## ----style, echo=FALSE, results="asis", message=FALSE---------------------- BiocStyle::markdown() knitr::opts_chunk$set(tidy = FALSE, warning = FALSE, message = FALSE) ## ----echo=FALSE, results='hide', message=FALSE----------------------------- library(dSimer) ## ---- eval=FALSE----------------------------------------------------------- # library(dSimer) # help("dSimer") ## -------------------------------------------------------------------------- data(d2s_hsdn) #get disease-symptom associations for constructing feature vectors ds <- sample(unique(d2s_hsdn[,2]), 5) #get disease names sample simmat <- CosineDFV(ds, ds, d2s_hsdn) simmat ## -------------------------------------------------------------------------- data(d2g_separation) #get disease-gene associations ds<-sample(names(d2g_separation),5) ds sim<-BOG(ds,ds,d2g_separation) Normalize(sim) #normalize BOG sim scores ## -------------------------------------------------------------------------- options(stringsAsFactors = FALSE) #this may be neccessary d2g_fundo_sample<-read.table(text = "DOID:5218 IL6 DOID:8649 EGFR DOID:8649 PTGS2 DOID:8649 VHL DOID:8649 ERBB2 DOID:8649 PDCD1 DOID:8649 KLRC1 DOID:5214 MPZ DOID:5214 EGR2 DOID:5210 AMH") d2g_fundo_list<-x2y_df2list(d2g_fundo_sample) d2g_fundo_list ## -------------------------------------------------------------------------- m<-matrix(1:9,3,3) m Normalize(m) ## -------------------------------------------------------------------------- ## get the data data(go2g_sample) data(d2go_sample) ds<-names(d2go_sample) sim<-PSB(ds,ds,d2go_sample,go2g_sample) sim Normalize(sim) ## -------------------------------------------------------------------------- ## in this method, we must use disease-gene associations ## which genes are represented by entrez ids because of ## HumanNet data(d2g_fundo_entrezid) ##get disease-gene associations data(HumanNet_sample) ## we specified 5 DOIDs to match Human_sample ds<-c("DOID:8176","DOID:2394","DOID:3744","DOID:8466","DOID:5679") llsnlist<-LLSn2List(HumanNet_sample) FunSim(ds,ds,d2g_fundo_entrezid,llsnlist) ## -------------------------------------------------------------------------- ## get disease-gene associations and HPRD PPI data data(d2g_fundo_symbol) data(PPI_HPRD) graph_hprd<-graph.data.frame(PPI_HPRD,directed=FALSE) #get a igraph object based on HPRD PPI data ds<-sample(names(d2g_fundo_symbol),5) ICod(ds,ds,d2g_fundo_symbol,graph_hprd) ## -------------------------------------------------------------------------- data(d2g_separation) ## get disease-gene associations ds<-sample(names(d2g_separation),5) Sun_annotation(ds,ds,d2g_separation) ## -------------------------------------------------------------------------- ## get a sample of disease-GO associations data(d2go_sample) ds<-names(d2go_sample) Sun_function(ds,ds,d2go_sample) ## -------------------------------------------------------------------------- data(d2g_fundo_symbol) data(graphlet_sig_hprd) #get graphlet signatures of genes in HPRD PPI network data(weight) ds<-sample(names(d2g_fundo_symbol),5) Sun_topology(ds,ds,d2g_fundo_symbol,graphlet_sig_hprd,weight) ## -------------------------------------------------------------------------- ## get the disease-gene association data and interactome data data(d2g_separation) data(interactome) ## import ppi data to R by igraph graph_interactome<-graph.data.frame(interactome,directed=FALSE) ## calculate separation of 5 sample diseases ds<-sample(names(d2g_separation),5) sep<-Separation(ds,ds,d2g_separation,graph_interactome) ## convert separation into simialrity sim<-Separation2Similarity(sep) sim ## -------------------------------------------------------------------------- data(d2g_fundo_symbol) d2g_sample<-d2g_fundo_symbol[1:10] plot_bipartite(d2g_sample) ## -------------------------------------------------------------------------- data("PPI_HPRD") g<-graph.data.frame(PPI_HPRD,directed = FALSE) #get an igraph graph data(d2g_fundo_symbol) a<-d2g_fundo_symbol[["DOID:8242"]] # get gene set a b<-d2g_fundo_symbol[["DOID:4914"]] # get gene set b plot_topo(a,b,g) ## -------------------------------------------------------------------------- data(d2g_separation) data(interactome) graph_interactome<-graph.data.frame(interactome,directed=FALSE) ds<-c("myocardial ischemia","myocardial infarction","coronary artery disease", "cerebrovascular disorders","arthritis, rheumatoid","diabetes mellitus, type 1", "autoimmune diseases of the nervous system","demyelinating autoimmune diseases, cns", "respiratory hypersensitivity","asthma","retinitis pigmentosa", "retinal degeneration","macular degeneration") sep<-Separation(ds,ds,d2g_separation,graph_interactome) sim<-Separation2Similarity(sep) plot_heatmap(sim,font.size = 3) plot_net(sim,cutoff=0.2)