Nothing
#Plot a graph with nodes in modules indicated by colors
PlotGraphWithModules <- function(Adj_directed,PlotDendrogramObj,GV,node.size=8,arrow.size = 5,label.size = 3,alpha = 1,...) {
AC <- PlotDendrogramObj$dynamicColors #All (genes and GV) colors
CT <- table(AC) #Color table
Adj_symmetric_matrix <- PlotDendrogramObj$Adj_symmetric_matrix #symmetric matrix from PlotDendrogram
G_Order <- vector("list")
n <- vector("list")
MO <- c()
for (i in 1:length(CT)) {
G_Order[[i]] <- (which(AC==rownames(CT)[i]))
n[[i]] <- length(G_Order[[i]])
MO <- c(MO, G_Order[[i]])
}
#New adjacency matrix by module order (MO)
New_Mat_MO <- Adj_directed[MO,MO]
MM <- 0:(ncol(Adj_directed)-1)
NN1 <- as.matrix(cumsum(n))
NN2 <- NN1#[-nrow(NN1),]
Module <- CutModules(MM,c(0,NN2))
col <- rownames(CT)
names(col) <- levels(Module)
#library(network) need for network function
#Craete a graphical network used for the next step
net <- network(New_Mat_MO, directed = TRUE)
#Make two group (i) for phenotypes (e.g., gene expression)=circle and (ii) genotypes=Triangle
#shape.palette = c("Genotype" = 17,"Phenotype" = 19)
#char=colnames(Adj_symmetric_matrix)[GV+1:(ncol(Adj_symmetric_matrix)-1)] #all GV
if(GV==0){
char <- NULL
}
else{
char <- colnames(Adj_symmetric_matrix)[1:GV] #all GV
}
#char=colnames(Adj_symmetric_matrix)[-c(1:GV)] #all GV
net%v%"phono" <- ifelse((colnames(New_Mat_MO) %in% char),"Genotype","Phenotype")
#library(GGally) #Need for ggnet2 function
#Plot the final graph
PlotGraphWithModulesObj <- ggnet2(net,color = Module,palette = col,node.size=node.size,arrow.size = arrow.size,label=TRUE,label.size = label.size,alpha = alpha,
shape.legend = "",edge.label.color = Module,shape = net%v%"phono",shape.palette = c("Genotype" = 17,"Phenotype" = 19),
color.legend = "Modules",legend.position = "bottom",arrow.gap = 0.0280)
return(PlotGraphWithModulesObj)
}
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