##########################################
##the code here is used to run simulation tests for
##checking the model and methods.
##1.testing whether the simulated data can be used to
## detecting the nonZero interaction effects. ----it should
##2.checking for the heterscedasticity cases vs. homoscedasticity
##3.checking for validity of the modeling without control data
##4.all above first using the smaller number of genes---- to keep
## the memory usage low so as to using the R built-in linear
## function for testing
##
## Oct 1st, 2016 Feng@BU
## see ModelTesting_run1.R for equal variance cases
##
##
##@Oct 23, 2016 by Feng
##ModelTesting code file #3
##simulate datafor unequal variance cases
##here, we need to do the transformation!!!
##since clearly the unequal variances have lower true discovery rate.
## To transform we need to calculate the expected variance using bayesian approach
## (smyth 2004), then transform to stabalize the variance by
## var_hat=(d0S0^2+diSi^2)/(d0+di)
##
## Y_t=(Y-Y_bar)/sqrt(var_hat)+Y_bar
##by doing this, we kind of stablized the variance, but did not
##changed the variance
##
##
##in the end we do the linear regression
##check the assumption
##as well as the evaluate the performance
##
##########################################
library(ARPPA)
##first checking for homoscedastical dataset
#setwd("H:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_NegativeCon_Trans")
setwd("E:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_NegativeCon_Trans")
nGene<-2000
nTreatment<-2 #number of different beta
sampleSize<-5 ##this is the number of repeats for each group
alpha.mean<-0 #variance for alpha prior
alpha.sigma<-3
beta.mean<-0
beta.sigma<-2 #variance for beta prior
gamma.sigma<-10 # the unscaled factor for gamma given gamma<=>0
prob.nonZero<-0.02
#priors for variance distribution
d0<-5
s0<-2
repeats<-100
#this time of around, we need a fixed variance for the gaussian variance
#epsilon.si<-2 #not use for this case
#call it
set.seed(2004);
#simulate *****the equal variance and !!!!!!!negative control data
#E-equal variance; N-negative control
i<-1
for(i in c(1:repeats))
{
cat("doing ", i, "/",repeats," analyses....\n")
dataExp_EN_List<-simulateExpression(nGene, nTreatment, sampleSize,
control.negative=TRUE, control.isotype=FALSE,
control.index=c(1),
alpha.mean=alpha.mean, alpha.sigma=alpha.sigma,
beta.mean=beta.mean, beta.sigma=beta.sigma,
prob.nonZero=prob.nonZero, gamma.sigma=gamma.sigma,
#epsilon.si=epsilon.si,
epsilon.d0=d0, epsilon.s0=s0
)
#calculate the sample variance from the data
dataExp_EN<-dataExp_EN_List[[1]]
svList<-sampleVariance(dataExp_EN,nTreatment, sampleSize)
#reformat it into a vector
s2<-as.vector(as.matrix(svList))
df<-as.vector(as.matrix(sampleSizes(dataExp_EN,nTreatment, sampleSize)))-1
r2<-sampleVariancePrior(s2,df)
#now do the transformation to stabilize the variance
dataTransformed<-transformData(dataExp_EN,nTreatment, sampleSize)
#we have the data, what to do.
#reformat the data from data matrix to
#dataframe for the linear regress
dExp_EN<-matrix2dframe(dataTransformed, nTreatment, sampleSize);#transformed
#dExp_EN<-matrix2dframe(dataExp_EN, nTreatment, sampleSize); #untransformed
#now do the linear regression with interaction
lreg_EN<-lm(exp~gene*group, data=dExp_EN)
lregSm<-summary(lreg_EN)
lstToSave<-list("data"=dataExp_EN_List, "lregCoef"=lregSm$coefficients, "var.prior"=r2);
save(lstToSave, file=paste("result_",i,".RData",sep=""));
}
###############
set.seed(2005)
setwd("H:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_IsotypeCon_Trans")
j<-1
for(j in c(1:repeats))
{
cat("doing ", j, "/",repeats," analyses....\n")
#simulate *****the equal variance and !!!!!!!isotype control data
#E-equal; I-isotype
dataExp_EI_List<-simulateExpression(nGene, nTreatment, sampleSize,
control.negative=FALSE, control.isotype=TRUE,
control.index=c(1),
alpha.mean=alpha.mean, alpha.sigma=alpha.sigma,
beta.mean=beta.mean, beta.sigma=beta.sigma,
prob.nonZero=prob.nonZero, gamma.sigma=gamma.sigma,
#epsilon.si=epsilon.si #
epsilon.d0=d0, epsilon.s0=s0
)
dataExp_EI<-dataExp_EI_List[[1]]
#calculate the sample variance from the data
svList<-sampleVariance(dataExp_EI,nTreatment, sampleSize)
#reformat it into a vector
s2<-as.vector(as.matrix(svList))
df<-as.vector(as.matrix(sampleSizes(dataExp_EI,nTreatment, sampleSize)))-1
r2<-sampleVariancePrior(s2,df)
#now do the transformation
dataTransformed<-transformData(dataExp_EI,nTreatment, sampleSize)
#we have the data, what to do.
#reformat the data from data matrix to
#dataframe for the linear regress
dExp_EI<-matrix2dframe(dataTransformed, nTreatment, sampleSize);
#now do the linear regression with interaction
if(j>65)
{
lreg_EI<-lm(exp~gene*group, data=dExp_EI)
lregSm_EI<-summary(lreg_EI)
lstToSave_EI<-list("data"=dataExp_EI_List, "lregCoef"=lregSm_EI$coefficients, "var.prior"=r2);
save(lstToSave_EI, file=paste("result_",j,".RData",sep=""));
cat("Done!!\n")
}
}
#####################
set.seed(2006)
setwd("H:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_UnControl_Trans")
k<-1
for(k in c(1:repeats))
{
cat("doing ", k, "/",repeats," analyses....\n")
#simulate *****the equal variance and !!!!!!!isotype control data
#E-equal; NC-NC
dataExp_ENC_List<-simulateExpression(nGene, nTreatment, sampleSize,
control.negative=FALSE, control.isotype=FALSE,
#control.index=c(1),
alpha.mean=alpha.mean, alpha.sigma=alpha.sigma,
beta.mean=beta.mean, beta.sigma=beta.sigma,
prob.nonZero=prob.nonZero, gamma.sigma=gamma.sigma,
#epsilon.si=epsilon.si
epsilon.d0=d0, epsilon.s0=s0
)
dataExp_ENC<-dataExp_ENC_List[[1]]
#calculate the sample variance from the data
svList<-sampleVariance(dataExp_ENC,nTreatment, sampleSize)
#reformat it into a vector
s2<-as.vector(as.matrix(svList))
df<-as.vector(as.matrix(sampleSizes(dataExp_ENC,nTreatment, sampleSize)))-1
r2<-sampleVariancePrior(s2,df)
#now do the transformation
dataTransformed<-transformData(dataExp_ENC,nTreatment, sampleSize)
#we have the data, what to do.
#reformat the data from data matrix to
#dataframe for the linear regress
dExp_ENC<-matrix2dframe(dataTransformed, nTreatment, sampleSize);
#now do the linear regression with interaction
if(k>64)
{
lreg_ENC<-lm(exp~gene*group, data=dExp_ENC)
lregSm_ENC<-summary(lreg_ENC)
lstToSave_ENC<-list("data"=dataExp_ENC_List, "lregCoef"=lregSm_ENC$coefficients, "var.prior"=r2);
save(lstToSave_ENC, file=paste("result_",k,".RData",sep=""));
cat("Done!!\n")
}
}
#now summary the data first
aggregate(exp~gene*group, dExp_ENC, FUN=mean)
###dianostic plots
op<-par(mfrow=c(2,2))
plot(lreg_EN)
par(op)
##############################################
#-------------------------------------------
##############################################
###the code below has not been used######
#===============================================================
#================data analysis=======
###start doing the summary statistics of the repeated runs
#
#loading the data
repeats<-100
setwd("H:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_NegativeCon");
#setwd("~/Desktop/arppr/EqualVar_NegativeCon/")
i<-1
portions<-seq(0.001, 0.5, 0.001)
stats_df<-data.frame("portions"=portions);
for(i in c(1:repeats))
{
cat("reading the ",i, "/", repeats," data sets.....\n")
load(paste("result_", i,".RData",sep=""))
cat("count the correct ones.....\n")
#depending on which data,
lstData<-lstToSave
rm(lstToSave)
#try to know which one is nonZero as the true values
gammaTrue<-lstData$data$gamma
gammaTrueIndex<-which(gammaTrue[,2]!=0)
#now need to sort the p-value of the
#and pick top n genes to be the significant ones as the analysis results
#
lr_coe<-lstData$lregCoef
lr_coe_names<-rownames(lr_coe)
lr_coe_gamma_index<-which(grepl("gene\\d*:group2",lr_coe_names));
lr_coe_int<-lr_coe[lr_coe_gamma_index,]
lr_coe_int_names<-lr_coe_names[lr_coe_gamma_index];
#sort the array according to the prob
lr_coe_int_sort_order<-order(lr_coe_int[,4]) #NOTE:the names is one more than its index, eg. 811 is gene812:group2
#lr_coe_int_sort<-lr_coe_int[lr_coe_int_sort_order,]
#now we have everything, just need to collect statistics
p_correct<-portions;#initialize the vector
for(j in c(1:length(portions)))
{
#for each portion, we need to check what is percentage to be correct
numToPick<-floor(lstData$data$params[1]*portions[j])
#picking from the order array
genesToPick<-lr_coe_int_sort_order[c(1:numToPick)]+1
#now we got the genes, just need to check whether the genes so far are the TRUE ones
numOfCorrect<-sum(is.element(genesToPick, gammaTrueIndex));
p_correct[j]<-numOfCorrect/numToPick
}
stats_df[,paste("repeat_",i)]<-p_correct;
}
#statistics with the stats_df array
mean_correct<-portions
max_correct<-portions
min_correct<-portions
std_correct<-portions
m_stats_df<-as.matrix(stats_df)
for(k in c(1:length(portions)))
{
mean_correct[k]<-mean(m_stats_df[k,c(-1)])
max_correct[k]<-max(m_stats_df[k,c(-1)])
min_correct[k]<-min(m_stats_df[k,c(-1)])
std_correct[k]<-sqrt(var(m_stats_df[k,c(-1)]))
}
stats_df[,"mean"]<-mean_correct
stats_df[,"min"]<-min_correct
stats_df[,"max"]<-max_correct
stats_df[,"std"]<-std_correct
plot(c(0.001,0.51),c(0.02, 1.1), type="n", main="true discovery rate for protein selection",
xlab="portion of protein selected", ylab="portion of true discovery", log="xy")
lines(stats_df[,1], stats_df[,"mean"], col=1, lty=1, lwd=2)
lines(stats_df[,1], stats_df[,"min"], col="grey", lty=2, lwd=1)
lines(stats_df[,1], stats_df[,"max"], col="grey", lty=2, lwd=1)
lines(c(0.01, 0.01), c(0.1,1.2), col=2, lty=3)
#show stats
stats_df[c(1:30),c("portions", "mean", "min", "max", "std")]
########=================Isotype control data analysis
#loading the data
repeats<-100
setwd("H:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_IsotypeCon");
#setwd("~/Desktop/arppr/EqualVar_NegativeCon/")
i<-1
portions<-seq(0.001, 0.5, 0.001)
stats_df<-data.frame("portions"=portions);
for(i in c(1:repeats))
{
cat("reading the ",i, "/", repeats," data sets.....\n")
load(paste("result_", i,".RData",sep=""))
cat("count the correct ones.....\n")
#depending on which data,
lstData<-lstToSave_EI
rm(lstToSave_EI)
#try to know which one is nonZero as the true values
gammaTrue<-lstData$data$gamma
gammaTrueIndex<-which(gammaTrue[,2]!=0)
#now need to sort the p-value of the
#and pick top n genes to be the significant ones as the analysis results
#
lr_coe<-lstData$lregCoef
lr_coe_names<-rownames(lr_coe)
lr_coe_gamma_index<-which(grepl("gene\\d*:group2",lr_coe_names));
lr_coe_int<-lr_coe[lr_coe_gamma_index,]
lr_coe_int_names<-lr_coe_names[lr_coe_gamma_index];
#sort the array according to the prob
lr_coe_int_sort_order<-order(lr_coe_int[,4]) #NOTE:the names is one more than its index, eg. 811 is gene812:group2
#lr_coe_int_sort<-lr_coe_int[lr_coe_int_sort_order,]
#now we have everything, just need to collect statistics
p_correct<-portions;#initialize the vector
for(j in c(1:length(portions)))
{
#for each portion, we need to check what is percentage to be correct
numToPick<-floor(lstData$data$params[1]*portions[j])
#picking from the order array
genesToPick<-lr_coe_int_sort_order[c(1:numToPick)]+1
#now we got the genes, just need to check whether the genes so far are the TRUE ones
numOfCorrect<-sum(is.element(genesToPick, gammaTrueIndex));
p_correct[j]<-numOfCorrect/numToPick
}
stats_df[,paste("repeat_",i)]<-p_correct;
}
#statistics with the stats_df array
mean_correct<-portions
max_correct<-portions
min_correct<-portions
std_correct<-portions
m_stats_df<-as.matrix(stats_df)
for(k in c(1:length(portions)))
{
mean_correct[k]<-mean(m_stats_df[k,c(-1)])
max_correct[k]<-max(m_stats_df[k,c(-1)])
min_correct[k]<-min(m_stats_df[k,c(-1)])
std_correct[k]<-sqrt(var(m_stats_df[k,c(-1)]))
}
stats_df[,"mean"]<-mean_correct
stats_df[,"min"]<-min_correct
stats_df[,"max"]<-max_correct
stats_df[,"std"]<-std_correct
plot(c(0.001,0.51),c(0.02, 1.1), type="n", main="true discovery rate for protein selection",
xlab="portion of protein selected", ylab="portion of true discovery", log="xy")
lines(stats_df[,1], stats_df[,"mean"], col=1, lty=1, lwd=2)
lines(stats_df[,1], stats_df[,"min"], col="grey", lty=2, lwd=1)
lines(stats_df[,1], stats_df[,"max"], col="grey", lty=2, lwd=1)
lines(c(0.01, 0.01), c(0.02,1.2), col=2, lty=3)
#show stats
stats_df[c(1:30),c("portions", "mean", "min", "max", "std")]
#######====================No control++++++++++
#loading the data
repeats<-100
setwd("H:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_UnControl");
#setwd("~/Desktop/arppr/EqualVar_NegativeCon/")
i<-1
portions<-seq(0.001, 0.5, 0.001)
stats_df<-data.frame("portions"=portions);
for(i in c(1:repeats))
{
cat("reading the ",i, "/", repeats," data sets.....\n")
load(paste("result_", i,".RData",sep=""))
cat("count the correct ones.....\n")
#depending on which data,
lstData<-lstToSave_ENC
rm(lstToSave_ENC)
#try to know which one is nonZero as the true values
gammaTrue<-lstData$data$gamma
gammaTrueIndex<-which(gammaTrue[,2]!=0)
#now need to sort the p-value of the
#and pick top n genes to be the significant ones as the analysis results
#
lr_coe<-lstData$lregCoef
lr_coe_names<-rownames(lr_coe)
lr_coe_gamma_index<-which(grepl("gene\\d*:group2",lr_coe_names));
lr_coe_int<-lr_coe[lr_coe_gamma_index,]
lr_coe_int_names<-lr_coe_names[lr_coe_gamma_index];
#sort the array according to the prob
lr_coe_int_sort_order<-order(lr_coe_int[,4]) #NOTE:the names is one more than its index, eg. 811 is gene812:group2
#lr_coe_int_sort<-lr_coe_int[lr_coe_int_sort_order,]
#now we have everything, just need to collect statistics
p_correct<-portions;#initialize the vector
for(j in c(1:length(portions)))
{
#for each portion, we need to check what is percentage to be correct
numToPick<-floor(lstData$data$params[1]*portions[j])
#picking from the order array
genesToPick<-lr_coe_int_sort_order[c(1:numToPick)]+1
#now we got the genes, just need to check whether the genes so far are the TRUE ones
numOfCorrect<-sum(is.element(genesToPick, gammaTrueIndex));
p_correct[j]<-numOfCorrect/numToPick
}
stats_df[,paste("repeat_",i)]<-p_correct;
}
#statistics with the stats_df array
mean_correct<-portions
max_correct<-portions
min_correct<-portions
std_correct<-portions
m_stats_df<-as.matrix(stats_df)
for(k in c(1:length(portions)))
{
mean_correct[k]<-mean(m_stats_df[k,c(-1)])
max_correct[k]<-max(m_stats_df[k,c(-1)])
min_correct[k]<-min(m_stats_df[k,c(-1)])
std_correct[k]<-sqrt(var(m_stats_df[k,c(-1)]))
}
stats_df[,"mean"]<-mean_correct
stats_df[,"min"]<-min_correct
stats_df[,"max"]<-max_correct
stats_df[,"std"]<-std_correct
plot(c(0.001,0.51),c(0.02, 1.1), type="n", main="true discovery rate for protein selection",
xlab="portion of protein selected", ylab="portion of true discovery", log="")
lines(stats_df[,1], stats_df[,"mean"], col=1, lty=1, lwd=2)
lines(stats_df[,1], stats_df[,"min"], col="grey", lty=2, lwd=1)
lines(stats_df[,1], stats_df[,"max"], col="grey", lty=2, lwd=1)
lines(c(0.01, 0.01), c(0.02,1.2), col=2, lty=3)
#show stats
stats_df[c(1:30),c("portions", "mean", "min", "max", "std")]
####------------------------------------------------------------------------------------
#####the following part is also for the ---no control---
###this is special for no control cases, because in this case
###the differential ones could possibly from both groups
###if we only count one group, this will lower the true positive rate
###here we need to check both groups for the true positive ones
#loading the data
repeats<-100
setwd("H:\\feng\\LAB\\hg\\proteinArray_Masa\\ARPPA\\data\\UnEqualVar_UnControl");
#setwd("~/Desktop/arppr/EqualVar_NegativeCon/")
i<-1
portions<-seq(0.001, 0.5, 0.001)
stats_df<-data.frame("portions"=portions);
for(i in c(1:repeats))
{
cat("reading the ",i, "/", repeats," data sets.....\n")
load(paste("result_", i,".RData",sep=""))
cat("count the correct ones.....\n")
#depending on which data,
lstData<-lstToSave_ENC
rm(lstToSave_ENC)
#try to know which one is nonZero as the true values
gammaTrue<-lstData$data$gamma
gammaTrueIndex2<-which(gammaTrue[,2]!=0)
gammaTrueIndex1<-which(gammaTrue[,1]!=0)
#now need to sort the p-value of the
#and pick top n genes to be the significant ones as the analysis results
#
lr_coe<-lstData$lregCoef
lr_coe_names<-rownames(lr_coe)
lr_coe_gamma_index<-which(grepl("gene\\d*:group2",lr_coe_names));
lr_coe_int<-lr_coe[lr_coe_gamma_index,]
lr_coe_int_names<-lr_coe_names[lr_coe_gamma_index];
#sort the array according to the prob
lr_coe_int_sort_order<-order(lr_coe_int[,4]) #NOTE:the names is one more than its index, eg. 811 is gene812:group2
#lr_coe_int_sort<-lr_coe_int[lr_coe_int_sort_order,]
#now we have everything, just need to collect statistics
p_correct<-portions;#initialize the vector
for(j in c(1:length(portions)))
{
#for each portion, we need to check what is percentage to be correct
numToPick<-floor(lstData$data$params[1]*portions[j]*2)
#picking from the order array
genesToPick<-lr_coe_int_sort_order[c(1:numToPick)]+1
#now we got the genes, just need to check whether the genes so far are the TRUE ones
numOfCorrect1<-sum(is.element(genesToPick, gammaTrueIndex1));
numOfCorrect2<-sum(is.element(genesToPick, gammaTrueIndex2));
p_correct[j]<-(numOfCorrect1+numOfCorrect2)/numToPick
}
stats_df[,paste("repeat_",i)]<-p_correct;
}
#statistics with the stats_df array
mean_correct<-portions
max_correct<-portions
min_correct<-portions
std_correct<-portions
m_stats_df<-as.matrix(stats_df)
for(k in c(1:length(portions)))
{
mean_correct[k]<-mean(m_stats_df[k,c(-1)])
max_correct[k]<-max(m_stats_df[k,c(-1)])
min_correct[k]<-min(m_stats_df[k,c(-1)])
std_correct[k]<-sqrt(var(m_stats_df[k,c(-1)]))
}
stats_df[,"mean"]<-mean_correct
stats_df[,"min"]<-min_correct
stats_df[,"max"]<-max_correct
stats_df[,"std"]<-std_correct
plot(c(0.001,0.51),c(0.02, 1.1), type="n", main="true discovery rate for protein selection",
xlab="portion of protein selected", ylab="portion of true discovery", log="xy")
lines(stats_df[,1], stats_df[,"mean"], col=1, lty=1, lwd=2)
lines(stats_df[,1], stats_df[,"min"], col="grey", lty=2, lwd=1)
lines(stats_df[,1], stats_df[,"max"], col="grey", lty=2, lwd=1)
lines(c(0.01, 0.01), c(0.02,1.2), col=2, lty=3)
#show stats
stats_df[c(1:30),c("portions", "mean", "min", "max", "std")]
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