## ----style, eval=TRUE, echo=FALSE, results='asis'-------------------------- BiocStyle::latex() ## ----options,echo=FALSE----------------------------------- options(width=60) ## ----env, echo=FALSE, warning=FALSE----------------------- suppressPackageStartupMessages(library("GenomicRanges")) suppressPackageStartupMessages(library("igraph")) ## ----init-ChiapetExperimentData, results = 'hide', warning=FALSE, message=FALSE, prompt=TRUE, tidy=TRUE---- library(R3CPET) petFile <- file.path(system.file("example",package="R3CPET"),"HepG2_interactions.txt") tfbsFile <- file.path(system.file("example",package="R3CPET"),"HepG2_TF.txt.gz") x <- ChiapetExperimentData(pet = petFile, tfbs= tfbsFile, IsBed = FALSE, ppiType="HPRD", filter= TRUE) ## ----Read_chiapetData_chiapetTools, warning=FALSE, tidy=TRUE, prompt=TRUE---- petPath <- system.file("example","HepG2_interactions.txt",package="R3CPET") petFile <- read.table(petPath, sep = "\t", header = TRUE) head(petFile) ## ----Read_chiapetData_BED, warning=FALSE, tidy=TRUE, prompt=TRUE---- petPath <- system.file("example","HepG2_centered.bed",package="R3CPET") petFile <- read.table(petPath, sep = "\t", header = FALSE, comment.char = '+') head(petFile) ## ----loadPETs_example, warning=FALSE, tidy=TRUE, prompt=TRUE---- ## if it has 6 columns format IsBed = FALSE petPath <- system.file("example","HepG2_interactions.txt",package="R3CPET") x <- loadPETs(x,petFile=petPath, IsBed=FALSE) ## ----loadPETs_BED_example, warning=FALSE, tidy=TRUE, prompt=TRUE, eval=FALSE---- # ## loading a 4 columns BED file # petPath <- system.file("example","HepG2_centered.bed",package="R3CPET") # x <- loadPETs(x,petFile=petPath, IsBed=TRUE, header = FALSE) ## ----TF_example, warning=FALSE, tidy=TRUE, prompt=TRUE---- ## loading a 4 columns BED file TFPath <- system.file("example","HepG2_TF.txt.gz",package="R3CPET") TF <- read.table(TFPath, sep = "\t", header= FALSE) head(TF) x <- loadTFBS(x,tfbsFile=TFPath) ## ----HPRD_Biogrid_example, warning=FALSE, tidy=TRUE, prompt=TRUE---- data(HPRD) data(Biogrid) PPI.HPRD PPI.Biogrid ## ----loadPPI_usage, warning=FALSE, tidy=TRUE, eval=FALSE, prompt=TRUE---- # loadPPI(object,type=c("HPRD","Biogid"),customPPI = NULL, # filter = FALSE,term = "GO:0005634", # annot = NULL, RPKM = NULL, threshold = 1 ) ## ----loadPPI_loading, warning=FALSE, tidy=TRUE, prompt=TRUE---- ## loading the PPI with GO filtering x <- loadPPI(x,type="HPRD", filter=TRUE) ## ----createIndexes, warning=FALSE, tidy=TRUE, prompt=TRUE---- x <- createIndexes(x) x ## ----buildingNetworks, warning=FALSE, tidy=TRUE,cache=TRUE, prompt=TRUE, eval=FALSE---- # nets<- buildNetworks(x, minFreq=0.1, maxFreq=0.9) # nets ## ----load_nets, warning=FALSE, echo=FALSE, message=TRUE---- load(system.file("example","nets.RData",package="R3CPET")) nets ## ----InferNetowrks_usage, warning=FALSE, tidy=TRUE, eval=FALSE---- # InferNetworks(object,thr =0.5,max_iter = 500L, max_time = 3600L, eta=0.01,gamma=1,alpha=1) ## ----InferNetowrks, warning=FALSE, tidy=TRUE,cache=TRUE, prompt=TRUE, eval=FALSE---- # hlda <- InferNetworks(nets) # hlda ## ----load_hlda, warning=FALSE, echo=FALSE, message=TRUE---- load(system.file("example","hlda.RData",package="R3CPET")) hlda ## ----InferNetowrks_topElemets, warning=FALSE, tidy=TRUE, prompt=TRUE---- head(topEdges(hlda)) head(topNodes(hlda)) ## ----InferNetowrks_Networks, warning=FALSE, tidy=TRUE, prompt=TRUE---- hlda <- GenerateNetworks(hlda) head(networks(hlda)) ## ----Annotate expression, warning=FALSE, tidy=TRUE, prompt=TRUE---- data(RPKMS) hlda<- annotateExpression(hlda,RPKMS) networks(hlda)[[1]] ## ----cluster_usage, tidy=TRUE, eval=FALSE---------------- # clusterInteractions(object, method=c("clues","sota"), nbClus=20 ) ## ----clusterInteractions, tidy=TRUE,cache=TRUE, prompt=TRUE---- ## clustering using the "clues" method hlda <- clusterInteractions(hlda, method="clues") ## ----plot3CPETRes_usage, tidy=TRUE, eval=FALSE----------- # # plot3CPETRes(object, path="", W=14, H=7 , # type=c("heatmap","clusters","curve","avgCurve","netSim", "networks"), # byEdge=TRUE, layoutfct=layout.kamada.kawai, ...) ## ----getRegionsIncluster, tidy=TRUE, prompt=TRUE---------- getRegionsIncluster(hlda,x,cluster=3) ## ----heatmap, tidy=TRUE,cache=TRUE, prompt=TRUE,fig.align='center' , fig.height=3---- plot3CPETRes( hlda, type = 'heatmap') ## ----plot_curve, tidy=TRUE,cache=TRUE, prompt=TRUE,fig.align='center', fig.width=6, fig.height=3.5---- ## plotting curves plot3CPETRes(hlda, type = 'curve') ## ----plot_avgCurve, tidy=TRUE,cache=TRUE, prompt=TRUE,fig.align='center', fig.width=6, fig.height=3.5---- ## plotting Average curves plot3CPETRes(hlda, type = 'avgCurve') ## ----plot_clusters, tidy=TRUE,cache=TRUE, prompt=TRUE,fig.align='center',fig.width=5, fig.height=5---- ## plotting pair-wise clusters scatter plots plot3CPETRes(hlda, type = 'clusters') ## ----plot_networks, tidy=TRUE,cache=TRUE, prompt=TRUE,fig.align='center', warning=FALSE, message=FALSE, fig.width=3.5, fig.height=3.5---- nets_plot <- plot3CPETRes(hlda, type = 'networks') plot(nets_plot[[4]]) ## ----plot_netSim, tidy=TRUE,cache=FALSE, prompt=TRUE,fig.align='center', warning=FALSE, fig.width=5, fig.height=3.5---- plot3CPETRes(hlda,type = 'netSim') ## ----visualizeCircos_usage,tidy=TRUE, eval=FALSE--------- # # visualizeCircos(object, data, cluster = 1, chrLenghts = NULL) ## ----plot_circos, tidy=TRUE, prompt=TRUE,fig.align='center', fig.height=6, fig.width=6, message=FALSE, warning=FALSE---- visualizeCircos(hlda,x, cluster = 4) ## ----GOEnrich.networks_usage, warning=FALSE, tidy=TRUE, eval=FALSE, prompt=TRUE---- # # GOEnrich.networks(object, fdr= 0.05, GOlimit = 5,path = "") ## ----GOEnrich.networks, warning=FALSE, tidy=TRUE, prompt=TRUE, fig.align='center', cache=TRUE, fig.show='hide', eval=FALSE---- # # GOEnrich.networks(hlda, path= '.') ## ----outputGenesPerClusterToDir_usage,tidy=TRUE, eval=FALSE---- # outputGenesPerClusterToDir(hdaRes,data,path="ClustersGenes", ...) ## ----outputGenesPerClusterToDir, warning=FALSE, tidy=TRUE, prompt=TRUE, message=FALSE, eval=FALSE---- # outputGenesPerClusterToDir(hlda, x) ## ----GOEnrich_folder, warning=FALSE, tidy=TRUE, prompt=TRUE, fig.align='center', fig.show='hide', message=FALSE, eval=FALSE---- # GOEnrich.folder(folder = "ClustersGenes/") ## ----createShinyServer,tidy=TRUE, eval=FALSE------------- # createServer(x,nets,hlda) ## ----sessioninfo------------------------------------------ sessionInfo()