Hello Everyone, I am trying to use GO for statistical analysis and I have several questions which I hope you could help me to answer. 1. How can I change configuration parameters from a goCluster object without running again config(goCluster_object)? Alternatively, where could I find documentation about doing this? For example, I would like to try different similarity measures or different pvalue cut-offs, etc. In the manual, it is showed how to change the visualization method so I assumed there is a slot for all other parameters. 2. goCluster will show only the most significant GO terms (with the lowest p-value) if there are overlaps. Is there a way to see all selected GO terms (and their p-value), similar to GOHyperG output? 3. Suppose I use GOHyperG to select the GO terms for a determined set of genes, can I use goCluster for visualization? if yes, how? 4. Is the code for the examples described in the vignette "Using GO for statistical analyses" available? 5. How can I get the locusLink ID for yeast genes? I would like to use GOHyperG but I have only ORF and gene names for yeast (and this is not microarray data). If I use lookUp(myGenes, "YEAST", "GO") I get the GO Annotations for those genes, but GOHyperG(myGenes, lib = "YEAST", what = "BP") doesn't work since there is not YEASTLOCUSID in YEAST. Do I have to build an annotation package? Thanks a lot! Lourdes

Hi Lourdes, > 1. How can I change configuration parameters from a goCluster object > without running again config(goCluster_object)? Alternatively, where > could I find documentation about doing this? For example, I would like > to try different similarity measures or different pvalue cut-offs, > etc. > In the manual, it is showed how to change the visualization method so > I assumed there is a slot for all other parameters. > You can use the "setup" function. This function retrieves a configuration from a goCluster object as a list. This configuration list can be modified and then assigned to a new goCluster object. > a <- setup(goCluster_object) > a$sign$threshold <- 0.1 > test <- new("goCluster") > execute(test) <- a Instead of directly executing in the last step you can also assign the configuration using "setup" again so that you can run the analysis at a later time: > setup(test) <- a > 2. goCluster will show only the most significant GO terms (with the > lowest p-value) if there are overlaps. Is there a way to see all > selected GO terms (and their p-value), similar to GOHyperG output? Yes, in the significance slot. > goCluster_object at sign@selected This list is splitted according to the different clusters that were identified (you'll find those in goCluster_object at algo@clusterset) > 3. Suppose I use GOHyperG to select the GO terms for a determined > set of genes, can I use goCluster for visualization? if yes, how? Actually using GOHyperG should result in the same values as goCluster since I used the GOHyperG code for verification. I'll add a section in the users manual that will verify that. But in principle nothing prevents you from replacing the statistical module as detailed in the developers manual of goCluster. The package is meant to be extensible. In case the developers manual is not clear enough (guess it might not ;) ) don't hesitate to contact me. > 4. Is the code for the examples described in the vignette "Using GO > for statistical analyses" available? It is part of the GOstats package so you should have a file in your R-library: R/library/GOstats/doc/GOstats.Rnw Cheers Gunnar