Hi, I am investigating the GO terms enrichment in two independent experiments in the same cell line: ctrl versus drug1 and ctrl versus drug2. Using GOstats I can visualize the graphs related to the two groups of enriched GO terms linked by their parents I will be very happy if someone could give me some advice where to find R code to search for similarities between graphs. Furthermore, there is any computational way to find the presence subgraphs in common between drug1 and drug2 GO graphs? Many thanks for the help Cheers Raffaele ######################################## This is the code I use to generate the graphical output for each of the two data sets: gNll #subset of differentially expressed Entrez Gene Ids gNuniverse #the subset of Entrez Gene Ids representing the universe under evaluation my.go <- "BP" p.value <- 0.05 params <- new("GOHyperGParams", geneIds = gNll, universeGeneIds = gNuniverse, annotation = lib, ontology = my.go, pvalueCutoff = p.value, conditional = FALSE, testDirection = "over") hgOver <- hyperGTest(params) hgOver.info <- paste(description(hgOver), paste(length(universeCounts(hgOver)),"GO BP ids tested","(",length(which(pvalues(hgOver) < p.value)),"have p<",p.value,")", sep=" "), paste("Selected gene set size:",length(geneIds(hgOver)), sep=" "), paste("Gene universe size:", universeMappedCount(hgOver), sep=" "), paste("Annotation package:", hgOver at annotation, sep=" "), sep="\n") conditional(params) <- TRUE ggMat <- summary(hgOver) if(my.go == "BP"){ tfG <- GOGraph(ggMat[,1], GOBPPARENTS) } else if (my.go == "MF"){ tfG <- GOGraph(ggMat[,1], GOMFPARENTS) } else if (my.go == "CC"){ tfG <- GOGraph(ggMat[,1], GOCCPARENTS) } gCol <- rep("lightblue", length(nodes(tfG)) gCol[which(nodes(tfG)%in%ggMat[,1])] <- "tomato" tGfnA <- makeNodeAttrs(tfG,label=nodes(tfG),shape="ellipse",fillcolor=gCol,fixe dsize=FALSE) plot(tfG, nodeAttrs=tGfnA) -- ---------------------------------------- Prof. Raffaele A. Calogero Bioinformatics and Genomics Unit Dipartimento di Scienze Cliniche e Biologiche c/o Az. Ospedaliera S. Luigi Regione Gonzole 10, Orbassano 10043 Torino tel. ++39 0116705417 Lab. ++39 0116705408 Fax ++39 0119038639 Mobile ++39 3333827080 email: raffaele.calogero at unito.it raffaele[dot]calogero[at]gmail[dot]com www: www.bioinformatica.unito.it

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There are some graph-related algorithms in bioC package RBGL available, with quite a few algorithms finding subgraphs of various kinds. You could take a look. If you have some algorithms/approaches in mind that will be useful, we'll be happy to research and implement them. Regards, Li > Hi, > I am investigating the GO terms enrichment in two independent > experiments in the same cell line: ctrl versus drug1 and ctrl versus > drug2. > Using GOstats I can visualize the graphs related to the two groups of > enriched GO terms linked by their parents > I will be very happy if someone could give me some advice where to find > R code to search for similarities between graphs. > Furthermore, there is any computational way to find the presence > subgraphs in common between drug1 and drug2 GO graphs? > Many thanks for the help > Cheers > Raffaele > > ######################################## > This is the code I use to generate the graphical output for each of the > two data sets: > gNll #subset of differentially expressed Entrez Gene Ids > gNuniverse #the subset of Entrez Gene Ids representing the universe > under evaluation > my.go <- "BP" > p.value <- 0.05 > params <- new("GOHyperGParams", geneIds = gNll, universeGeneIds = > gNuniverse, > annotation = lib, ontology = my.go, pvalueCutoff = p.value, > conditional = FALSE, testDirection = "over") > hgOver <- hyperGTest(params) > hgOver.info <- paste(description(hgOver), > paste(length(universeCounts(hgOver)),"GO BP ids > tested","(",length(which(pvalues(hgOver) < p.value)),"have > p<",p.value,")", sep=" "), > paste("Selected gene set size:",length(geneIds(hgOver)), > sep=" "), > paste("Gene universe size:", universeMappedCount(hgOver), > sep=" "), > paste("Annotation package:", hgOver at annotation, sep=" "), > sep="\n") > conditional(params) <- TRUE > ggMat <- summary(hgOver) > > if(my.go == "BP"){ > tfG <- GOGraph(ggMat[,1], GOBPPARENTS) > } else if (my.go == "MF"){ > tfG <- GOGraph(ggMat[,1], GOMFPARENTS) > } else if (my.go == "CC"){ > tfG <- GOGraph(ggMat[,1], GOCCPARENTS) > } > gCol <- rep("lightblue", length(nodes(tfG)) > gCol[which(nodes(tfG)%in%ggMat[,1])] <- "tomato" > > tGfnA <- > makeNodeAttrs(tfG,label=nodes(tfG),shape="ellipse",fillcolor=gCol,fi xedsize=FALSE) > plot(tfG, nodeAttrs=tGfnA) > > > -- > > ---------------------------------------- > Prof. Raffaele A. Calogero > Bioinformatics and Genomics Unit > Dipartimento di Scienze Cliniche e Biologiche > c/o Az. Ospedaliera S. Luigi > Regione Gonzole 10, Orbassano > 10043 Torino > tel. ++39 0116705417 > Lab. ++39 0116705408 > Fax ++39 0119038639 > Mobile ++39 3333827080 > email: raffaele.calogero at unito.it > raffaele[dot]calogero[at]gmail[dot]com > www: www.bioinformatica.unito.it > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >

Hi Raffaele, have a look here: http://rss.acs.unt.edu/Rdoc/library/GOstats/html/simLL.html hope this can help, Cheers, Maria --Maria Persico, PhD. student http://cbm.bio.uniroma2.it/~maria/ MINT database group Universita' di Tor Vergata, via della Ricerca scientifica 11 00133 Roma, Italy Tel +39 0672594315 (Supervisor's room) Fax +39 0672594766 Mobile phone: +393479715662 e-mail maria.persico at uniroma2.it Quoting rcaloger <raffaele.calogero at="" unito.it="">: > Hi, > I am investigating the GO terms enrichment in two independent > experiments in the same cell line: ctrl versus drug1 and ctrl versus drug2. > Using GOstats I can visualize the graphs related to the two groups of > enriched GO terms linked by their parents > I will be very happy if someone could give me some advice where to find > R code to search for similarities between graphs. > Furthermore, there is any computational way to find the presence > subgraphs in common between drug1 and drug2 GO graphs? > Many thanks for the help > Cheers > Raffaele > > ######################################## > This is the code I use to generate the graphical output for each of the > two data sets: > gNll #subset of differentially expressed Entrez Gene Ids > gNuniverse #the subset of Entrez Gene Ids representing the universe > under evaluation > my.go <- "BP" > p.value <- 0.05 > params <- new("GOHyperGParams", geneIds = gNll, universeGeneIds = > gNuniverse, > annotation = lib, ontology = my.go, pvalueCutoff = p.value, > conditional = FALSE, testDirection = "over") > hgOver <- hyperGTest(params) > hgOver.info <- paste(description(hgOver), > paste(length(universeCounts(hgOver)),"GO BP ids > tested","(",length(which(pvalues(hgOver) < p.value)),"have > p<",p.value,")", sep=" "), > paste("Selected gene set size:",length(geneIds(hgOver)), > sep=" "), > paste("Gene universe size:", universeMappedCount(hgOver), > sep=" "), > paste("Annotation package:", hgOver at annotation, sep=" "), > sep="\n") > conditional(params) <- TRUE > ggMat <- summary(hgOver) > > if(my.go == "BP"){ > tfG <- GOGraph(ggMat[,1], GOBPPARENTS) > } else if (my.go == "MF"){ > tfG <- GOGraph(ggMat[,1], GOMFPARENTS) > } else if (my.go == "CC"){ > tfG <- GOGraph(ggMat[,1], GOCCPARENTS) > } > gCol <- rep("lightblue", length(nodes(tfG)) > gCol[which(nodes(tfG)%in%ggMat[,1])] <- "tomato" > > tGfnA <- > makeNodeAttrs(tfG,label=nodes(tfG),shape="ellipse",fillcolor=gCol,fi xedsize=FALSE) > plot(tfG, nodeAttrs=tGfnA) > > > -- > > ---------------------------------------- > Prof. Raffaele A. Calogero > Bioinformatics and Genomics Unit > Dipartimento di Scienze Cliniche e Biologiche > c/o Az. Ospedaliera S. Luigi > Regione Gonzole 10, Orbassano > 10043 Torino > tel. ++39 0116705417 > Lab. ++39 0116705408 > Fax ++39 0119038639 > Mobile ++39 3333827080 > email: raffaele.calogero at unito.it > raffaele[dot]calogero[at]gmail[dot]com > www: www.bioinformatica.unito.it > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >