BuildNetwork {G1DBN}
Network object creation
Description
Given a list of scored edges and a REQUIRED vector of labels (e.g, 1:p), this function builds an object "Network". This is useful for exporting a network described by a list of edges to a network described by an adjacency matrix.
Usage
BuildNetwork(Edges,Labels,nonedges.val=NA)
Arguments
- Edges
- a n imes 3 matrix (each line contains a couple of vertices plus the associated score)
- Labels
- a vector of labels for the vertices
- nonedges.val
- optional. Value attributed to the not existing edges in the generated score matrix out$Score, default=NA
Values
a list that contains out$Vertices$Num the number of vertices, out$Vertices$Labels a vector of labels of the vertices, out$Vertices$Connected a vector of the connected vertices, out$Edges$Prop the proportion of edges, out$Edges$Num the number of edges, the graph of the network (out$AdjMatrix an adjacency matrix and out$Edges$List a list of edges) and out$Score a score matrix.
See Also
BuildEdges
Examples
library(G1DBN) ## ======================== ## SIMULATING THE NETWORK ## ________________________ ## number of genes p <- 10 ## number of time points n <- 20 ## proportion of genes geneProp <- 0.05 ## the network - adjacency Matrix MyNet <- SimulNetworkAdjMatrix(p,geneProp,c(-1.5,-0.5,0.5,1.5)) cat("\n==========================================\n") cat("SIMULATION\n") ## ====================================== ## SIMULATING THE TIME SERIES EXPERIMENTS ## ______________________________________ ## ## Autoregressive model ## cat("Time series experiments with") ## initializing the B vector B <- runif(p,-1,1) ## initializing the variance of the noise sigmaEps <- runif(p,0.1,0.5) ## initializing the process Xt X0 <- B + rnorm(p,0,sigmaEps*10) ## the times series process Xn <- SimulGeneExpressionAR1(MyNet$A,B,X0,sigmaEps,n) ## ====================================== ## NETWORK INFERENCE WITH G1DBN ## ______________________________________ ## cat("\n==========================================\n") cat("NETWORK INFERENCE\n") cat("Using a Dynamic Bayesian Network model\n") ## STEP 1 ## ------ cat("STEP 1...\n") S1 <- DBNScoreStep1(Xn, method='ls') ## STEP 2 ## ------ cat("STEP 2...\n") alpha1=0.5 S2 <- DBNScoreStep2(S1$S1ls, data=Xn, method='ls', alpha1=alpha1) ## ====================================== ## POST TREATMENTS ## building the inferred Graph G1 <- BuildEdges(S1$S1ls,threshold=alpha1,dec=FALSE) ## encoding as the adjancecy matrix graph Step1InferredNet <- BuildNetwork(G1,1:p) Step1InferredNet #Step 2 alpha2=0.05 G2 <- BuildEdges(S2,threshold=alpha2,dec=FALSE) Step2InferredNet <- BuildNetwork(G2,1:p) ## ====================================== ## PLOTTING THE RESULTS... ## ______________________________________ ## Not run: cat(" ========================================== ") cat("SUMMARY ") cat("Plotting the results... ") split.screen(c(1,3)) ## The Original graph and data ## --------------------------- # set the edges list of the simulated network G0 <- BuildEdges(MyNet$AdjMatrix,threshold=0.9,dec=TRUE) ## Nodes coordinates are calculated according to the global structure of the network all_parents=c(G0[,1],G1[,1], G2[,1]) all_targets=c(G0[,2],G1[,2], G2[,2]) posEdgesG0=1:dim(G0)[1] posEdgesG1=(dim(G0)[1]+1):(dim(G0)[1]+dim(G1)[1]) posEdgesG2=(dim(G0)[1]+dim(G1)[1]+1):length(all_parents) ## Global network with all the edges netAll = graph.edgelist(cbind(as.character(all_parents),as.character(all_targets))) ## Nodes coordinates nodeCoord=layout.fruchterman.reingold(netAll) #after Step 1 screen(1) # set the edges list netG1 = graph.edgelist(cbind(as.character(G1[,1]),as.character(G1[,2]))) # set the object for plotting the network with global coordinates of all nodes G1toPlot=delete.edges(netAll, E(netAll)[c(posEdgesG0,posEdgesG2)-1] ) # plot the network plot(G1toPlot, layout=nodeCoord, vertex.label = get.vertex.attribute(G1toPlot, name="name"), edge.arrow.size = 0.2, main="G1DBN Inferred network: Step 1") #after Step 2 screen(2) # set the edges list netG2 = graph.edgelist(cbind(as.character(G2[,1]),as.character(G2[,2]))) # set the object for plotting the network with global coordinates of all nodes G2toPlot=delete.edges(netAll, E(netAll)[c(posEdgesG0,posEdgesG1)-1 ] ) # plot the network plot(G2toPlot, layout=nodeCoord, vertex.label = get.vertex.attribute(G2toPlot, name="name"),edge.arrow.size = 0.2, main="G1DBN Inferred network: Step 2") screen(3) net0 = graph.edgelist(cbind(as.character(G0[,1]),as.character(G0[,2]))) # set the object for plotting the network with global coordinates of all nodes G0toPlot=delete.edges(netAll, E(netAll)[c(posEdgesG1,posEdgesG2)-1] ) plot(G0toPlot, layout=nodeCoord, vertex.label = get.vertex.attribute(G0toPlot, name="name"), edge.arrow.size = 0.2, main="Simulated network:") close.screen(all = TRUE) ## End(Not run) cat("") cat("\nDONE !\n")
Documentation reproduced from package G1DBN, version 3.1. License: GPL (>= 2)