[BioC] Measuring similarities beween GO terms graphs

[Li.Long at isb-sib.ch]

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,fixedsize=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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