Nothing
mGSZ.test.score <-
function(expr.data, gene.sets, wgt1 = 0.2, wgt2 = 0.5, pre.var = 0, var.constant = 10, start.val = 5, flip.gene.sets = FALSE, table = FALSE,...){
num.genes <- length(expr.data)
# Ordering of gene expression data and gene sets data
gene.sets <- toMatrix(expr.data,gene.sets,flip.gene.sets)
ord_out <- order(expr.data, decreasing= TRUE)
expr.data <- expr.data[ord_out]
set.dim <- dim(gene.sets)
cols <- set.dim[2]
gene.sets <- gene.sets[ord_out,]
expr.data.ud <- expr.data[num.genes:1] # expression values turned up-side down
# This does the analysis of the lower end
num.genes=length(expr.data)
num.classes=dim(gene.sets)[2]
set_sz <- apply(gene.sets,2,sum)
unique_class_sz_ln <- length(unique(set_sz))
### Defining variables for different output
mAllez <- rep(0,num.classes)
mgsa <- matrix(0,2,num.classes)
pre_z_var.1 <- sumVarMean_calc(expr.data, gene.sets, pre.var)
pre_z_var.2 <- sumVarMean_calc(expr.data.ud, gene.sets, pre.var)
Z_var1 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.1$Z_var,wgt2,var.constant)
Z_var2 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.2$Z_var,wgt2,var.constant)
out2 = matrix(0,2,num.classes)
mgsa1 <- numeric(num.classes)
mgsa2 <- mgsa1
mgsz1 <- mgsa1
mgsz2 <- mgsa2
for (k in 1:num.classes){
po1 <- which(gene.sets[,k]==1)
po0 <- which(gene.sets[,k]==0)
if(length(po1) > 0 & length(po0) > 0){
tmp1 = expr.data
tmp1[po0] = 0
tmp0 = expr.data
tmp0[po1] = 0
result1 = cumsum(tmp1)-cumsum(tmp0)-pre_z_var.1$Z_mean[,pre_z_var.1$class_size_index[k]]
result2 = cumsum(tmp1[num.genes:1])-cumsum(tmp0[num.genes:1])-pre_z_var.2$Z_mean[,pre_z_var.2$class_size_index[k]]
result1[1:start.val] <- 0
result2[1:start.val] <- 0
A = result1/Z_var1[,pre_z_var.1$class_size_index[k]]
B = result2/Z_var2[,pre_z_var.2$class_size_index[k]]
mAllez[k] <- A[num.genes]
mgsa1[k] <- A[round(num.genes/2)]
mgsa2[k] <- B[round(num.genes/2)]
mgsz1[k] <- max(abs(A))
mgsz2[k] <- max(abs(B))
}
}
mgsa[1,]<-mgsa1
mgsa[2,]<-mgsa2
out2[1,]<-mgsz1
out2[2,]<-mgsz2
if(table){
out <- cbind(Gene.sets = colnames(gene.sets), mGSZ.scores = apply(out2,2,max), mAllez.scores = mAllez, mGSA.scores = apply(abs(mgsa),2,max))
}
else{
out = list(mGSZ.scores = apply(out2,2,max), mAllez.scores = mAllez, mGSA.scores= apply(abs(mgsa),2,max))
return(out)}
}
####################
mGSZ.test.score.stb2 <-
function(gene.sets.stb, expr.data, gene.sets, wgt1, wgt2, pre.var, var.constant,start.val){
num.genes <- length(expr.data)
# Ordering of gene expression data and gene sets
ord_out <- order(expr.data, decreasing= TRUE)
expr.data <- expr.data[ord_out]
set.dim <- dim(gene.sets)
cols <- set.dim[2]
gene.sets <- gene.sets[ord_out,]
expr.data.ud <- expr.data[num.genes:1]
num.genes=length(expr.data)
num.classes=dim(gene.sets)[2]
set_sz <- apply(gene.sets,2,sum)
unique_class_sz_ln <- length(unique(set_sz))
### Defining variables for different output
mGSZ.up <- list()
#GSZ.down <- list()
pre_z_var.1 <- sumVarMean_calc(expr.data, gene.sets, pre.var)
pre_z_var.2 <- sumVarMean_calc(expr.data.ud, gene.sets, pre.var)
Z_var1 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.1$Z_var,wgt2,var.constant)
Z_var2 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.2$Z_var,wgt2,var.constant)
out2 = matrix(0,2,num.classes)
mgsz1 <- numeric(num.classes)
mgsz2 <- mgsz1
for (k in 1:num.classes){
po1 <- which(gene.sets[,k]==1)
po0 <- which(gene.sets[,k]==0)
if(length(po1) > 0 & length(po0) > 0){
tmp1 = expr.data
tmp1[po0] = 0
tmp0 = expr.data
tmp0[po1] = 0
result1 = cumsum(tmp1)-cumsum(tmp0)-pre_z_var.1$Z_mean[,pre_z_var.1$class_size_index[k]]
result2 = cumsum(tmp1[num.genes:1])-cumsum(tmp0[num.genes:1])-pre_z_var.2$Z_mean[,pre_z_var.2$class_size_index[k]]
result1[1:start.val] <- 0
result2[1:start.val] <- 0
A = result1/Z_var1[,pre_z_var.1$class_size_index[k]]
B = result2/Z_var2[,pre_z_var.2$class_size_index[k]]
if(colnames(gene.sets)[k] %in% gene.sets.stb){
mGSZ.up[[length(mGSZ.up)+1]] <- (A)
#GSZ.down[[length(mGSZ.down)+1]] <- (B)
}
mgsz1[k] <- max(abs(A))
mgsz2[k] <- max(abs(B))
}
}
out2[1,]<-mgsz1
out2[2,]<-mgsz2
result1 = list(GSZ.result = apply(out2,2,max))
result2 = list(Z_var1=Z_var1,Z_var2=Z_var2,Z_mean1=pre_z_var.1$Z_mean,Z_mean2=pre_z_var.2$Z_mean,class_size_index1=pre_z_var.1$class_size_index,class_size_index2=pre_z_var.2$class_size_index)
out = list(gene.set.scores=result1, var.attributes=result2,mGSZ.up=mGSZ.up)
return(out)
}
################
mGSZ.test.score.stb3 <-
function(gene.sets.stb, expr.data, gene.sets, wgt1, wgt2, pre.var, var.constant,start.val,...){
num.genes <- length(expr.data)
# Ordering of gene expression data and gene set data
ord_out <- order(expr.data, decreasing= TRUE)
expr.data <- expr.data[ord_out]
set.dim <- dim(gene.sets)
cols <- set.dim[2]
gene.sets <- gene.sets[ord_out,]
expr.data.ud <- expr.data[num.genes:1]
num.genes=length(expr.data)
num.classes=dim(gene.sets)[2]
set_sz <- apply(gene.sets,2,sum)
unique_class_sz_ln <- length(unique(set_sz))
### Defining variables for different output
mGSZ.up <- list()
#GSZ.down <- list()
pre_z_var.1 <- sumVarMean_calc(expr.data, gene.sets, pre.var)
pre_z_var.2 <- sumVarMean_calc(expr.data.ud, gene.sets, pre.var)
Z_var1 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.1$Z_var,wgt2,var.constant)
Z_var2 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.2$Z_var,wgt2,var.constant)
out2 = matrix(0,2,num.classes)
mgsz1 <- numeric(num.classes)
mgsz2 <- mgsz1
for (k in 1:num.classes){
po1 <- which(gene.sets[,k]==1)
po0 <- which(gene.sets[,k]==0)
if(length(po1) > 0 & length(po0) > 0){
tmp1 = expr.data
tmp1[po0] = 0
tmp0 = expr.data
tmp0[po1] = 0
result1 = cumsum(tmp1)-cumsum(tmp0)-pre_z_var.1$Z_mean[,pre_z_var.1$class_size_index[k]]
result2 = cumsum(tmp1[num.genes:1])-cumsum(tmp0[num.genes:1])-pre_z_var.2$Z_mean[,pre_z_var.2$class_size_index[k]]
result1[1:start.val] <- 0
result2[1:start.val] <- 0
A = result1/Z_var1[,pre_z_var.1$class_size_index[k]]
B = result2/Z_var2[,pre_z_var.2$class_size_index[k]]
if(colnames(gene.sets)[k] %in% gene.sets.stb){
mGSZ.up[[length(mGSZ.up)+1]] <- (A)
#mGSZ.down[[length(mGSZ.down)+1]] <- (B)
}
mgsz1[k] <- max(abs(A))
mgsz2[k] <- max(abs(B))
}
}
out2[1,]<-mgsz1
out2[2,]<-mgsz2
out = list(mGSZ.result = apply(out2,2,max),mGSZ.up=mGSZ.up)
return(out)
}
#################
mGSZ.test.score.stb4 <-
function(gene.sets.stb, expr.data,gene.sets,Z_var1,Z_var2,Z_mean1,Z_mean2,class_size_index1,class_size_index2,start.val){
num.genes <- length(expr.data)
# Ordering of gene expression data and gene sets
ord_out <- order(expr.data, decreasing= TRUE)
expr.data <- expr.data[ord_out]
set.dim <- dim(gene.sets)
cols <- set.dim[2]
gene.sets <- gene.sets[ord_out,]
expr.data.ud <- expr.data[num.genes:1]
num.genes=length(expr.data)
num.classes=dim(gene.sets)[2]
set_sz <- apply(gene.sets,2,sum)
unique_class_sz_ln <- length(unique(set_sz))
### Defining variables for different output
mGSZ.up <- list()
#mGSZ.down <- list()
out2 = matrix(0,2,num.classes)
mgsz1 <- numeric(num.classes)
mgsz2 <- mgsz1
for (k in 1:num.classes){
po1 <- which(gene.sets[,k]==1)
po0 <- which(gene.sets[,k]==0)
if(length(po1) > 0 & length(po0) > 0){
tmp1 = expr.data
tmp1[po0] = 0
tmp0 = expr.data
tmp0[po1] = 0
result1 = cumsum(tmp1)-cumsum(tmp0)-Z_mean1[,class_size_index1[k]]
result2 = cumsum(tmp1[num.genes:1])-cumsum(tmp0[num.genes:1])-Z_mean2[,class_size_index2[k]]
result1[1:start.val] <- 0
result2[1:start.val] <- 0
A = result1/Z_var1[,class_size_index1[k]]
B = result2/Z_var2[,class_size_index2[k]]
if(colnames(gene.sets)[k] %in% gene.sets.stb){
mGSZ.up[[length(mGSZ.up)+1]] <- (A)
#mGSZ.down[[length(mGSZ.down)]] <- (B)
}
mgsz1[k] <- max(abs(A))
mgsz2[k] <- max(abs(B))
}
}
out2[1,]<-mgsz1
out2[2,]<-mgsz2
out = list(mGSZ.result = apply(out2,2,max),mGSZ.up=mGSZ.up)
return(out)
}
###############
mGSZ.test.score2 <-
function(expr.data, gene.sets, wgt1, wgt2, pre.var, var.constant,start.val, flip.gene.sets = FALSE,...){
num.genes <- length(expr.data)
# Ordering of gene expression data and gene sets data
gene.sets <- toMatrix(expr.data,gene.sets,flip.gene.sets)
ord_out <- order(expr.data, decreasing= TRUE)
expr.data <- expr.data[ord_out]
set.dim <- dim(gene.sets)
cols <- set.dim[2]
gene.sets <- gene.sets[ord_out,]
expr.data.ud <- expr.data[num.genes:1] # expression values turned up-side down
# This does the analysis of the lower end
num.genes=length(expr.data)
num.classes=dim(gene.sets)[2]
set_sz <- apply(gene.sets,2,sum)
unique_class_sz_ln <- length(unique(set_sz))
mAllez <- rep(0,num.classes)
mgsa <- matrix(0,2,num.classes)
pre_z_var.1 <- sumVarMean_calc(expr.data, gene.sets, pre.var)
pre_z_var.2 <- sumVarMean_calc(expr.data.ud, gene.sets, pre.var)
Z_var1 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.1$Z_var,wgt2,var.constant)
Z_var2 = calc_z_var(num.genes,unique_class_sz_ln, pre_z_var.2$Z_var,wgt2,var.constant)
out2 = matrix(0,2,num.classes)
mgsa1 <- numeric(num.classes)
mgsa2 <- mgsa1
mgsz1 <- mgsa1
mgsz2 <- mgsa2
for (k in 1:num.classes){
po1 <- which(gene.sets[,k]==1)
po0 <- which(gene.sets[,k]==0)
if(length(po1) > 0 & length(po0) > 0){
tmp1 = expr.data
tmp1[po0] = 0
tmp0 = expr.data
tmp0[po1] = 0
result1 = cumsum(tmp1)-cumsum(tmp0)-pre_z_var.1$Z_mean[,pre_z_var.1$class_size_index[k]]
result2 = cumsum(tmp1[num.genes:1])-cumsum(tmp0[num.genes:1])-pre_z_var.2$Z_mean[,pre_z_var.2$class_size_index[k]]
result1[1:start.val] <- 0
result2[1:start.val] <- 0
A = result1/Z_var1[,pre_z_var.1$class_size_index[k]]
B = result2/Z_var2[,pre_z_var.2$class_size_index[k]]
mAllez[k] <- A[num.genes]
mgsa1[k] <- A[round(num.genes/2)]
mgsa2[k] <- B[round(num.genes/2)]
mgsz1[k] <- max(abs(A))
mgsz2[k] <- max(abs(B))
}
}
mgsa[1,]<-mgsa1
mgsa[2,]<-mgsa2
out2[1,]<-mgsz1
out2[2,]<-mgsz2
result1 = list(mGSZ.scores = apply(out2,2,max), mAllez.scores = mAllez, mGSA.scores= apply(abs(mgsa),2,max))
result2 = list(Z_var1=Z_var1,Z_var2=Z_var2,Z_mean1=pre_z_var.1$Z_mean,Z_mean2=pre_z_var.2$Z_mean,class_size_index1=pre_z_var.1$class_size_index,class_size_index2=pre_z_var.2$class_size_index)
out = list(gene.set.scores=result1, var.attributes=result2)
return(out)
}
################
mGSZ.test.score4 <-
function(expr.data,gene.sets,Z_var1,Z_var2,Z_mean1,Z_mean2,class_size_index1,class_size_index2,start.val,flip.gene.sets=FALSE){
num.genes <- length(expr.data)
# Ordering of gene expression data and gene sets data
gene.sets <- toMatrix(expr.data,gene.sets,flip.gene.sets)
ord_out <- order(expr.data, decreasing= TRUE)
expr.data <- expr.data[ord_out]
set.dim <- dim(gene.sets)
cols <- set.dim[2]
gene.sets <- gene.sets[ord_out,]
expr.data.ud <- expr.data[num.genes:1] # expression values turned up-side down
# This does the analysis of the lower end
num.genes=length(expr.data)
num.classes=dim(gene.sets)[2]
set_sz <- apply(gene.sets,2,sum)
unique_class_sz_ln <- length(unique(set_sz))
### Defining variables for different output
mAllez <- rep(0,num.classes)
mgsa <- matrix(0,2,num.classes)
out2 = matrix(0,2,num.classes)
mgsa1 <- numeric(num.classes)
mgsa2 <- mgsa1
mgsz1 <- mgsa1
mgsz2 <- mgsa2
for (k in 1:num.classes){
po1 <- which(gene.sets[,k]==1)
po0 <- which(gene.sets[,k]==0)
if(length(po1) > 0 & length(po0) > 0){
tmp1 = expr.data
tmp1[po0] = 0
tmp0 = expr.data
tmp0[po1] = 0
result1 = cumsum(tmp1)-cumsum(tmp0)-Z_mean1[,class_size_index1[k]]
result2 = cumsum(tmp1[num.genes:1])-cumsum(tmp0[num.genes:1])-Z_mean2[,class_size_index2[k]]
result1[1:start.val] <- 0
result2[1:start.val] <- 0
A = result1/Z_var1[,class_size_index1[k]]
B = result2/Z_var2[,class_size_index2[k]]
mAllez[k] <- A[num.genes]
mgsa1[k] <- A[round(num.genes/2)]
mgsa2[k] <- B[round(num.genes/2)]
mgsz1[k] <- max(abs(A))
mgsz2[k] <- max(abs(B))
}
}
mgsa[1,]<-mgsa1
mgsa[2,]<-mgsa2
out2[1,]<-mgsz1
out2[2,]<-mgsz2
out = list(mGSZ.scores = apply(out2,2,max), mAllez.scores = mAllez, mGSA.scores= apply(abs(mgsa),2,max))
return(out)
}
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