I forgot to mention there is also a very nice book by David Marchette called Random Graphs for Statistical Pattern Recognition that probably discusses distances among graphs.
   
  Thank you.
   
  Bill Shannon, PhD
BioRankings, LLC
Founder and President
314-704-8725 
http://BioRankings.com
   
  "Helping you gain understanding of statistical data to protect your investment."
   
  Associate Professor of Biostatistics in Medicine
Washington University School of Medicine
http://ilya.wustl.edu/~shannon

  

rcaloger <raffaele.calogero@unito.it> wrote:
  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@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@unito.it
raffaele[dot]calogero[at]gmail[dot]com
www: www.bioinformatica.unito.it

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