plotData, aes(y = Mean, x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = Max, ymin = Min), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/log/After step1.jpg")
ggsave(path, width = 12, height = 9)
ggplot(plotData, aes(y = exp(Mean), x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = exp(Max), ymin = exp(Min)), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/org/After step1.jpg")
ggsave(path, width = 12, height = 9)
##########################################################################################
# Step 2. Use Cochran test to look for outliers in the variances of the results from #
# each subject to see if any individual's dispersion of results is larger or smaller #
# than that of the group as a whole (i.e. examining the heterogeneity of within-subject #
# biological variation [1]. #
##########################################################################################
cat("\n------------------------------------","\nStep 2. Use Cochran test to look for outliers in the variances of the results from each subject","\n------------------------------------")
dataFull2 = dataFull
value = 1
criticalValue = 0
outlierDataList2 = list()
while(value > criticalValue){
# dataFull3 = dataFull2[,c("subject", "value")]
vars = tapply(dataFull2$value, as.factor(dataFull2$subject), var)
#names(vars) = unique(dataFull3$subject)
subjectNumber = length(vars)
df = length(unique(dataFull2$time))-1
value <- max(vars)/sum(vars)
criticalValue = prospectr:::Cul(0.05, df, subjectNumber)
if(value > criticalValue){
outlierSubject = dataFull2[dataFull2$subject == names(which.max(vars)),]
outlierDataList2[[outlierSubject$subject[1]]] = outlierSubject
dataFull2 = dataFull2[!(rownames(dataFull2) %in% rownames(outlierSubject)), ]
cat("\nOutlier Found", "\nValue", value, "\nCritical Value:", criticalValue, "\n")
cat("subject",outlierSubject$subject[1], "is outlier\n")
}else{
cat("\nNo Outlier Found\n")
}
}
outlierData2 = do.call(rbind.data.frame, outlierDataList2)
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/outliers/Step2_outliers.xls")
WriteXLS(outlierData2, path)
cat("\n------------------------------------","\nCheck for the outliers in the 'outlierData2' table above","\n------------------------------------")
cat("\n------------------------------------","\nStep 2 is succesfully completed. Moving on Step 3","\n------------------------------------")
## Plot
data_grp <- group_by(dataFull2, subject)
plotData <- summarise(data_grp, N = n(), Mean = mean(value), Min = min(value), Max = max(value))
plotData$subject <- as.factor(plotData$subject)
ggplot(plotData, aes(y = Mean, x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = Max, ymin = Min), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/log/After step2.jpg")
ggsave(path, width = 12, height = 9)
ggplot(plotData, aes(y = exp(Mean), x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = exp(Max), ymin = exp(Min)), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/org/After step2.jpg")
ggsave(path, width = 12, height = 9)
###############################################################################################
# Step 3. Use Reed's criterion to see if any individual has a mean value that differs greatly #
# from the other subjects [1]. #
###############################################################################################
cat("\n------------------------------------","\nStep 3. Use Reed's criterion to see if any individual has a mean value that differs greatly from the other subjects","\n------------------------------------")
dataReed = dataFull2
analyteValue = dataReed$value
subjects = as.factor(dataReed$subject)
means <- tapply(analyteValue, subjects, mean)
names(means) <- levels(subjects)
maxDifference = 1
absoluteDifference = 1
while(maxDifference > absoluteDifference/3){
outlierListReed = list()
## High-value outliers
order = sort(means, decreasing = TRUE)
maxDifference = abs(diff(order[1:2]))
absoluteDifference = abs((diff(range(order))))
if(maxDifference > absoluteDifference/3){
outlier = order[1]
outSubject = names(outlier)
outlierListReed[[as.numeric(outSubject)]] = as.numeric(outSubject)
cat("subject", outSubject, "is a high-value outlier \n-------------------------------------------- \nMoving on to check low-value outlier \n--------------------------------------------")
}else{
cat("\nNo high-value outlier\n")
}
order = sort(means, decreasing = FALSE)
maxDifference = abs(diff(order[1:2]))
absoluteDifference = abs((diff(range(order))))
if(maxDifference > absoluteDifference/3){
outlier = order[1]
outSubject = names(outlier)
outlierListReed[[as.numeric(outSubject)]] = as.numeric(outSubject)
cat("subject", outSubject, "is a low-value outlier \n-------------------------------------------- \nOutlier detection process is successfully completed \n--------------------------------------------")
}else{
cat("\nNo low-value outlier\n" )
}
if(length(outlierListReed) != 0){
outlierSubjectsReed = do.call(rbind.data.frame,outlierListReed)
names(outlierSubjectsReed) = "Subjects"
dataReed = dataReed[!(dataReed$subject %in% outlierSubjectsReed[,1]),]
means = means[!(names(means) %in% outlierSubjectsReed[,1])]
}else{
dataReed = dataReed
}
}
cat("\n------------------------------------","\nStep 3 is succesfully completed. All outliers found and removed from dataset.","\n------------------------------------")
dataWithoutOutliers = dataReed
dim(dataFull)
dim(dataWithoutOutliers)
write.table(dataWithoutOutliers, paste0(testName,"_withoutOutlier.txt"), quote=F, sep="\t", row.names = F)
## Plot
data_grp <- group_by(dataWithoutOutliers, subject)
plotData <- summarise(data_grp, N = n(), Mean = mean(value), Min = min(value), Max = max(value))
plotData$subject <- as.factor(plotData$subject)
ggplot(plotData, aes(y = Mean, x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = Max, ymin = Min), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/log/After step3.jpg")
ggsave(path, width = 12, height = 9)
ggplot(plotData, aes(y = exp(Mean), x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = exp(Max), ymin = exp(Min)), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/org/After step3.jpg")
ggsave(path, width = 12, height = 9)
##########################################################################
# Between and Within Subject Homogenity
#########################################################################
data = dataWithoutOutliers
analyteValue = data$value
replicate = as.factor(data$subject)
nreplicate = length(levels(replicate))
nameanalyte = toupper(as.character(levels(data$analyte)))
# #Between-subject variance homogeneity
# betweenVar = tapply(analyteValue, factor(replicate), var)
# names(betweenVar) = levels(unique( factor(replicate)))
# betweenVar = cbind.data.frame(subject = names(betweenVar), variance = betweenVar)
#
# path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/betweenSubjectVariance.xls")
# WriteXLS(betweenVar, path, row.names = F)
#
#
# betwSampBartRes = bartlett.test(analyteValue, factor(replicate))
#Between-duplicate variance homogeneity
bartletres = matrix(NA,length(levels(replicate)),1)
rownames(bartletres) =levels(replicate)
colnames(bartletres) = "p.val"
for (i in 1:length(levels(replicate))){
x = data[data$subject == as.numeric(levels(replicate))[i],]
bart = bartlett.test(x$value, factor(x$replicate.subject))
bartletres[i,1] = bart$p.value
}
bartletlogres = data.frame(row = rownames(bartletres), bartletres)
bartletlogres = bartletlogres[complete.cases(bartletlogres),]
withinsubjectHomogenity = data.frame(matrix(NA,1,2))
names(withinsubjectHomogenity) = c("Within subject homogenity", "Subjects")
tbl = table(bartletlogres$p.val<0.05)
if(TRUE %in% names(tbl)){
withinsubjectHomogenity[1,1] = "Some subjects have heterogeneous variances"
subjects = rownames(bartletlogres[bartletlogres$p.val<0.05,])
if(length(subjects) == 1){
withinsubjectHomogenity[1,2] = subjects[[1]]
}else{
withinsubjectHomogenity[1,2] = paste(subjects, collapse = ",")
}
}else{
withinsubjectHomogenity[1,1] = "homogeneous"
withinsubjectHomogenity[1,2] = "No subject"
}
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/withinsubjectHomogenity.xls")
WriteXLS(withinsubjectHomogenity, path)
head(data)
dim(data)
if(withinsubjectHomogenity$Subjects != "No subject"){
nonHomogenSubjects = as.numeric(withinsubjectHomogenity$Subjects)
data = data[!(data$subject %in% nonHomogenSubjects),]
}
#############################################################################################
analyteValue = data$value
replicate = as.factor(data$subject)
nreplicate = length(levels(replicate))
nameanalyte = toupper(as.character(levels(data$analyte)))
#Between-subject variance homogeneity
betweenVar = tapply(analyteValue, factor(replicate), var)
names(betweenVar) = levels(unique( factor(replicate)))
betweenVar = cbind.data.frame(subject = names(betweenVar), variance = betweenVar)
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/betweenSubjectVariance.xls")
WriteXLS(betweenVar, path, row.names = F)
betwSampBartRes = bartlett.test(analyteValue, factor(replicate))
bartletTest = data.frame(matrix(NA,1,5))
names(bartletTest) = c("Test", "Statistic", "df", "p-value", "result")
bartletTest[1,1] = "Bartlett"
bartletTest[1,2] = betwSampBartRes$statistic[[1]]
bartletTest[1,3] = betwSampBartRes$parameter[[1]]
bartletTest[1,4] = betwSampBartRes$p.value[[1]]
bartletTest[1,5] = if(bartletTest[1,4] > 0.05){"homogeneous"}else{"heterogeneous"}
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/betweenSubjectHomogenity.xls")
WriteXLS(bartletTest, path)
## Plot
data_grp <- group_by(data, subject)
plotData <- summarise(data_grp, N = n(), Mean = mean(value), Min = min(value), Max = max(value))
plotData$subject <- as.factor(plotData$subject)
ggplot(plotData, aes(y = Mean, x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = Max, ymin = Min), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/log/After within variance homogenity.jpg")
ggsave(path, width = 12, height = 9)
ggplot(plotData, aes(y = exp(Mean), x = subject)) +
geom_point() +
geom_errorbar(aes(ymax = exp(Max), ymin = exp(Min)), width = 0.35) +
coord_flip() +
theme_bw(base_size = 14) +
ylab(bquote('PLT-I'~(x10^3 / mu~'mol'))) +
xlab("Subjects")
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/plots/org/After within variance homogenity.jpg")
ggsave(path, width = 12, height = 9)
#####################################################################################
# 1. Homogenity for Gender #
#####################################################################################
# data = dataWithoutOutliers
dataHomogenity = data
analyteValue = data$value
subjects = as.factor(data$subject)
gender = unique(data[-c(1,3,5:8)])[,"gender"]
means <- tapply(analyteValue, subjects, mean)
names(means) <- levels(subjects)
bartlet = bartlett.test(means, gender)
dataTtest = cbind.data.frame(means, gender)
HomogenityGenderResult = data.frame(matrix(NA,1,5))
names(HomogenityGenderResult) = c("Test", "Statistic", "df", "p-value", "result")
HomogenityGenderResult[1,1] = "Bartlett"
HomogenityGenderResult[1,2] = bartlet$statistic[[1]]
HomogenityGenderResult[1,3] = bartlet$parameter[[1]]
HomogenityGenderResult[1,4] = bartlet$p.value[[1]]
HomogenityGenderResult[1,5] = if(bartlet$p.value > 0.05){"homogeneous"}else{"heterogeneous"}
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/HomogenityGenderResult.xls")
WriteXLS(HomogenityGenderResult, path)
dataHomogenity2 = data[,c("subject", "value", "gender")]
vars = tapply(dataHomogenity2$value, as.factor(dataHomogenity2$subject), var)
names(vars) = unique(dataHomogenity2$subject)
dataHomogenity3 = cbind.data.frame(vars, names(vars),gender)
bartletGender = bartlett.test(dataHomogenity3$vars, dataHomogenity3$gender)
if(bartlet$p.value > 0.05){ varEqual = TRUE}else{ varEqual = FALSE}
ttest = t.test(means~gender, data = dataTtest, var.equal = varEqual)
sd = tapply(means, gender, sd)
ttestResult = data.frame(matrix(NA,1,6))
names(ttestResult) = c("Analyte", "Mean_female", "SD_female", "Mean_male", "SD_male", "pvalue")
ttestResult[1,1] = toupper(as.character(levels(dataFull$analyte)))
ttestResult[1,2] = ttest$estimate[[1]]
ttestResult[1,3] = sd[[1]]
ttestResult[1,4] = ttest$estimate[[2]]
ttestResult[1,5] = sd[[2]]
ttestResult[1,6] = ttest$p.value
path = paste0("Results/Analyte/", toupper(as.character(levels(dataFull$analyte))),"/ttest/ttest.xls")
#write.table(ttestResult, path, quote=F, sep="\t", row.names = F)
WriteXLS(ttestResult, path)
#####################################################################################
# 1. Linear Mixed Effects Models #
#####################################################################################
#####################################################################################
# a. All Group #
#####################################################################################
library(lme4)
library(multcomp)
library(nlme)
dataFull = data
cat("\n------------------------------------\nStarting LME analysis for", toupper(as.character(levels(dataFull$analyte))),"\n------------------------------------")
dataFull$subject = as.factor(dataFull$subject)
dataFull$replicate = as.factor(dataFull$replicate)
dataFull$time = as.factor(dataFull$time)
N <- nrow(dataFull)
r <- length(unique(dataFull$subject))
ni <- as.numeric(table(dataFull[ ,"subject"]))
sgConstant <- (1/(r - 1))*sum((ni^2)*(1/ni - 1/N))
## lme model
bv <- lme(value ~1, random=~1|subject/time,data=dataFull)
# bv <- lmer(data=dataFull, REML = TRUE, value ~ 1 + (1|subject) + (1|subject:time))
summary = summary(bv)
grandMean = summary$coefficients$fixed[[1]]
seGrandMean = sqrt(summary$varFix)[[1]]/sqrt(N)
llMean = grandMean-1.96*seGrandMean
ulMean = grandMean+1.96*seGrandMean
table= nlme::VarCorr(bv)
sigma_between = as.numeric(table[2,2])
sigma_within = as.numeric(table[4,2])
sigma_analytical = as.numeric(table[5,2])
sigma_total = sqrt(sigma_analytical^2 + sigma_within^2 + sigma_between^2)
CV_analytical = abs(sigma_analytical/grandMean)
CV_within = abs(sigma_within/grandMean)
CV_between = abs(sigma_between/grandMean)
CV_total = abs(sigma_total/grandMean)
w1 = 2
w2 = sgConstant
w3 = 2
w1u = 2
w2u = sgConstant
w3u = 2
c2u = (w2u-w1u)/(w3u)
c3u = w2u-1-c2u
c2 = 1
c3 = 0
alpha = 0.025
inf = 999999999
ll_analytical = sqrt((sigma_analytical^2)/qf(alpha, bv$dims$ngrps[[1]], inf, lower.tail = F))
ul_analytical = sqrt((sigma_analytical^2)/qf(alpha, bv$dims$ngrps[[1]], inf, lower.tail = T))
lower_analytical = ll_analytical/grandMean
upper_analytical = ul_analytical/grandMean
sigma2_between = sigma_between^2
sigma2_within = sigma_within^2
sigma2_analytical = sigma_analytical^2
msu_within = 2*sigma2_within+sigma2_analytical
msu_between = sigma2_between*sgConstant + sigma2_analytical + sigma2_within*2
dfbetween = (bv$dims$ngrps[[2]]-1)
dfwithin = (bv$dims$ngrps[[1]]-bv$dims$ngrps[[2]])
dfanalytical = (bv$dims$ngrps[[1]])
g1 = 1-1/(qf(alpha, dfbetween, inf, lower.tail = F))
g2 = 1-1/(qf(alpha, dfwithin, inf, lower.tail = F))
g3 = 1-1/(qf(alpha, dfanalytical, inf, lower.tail = F))
h1 =1/(qf(1-alpha, dfbetween, inf, lower.tail = F))-1
h2 = 1/(qf(1-alpha, dfwithin, inf, lower.tail = F))-1
h3 = 1/(qf(1-alpha, dfanalytical, inf, lower.tail = F))-1
g12 = (((qf(alpha, dfbetween, dfwithin, lower.tail = F)-1)^2)-g1^2*(qf(alpha, dfbetween, dfwithin, lower.tail = F)^2)-h2^2)/(qf(alpha, dfbetween, dfwithin, lower.tail = F))
g13 = (((qf(alpha, dfbetween, dfanalytical, lower.tail = F)-1)^2)-g1^2*(qf(alpha, dfbetween, dfanalytical, lower.tail = F)^2)-h3^2)/(qf(alpha, dfbetween, dfanalytical, lower.tail = F))
g23 = (((qf(alpha, dfwithin, dfanalytical, lower.tail = F)-1)^2)-g2^2*(qf(alpha, dfwithin, dfanalytical, lower.tail = F))^2-h3^2)/(qf(alpha, dfwithin, dfanalytical, lower.tail = F))
g32 = (((qf(alpha, dfanalytical, dfwithin, lower.tail = F)-1)^2)-g3^2*(qf(alpha, dfanalytical, dfwithin, lower.tail = F))^2-h2^2)/(qf(alpha, dfanalytical, dfwithin, lower.tail = F))
h12 = (((1-qf(1-alpha, dfbetween, dfwithin, lower.tail = F))^2)-h1^2*(qf(1-alpha, dfbetween, dfwithin, lower.tail = F))^2-g2^2)/(qf(1-alpha, dfbetween, dfwithin, lower.tail = F))
h13 = (((1-qf(1-alpha, dfbetween, dfanalytical, lower.tail = F))^2)-h1^2*(qf(1-alpha, dfbetween, dfanalytical, lower.tail = F))^2-g3^2)/(qf(1-alpha, dfbetween, dfanalytical, lower.tail = F))
h23 = (((1-qf(1-alpha, dfwithin, dfanalytical, lower.tail = F))^2)-h2^2*(qf(1-alpha, dfwithin, dfanalytical, lower.tail = F))^2-g3^2)/(qf(1-alpha, dfwithin, dfanalytical, lower.tail = F))
h32 = (((1-qf(1-alpha, dfanalytical, dfwithin, lower.tail = F))^2)-h3^2*(qf(1-alpha, dfanalytical, dfwithin, lower.tail = F))^2-g2^2)/(qf(1-alpha, dfanalytical, dfwithin, lower.tail = F))
VLba = (g2^2)*(msu_within^2)+(h3^2)*(sigma2_analytical^2)+g23*msu_within*sigma2_analytical
VUba = (h2^2)*(msu_within^2)+(g3^2)*(sigma2_analytical^2)+h23*msu_within*sigma2_analytical
VLa = g1^2*msu_between^2+h2^2*c2^2*msu_within^2+h3^2*c3^2*sigma2_analytical^2+g12*c2*msu_between*msu_within+g13*abs(c3)*msu_between*sigma2_analytical
VLa2 = h1^2*msu_between^2+g2^2*c2^2*msu_within^2+g3^2*c3^2*sigma2_analytical^2+h12*c2*msu_between*msu_within+h13*abs(c3)*msu_between*sigma2_analytical
ll_within = sqrt((msu_within-sigma2_analytical-sqrt(VLba))/2)
ul_within = sqrt((msu_within-sigma2_analytical+sqrt(VUba))/2)
lower_within = ll_within/grandMean
upper_within = ul_within/grandMean
ll_between = sqrt((msu_between - msu_within - sqrt(VLa))/sgConstant)
ul_between = sqrt((msu_between - msu_within + sqrt(VLa2))/sgConstant)
lower_between = ll_between/grandMean
upper_between = ul_between/grandMean
c34 = (sgConstant-2)/2
c35 = sgConstant-1-c34
ll_total = sqrt((msu_between + c34*msu_within + c35*sigma2_analytical- sqrt(VLa))/sgConstant)
ul_total = sqrt((msu_between + c34*msu_within + c35*sigma2_analytical+ sqrt(VLa2))/sgConstant)
lower_total = ll_total/grandMean
upper_total = ul_total/grandMean
## CV table (log transfomed)
########################################################################################
CVTable = data.frame(matrix(NA,4,6))
colnames(CVTable) = c("Source", "sigma (log)", "CV (log)", "Lower (log)", "Upper (log)", "CV% (log)")
CVTable[1,"Source"] = "Between"
CVTable[2,"Source"] = "Within"
CVTable[3,"Source"] = "Analytical"
CVTable[4,"Source"] = "Total"