inst/doc/with.BioGRID.STRING.R

## ----setup, include=FALSE------------------------------------------------
library(GeneNetworkBuilder)
library(simpIntLists)
library(knitr)
library(STRINGdb)

## ----BioGRID-------------------------------------------------------------
library(GeneNetworkBuilder)
library(simpIntLists)
i <- findInteractionList("human", "Official")
i <- lapply(i, function(.ele) cbind(from=as.character(.ele$name), to=as.character(.ele$interactors)))
i <- do.call(rbind, i)
set.seed(123)
## generate a random ChIP-seq binding table
rootgene <- sample(i[, 1], 1)
TFbindingTable <- i[i[, 1] == rootgene, ]
interactionmap <- i
# build network
sifNetwork<-buildNetwork(TFbindingTable=TFbindingTable, 
                        interactionmap=interactionmap, level=2)
ID=unique(as.character(sifNetwork))
## create a random expression data
expressionData <- data.frame(ID=ID,
                             logFC=sample(-3:3, length(ID), replace=TRUE),
                             P.Value=runif(n=length(ID), max=0.25))
## filter network
cifNetwork<-filterNetwork(rootgene=rootgene, sifNetwork=sifNetwork, 
                    exprsData=expressionData, mergeBy="ID",
                    miRNAlist=character(0), 
                    tolerance=1, cutoffPVal=0.01, cutoffLFC=1)
## polish network
gR<-polishNetwork(cifNetwork)
## browse network
browseNetwork(gR)

## ----STRING--------------------------------------------------------------
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)
    rootgene <- sample(i[, 1], 1) # random set a rootgene. It should be set by your experiment.
    TFbindingTable <- i[i[, 1] == rootgene, c("from", "to")]
    interactionmap <- i[, c("from", "to")]
    sifNetwork<-buildNetwork(TFbindingTable=TFbindingTable, 
                            interactionmap=interactionmap, level=2)
    ## filter network
    colnames(example1_mapped) <- c("gene", "P.Value", "logFC", "symbols")
    ## 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=rootgene, 
                              sifNetwork=sifNetwork, 
                              exprsData=expressionData, mergeBy="symbols",
                              miRNAlist=character(0), 
                              tolerance=1, cutoffPVal=0.01, 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)
})

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GeneNetworkBuilder documentation built on Nov. 17, 2017, 12:12 p.m.