R/simulate_data_3gr.R
In ryananeff/superNOVA: Differential coexpression within multiple subgroups of data
Defines functions
simulate_data_3gr
Documented in
simulate_data_3gr
#' @title Simulate differentially linearly coexpressed genes between three groups.
#' @description Simulates sets of genes coexpressed with an eigengene which show differential coexpression among three groups. This tool simulates 9 possible classes of possible gene coexpression changes (positive, negative, null correlation in group A, group B, and groupC) but does not test all 27 possible classes.
#' @param samples_per_group The number of samples per group to simulate. Default=50
#' @param total_genes The number of genes among 9 differential coexpression classes to simulate. Will actually return a number of genes which is a multiple of 9 that is less than this number.
#' @param maxCor_true The absolute value of the maximum correlation rho value for highly correlated pairs.
#' @param minCor_true The absolute value of the minimum correlation rho value for highly correlated pairs.
#' @param maxCor_null The absolute value of the maximum correlation rho value for null correlated pairs. The minimum is 0 correlation.
#' @param verbose Whether or not to print additional information about the simulation when it runs.
#' @param corr_slope The slope of the correlations to simulate. All correlated pairs will have this slope.
#' @param corr_intercept The correlation intercept of the genes in matrix B.
#' @param group1_offset The correlation intercept of group1 (e.g. its mean expression value)
#' @param group2_offset The correlation intercept of group2 (e.g. its mean expression value)
#' @param group3_offset The correlation intercept of group3 (e.g. its mean expression value)
#' @return Returns a list of inputs for chowCor, including a matA, matB, design_mat, subgroup names (conditions), and the actual classes of differential coexpression.
#' @keywords superNOVA simulate three
#' @importFrom stats model.matrix
#' @export
simulate_data_3gr <- function(samples_per_group=50,total_genes=2000,
maxCor_true=0.95,minCor_true=0.3,maxCor_null=0.2,verbose=T,
corr_slope = 1, corr_intercept = 0, group1_offset=0, group2_offset=0,
group3_offset=0){
samples = samples_per_group
if(verbose){
print("=============")
print(paste("samples:",samples*3))
print(paste("maxCor_true:",maxCor_true))
print(paste("minCor_true:",minCor_true))
print(paste("maxCor_null:",maxCor_null))
}
int <- function(n){as.integer(n)}
genes_per_group = int(total_genes/9)
#linear uniform right now, but we should try other relationship types (anscombe's quartet)
eigengeneg1 = stats::rnorm(samples) + group1_offset #linear uniform right now, but we should try other relationship types (anscombe's quartet)
eigengeneg2 = stats::rnorm(samples) + group2_offset
eigengeneg3 = stats::rnorm(samples) + group3_offset
#group1
pos_corg1 = WGCNA::simulateModule(eigengeneg1,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #module is positively correlated
neg_corg1 = WGCNA::simulateModule(eigengeneg1,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #module is negatively correlated
null_corg1a = WGCNA::simulateModule(eigengeneg1,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #no correlation
null_corg1b = WGCNA::simulateModule(eigengeneg1,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #no correlation
null_corg1 = cbind(null_corg1a,null_corg1b)
true_posg1 = attributes(pos_corg1)$trueKME
true_negg1 = attributes(neg_corg1)$trueKME
true_nullg1a = attributes(null_corg1a)$trueKME
true_nullg1b = attributes(null_corg1b)$trueKME*-1
true_nullg1 = c(true_nullg1a,true_nullg1b)
#group2
pos_corg2 = WGCNA::simulateModule(eigengeneg2,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #module is positively correlated
neg_corg2 = WGCNA::simulateModule(eigengeneg2,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #module is negatively correlated
null_corg2a = WGCNA::simulateModule(eigengeneg2,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #no correlation
null_corg2b = WGCNA::simulateModule(eigengeneg2,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #no correlation
null_corg2 = cbind(null_corg2a,null_corg2b)
true_posg2 = attributes(pos_corg2)$trueKME
true_negg2 = attributes(neg_corg2)$trueKME
true_nullg2a = attributes(null_corg2a)$trueKME
true_nullg2b = attributes(null_corg2b)$trueKME*-1
true_nullg2 = c(true_nullg2a,true_nullg2b)
#group3
pos_corg3 = WGCNA::simulateModule(eigengeneg3,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #module is positively correlated
neg_corg3 = WGCNA::simulateModule(eigengeneg3,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #module is negatively correlated
null_corg3a = WGCNA::simulateModule(eigengeneg3,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #no correlation
null_corg3b = WGCNA::simulateModule(eigengeneg3,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #no correlation
null_corg3 = cbind(null_corg3a,null_corg3b)
true_posg3 = attributes(pos_corg3)$trueKME
true_negg3 = attributes(neg_corg3)$trueKME
true_nullg3a = attributes(null_corg3a)$trueKME
true_nullg3b = attributes(null_corg3b)$trueKME*-1
true_nullg3 = c(true_nullg3a,true_nullg3b)
order_control = cbind(pos_corg1,pos_corg1,pos_corg1,
null_corg1,null_corg1,null_corg1,
neg_corg1,neg_corg1,neg_corg1) #samples on rows, genes on columns, so bind columns
order_diseaseA = cbind(pos_corg2,null_corg2,neg_corg2,
pos_corg2,null_corg2,neg_corg2,
pos_corg2,null_corg2,neg_corg2) #samples on rows, genes on columns, so bind columns
order_diseaseB = cbind(pos_corg3,null_corg3,null_corg3,
pos_corg3,neg_corg3,neg_corg3,
pos_corg3,null_corg3,neg_corg3) #samples on rows, genes on columns, so bind columns
matB = data.frame(rbind(order_control, order_diseaseA, order_diseaseB))
eigengeneg1 = as.matrix(eigengeneg1)
eigengeneg2 = as.matrix(eigengeneg2)
eigengeneg3 = as.matrix(eigengeneg3)
matA = data.frame(rbind(eigengeneg1,eigengeneg2,eigengeneg3))
genenames = apply(as.matrix(1:ncol(matB)),1,function(x) paste0("gene",x))
samplenames = apply(as.matrix(1:nrow(matA)),1,function(x) paste0("sample",x))
colnames(matA) = c("eigengene")
colnames(matB) = genenames
rownames(matA) = samplenames
rownames(matB) = samplenames
matA = t(matA)
matB = t(matB)
## calculate true pairs classes and control/exp true correlations
colnames_truth = c("Gene1","Gene2","corA","corB","corC","true_class","true_signif")
true_pairs = data.frame(matrix(nrow = nrow(matB),ncol=length(colnames_truth)))
colnames(true_pairs) = colnames_truth #you can't handle the truth
count = 1 #this is the row no. in the true pairs array
# positive to positive to positive
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_posg1[iter]
corB = true_posg2[iter]
corC = true_posg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"+/+/+",F)
count = count + 1
}
#positive to null to null
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_posg1[iter]
corB = true_nullg2[iter]
corC = true_nullg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"+/0/0",T)
count = count + 1
}
#positive to negative to null
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_posg1[iter]
corB = -1*true_negg2[iter]
corC = true_nullg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"+/-/0",T)
count = count + 1
}
#null to posiitve to positive
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_nullg1[iter]
corB = true_posg2[iter]
corC = true_posg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"0/+/+",T)
count = count + 1
}
#null to null to negative
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_nullg1[iter]
corB = true_nullg2[iter]
corC = -1*true_negg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"0/0/-",T)
count = count + 1
}
#null to negative to negative
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_nullg1[iter]
corB = -1*true_negg2[iter]
corC = -1*true_negg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"0/-/-",T)
count = count + 1
}
#negative to posiitve to positive
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = -1*true_negg1[iter]
corB = true_posg2[iter]
corC = true_posg2[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"-/+/+",T)
count = count + 1
}
#negative to null to null
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = -1*true_negg1[iter]
corB = true_nullg2[iter]
corC = true_nullg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"-/0/0",T)
count = count + 1
}
#negative to negative to negative
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = -1*true_negg1[iter]
corB = -1*true_negg2[iter]
corC = -1*true_negg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"-/-/-",F)
count = count + 1
}
true_pairs[["corA"]] = as.numeric(true_pairs[["corA"]])
true_pairs[["corB"]] = as.numeric(true_pairs[["corB"]])
true_pairs[["corC"]] = as.numeric(true_pairs[["corC"]])
rownames(true_pairs) = paste0(true_pairs$Gene1,"_",true_pairs$Gene2)
#======
#TODO: concat the matrices for control and disease, make matrices with information about the correlation before and after, etc.
#======
design_mat = data.frame(rbind(cbind(rep(1,samples),rep(0,samples),rep(0,samples)),
cbind(rep(0,samples),rep(1,samples),rep(0,samples)),
cbind(rep(0,samples),rep(0,samples),rep(1,samples))
))
conditions = c("group_control","group_expA","group_expB")
colnames(design_mat) = conditions
design_mat = model.matrix(~0+group_control+group_expA+group_expB,design_mat)
colnames(design_mat) = conditions
matA_in = matA
matB_in = matB
return(list(matA=matA_in,
matB=matB_in,
design_mat=design_mat,
conditions=conditions,
true_pairs=true_pairs))
}
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Defines functions simulate_data_3gr
Documented in simulate_data_3gr
#' @title Simulate differentially linearly coexpressed genes between three groups.
#' @description Simulates sets of genes coexpressed with an eigengene which show differential coexpression among three groups. This tool simulates 9 possible classes of possible gene coexpression changes (positive, negative, null correlation in group A, group B, and groupC) but does not test all 27 possible classes.
#' @param samples_per_group The number of samples per group to simulate. Default=50
#' @param total_genes The number of genes among 9 differential coexpression classes to simulate. Will actually return a number of genes which is a multiple of 9 that is less than this number.
#' @param maxCor_true The absolute value of the maximum correlation rho value for highly correlated pairs.
#' @param minCor_true The absolute value of the minimum correlation rho value for highly correlated pairs.
#' @param maxCor_null The absolute value of the maximum correlation rho value for null correlated pairs. The minimum is 0 correlation.
#' @param verbose Whether or not to print additional information about the simulation when it runs.
#' @param corr_slope The slope of the correlations to simulate. All correlated pairs will have this slope.
#' @param corr_intercept The correlation intercept of the genes in matrix B.
#' @param group1_offset The correlation intercept of group1 (e.g. its mean expression value)
#' @param group2_offset The correlation intercept of group2 (e.g. its mean expression value)
#' @param group3_offset The correlation intercept of group3 (e.g. its mean expression value)
#' @return Returns a list of inputs for chowCor, including a matA, matB, design_mat, subgroup names (conditions), and the actual classes of differential coexpression.
#' @keywords superNOVA simulate three
#' @importFrom stats model.matrix
#' @export
simulate_data_3gr <- function(samples_per_group=50,total_genes=2000,
maxCor_true=0.95,minCor_true=0.3,maxCor_null=0.2,verbose=T,
corr_slope = 1, corr_intercept = 0, group1_offset=0, group2_offset=0,
group3_offset=0){
samples = samples_per_group
if(verbose){
print("=============")
print(paste("samples:",samples*3))
print(paste("maxCor_true:",maxCor_true))
print(paste("minCor_true:",minCor_true))
print(paste("maxCor_null:",maxCor_null))
}
int <- function(n){as.integer(n)}
genes_per_group = int(total_genes/9)
#linear uniform right now, but we should try other relationship types (anscombe's quartet)
eigengeneg1 = stats::rnorm(samples) + group1_offset #linear uniform right now, but we should try other relationship types (anscombe's quartet)
eigengeneg2 = stats::rnorm(samples) + group2_offset
eigengeneg3 = stats::rnorm(samples) + group3_offset
#group1
pos_corg1 = WGCNA::simulateModule(eigengeneg1,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #module is positively correlated
neg_corg1 = WGCNA::simulateModule(eigengeneg1,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #module is negatively correlated
null_corg1a = WGCNA::simulateModule(eigengeneg1,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #no correlation
null_corg1b = WGCNA::simulateModule(eigengeneg1,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #no correlation
null_corg1 = cbind(null_corg1a,null_corg1b)
true_posg1 = attributes(pos_corg1)$trueKME
true_negg1 = attributes(neg_corg1)$trueKME
true_nullg1a = attributes(null_corg1a)$trueKME
true_nullg1b = attributes(null_corg1b)$trueKME*-1
true_nullg1 = c(true_nullg1a,true_nullg1b)
#group2
pos_corg2 = WGCNA::simulateModule(eigengeneg2,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #module is positively correlated
neg_corg2 = WGCNA::simulateModule(eigengeneg2,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #module is negatively correlated
null_corg2a = WGCNA::simulateModule(eigengeneg2,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #no correlation
null_corg2b = WGCNA::simulateModule(eigengeneg2,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #no correlation
null_corg2 = cbind(null_corg2a,null_corg2b)
true_posg2 = attributes(pos_corg2)$trueKME
true_negg2 = attributes(neg_corg2)$trueKME
true_nullg2a = attributes(null_corg2a)$trueKME
true_nullg2b = attributes(null_corg2b)$trueKME*-1
true_nullg2 = c(true_nullg2a,true_nullg2b)
#group3
pos_corg3 = WGCNA::simulateModule(eigengeneg3,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #module is positively correlated
neg_corg3 = WGCNA::simulateModule(eigengeneg3,genes_per_group,nNearGenes=0,minCor=minCor_true,maxCor=maxCor_true,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #module is negatively correlated
null_corg3a = WGCNA::simulateModule(eigengeneg3,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=T,geneMeans=NULL)*corr_slope+corr_intercept #no correlation
null_corg3b = WGCNA::simulateModule(eigengeneg3,genes_per_group/2,nNearGenes=0,minCor=0.01,maxCor=maxCor_null,corPower=1,signed=F,geneMeans=NULL,propNegativeCor = 1)*corr_slope+corr_intercept #no correlation
null_corg3 = cbind(null_corg3a,null_corg3b)
true_posg3 = attributes(pos_corg3)$trueKME
true_negg3 = attributes(neg_corg3)$trueKME
true_nullg3a = attributes(null_corg3a)$trueKME
true_nullg3b = attributes(null_corg3b)$trueKME*-1
true_nullg3 = c(true_nullg3a,true_nullg3b)
order_control = cbind(pos_corg1,pos_corg1,pos_corg1,
null_corg1,null_corg1,null_corg1,
neg_corg1,neg_corg1,neg_corg1) #samples on rows, genes on columns, so bind columns
order_diseaseA = cbind(pos_corg2,null_corg2,neg_corg2,
pos_corg2,null_corg2,neg_corg2,
pos_corg2,null_corg2,neg_corg2) #samples on rows, genes on columns, so bind columns
order_diseaseB = cbind(pos_corg3,null_corg3,null_corg3,
pos_corg3,neg_corg3,neg_corg3,
pos_corg3,null_corg3,neg_corg3) #samples on rows, genes on columns, so bind columns
matB = data.frame(rbind(order_control, order_diseaseA, order_diseaseB))
eigengeneg1 = as.matrix(eigengeneg1)
eigengeneg2 = as.matrix(eigengeneg2)
eigengeneg3 = as.matrix(eigengeneg3)
matA = data.frame(rbind(eigengeneg1,eigengeneg2,eigengeneg3))
genenames = apply(as.matrix(1:ncol(matB)),1,function(x) paste0("gene",x))
samplenames = apply(as.matrix(1:nrow(matA)),1,function(x) paste0("sample",x))
colnames(matA) = c("eigengene")
colnames(matB) = genenames
rownames(matA) = samplenames
rownames(matB) = samplenames
matA = t(matA)
matB = t(matB)
## calculate true pairs classes and control/exp true correlations
colnames_truth = c("Gene1","Gene2","corA","corB","corC","true_class","true_signif")
true_pairs = data.frame(matrix(nrow = nrow(matB),ncol=length(colnames_truth)))
colnames(true_pairs) = colnames_truth #you can't handle the truth
count = 1 #this is the row no. in the true pairs array
# positive to positive to positive
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_posg1[iter]
corB = true_posg2[iter]
corC = true_posg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"+/+/+",F)
count = count + 1
}
#positive to null to null
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_posg1[iter]
corB = true_nullg2[iter]
corC = true_nullg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"+/0/0",T)
count = count + 1
}
#positive to negative to null
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_posg1[iter]
corB = -1*true_negg2[iter]
corC = true_nullg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"+/-/0",T)
count = count + 1
}
#null to posiitve to positive
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_nullg1[iter]
corB = true_posg2[iter]
corC = true_posg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"0/+/+",T)
count = count + 1
}
#null to null to negative
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_nullg1[iter]
corB = true_nullg2[iter]
corC = -1*true_negg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"0/0/-",T)
count = count + 1
}
#null to negative to negative
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = true_nullg1[iter]
corB = -1*true_negg2[iter]
corC = -1*true_negg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"0/-/-",T)
count = count + 1
}
#negative to posiitve to positive
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = -1*true_negg1[iter]
corB = true_posg2[iter]
corC = true_posg2[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"-/+/+",T)
count = count + 1
}
#negative to null to null
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = -1*true_negg1[iter]
corB = true_nullg2[iter]
corC = true_nullg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"-/0/0",T)
count = count + 1
}
#negative to negative to negative
for(iter in 1:length(true_posg1)){
gene_name = paste0("gene",count) #this is the gene name from genenames
corA = -1*true_negg1[iter]
corB = -1*true_negg2[iter]
corC = -1*true_negg3[iter]
true_pairs[count,] = c("eigengene",gene_name,corA,corB,corC,"-/-/-",F)
count = count + 1
}
true_pairs[["corA"]] = as.numeric(true_pairs[["corA"]])
true_pairs[["corB"]] = as.numeric(true_pairs[["corB"]])
true_pairs[["corC"]] = as.numeric(true_pairs[["corC"]])
rownames(true_pairs) = paste0(true_pairs$Gene1,"_",true_pairs$Gene2)
#======
#TODO: concat the matrices for control and disease, make matrices with information about the correlation before and after, etc.
#======
design_mat = data.frame(rbind(cbind(rep(1,samples),rep(0,samples),rep(0,samples)),
cbind(rep(0,samples),rep(1,samples),rep(0,samples)),
cbind(rep(0,samples),rep(0,samples),rep(1,samples))
))
conditions = c("group_control","group_expA","group_expB")
colnames(design_mat) = conditions
design_mat = model.matrix(~0+group_control+group_expA+group_expB,design_mat)
colnames(design_mat) = conditions
matA_in = matA
matB_in = matB
return(list(matA=matA_in,
matB=matB_in,
design_mat=design_mat,
conditions=conditions,
true_pairs=true_pairs))
}
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