## ---- eval = FALSE------------------------------------------------------- # Case_Study1_loading_1_network<-function(species){ # org<-SpidermiRquery_species(species) # net_shar_prot<-SpidermiRquery_spec_networks(organismID = org[6,], # network = "SHpd") # out_net<-SpidermiRdownload_net(net_shar_prot) # geneSymb_net<-SpidermiRprepare_NET(organismID = org[6,],data = out_net) # ds<-do.call("rbind", geneSymb_net) # data2<-as.data.frame(ds[!duplicated(ds), ]) # m<-c(data2$gene_symbolA) # m2<-c(data2$gene_symbolB) # s<-c(m,m2) # fr<- unique(s) # network = "SHpd" # print(paste("Downloading of 1 ",network, " network ", # "in ",org[6,]," with number of nodes: ", # length(fr)," and number of edges: ",nrow(data2), # sep = "")) # return(geneSymb_net) # } ## ---- eval = FALSE------------------------------------------------------- # Case_Study1_loading_2_network<-function(data){ # miRNA_complNET<-SpidermiRanalyze_mirna_gene_complnet(data, # disease="prostate cancer", # miR_trg="val") # m2<-c(miRNA_complNET$V1) # m3<-c(miRNA_complNET$V2) # s2<-c(m2,m3) # fr2<- as.data.frame(unique(s2)) # print(paste("Downloading of 2 network with the # integration of miRNA-gene-gene interaction with number of nodes ", # nrow(fr2)," and number of edges ", nrow(miRNA_complNET), sep = "")) # return(miRNA_complNET) # } ## ---- eval = FALSE------------------------------------------------------- # Case_Study1_loading_3_network<-function(data,dataFilt,dataClin){ # highstage <- dataClin[grep("7|8|9|10", dataClin$gleason_score), ] # highstage<-highstage[,c("bcr_patient_barcode","gleason_score")] # highstage<-t(highstage) # samples_hight<-highstage[1,2:ncol(highstage)] # dataSmTP <- TCGAquery_SampleTypes(barcode = colnames(dataFilt), # typesample = "TP") # dataSmNT <- TCGAquery_SampleTypes(barcode = colnames(dataFilt), # typesample ="NT") # colnames(dataFilt)<-substr(colnames(dataFilt),1,12) # se<-substr(dataSmTP, 1, 12) # common<-intersect(colnames(dataFilt),samples_hight) # dataSmNT<-substr(dataSmNT, 1, 12) # sub_net2<-SpidermiRanalyze_DEnetworkTCGA(data, # TCGAmatrix=dataFilt, # tumour=common,normal=dataSmNT) # ft<-sub_net2$V1 # ft1<-sub_net2$V2 # fgt<-c(ft,ft1) # miRNA_NET<-SpidermiRanalyze_mirna_network(sub_net2, # disease="prostate cancer",miR_trg="val") # TERZA_NET<-rbind(miRNA_NET,sub_net2) # print(paste("In the 3 network we found",length(unique(miRNA_NET$V1)), # " miRNAs and ", # length(unique(fgt)), " genes with ", nrow(TERZA_NET), # " edges " )) # return(TERZA_NET) # } ## ---- eval = FALSE------------------------------------------------------- # Case_Study1_loading_4_network<-function(TERZA_NET){ # comm<- SpidermiRanalyze_Community_detection(data=TERZA_NET,type="FC") # #SpidermiRvisualize_mirnanet(TERZA_NET) # cd_net<-SpidermiRanalyze_Community_detection_net(data=TERZA_NET, # comm_det=comm,size=5) # ft<-cd_net$V1 # ft1<-cd_net$V2 # fgt<-c(ft,ft1) # print(paste("In the 4 network we found",length(unique(fgt)), # " nodes and ", nrow(cd_net), " edges " )) # return(cd_net) # } ## ---- eval = FALSE------------------------------------------------------- # Case_Study2_loading_1_network<-function(species){ # org<-SpidermiRquery_species(species) # net_PHint<-SpidermiRquery_spec_networks(organismID = org[6,], # network = "PHint") # out_net<-SpidermiRdownload_net(net_PHint) # geneSymb_net<-SpidermiRprepare_NET(organismID = org[6,],data = out_net) # ds<-do.call("rbind", geneSymb_net) # data1<-as.data.frame(ds[!duplicated(ds), ]) # sdas<-cbind(data1$gene_symbolA,data1$gene_symbolB) # sdas<-as.data.frame(sdas[!duplicated(sdas), ]) # m<-c(data1$gene_symbolA) # m2<-c(data1$gene_symbolB) # s<-c(m,m2) # fr<- unique(s) # network="PHint" # print(paste("Downloading of 1 ",network, # " network ","in ",org[6,], # " with number of nodes: ",length(fr), # " and number of edges: ",nrow(sdas), sep = "")) # return(geneSymb_net) # } ## ---- eval = FALSE------------------------------------------------------- # Case_Study2_loading_2_network<-function(data){ # miRNA_NET<-SpidermiRanalyze_mirna_network(data, # disease="breast cancer",miR_trg="val") # m2<-c(miRNA_NET$V1) # m3<-c(miRNA_NET$V2) # s2<-c(m2,m3) # fr2<- as.data.frame(unique(s2)) # print(paste("Downloading of 2 network with the integration of # miRNA-gene interaction with number of nodes ", nrow(fr2)," # and number of edges ", nrow(miRNA_NET), sep = "")) # return(miRNA_NET) # } ## ---- eval = FALSE------------------------------------------------------- # Case_Study2_loading_3_network<-function(sdas,miRNA_NET){ # ds<-do.call("rbind", sdas) # data1<-as.data.frame(ds[!duplicated(ds), ]) # sdas<-cbind(data1$gene_symbolA,data1$gene_symbolB) # sdas<-as.data.frame(sdas[!duplicated(sdas), ]) # topwhol<-SpidermiRanalyze_degree_centrality(sdas) # topwhol_mirna<-SpidermiRanalyze_degree_centrality(miRNA_NET) # # miRNA_degree<-topwhol_mirna[grep("hsa",topwhol_mirna$dfer),] # seq_gd<-as.data.frame(seq(1, 15400, by = 50)) # even<-seq_gd[c(F,T),] # even2<-even # odd<-seq_gd[c(T,F),] # odd2<-odd[-1] # odd2[154]<-15400 # f<-cbind(even2,odd2-1) # # SQ<-cbind(odd,even-1) # # h<-as.data.frame(rbind(f,SQ)) # SQ <- as.data.frame(h[order(h$even2,decreasing=FALSE),]) # # table_pathway_enriched <- matrix(1, nrow(SQ),4) # colnames(table_pathway_enriched) <- c("interval min", # "interval max","gene","miRNA") # table_pathway_enriched <- as.data.frame(table_pathway_enriched) # # j=1 # for (j in 1:nrow(SQ)){ # a<-SQ$even2[j] # b<-SQ$V2[j] # d<-c(a,b) # gene_degree10<-topwhol[a:b,] # vfg<-rbind(miRNA_degree[1:10,],gene_degree10) # subnet<-SpidermiRanalyze_direct_subnetwork(data=miRNA_NET,BI=vfg$dfer) # # table_pathway_enriched[j,"interval min"] <- d[1] # table_pathway_enriched[j,"interval max"] <- d[2] # s<-unique(subnet$V1) # x<-unique(subnet$V2) # table_pathway_enriched[j,"miRNA"]<-length(s) # table_pathway_enriched[j,"gene"]<-length(x) # } # # df<-cbind(table_pathway_enriched$gene,table_pathway_enriched$miRNA) # rownames(df)<-table_pathway_enriched$`interval max` # categories <- c("protein", "miRNA") # colors <- c("green", "magenta") # op <- par(mar = c(5, 5, 4, 2) + 0.1) # matplot(df, type="l",col=colors,xlab = "N of Clusters", # main = "",ylab = "Interactions",cex.axis=2,cex.lab=2,cex.main=2) # legend("topright", col=colors, categories, bg="white", lwd=1,cex=2) # j=1 # a<-SQ$even2[j] # b<-SQ$V2[j] # d<-c(a,b) # gene_degree10<-topwhol[a:b,] # vfg<-rbind(miRNA_degree[1:10,],gene_degree10) # subnet<-SpidermiRanalyze_direct_subnetwork(data=miRNA_NET,BI=vfg$dfer) # m2<-c(subnet$V1) # m3<-c(subnet$V2) # s2<-c(m2,m3) # fr2<- as.data.frame(unique(s2)) # print(paste("Downloading of 3 network with proteins and miRNAs # with highest degree # centrality with ", nrow(fr2)," nodes and number of # edges ", nrow(subnet), sep = "")) # return(subnet) # } #