## ----setup, include=FALSE------------------------------------------------ knitr::opts_chunk$set(dpi = 300) knitr::opts_chunk$set(cache=FALSE) ## ---- eval = TRUE, echo = FALSE,hide=TRUE, message=FALSE,warning=FALSE---- devtools::load_all() ## ---- eval = FALSE------------------------------------------------------- # source("https://bioconductor.org/biocLite.R") # biocLite("SpidermiR") ## ---- eval = TRUE-------------------------------------------------------- org<-SpidermiRquery_species(species) ## ---- eval = TRUE, echo = FALSE------------------------------------------ knitr::kable(org, digits = 2, caption = "List of species",row.names = TRUE) ## ---- eval = TRUE-------------------------------------------------------- net_type<-SpidermiRquery_networks_type(organismID=org[9,]) ## ---- eval = TRUE, echo = FALSE------------------------------------------ net_type ## ---- eval = TRUE-------------------------------------------------------- net_shar_prot<-SpidermiRquery_spec_networks(organismID = org[9,], network = "SHpd") ## ---- eval = TRUE, echo = FALSE------------------------------------------ net_shar_prot ## ---- eval = TRUE-------------------------------------------------------- disease<-SpidermiRquery_disease(diseaseID) ## ---- eval = TRUE, echo = FALSE------------------------------------------ disease ## ---- eval = TRUE-------------------------------------------------------- out_net<-SpidermiRdownload_net(net_shar_prot) ## ---- eval = TRUE, echo = FALSE------------------------------------------ str(out_net) ## ---- eval = FALSE------------------------------------------------------- # mirna<-c('hsa-miR-567','hsa-miR-566') # SpidermiRdownload_miRNAprediction(mirna_list=mirna) ## ---- eval = FALSE------------------------------------------------------- # list<-SpidermiRdownload_miRNAvalidate(validated) ## ---- eval = FALSE------------------------------------------------------- # list<-SpidermiRdownload_miRNAextra_cir(miRNAextra_cir) ## ---- eval = TRUE-------------------------------------------------------- mir_pharmaco<-SpidermiRdownload_pharmacomir(pharmacomir=pharmacomir) ## ---- eval = TRUE-------------------------------------------------------- geneSymb_net<-SpidermiRprepare_NET(organismID = org[9,], data = out_net) ## ---- eval = TRUE, echo = FALSE------------------------------------------ knitr::kable(geneSymb_net[[1]][1:5,c(1,2,3,5,8)], digits = 2, caption = "shared protein domain",row.names = FALSE) ## ---- eval = TRUE-------------------------------------------------------- miRNA_NET<-SpidermiRanalyze_mirna_network(data=geneSymb_net,disease="prostate cancer",miR_trg="val") ## ---- eval = TRUE, echo = FALSE------------------------------------------ str(miRNA_NET) ## ---- eval = FALSE------------------------------------------------------- # miRNA_complNET<-SpidermiRanalyze_mirna_gene_complnet(data=geneSymb_net,disease="prostate cancer",miR_trg="val") ## ---- eval = TRUE-------------------------------------------------------- mir_pharmnet<-SpidermiRanalyze_mirnanet_pharm(mir_ph=mir_pharmaco,net=miRNA_NET) ## ---- eval = FALSE------------------------------------------------------- # miRNA_NET_ext_circmT<-SpidermiRanalyze_mirna_extra_cir(data=miRNA_complNET,"mT") ## ---- eval = FALSE------------------------------------------------------- # miRNA_NET_ext_circmCT<-SpidermiRanalyze_mirna_extra_cir(data=miRNA_complNET,"mCT") ## ---- eval = TRUE-------------------------------------------------------- biomark_of_interest<-c("hsa-miR-214","PTEN","FOXO1","hsa-miR-27a") GIdirect_net<-SpidermiRanalyze_direct_net(data=miRNA_NET,BI=biomark_of_interest) ## ---- eval = TRUE, echo = FALSE------------------------------------------ str(GIdirect_net) ## ---- eval = FALSE------------------------------------------------------- # # subnet<-SpidermiRanalyze_direct_subnetwork(data=miRNA_NET,BI=biomark_of_interest) # ## ---- eval = FALSE------------------------------------------------------- # # GIdirect_net_neigh<-SpidermiRanalyze_subnetwork_neigh(data=miRNA_NET,BI=biomark_of_interest) ## ---- eval = FALSE------------------------------------------------------- # top10_cent_gene<-SpidermiRanalyze_degree_centrality(miRNA_NET,cut=10) ## ---- eval = FALSE------------------------------------------------------- # comm<- SpidermiRanalyze_Community_detection(data=miRNA_NET,type="FC") ## ---- eval = FALSE------------------------------------------------------- # cd_net<-SpidermiRanalyze_Community_detection_net(data=miRNA_NET,comm_det=comm,size=1) ## ---- eval = FALSE------------------------------------------------------- # gi=c("CF","ROCK1","KIT","CCND2") # mol<-SpidermiRanalyze_Community_detection_bi(data=comm,BI=gi) ## ---- eval = TRUE-------------------------------------------------------- miRNA_cN <-data.frame(gA=c('IGFL3','GABRA1'),gB=c('IGFL2','KRT13'),stringsAsFactors=FALSE) tumour<-c("TCGA-E9-A1RD-01A","TCGA-E9-A1RC-01A") normal<-c("TCGA-BH-A18P-11A","TCGA-BH-A18L-11A") de_int<-SpidermiRanalyze_DEnetworkTCGA(data=miRNA_cN, TCGAmatrix=Data_CANCER_normUQ_filt, tumour, normal ) ## ---- eval = TRUE-------------------------------------------------------- library(networkD3) SpidermiRvisualize_mirnanet(data=mir_pharmnet[sample(nrow(mir_pharmnet), 100), ] ) ## ---- eval = TRUE-------------------------------------------------------- biomark_of_interest<-c("hsa-let-7b","MUC1","PEX7","hsa-miR-222") SpidermiRvisualize_BI(data=mir_pharmnet[sample(nrow(mir_pharmnet), 100), ],BI=biomark_of_interest) ## ---- eval = TRUE-------------------------------------------------------- library(visNetwork) SpidermiRvisualize_direction(data=mir_pharmnet[sample(nrow(mir_pharmnet), 100), ] ) ## ---- eval = TRUE-------------------------------------------------------- SpidermiRvisualize_plot_target(data=miRNA_NET[1:15,]) ## ----fig.width=4, fig.height=4, eval = TRUE------------------------------ SpidermiRvisualize_degree_dist(data=miRNA_NET) ## ---- fig.width=10, fig.height=10,eval = TRUE---------------------------- SpidermiRvisualize_adj_matrix(data=miRNA_NET[1:30,]) ## ----fig.width=4, fig.height=4, eval = TRUE------------------------------ SpidermiRvisualize_3Dbarplot(Edges_1net=1041003,Edges_2net=100016,Edges_3net=3008,Edges_4net=1493,Edges_5net=1598,NODES_1net=16502,NODES_2net=13338,NODES_3net=1429,NODES_4net=675,NODES_5net=712,nmiRNAs_1net=0,nmiRNAs_2net=74,nmiRNAs_3net=0,nmiRNAs_4net=0,nmiRNAs_5net=37) ## ---- eval = TRUE,echo = FALSE------------------------------------------- B<-matrix( c("Gene network", "Validated miRNA-target","","", "Predicted miRNA-target","","","", "Extracellular Circulating microRNAs", "miRNA-disease", "Drug Associations", "GeneMania", "miRTAR", "miRwalk","miRTarBase", "DIANA", "Miranda", "PicTar","TargetScan","miRandola","miR2disease","Pharmaco-miR", "Current","N/A","miRwalk2","miRTarBase 6.1","DIANA- 5.0","N/A","N/A","TargetScan7.1","miRandola v 02/2017","N/A","N/A", 2016,2009,2015,"N/A",2013,2010,"N/A","2016",2017,2009,"N/A", "http://genemania.org/data/current/","http://watson.compbio.iupui.edu:8080/miR2Disease/download/miRtar.txt","http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/downloads/vtm/hsa-vtm-gene.rdata.zip","http://mirtarbase.mbc.nctu.edu.tw/cache/download/6.1/miRTarBase_SE_WR.xls","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","http://mirandola.iit.cnr.it/download/miRandola_version_02_2017.txt","http://watson.compbio.iupui.edu:8080/miR2Disease/download/AllEntries.txt","http://pharmaco-mir.org/home/download_VERSE_db/pharmacomir_VERSE_DB.csv" ), nrow=11, ncol=5) colnames(B)<-c("CATEGORY","EXTERNAL DATABASE","VERSION","LAST UPDATE","LINK") ## ---- eval = TRUE, echo = FALSE------------------------------------------ knitr::kable(B, digits = 2, caption = "Features",row.names = FALSE) ## ---- eval = FALSE------------------------------------------------------- # # a<-Case_Study1_loading_1_network(species) # b<-Case_Study1_loading_2_network(data=a) # c<-Case_Study1_loading_3_network(data=b,dataFilt=dataFilt,dataClin=dataClin) # d<-Case_Study1_loading_4_network(TERZA_NET=c) # ## ---- eval = FALSE------------------------------------------------------- # a2<-Case_Study2_loading_1_network(species) # b2<-Case_Study2_loading_2_network(data=a2) # c2<-Case_Study2_loading_3_network(sdas=a2,miRNA_NET=b2) ## ----sessionInfo--------------------------------------------------------- sessionInfo()