## ---- eval=FALSE-------------------------------------------------------------- # ## try http if https is not available # if (!requireNamespace("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # BiocManager::install("EGAD") ## ---- eval=FALSE-------------------------------------------------------------- # browseVignettes("EGAD") ## ----library, results="hide"-------------------------------------------------- library(EGAD) ## ----eval=FALSE--------------------------------------------------------------- # data <- get_phenocarta(species="human", type="all") ## ----eval=FALSE--------------------------------------------------------------- # data <- get_biogrid(species="9606") ## ----eval=FALSE--------------------------------------------------------------- # data <- get_biogrid(species="9606", version="3.4.131") ## ----eval=FALSE--------------------------------------------------------------- # exprs <- get_expression_matrix_from_GEO(gseid="GSE34308") ## ----eval=FALSE--------------------------------------------------------------- # exprs <- get_expression_data_gemma(gseid="GSE34308", filtered = "true") ## ----eval=TRUE---------------------------------------------------------------- data("example_annotations") annotations <- make_annotations(example_annotations$gene2Annot, example_annotations$genes, example_annotations$annotationlist) head(colnames(annotations)) ## ----eval=TRUE---------------------------------------------------------------- data("example_binary_network") binary_net <- build_binary_network(example_binary_network$genepairs, example_binary_network$genelist) hist <- plot_distribution(node_degree(binary_net), b=10, xlim=c(0,14), ylim=c(0,2), xlab="Node degree") ## ----eval=TRUE---------------------------------------------------------------- extended_net <- extend_network(binary_net) hist <- plot_distribution(node_degree(extended_net), b=10, xlab="Node degree", ) ## ----eval=TRUE---------------------------------------------------------------- semantic_net <- build_semantic_similarity_network(annotations, example_binary_network$genelist) hist <- plot_distribution(node_degree(semantic_net), b=20, xlab="Node degree", bars=TRUE) ## ----eval=FALSE--------------------------------------------------------------- # data("example_coexpression") # coexp_net <- build_coexp_expressionSet(exprsSet, genelist, method="spearman", flag="rank") # # or # coexp_net <- build_coexp_network(exprs, genelist, method="spearman", flag="rank") ## ----eval=TRUE---------------------------------------------------------------- data("example_neighbor_voting") network <- example_neighbor_voting$network gba_auc_nv <- neighbor_voting(annotations, network, nFold=3, output="AUROC") # or gba_pr_nv <- neighbor_voting(annotations, network, nFold=3, output="PR") head(gba_auc_nv) head(gba_pr_nv) ## ----eval=TRUE---------------------------------------------------------------- multifunc_assessment <- calculate_multifunc(annotations) auc_mf <- auc_multifunc(annotations, multifunc_assessment[,4]) hist <- plot_distribution(auc_mf, xlab="AUROC", med=FALSE, avg=FALSE) ## ----eval=TRUE---------------------------------------------------------------- X <- runif(1000) Y <- runif(1000) + X*0.3 window <- 50 smoothedxy <- conv_smoother(X,Y, window, xlab="X",ylab="Y") ## ----eval=TRUE---------------------------------------------------------------- filt <- !is.na(gba_auc_nv[,1]) aucA <- gba_auc_nv[filt,1] aucB <- gba_auc_nv[filt,3] hist <- plot_distribution(aucA, xlab="AUROCs") avgs <- plot_density_compare(aucA, aucB, xlab="AUROCs") plot_value_compare(aucA, aucB) ## ----eval=TRUE, include=FALSE------------------------------------------------- scores <- 100:1 labels <- sample(c(0,1), 100, replace=TRUE) labels[1:13]<-1 labels[18:30]<-1 ## ----eval=TRUE---------------------------------------------------------------- roc <- plot_roc(scores, labels) ## ----eval=FALSE--------------------------------------------------------------- # scores <- predictions(labels, network) # plot_roc_overlay(scores, labels) ## ----eval=TRUE---------------------------------------------------------------- roc <- get_roc(scores, labels) ## ----eval=TRUE---------------------------------------------------------------- auroc <- get_auc(roc[,1], roc[,2]) print(auroc) ## ----eval=TRUE---------------------------------------------------------------- auroc <- auroc_analytic(scores, labels) print(auroc) ## ----eval=TRUE---------------------------------------------------------------- prc <- get_prc(scores, labels) prc <- plot_prc(scores, labels) ## ----eval=TRUE---------------------------------------------------------------- auprc <- auprc(scores, labels) print(auprc) ## ----eval=TRUE---------------------------------------------------------------- fbeta <- fmeasure(prc[,1], prc[,2], beta=1) ## ----eval=TRUE---------------------------------------------------------------- nd <- node_degree(network) hist <- plot_distribution(nd, xlab="Node degree", med = FALSE) ## ----eval=TRUE---------------------------------------------------------------- assort <- assortativity(network) print(assort) ## ----eval=FALSE--------------------------------------------------------------- # data("biogrid") # data("GO.human") ## ----eval=FALSE--------------------------------------------------------------- # genelist <- make_genelist(biogrid) # gene_network <- make_gene_network(biogrid,genelist) ## ----eval=FALSE--------------------------------------------------------------- # annotations <- make_annotations(GO.human[,c(2,3)],genelist,goterms) ## ----eval=FALSE--------------------------------------------------------------- # GO_groups_voted <- run_GBA(gene_network, annotations) ## ----eval=FALSE--------------------------------------------------------------- # GO_multifunc_assessment <- calculate_multifunc(annotations) ## ----eval=FALSE--------------------------------------------------------------- # # For genes # optimallist_genes <- GO_multifunc_assessment[,4] # auc_GO_mf <- auc_multifunc(annotations, GO_multifunc_assessment) # # # Or for GO groups # GO_genes_multifunc_assessment <- calculate_multifunc(t(annotations) ) # optimallist_GO <- GO_genes_multifunc_assessment[,4] # auc_gene_mf <- auc_multifunc(t(annotations), optimallist_GO) # ## ----eval=FALSE--------------------------------------------------------------- # auc_GO_nv <- GO_groups_voted[,1] # plot_distribution(auc_GO_nv, xlab="Neighbor voting AUROC ", # ylab="Number of functional terms", # b=30, xlim=c(0.4,1), ylim=c(0, 440), col="gray64", # density=FALSE, avg=FALSE, bars=TRUE) ## ----eval=FALSE--------------------------------------------------------------- # plot_distribution(auc_GO_mf, xlab="Optimal GO Ranking AUROC", # ylab="Number of functional terms", # b=20, xlim=c(0.2,1), ylim=c(0,4400), col="gray64", # density=FALSE, avg=FALSE, bars=TRUE) ## ----eval=FALSE--------------------------------------------------------------- # data("attr.human") # genelist <- unique(attr.human$entrezID[attr.human$type == "protein_coding" # & !is.na(attr.human$entrezID)] ) # exprs <- get_expression_matrix_from_GEO("GSE34308") ## ----eval=FALSE--------------------------------------------------------------- # network <- build_coexp_network(exprs, gene.list) # network <- build_coexp_expressionSet(exprsSet, genelist) ## ----eval=FALSE--------------------------------------------------------------- # assort <- assortativity(network) # nd <- node_degrees(network) # plot_density(nd) ## ----eval=FALSE--------------------------------------------------------------- # data("GO.human") # annotations <- make_annotations(GO.human[,c(2,3)],genelist,goterms) # annotations_sub <- filter_network_cols(annotations, min=20, max=300) # GO_groups_voted <- run_GBA(network, annotations_sub) ## ----eval=FALSE--------------------------------------------------------------- # data("biogrid") # data("GO.human") # data("ortho") # # # Prepare gene network and gene labels/annotations # genelist <- make_genelist(BIOGRID ) # gene_network <- make_gene_network(BIOGRID, genelist) # annotations <- make_annotations(GO.human[,c(2,3)],genelist,goterms) # # neighbor voting # nv_results <- run_GBA(gene_network, annotations) # mf_optimal <- calculate_multifunc(annotations) # optimal_list<- as.numeric(mf_optimal[,4]) # mf_results <- auc_multifunc(annotations, optimal_list) # # neighbor voting, ortholog constraints (e.g. yeast) # annotations_yeast <- filter_orthologs(annotations, genelist, ortho_yeast) # # nv_results_yeast <- run_GBA(gene_network, annotations_yeast) # mf_optimal_yeast <- calculate_multifunc(annotations_yeast) # optimal_list_yeast<- as.numeric(mf_optimal_yeast[,4]) # mf_results_yeast <- auc_multifunc(annotations_yeast, optimal_list_yeast) ## ----eval=FALSE--------------------------------------------------------------- # # Load data # load("net.RData") # load("ortho.RData") # # # Extend network # ext_gene_network <- extend_network(gene_network, max=6) # # neighbor voting # nv_results <- run_GBA(ext_gene_network, annotations) # # neighbor voting, ortholog constraints (e.g. yeast) # annotations_yeast <- filter_orthologs(annotations, genelist, ortho_yeast) # nv_results_yeast <- run_GBA(ext_gene_network, annotations_yeast)