--- title: "GeneNetworkBuilder Guide" author: "Jianhong Ou, Lihua Julie Zhu" date: "`r doc_date()`" package: "`r pkg_ver('GeneNetworkBuilder')`" bibliography: bibliography.bib csl: nature.csl abstract: > Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. vignette: > %\VignetteIndexEntry{GeneNetworkBuilder Vignette} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document --- ```{r, echo=FALSE, results="hide", warning=FALSE} suppressPackageStartupMessages({ library(GeneNetworkBuilder) library(Rgraphviz) library(XML) }) knitr::opts_chunk$set(warning=FALSE, message=FALSE) ``` # Introduction Transcription factors (TFs), chromatin modifications and microRNAs (miRNAs) are important in regulating gene expression[@Walhout2010]. Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq)[@Peter2009] or genome tiling array analysis (ChIP-chip)[@Tae2006] are widely used technologies for identifying genome-wide binding sites of TFs (TFBDs)[@Kerstin2010]. The role of the TF on genome-wide gene expression can be determined by expression microarray or RNA-seq experiments[@Zhong2009]. By combining both technologies, researchers have the potential to decipher the regulatory network of the TF. Genes bound by the TF and altered in expression are considered direct targets of the TF, and genes altered in expression but not bound by the TF are the indirect targets of the TF. The indirect targets can potentially form a complex network itself, especially when the TF is a master regulator who regulates other TFs. To facilitate identification of the complex regulatory network of TFs and how indirect targets are inter-connected, we have developed GeneNetworkBuilder (GNB). Each genereated network is consisted of a directed acyclic graph with each edge representing TF -> gene, TF -> miRNA, or miRNA -> gene where a -> b represents "a regulates b". # Examples of using GeneNetworkBuilder To use GNB, users need to input a list of genes bound by a given TF and another list of genes/miRNAs with altered expression by knockdown/knockout of the same TF. The bound gene list could be obtained from ChIP-seq or ChIP-chip experiment. The gene list with altered expression are from RNA-seq or expression microarray experiment. ChIP experiments and expression experiments should preferably be performed in similar experimental condition such as tissue type, development stage etc. In addition, users need to select a TF regulatory network from GNB or upload customized TF regulatory network. GNB provides two embedded regulatory networks. One is designed for _Caenorhabditis elegans_ combining database EDGEdb[@Barrasa2007] and microCosm Targets[@Sam2008]. Database MicroCosm Targets contains computationally predicted targets for miRNAs across many species. EDGEdb contains experimentally determined interactions of ~934 worm TFs by high-throughput yeast on-hybrid (Y1H) assay. And the other embedded regulatory network is designed for _Homo sapiens_ combining database FANTOM[@Ravasi2010], miRGen[@Panagiotis2010] and microCosm Targets[@Sam2008]. FANTOM stores physical interactions among the majority of human/mouse DNA-binding TFs. The miRGen is an integrated database of miRNA regulation by TFs and miRNA targets mainly for human. Figure 1 depicts the relationships of various databases and its role in GNB. ![Workflow of GeneNetworkBuilder](workflow.png) # Quick start Here is an example to use GNB to generate a simple regulatory network for _C. elegans_. There are three steps, 1. buildNetwork, build the network by GNB-embedded or user-defined regulatory network starting from the bound gene list. 2. filterNetwork, filter the network by differential expressed genes/miRNAs. 3. polishNetwork, generate the graphNEL object with display style. ```{r quickStart} library(GeneNetworkBuilder) ##load C. elegans miRNA ID lists data("ce.miRNA.map") ##load GNB-embedded regulatory network of C. elegans. data("ce.interactionmap") ##load data required data("example.data") ##build the network by binding list and interaction map sifNetwork<-buildNetwork(TFbindingTable=example.data$ce.bind, interactionmap=ce.interactionmap, level=2) ##filter the network by expression data cifNetwork<-filterNetwork(rootgene="WBGene00000912", sifNetwork=sifNetwork, exprsData=uniqueExprsData(example.data$ce.exprData), mergeBy="symbols", miRNAlist=as.character(ce.miRNA.map[ , 1]), remove_miRNA=FALSE, tolerance=1) ##generate graphNEL object for the network gR<-polishNetwork(cifNetwork=cifNetwork, nodecolor=colorRampPalette(c("green", "yellow", "red"))(5)) ##show network (html <- browseNetwork(gR)) ##save network exportNetwork(html, "network.html") ``` # Example using gene expression profile Here is an example to use GNB to generate a simple regulatory network for _C. elegans_. And also show some examples how to use the `graphNEL` object for further analysis. ```{r ce_example} library(GeneNetworkBuilder) data("example.data") ##Initialize a binding matrix by TF and the related gene lists of TFBDs. ##For example, TF is daf-16, and the ChIP-chip result indicates that it can bind to ##upstream regions of gene "zip-2", "zip-4", "nhr-3" and "nhr-66". bind<-cbind(from="daf-16", to=c("zip-2", "zip-4", "nhr-3", "nhr-66")) ##For same gene, there are multple gene alias. In order to eliminate the possibility of ##missing any interactions, convert the gene symbols to unique gene ids is important. data("ce.IDsMap") bind<-convertID(toupper(bind), IDsMap=ce.IDsMap, ByName=c("from", "to")) ##build the network by binding list and interaction map data("ce.interactionmap") sifNetwork<-buildNetwork(TFbindingTable=example.data$ce.bind, interactionmap=ce.interactionmap, level=2) ##filter the network by expression data ##For each gene id, it should have only single record for expression change. unique.ce.microarrayData<-uniqueExprsData(example.data$ce.exprData, method="Max", condenseName='logFC') data("ce.miRNA.map") cifNetwork<-filterNetwork(rootgene="WBGene00000912", sifNetwork=sifNetwork, exprsData=unique.ce.microarrayData, mergeBy="symbols", miRNAlist=as.character(ce.miRNA.map[ , 1]), tolerance=1, cutoffPVal=0.01, cutoffLFC=1) ##convert the unique gene ids back to gene symbols data("ce.mapIDs") cifNetwork<-convertID(cifNetwork, ce.mapIDs, ByName=c("from","to")) ##generate graphNEL object for the network gR<-polishNetwork(cifNetwork, nodecolor=colorRampPalette(c("green", "yellow", "red"))(10)) ##plot the figure browseNetwork(gR) ``` ```{r} ## or plot by Rgraphviz library(Rgraphviz) plotNetwork<-function(gR, layouttype="dot", ...){ if(!is(gR,"graphNEL")) stop("gR must be a graphNEL object") if(!(GeneNetworkBuilder:::inList(layouttype, c("dot", "neato", "twopi", "circo", "fdp")))){ stop("layouttype must be dot, neato, twopi, circo or fdp") } g1<-Rgraphviz::layoutGraph(gR, layoutType=layouttype, ...) nodeRenderInfo(g1)$col <- nodeRenderInfo(gR)$col nodeRenderInfo(g1)$fill <- nodeRenderInfo(gR)$fill renderGraph(g1) } plotNetwork(gR) ``` ```{r exportxml} ##output file for cytoscape exportNetwork(browseNetwork(gR), file="network.xgmml", format = "XGMML") ##output the GXL file library("XML") xml<-saveXML(toGXL(gR)$value()) z<-textConnection(xml) cat(readLines(z, 8), sep="\n") ##calculate shortest path, ... library(RBGL) sp.between(gR,"daf-16","lam-2") ``` # Example using both gene and miRNA expression profile Using several advanced genomic technologies including micorarray profiling and miRNA sequencing, not only the gene expression profile, but also the miRNA expression profile can be obtained. A more robust network can be built if miRNA expression profiling is available. Here is an example to build *SOX2* response network for _H. sapiens_. The data was downloaded from [BMC Genomics](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3022822/?tool=pubmed)[@xuefeng2011]. ```{r hs_example} library(GeneNetworkBuilder) data("hs.interactionmap") data("hs.miRNA.map") data("hs.IDsMap") data("hs.mapIDs") data("example.data") rootgene<-"6657" sifNetwork<-buildNetwork(example.data$hs.bind, hs.interactionmap, level=3) ##example.data$ce$exprData is the combination of gene/miRNA expression profile ##note, here should set the miRNAtol to TRUE cifNetwork<-filterNetwork(rootgene=rootgene, sifNetwork=sifNetwork, exprsData=example.data$hs.exprData, mergeBy="symbols", miRNAlist=as.character(hs.miRNA.map[,1]), tolerance=0, miRNAtol=TRUE) cifNetwork<-convertID(cifNetwork, hs.mapIDs, ByName=c("from","to")) gR<-polishNetwork(cifNetwork) ##plot the figure browseNetwork(gR) ## or plot by Rcyjs # library(RCyjs) # rcy <- RCyjs(portRange=9047:9067, title='sox2', graph=gR) # setBrowserWindowTitle(rcy, "sox2") # setNodeLabelRule(rcy, "label") # setNodeLabelAlignment(rcy, "center", "center") # setDefaultNodeColor(rcy, "white") # setDefaultNodeBorderColor(rcy, "black") # setDefaultNodeBorderWidth(rcy, 1) # logFC.range <- range(cifNetwork$logFC) # logFC.range <- max(abs(logFC.range)) # setNodeColorRule(rcy, "logFC", c(-logFC.range, 0, logFC.range), c("green", "yellow", "red"), mode="interpolate") # size.range <- range(noa(gR, "size")) # setNodeSizeRule(rcy, "size", size.range, size.range) # layout(rcy, "cose") # redraw(rcy) ``` # Session Info ```{r sessionInfo} sessionInfo() ``` # References