R/MODA.R In MODA: MODA: MOdule Differential Analysis for weighted gene co-expression network

Documented in CompareAllNetscomparemodulestwonetsModuleFrequencyPartitionDensityPartitionModularityWeightedModulePartitionAmoutainWeightedModulePartitionHierarchicalWeightedModulePartitionLouvainWeightedModulePartitionSpectral

```#' Illustration of partition density
#'
#' Calculate the average density of all resulting modules from a partition. The
#' density of each module is defined as the average adjacency of the module
#' genes.
#'
#' @param ADJ gene similarity matrix
#' @param PartitionSet vector indicates the partition label for genes
#'
#' @return partition density, defined as average density of all modules
#'
#' @references Langfelder, Peter, and Steve Horvath. "WGCNA: an R package for
#' weighted correlation network analysis." BMC bioinformatics 9.1 (2008): 1.
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords density
#'
#' @examples
#' data(synthetic)
#' groups <- cutree(hierADJ, h = 0.8)
#' @export
#'
labels <- unique(PartitionSet)
numP <- length(labels)
pdensity <- 0
for (i in seq_len(numP)){
idx = which(PartitionSet == labels[i])

if (length(idx) == 1){
pdensity <- pdensity + 0
}else{
sum = sum(Aq)
nrow = dim(Aq)[1]
pdensity <- pdensity + sum*(sum-nrow)/(nrow*nrow-nrow)
}

}
pdensity
}

#' Illustration of modularity density
#'
#' Calculate the average modularity of a partition. The modularity of each
#' module is defined from a natural generalization of unweighted case.
#'
#' @param ADJ gene similarity matrix
#' @param PartitionSet vector indicates the partition label for genes
#'
#' @return partition modularity, defined as average modularity of all modules
#'
#' @references Newman, Mark EJ. "Analysis of weighted networks." Physical
#' review E 70.5 (2004): 056131.
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords modularity
#'
#' @examples
#' data(synthetic)
#' groups <- cutree(hierADJ, h = 0.8)
#'
#' @export
#'
labels <- unique(PartitionSet)
numP <- length(labels)
pModularity <- 0
for (i in seq_len(numP)){
idx = which(PartitionSet == labels[i])

if (length(idx) == 1){
pModularity <- pModularity + 0
}else{
deg = K[idx]
pModularity = pModularity + sum(Aq)- sum(deg*deg)/m

}
}
pModularity <- pModularity/m
pModularity
}

#' Modules detection by hierarchical clustering
#'
#' Module detection based on the optimal cutting height of dendrogram, which is
#' selected to make the average density or modularity of resulting partition
#' maximal. The clustering and visulization function are from WGCNA.
#'
#' @param datExpr gene expression profile, rows are samples and columns genes
#' @param foldername where to store the clusters
#' @param indicatename normally a specific tag of condition
#' @param cutmethod cutting the dendrogram based on maximal average Density
#' or Modularity
#' @param power the power parameter of WGCNA, W_{ij}=|cor(x_i,x_j)|^power
#'
#' @return The number of clusters
#'
#' @references Langfelder, Peter, and Steve Horvath. "WGCNA: an R package for
#' weighted correlation network analysis." BMC bioinformatics 9.1 (2008): 1.
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords cutting dendrogram
#'
#' @import WGCNA
#' @import igraph
#' @importFrom dynamicTreeCut cutreeDynamic
#' @examples
#' data(synthetic)
#' ResultFolder = 'ForSynthetic' # where middle files are stored
#' CuttingCriterion = 'Density' # could be Density or Modularity
#' indicator1 = 'X'     # indicator for data profile 1
#' indicator2 = 'Y'      # indicator for data profile 2
#' specificTheta = 0.1 #threshold to define condition specific modules
#' conservedTheta = 0.1#threshold to define conserved modules
#' intModules1 <- WeightedModulePartitionHierarchical(datExpr1,ResultFolder,
#' indicator1,CuttingCriterion)
#' #mymodule <- getPartition(ResultFolder)

#' @export
#'
WeightedModulePartitionHierarchical <- function(datExpr,foldername,indicatename,
cutmethod=c('Density','Modularity'), power=10){
dir.create(file.path('./', foldername), showWarnings = FALSE)

#NumCutHeights = 27440 for DaphIX, too much, just change
cutHeights <- seq(0.05,1,by=0.05)
NumCutHeights <- length(cutHeights)
pDensity <- numeric(length <- NumCutHeights)
maxpDensity <- 0
maxHeight <- 0
for (i in 1:NumCutHeights) {
groups <- cutree(hierADJ, h = cutHeights[i])
if (cutmethod == 'Density'){
}else{
}

if (pDensity[i] > maxpDensity){
maxpDensity <- pDensity[i]
maxHeight <- cutHeights[i]
}
}

# We like large modules, so we set the minimum module size relatively high:
minModuleSize = 30;
# Module identification using dynamic tree cut:
maxHeight,deepSplit = 2, pamRespectsDendro = FALSE,
minClusterSize = minModuleSize)

dynamicColors = labels2colors(dynamicMods)
intModules = table(dynamicColors)

#######Visualization#############
#pdf(paste(foldername,"/Partitions_",indicatename,".pdf",sep=""),width = 11,
#    height = 8)
png(paste(foldername,"/Partitions_",indicatename,".png",sep=""))
marAll = c(1, 5, 3, 1)
layout(matrix(c(1,2,3,0), 2, 2, byrow = TRUE), widths=c(0.8,0.2),
heights=c(0.8,0.2))
par(mar = c(0, marAll[2], marAll[3], marAll[4]))
plot(hierADJ, labels = FALSE, xlab="", sub="", ylim=c(0,1),hang = -1,
axes=FALSE,main = "Gene hierarchical clustering dendrogram")
abline(h = maxHeight, col = 'red')
axis(side = 2, at = seq(1,0,-0.2),labels =seq(1,0,-0.2))
par(mar = c(0, 1, marAll[3], marAll[4]),mgp = c(0, 1, 0))
plot(pDensity, cutHeights, type = 'n', ylim=c(0,1), axes=FALSE,ylab = "")
title(paste('Average',cutmethod), font.main = 1, cex.main = 1,line  = 0)
axis(side = 1, at	= seq(0,(max(pDensity)+0.1),0.1),
labels = seq(0,(max(pDensity)+0.1),0.1))
axis(side = 2, at	= seq(1,0,-0.2),labels =seq(1,0,-0.2))
lines(pDensity, cutHeights, lwd = 2, col = 'blue')
abline(h = maxHeight, col = 'red')
par(mar = c(marAll[1], marAll[2], 0, marAll[4]))
rowText = NULL, rowLabels = 'Clusters')
dev.off()

for(J in 1:length(intModules)){
idx <- which(dynamicColors == names(intModules)[J])
DenseGenes = colnames(datExpr)[idx]
densegenefile <- paste(foldername,"/DenseModuleGene_",
indicatename,"_",J,".txt",sep="")
write.table(idx,file = paste(foldername,'/DenseModuleGeneID_',indicatename,'_',J,'.txt',sep=''),
quote = FALSE, row.names = FALSE, col.names = FALSE)
write.table(DenseGenes,densegenefile,sep = "\n",col.names = FALSE,
row.names = FALSE,quote = FALSE)
}

return (length(intModules))
}

#' Modules detection by spectral clustering
#'
#' Module detection based on the spectral clustering algorithm, which mainly
#' solve the eigendecomposition on Laplacian matrix
#'
#' @param datExpr gene expression profile, rows are samples and columns genes
#' @param foldername where to store the clusters
#' @param indicatename normally a specific tag of condition
#' @param GeneNames normally the gene official names to replace the colnames of datExpr
#' @param power the power parameter of WGCNA, W_{ij}=|cor(x_i,x_j)|^power
#' @param nn the number of nearest neighbor, used to construct the affinity matrix
#' @param k the number of clusters(modules)
#'
#' @return None
#'
#' @references Von Luxburg, Ulrike. "A tutorial on spectral clustering."
#' Statistics and computing 17.4 (2007): 395-416.
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords cutting dendrogram
#'
#'
#' @import igraph
#' @import cluster
#' @examples
#' data(synthetic)
#' ResultFolder <- 'ForSynthetic' # where middle files are stored
#' indicator <- 'X'     # indicator for data profile 1
#' GeneNames <- colnames(datExpr1)
#' WeightedModulePartitionSpectral(datExpr1,ResultFolder,indicator,
#' GeneNames,k=5)
#' truemodule <- c(rep(1,100),rep(2,100),rep(3,100),rep(4,100),rep(5,100))
#' #mymodule <- getPartition(ResultFolder)
#' @export
#'
WeightedModulePartitionSpectral <- function(datExpr, foldername, indicatename,
GeneNames, power=6, nn=10, k=2){
dir.create(file.path('./', foldername), showWarnings = FALSE)

W = TOMsimilarity(ADJ1)   # similarity matrix
A = make.affinity(W,nn)
d <- apply(A, 1, sum)
L <- diag(d)-A                        # unnormalized version
#bottleneck
L <- diag(d^-0.5)%*%L%*% diag(d^-0.5) # normalized version
evL <- eigen(L,symmetric=TRUE)  # evL\$values is decreasing sorted when symmetric=TRUE
#bottleneck
Z <- evL\$vectors[,(ncol(evL\$vectors)-k+1):ncol(evL\$vectors)]
spc <- pam(Z,k)
colorSpectralTom <- labels2colors(spc\$cluster)
intModules = table(colorSpectralTom)
for(J in 1:length(intModules)){
idx <- which(colorSpectralTom == names(intModules)[J])
DenseGenes = GeneNames[idx]
densegenefile <- paste(foldername,"/DenseModuleGene_",
indicatename,"_",J,".txt",sep="")
write.table(idx,file = paste(foldername,'/DenseModuleGeneID_',indicator,'_',J,'.txt',sep=''),
quote = FALSE, row.names = FALSE, col.names = FALSE)
write.table(DenseGenes,densegenefile,sep = "\n",col.names = FALSE,
row.names = FALSE,quote = FALSE)
}
}

#' Modules detection by Louvain algorithm
#'
#' Module detection based on the Louvain algorithm, which tries to maximize
#' overall modularity of resulting partition.
#'
#' @param datExpr gene expression profile, rows are samples and columns genes
#' @param foldername where to store the clusters
#' @param indicatename normally a specific tag of condition
#' @param GeneNames normally the gene official names to replace the colnames of datExpr
#' @param maxsize the maximal nodes allowed in one module
#' @param minsize the minimal nodes allowed in one module
#' @param power the power parameter of WGCNA, W_{ij}=|cor(x_i,x_j)|^power
#' @param tao the threshold to cut the adjacency matrix
#'
#' @return The number of clusters
#'
#' @references Blondel, Vincent D., et al. "Fast unfolding of communities in
#' large networks." Journal of statistical mechanics: theory and experiment
#' 2008.10 (2008): P10008.
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords cutting dendrogram
#'
#'
#' @import igraph
#' @examples
#' data(synthetic)
#' ResultFolder <- 'ForSynthetic' # where middle files are stored
#' indicator <- 'X'     # indicator for data profile 1
#' GeneNames <- colnames(datExpr1)
#' intModules1 <- WeightedModulePartitionLouvain(datExpr1,ResultFolder,indicator,GeneNames)
#' truemodule <- c(rep(1,100),rep(2,100),rep(3,100),rep(4,100),rep(5,100))
#' #mymodule <- getPartition(ResultFolder)
#' @export
#'
WeightedModulePartitionLouvain <- function(datExpr,foldername,indicatename,GeneNames,
maxsize=200, minsize=30, power=6, tao=0.2){
V(g)\$name=1:length(V(g))
dir.create(foldername, showWarnings = FALSE)
tmfile <- paste(foldername,'tmp.txt',sep='')
recursiveigraph(g,tmfile,'louvain',maxsize, minsize)
if(!file.exists(tmfile)){
print('Not suitable for Louvain! Try another module identification method')
return (0)
}else
{
num <- modulesRank(foldername,indicatename,GeneNames)
return (num)
}
}

#' Modules detection by AMOUNTAIN algorithm
#'
#' Module detection based on the AMOUNTAIN algorithm, which tries to find the
#' optimal module every time and use a modules extraction way
#'
#' @param datExpr gene expression profile, rows are samples and columns genes
#' @param Nmodule the number of clusters(modules)
#' @param foldername where to store the clusters
#' @param indicatename normally a specific tag of condition
#' @param GeneNames normally the gene official names to replace the colnames of datExpr
#' @param maxsize the maximal nodes allowed in one module
#' @param minsize the minimal nodes allowed in one module
#' @param power the power parameter of WGCNA, W_{ij}=|cor(x_i,x_j)|^pwr
#' @param tao the threshold to cut the adjacency matrix
#'
#' @return None
#'
#' @references Blondel, Vincent D., et al. "Fast unfolding of communities in
#' large networks." Journal of statistical mechanics: theory and experiment
#' 2008.10 (2008): P10008.
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords optimization
#'
#' @import AMOUNTAIN
#' @examples
#' data(synthetic)
#' ResultFolder <- 'ForSynthetic' # where middle files are stored
#' GeneNames <- colnames(datExpr1)
#' intModules1 <- WeightedModulePartitionAmoutain(datExpr1,5,ResultFolder,'X',
#' GeneNames,maxsize=100,minsize=50)
#' truemodule <- c(rep(1,100),rep(2,100),rep(3,100),rep(4,100),rep(5,100))
#' #mymodule <- getPartition(ResultFolder)
#' @export
#'
WeightedModulePartitionAmoutain <- function(datExpr,Nmodule,foldername,indicatename,
GeneNames, maxsize=200, minsize=3, power=6, tao=0.2){
W <- abs(cor(datExpr,use="p"))^power
W[W < tao] <- 0
N = dim(W)[1]
z <- rep(0,N)
idx <- 1
dir.create(foldername, showWarnings = FALSE)
idxname <- 1:N
saveAtomfile <- paste(foldername,'/AtomModule',sep='')
for (ii in 1:Nmodule) {
abegin = 0.01
aend = 0.9
for (i in 1:20) {
#x <- CGPFixSS(W,z,rep(1/N,N),a=(abegin+aend)/2,lambda=0,maxiter = 50)
x <- moduleIdentificationGPFixSS(W,z,rep(1/N,N),a=(abegin+aend)/2,
lambda=0,maxiter = 50)
predictedid <- which(x[[2]]!=0)
if(length(predictedid) > maxsize){
abegin = (abegin+aend)/2
}else if (length(predictedid) < minsize){
aend = (abegin+aend)/2
}else
break
}

if(length(predictedid) <= maxsize){
modulescore = sum(W[predictedid,predictedid])

write.table(as.character(idxname[predictedid]),file = paste(foldername,
'/DenseModuleGeneID_',indicatename,'_',idx,'.txt',sep=''),
quote = FALSE, row.names = FALSE, col.names = FALSE)

write.table(GeneNames[predictedid],file = paste(foldername,'/',
floor(modulescore),'-moduleid-',idx,'.txt',sep=''),
quote = FALSE, row.names = FALSE, col.names = FALSE)
idx <- idx+1
}
else if(length(predictedid) > maxsize){
modulescoreW = W[predictedid,predictedid]
print(paste('Atom! with size',length(predictedid),sep=' '))
tmpstr = as.numeric(GeneNames[predictedid])-1
forTotalcompletegraph(tmpstr,modulescoreW,saveAtomfile)
}
W = W[-predictedid,-predictedid]
GeneNames = GeneNames[-predictedid]
z = z[-predictedid]
idxname = idxname[-predictedid]
N = length(GeneNames)
print(paste('Finishing module ',ii,sep=''))

if(N < 3 | sum(W)==0)
break
}
}

#' Illustration of two networks comparison
#'
#' Compare the background network and a condition-specific network. A Jaccard
#' index is used to measure the similarity of two sets, which represents the
#' similarity of each module pairs from two networks.
#'
#' @param sourcehead prefix of where to store results
#' @param nm1 how many modules in the background network
#' @param nm2 how many modules in the condition-specific network
#' @param ind1 indicator of the background network
#' @param ind2 indicator of the condition-specific network
#'
#' @return A matrix where each entry is the Jaccard index of corresponding
#' modules from two networks
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords module comparison
#'
#' @examples
#' data(synthetic)
#' ResultFolder = 'ForSynthetic' # where middle files are stored
#' CuttingCriterion = 'Density' # could be Density or Modularity
#' indicator1 = 'X'     # indicator for data profile 1
#' indicator2 = 'Y'      # indicator for data profile 2
#' intModules1 <- WeightedModulePartitionHierarchical(datExpr1,ResultFolder,
#' indicator1,CuttingCriterion)
#' intModules2 <- WeightedModulePartitionHierarchical(datExpr2,ResultFolder,
#' indicator2,CuttingCriterion)
#' JaccardMatrix <- comparemodulestwonets(ResultFolder,intModules1,intModules2,
#' paste('/DenseModuleGene_',indicator1,sep=''),
#' paste('/DenseModuleGene_',indicator2,sep=''))
#'
#' @export
#'
my.array<-array(0,dim=c(nm1,nm2))
for(i1 in 1:nm1){
for (i2 in 1:nm2){
my.array[i1,i2] = length(intersect(list1,list2))/
length(union(list1,list2))
}
}
return (my.array)
}

#' Illustration of network comparison
#'
#' Compare the background network and a set of condition-specific network.
#' Conserved or condition-specific modules are indicated by the plain files,
#' based on the statistics
#'
#' @param ResultFolder where to store results
#' @param intModules how many modules in the background network
#' @param indicator identifier of current profile, served as a tag in name
#' @param intconditionModules a numeric vector, each of them is the number
#' of modules in each condition-specific network. Or just single number
#' @param conditionNames a character vector, each of them is the name
#' of condition. Or just single name
#' @param specificTheta the threshold to define min(s)+specificTheta,
#' less than which is considered as condition specific module.
#' s is the sums of rows in Jaccard index matrix. See supplementary file.
#' @param conservedTheta The threshold to define max(s)-conservedTheta,
#' greater than which is considered as condition conserved module.
#' s is the sums of rows in Jaccard index matrix. See supplementary file.
#'
#' @return None
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords module differential
#'
#' @examples
#' data(synthetic)
#' ResultFolder = 'ForSynthetic' # where middle files are stored
#' CuttingCriterion = 'Density' # could be Density or Modularity
#' indicator1 = 'X'     # indicator for data profile 1
#' indicator2 = 'Y'      # indicator for data profile 2
#' specificTheta = 0.1 #threshold to define condition specific modules
#' conservedTheta = 0.1#threshold to define conserved modules
#' intModules1 <- WeightedModulePartitionHierarchical(datExpr1,ResultFolder,
#' indicator1,CuttingCriterion)
#' intModules2 <- WeightedModulePartitionHierarchical(datExpr2,ResultFolder,
#' indicator2,CuttingCriterion)
#' CompareAllNets(ResultFolder,intModules1,indicator1,intModules2,indicator2,
#' specificTheta,conservedTheta)
#'
#' @export
#'
CompareAllNets <- function(ResultFolder,intModules,indicator,
intconditionModules,conditionNames,specificTheta,
conservedTheta){
for (i in 1:length(conditionNames)) {
ArrayGroup1 <- comparemodulestwonets(ResultFolder,intModules,
intconditionModules[i],paste('/DenseModuleGeneID_',
indicator,sep=''),paste('/DenseModuleGeneID_',
conditionNames[i],sep=''))
dir.create(paste(ResultFolder,'/',conditionNames[i],sep=''), showWarnings = FALSE)
fileprefix <- paste(ResultFolder,'/',conditionNames[i],'/',sep='')

#pdf(paste(fileprefix,'module_overlap_remove',conditionNames[i],
#          '.pdf',sep=''),width = 10, height = 8)
png(paste(fileprefix,'module_overlap_remove',conditionNames[i],
'.png',sep=''))
plot(1:intModules,rowSums(ArrayGroup1),xlim = c(0,(intModules + 1)),
ylim = c(0,max(rowSums(ArrayGroup1)) + 0.1),
xlab="Module ID",ylab = 'RowSums of similarity matrix',
type = 'n',main="Overlapped ratio")
lines(1:intModules, rowSums(ArrayGroup1), col = 'black', type = "b")
abline(h = min(rowSums(ArrayGroup1)) + 0.1, col='red')
abline(h = max(rowSums(ArrayGroup1)) - 0.1, col='green')
text(5, min(rowSums(ArrayGroup1)) - 0.05, "condition response",
col = "red",cex = 0.75)
text(5, max(rowSums(ArrayGroup1)) + 0.05, "conserved response",
col = "green",cex = 0.75)
dev.off()
specificmoduleid <- which(rowSums(ArrayGroup1) <=
min(rowSums(ArrayGroup1)) + specificTheta)
conservedmoduleid <- which(rowSums(ArrayGroup1) >=
max(rowSums(ArrayGroup1)) - conservedTheta)
write.table(specificmoduleid,file = paste(fileprefix,
'sepcificModuleid.txt',sep=''),
row.names = FALSE, col.names = FALSE)
write.table(conservedmoduleid,file = paste(fileprefix,
'conservedModuleid.txt',sep=''),
row.names = FALSE, col.names = FALSE)
}
}

#' Statistics of all conditions
#'
#' Statistics of all conditions. To highlight conserved or condition-specific
#' by counting how frequent each module is lablelled as which, and then
#' visualize the frequency by bar plot.
#'
#' @param ResultFolder where to store results
#' @param intModules how many modules in the background network
#' @param indicator identifier of current profile, served as a tag in name
#' @param conditionNames a character vector, each of them is the name
#' @param legendNames a character vector, each of them is the condition name
#' showing up in the frequency barplot
#' of condition. Or just single name
#' @return None
#'
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords module differential Statistics
#'
#' @import RColorBrewer
#'
#' @export
#'
ModuleFrequency <- function(ResultFolder,intModules, conditionNames,
legendNames, indicator){
Ncon <- length(conditionNames)
wide <- matrix (0,nrow = Ncon, ncol = intModules)
for (i in 1:Ncon) {
fileprefix <- paste(ResultFolder,'/',conditionNames[i],'/',sep='')
}
idx1 <- which(colSums(wide)!=0)
# sequential <- brewer.pal(length(conditionNames), "Greens")
# sequential <- brewer.pal(length(conditionNames), col=c('black', 'white', 'blue', 'red', 'yellow', 'purple', 'green'))
# sequential <- c('black', 'white', 'blue', 'red', 'yellow', 'purple', 'green')

if (Ncon > 9){
qual_col_pals <- brewer.pal.info[brewer.pal.info\$category == 'qual',]
col_vector <- unlist(mapply(brewer.pal, qual_col_pals\$maxcolors, rownames(qual_col_pals)))
sequential <- sample(col_vector, Ncon)
} else
sequential <- brewer.pal(Ncon, "Set1")

pdf(paste(ResultFolder,'/specificmembership.pdf',sep=''),width = 10, height = 5)
#  png('specificmembership.png',width = 1000, height = 500)
barplot(wide[,idx1],
names.arg = idx1,
cex.names = 0.7, # makes x-axis labels small enough to show all
col = sequential, # colors
xlab = "module id",
ylab = "if in condition specific network",
xlim = c(0,length(idx1)), # these two lines allow space for the legend
main = 'Frequency of condition specific module',
width = 0.75) # these two lines allow space for the legend
legend("topleft",
legend = legendNames, #in order from top to bottom
fill = sequential, # 6:1 reorders so legend order matches graph
title = "conditions",cex = 0.75)
dev.off()

for (ispec in 1:Ncon) {
idx1 <- intersect(which(colSums(wide)!=0), which(colSums(wide) <= ispec))
uniqueaffiliation <- vector('list',Ncon)
names (uniqueaffiliation) <- conditionNames
for (i in 1:length(idx1)) {
tmp <- which(wide[,idx1[i]] == 1)
for (j in 1:length(tmp)) {
uniqueaffiliation[[tmp[j]]] <- union(uniqueaffiliation[[tmp[j]]],idx1[i])
}
}
write(paste('If we allow at most ', ispec ,' share one condition\n',sep=''),
file = paste(ResultFolder,'/specificmembership.txt',sep=''),append=TRUE)
for (i in 1:Ncon) {
write(conditionNames[i],file = paste(ResultFolder,'/specificmembership.txt',sep=''),append=TRUE)
if(is.null(uniqueaffiliation[[i]])){
write('NULL',file = paste(ResultFolder,'/specificmembership.txt',sep=''),append = TRUE)
}else{
write(uniqueaffiliation[[i]],file = paste(ResultFolder,'/specificmembership.txt',sep=''),append = TRUE)
}
write('\n',file = paste(ResultFolder,'/specificmembership.txt',sep=''),append=TRUE)
}
}

wide2 <- matrix (0,nrow = Ncon, ncol = intModules)
for (i in 1:length(conditionNames)) {
fileprefix <- paste(ResultFolder,'/',conditionNames[i],'/',sep='')
#wide2[i,moduleid] <- 1
}
idx2 <- which(colSums(wide2)!=0)

#sequential <- c('black', 'white', 'blue', 'red', 'yellow', 'purple', 'green')
#sequential <- brewer.pal(Ncon, "Set1")
#sequential <- sample(col_vector, Ncon)
pdf(paste(ResultFolder,'/conservedmembership.pdf',sep=''),width = 10, height = 5)
#  png('specificmembership.png',width = 1000, height = 500)
barplot(wide2[,idx2],
names.arg = idx2,
cex.names = 0.7, # makes x-axis labels small enough to show all
col = sequential, # colors
xlab = "module id",
ylab = "if in condition specific network",
xlim = c(0,length(idx2)), # these two lines allow space for the legend
main = 'Frequency of conserved module',
width = 0.75) # these two lines allow space for the legend
legend("topleft",
legend =legendNames, #in order from top to bottom
fill = sequential, # 6:1 reorders so legend order matches graph
title = "conditions",cex = 0.75)

dev.off()

idx <- sort(union(idx1,idx2))
pdf(paste(ResultFolder,'/membership.pdf',sep=''),width = 10, height = 5)
plot(1:length(idx),xaxt = "n",yaxt = "n",
xlim = c(1,length(idx)),ylim = c(-max(colSums(wide2)),max(colSums(wide))),type = "n",
xlab='Module ID',ylab='Membership',main = 'Specification of conditon-specific and conserved module')
#axis(1, at = seq(1, intModules, by = 1), labels=1:intModules)

bp <- barplot(wide[,idx],axes=FALSE,
names.arg = idx,
cex.names = 0.7, # makes x-axis labels small enough to show all
col = sequential, # colors
width = 0.75,
add = TRUE) # these two lines allow space for the legend
barplot(-wide2[,idx],axes=FALSE,
col = sequential, # colors
legend("bottomleft",
legend = legendNames, #in order from top to bottom
fill = sequential, # 6:1 reorders so legend order matches graph
cex = 0.75,bty = "n")# without borders
text(length(idx)-1, 1, 'Conditon specific',cex = 0.75)
text(length(idx)-1, -1, 'Conserved',cex = 0.75)
dev.off()

write.table(idx2,file = paste(ResultFolder,'/conservedmembership.txt',sep=''),
row.names = FALSE, col.names = FALSE)
dir.create(paste(ResultFolder,'/','interestedModules',sep=''))
for (i in idx1) {
file.copy(paste(ResultFolder,"/DenseModuleGene_",indicator,"_",i,".txt",sep=""),
paste(ResultFolder,'/','interestedModules',sep=''))
}
for (i in idx2) {
file.copy(paste(ResultFolder,"/DenseModuleGene_",indicator,"_",i,".txt",sep=""),
paste(ResultFolder,'/','interestedModules',sep=''))
}
}

#' datExpr1
#'
#' Synthetic gene expression profile with 20 samples and 500 genes.
#'
#' @name datExpr1
#' @docType data
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords data
#' @format A matrix with 20 rows and 500 columns.
#' @examples
#' data(synthetic)
#' ## plot the heatmap of the correlation matrix ...
#' \dontrun{heatmap(cor(as.matrix(datExpr1)))}
NULL

#' datExpr2
#'
#' Synthetic gene expression profile with 25 samples and 500 genes.
#'
#' @name datExpr2
#' @docType data
#' @author Dong Li, \email{dxl466@cs.bham.ac.uk}
#' @keywords data
#' @format A matrix with 25 rows and 500 columns.
#' @examples
#' data(synthetic)
#' ## plot the heatmap of the correlation matrix ...
#' \dontrun{heatmap(cor(as.matrix(datExpr2)))}
NULL
```

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MODA documentation built on Nov. 8, 2020, 6:39 p.m.