## ----setup, include=FALSE----------------------------------------------------- library(GeneNetworkBuilder) library(knitr) library(STRINGdb) ## ----STRING------------------------------------------------------------------- library(GeneNetworkBuilder) try({ ## just in case STRINGdb not work library(STRINGdb) string_db <- STRINGdb$new( version="10", species=9606, score_threshold=400) data(diff_exp_example1) example1_mapped <- string_db$map( diff_exp_example1, "gene", removeUnmappedRows = TRUE ) i <- string_db$get_interactions(example1_mapped$STRING_id) colnames(example1_mapped) <- c("gene", "P.Value", "logFC", "symbols") ## get significant up regulated genes. genes <- unique(example1_mapped$symbols[example1_mapped$P.Value<0.005 & example1_mapped$logFC>3]) x<-networkFromGenes(genes = genes, interactionmap=i, level=3) ## filter network ## unique expression data by symbols column expressionData <- uniqueExprsData(example1_mapped, method = 'Max', condenseName = "logFC") ## merge binding table with expression data by symbols column cifNetwork<-filterNetwork(rootgene=x$rootgene, sifNetwork=x$sifNetwork, exprsData=expressionData, mergeBy="symbols", miRNAlist=character(0), tolerance=1, cutoffPVal=0.001, cutoffLFC=1) ## convert the id back to symbol IDsMap <- expressionData$gene names(IDsMap) <- expressionData$symbols cifNetwork <- convertID(cifNetwork, IDsMap) ## polish network gR<-polishNetwork(cifNetwork) ## browse network browseNetwork(gR) })