### R code from vignette source 'RandomWalkRestartMH1.Rnw' ### Encoding: UTF-8 ################################################### ### code chunk number 1: style ################################################### BiocStyle::latex() ################################################### ### code chunk number 2: installation (eval = FALSE) ################################################### ## if (!requireNamespace("BiocManager", quietly=TRUE)) ## install.packages("BiocManager") ## BiocManager::install("RandomWalkRestartMH") ################################################### ### code chunk number 3: RandomWalkRestartMH1.Rnw:151-159 ################################################### library(RandomWalkRestartMH) library(igraph) data(PPI_Network) # We load the PPI_Network ## We create a Multiplex object composed of 1 layer (It's a Monoplex Network) ## and we display how it looks like PPI_MultiplexObject <- create.multiplex(PPI_Network,Layers_Name=c("PPI")) PPI_MultiplexObject ################################################### ### code chunk number 4: RandomWalkRestartMH1.Rnw:165-167 ################################################### AdjMatrix_PPI <- compute.adjacency.matrix(PPI_MultiplexObject) AdjMatrixNorm_PPI <- normalize.multiplex.adjacency(AdjMatrix_PPI) ################################################### ### code chunk number 5: RandomWalkRestartMH1.Rnw:175-181 ################################################### SeedGene <- c("PIK3R1") ## We launch the algorithm with the default parameters (See details on manual) RWR_PPI_Results <- Random.Walk.Restart.Multiplex(AdjMatrixNorm_PPI, PPI_MultiplexObject,SeedGene) # We display the results RWR_PPI_Results ################################################### ### code chunk number 6: RandomWalkRestartMH1.Rnw:190-194 ################################################### ## In this case we selected to induce a network with the Top 15 genes. TopResults_PPI <- create.multiplexNetwork.topResults(RWR_PPI_Results,PPI_MultiplexObject, k=15) ################################################### ### code chunk number 7: fig1 ################################################### par(mar=c(0.1,0.1,0.1,0.1)) plot(TopResults_PPI, vertex.label.color="black",vertex.frame.color="#ffffff", vertex.size= 20, edge.curved=.2, vertex.color = ifelse(igraph::V(TopResults_PPI)$name == "PIK3R1","yellow", "#00CCFF"), edge.color="blue",edge.width=0.8) ################################################### ### code chunk number 8: RandomWalkRestartMH1.Rnw:231-249 ################################################### data(Disease_Network) # We load our disease Network ## We load a data frame containing the gene-disease associations. ## See ?create.multiplexHet for details about its format data(GeneDiseaseRelations) ## We keep gene-diseases associations where genes are present in the PPI ## network GeneDiseaseRelations_PPI <- GeneDiseaseRelations[which(GeneDiseaseRelations$hgnc_symbol %in% PPI_MultiplexObject$Pool_of_Nodes),] ## We create the MultiplexHet object. PPI_Disease_Net <- create.multiplexHet(PPI_MultiplexObject, Disease_Network, GeneDiseaseRelations_PPI, c("Disease")) ## The results look like that PPI_Disease_Net ################################################### ### code chunk number 9: RandomWalkRestartMH1.Rnw:256-257 ################################################### PPIHetTranMatrix <- compute.transition.matrix(PPI_Disease_Net) ################################################### ### code chunk number 10: RandomWalkRestartMH1.Rnw:264-273 ################################################### SeedDisease <- c("269880") ## We launch the algorithm with the default parameters (See details on manual) RWRH_PPI_Disease_Results <- Random.Walk.Restart.MultiplexHet(PPIHetTranMatrix, PPI_Disease_Net,SeedGene,SeedDisease) # We display the results RWRH_PPI_Disease_Results ################################################### ### code chunk number 11: RandomWalkRestartMH1.Rnw:280-285 ################################################### ## In this case we select to induce a network with the Top 10 genes ## and the Top 10 diseases. TopResults_PPI_Disease <- create.multiplexHetNetwork.topResults(RWRH_PPI_Disease_Results, PPI_Disease_Net, GeneDiseaseRelations_PPI, k=10) ################################################### ### code chunk number 12: fig2 ################################################### par(mar=c(0.1,0.1,0.1,0.1)) plot(TopResults_PPI_Disease, vertex.label.color="black", vertex.frame.color="#ffffff", vertex.size= 20, edge.curved=.2, vertex.color = ifelse(V(TopResults_PPI_Disease)$name == "PIK3R1" | V(TopResults_PPI_Disease)$name == "269880","yellow", ifelse(V(TopResults_PPI_Disease)$name %in% PPI_Disease_Net$Pool_of_Nodes, "#00CCFF","Grey75")), edge.color=ifelse(E(TopResults_PPI_Disease)$type == "PPI","blue", ifelse(E(TopResults_PPI_Disease)$type == "Disease","black","grey50")), edge.width=0.8, edge.lty=ifelse(E(TopResults_PPI_Disease)$type == "bipartiteRelations", 2,1), vertex.shape= ifelse(V(TopResults_PPI_Disease)$name %in% PPI_Disease_Net$Pool_of_Nodes,"circle","rectangle")) ################################################### ### code chunk number 13: RandomWalkRestartMH1.Rnw:336-342 ################################################### data(Pathway_Network) # We load the Pathway Network ## We create a 2-layers Multiplex object PPI_PATH_Multiplex <- create.multiplex(PPI_Network,Pathway_Network, Layers_Name=c("PPI","PATH")) PPI_PATH_Multiplex ################################################### ### code chunk number 14: RandomWalkRestartMH1.Rnw:348-350 ################################################### AdjMatrix_PPI_PATH <- compute.adjacency.matrix(PPI_PATH_Multiplex) AdjMatrixNorm_PPI_PATH <- normalize.multiplex.adjacency(AdjMatrix_PPI_PATH) ################################################### ### code chunk number 15: RandomWalkRestartMH1.Rnw:356-361 ################################################### ## We launch the algorithm with the default parameters (See details on manual) RWR_PPI_PATH_Results <- Random.Walk.Restart.Multiplex(AdjMatrixNorm_PPI_PATH, PPI_PATH_Multiplex,SeedGene) # We display the results RWR_PPI_PATH_Results ################################################### ### code chunk number 16: RandomWalkRestartMH1.Rnw:367-371 ################################################### ## In this case we select to induce a multiplex network with the Top 15 genes. TopResults_PPI_PATH <- create.multiplexNetwork.topResults(RWR_PPI_PATH_Results, PPI_PATH_Multiplex, k=15) ################################################### ### code chunk number 17: fig3 ################################################### par(mar=c(0.1,0.1,0.1,0.1)) plot(TopResults_PPI_PATH, vertex.label.color="black", vertex.frame.color="#ffffff", vertex.size= 20, edge.curved= ifelse(E(TopResults_PPI_PATH)$type == "PPI", 0.4,0), vertex.color = ifelse(igraph::V(TopResults_PPI_PATH)$name == "PIK3R1", "yellow","#00CCFF"),edge.width=0.8, edge.color=ifelse(E(TopResults_PPI_PATH)$type == "PPI", "blue","red")) ################################################### ### code chunk number 18: RandomWalkRestartMH1.Rnw:412-424 ################################################### ## We keep gene-diseases associations where genes are present in the PPI ## or in the pathway network GeneDiseaseRelations_PPI_PATH <- GeneDiseaseRelations[which(GeneDiseaseRelations$hgnc_symbol %in% PPI_PATH_Multiplex$Pool_of_Nodes),] ## We create the MultiplexHet object. PPI_PATH_Disease_Net <- create.multiplexHet(PPI_PATH_Multiplex, Disease_Network, GeneDiseaseRelations_PPI_PATH, c("Disease")) ## The results look like that PPI_PATH_Disease_Net ################################################### ### code chunk number 19: RandomWalkRestartMH1.Rnw:431-432 ################################################### PPI_PATH_HetTranMatrix <- compute.transition.matrix(PPI_PATH_Disease_Net) ################################################### ### code chunk number 20: RandomWalkRestartMH1.Rnw:438-445 ################################################### ## We launch the algorithm with the default parameters (See details on manual) RWRH_PPI_PATH_Disease_Results <- Random.Walk.Restart.MultiplexHet(PPI_PATH_HetTranMatrix, PPI_PATH_Disease_Net,SeedGene,SeedDisease) # We display the results RWRH_PPI_PATH_Disease_Results ################################################### ### code chunk number 21: RandomWalkRestartMH1.Rnw:452-457 ################################################### ## In this case we select to induce a network with the Top 10 genes. ## and the Top 10 diseases. TopResults_PPI_PATH_Disease <- create.multiplexHetNetwork.topResults(RWRH_PPI_PATH_Disease_Results, PPI_PATH_Disease_Net, GeneDiseaseRelations_PPI_PATH, k=10) ################################################### ### code chunk number 22: fig4 ################################################### par(mar=c(0.1,0.1,0.1,0.1)) plot(TopResults_PPI_PATH_Disease, vertex.label.color="black", vertex.frame.color="#ffffff", vertex.size= 20, edge.curved=ifelse(E(TopResults_PPI_PATH_Disease)$type == "PATH", 0,0.3), vertex.color = ifelse(V(TopResults_PPI_PATH_Disease)$name == "PIK3R1" | V(TopResults_PPI_Disease)$name == "269880","yellow", ifelse(V(TopResults_PPI_PATH_Disease)$name %in% PPI_PATH_Disease_Net$Pool_of_Nodes, "#00CCFF","Grey75")), edge.color=ifelse(E(TopResults_PPI_PATH_Disease)$type == "PPI","blue", ifelse(E(TopResults_PPI_PATH_Disease)$type == "PATH","red", ifelse(E(TopResults_PPI_PATH_Disease)$type == "Disease","black","grey50"))), edge.width=0.8, edge.lty=ifelse(E(TopResults_PPI_PATH_Disease)$type == "bipartiteRelations", 2,1), vertex.shape= ifelse(V(TopResults_PPI_PATH_Disease)$name %in% PPI_PATH_Disease_Net$Pool_of_Nodes,"circle","rectangle")) ################################################### ### code chunk number 23: sessionInfo ################################################### toLatex(sessionInfo())