Hi Scott, sorry for the late reply. I hope you are still interested in the problem you raised. Your problem is very interested and I must admit I didn't thought about it when I wrote the topGO code. And as you already mentioned there is no trivial way to use more than one score in the gene selection function. However, one could do this with a bit of hacking... and I see two solutions to your problem. 1) If you want to be able to compare the results for a KS test with the ones from some count based test like Fisher's exact test, and for the count based tests you want to use a section criteria involving more than one gene score, then the most easiest way to do it is to build two "topGOdata" objects, one for the KS test, thus containing only the gene score and a dummy selection function and the other one containing a predefined list of interesting genes. Note that once a "topGOdata" object is instantiated, one can update the gene score or the predefined list of genes via the "updateGenes" method. Also very important to note here is that you need to have the same gene universe for the same objects. It will be a bit strange to compare results which are obtained using a different gene universe. Once you have the results, you can use the "GenTable" function to generate a summary of the results. You could feed any of the two "topGOdata" objects to this function (the "topGOresult" object do not directly depend on it). The disadvantage of this approach is the redundancy in the two "topGOdata" objects. However, it should be strait forward to use. 2) If you plan to use KS test and cont based tests, then one can do the following trick. The main observation is that the KS test used here is a ranked based test and thus only the ranking of the genes, based on their p-value, counts. Therefore, one can transform the vector of p-values into a vector of ranks, before building the "topGOdata" object. Now, since the ranks are unique (and we can compute them in this way), we can use them as to index a vector/table. We can build a table, similar with "input" in your first example, in which we can store multiple information on the genes. Then, if you build the "topGOdata" object using these ranks as scores, one could compute of course the KS test, and also define a selection function which will have access the data in the table using the ranks as indices. Of course, the table in this case needs to be a global variable. Once the "topGOdata" object is build it is very easy to update it with a new gene selection function using the "geneSelectionFun<-" method. Note that this method works only with ranked based test statistics and it will not generalize to tests which use the score itself easily! Bellow, you will find a chunk of code which should exemplify the second approach on the ALL data. I hope you'll find the answer in one of the solutions describe. Please let me know if you have further questions. Regards, Adrian ---------------------------------------------------------------------- -------- library(topGO) library(ALL); data(ALL) affyLib <- paste(annotation(ALL), "db", sep = ".") library(package = affyLib, character.only = TRUE) library(genefilter) f1 <- pOverA(0.25, log2(100)) f2 <- function(x) (IQR(x) > 0.5) ff <- filterfun(f1, f2) ## select genes based on the defined filters selGenes <- genefilter(ALL, ff) ## obtain the list of differentially expressed genes classLabel <- as.integer(sapply(ALL$BT, function(x) return(substr(x, 1, 1) == 'T'))) geneList <- getPvalues(exprs(ALL), classlabel = classLabel) ## select 1000 random genes randSel <- integer(length(geneList)) randSel[sample(length(geneList), 1000)] <- as.integer(1) names(randSel) <- names(geneList) ## transform the p-values into ranks scoreIndex <- rank(geneList, ties.method = "first") ## generate a data frame containing all the information you need to select interesting genes GENE.INFO <- data.frame(p.value = geneList[names(geneList)], filtered = selGenes[names(geneList)], random = randSel[names(geneList)]) ## index the data frame with the ranks GENE.INFO <- GENE.INFO[order(scoreIndex), ] ## define a function to select the "significant" genes mySelGenes <- function(allScore) { returnGENE.INFO[allScore, "p.value"] < 0.01) } ## select the genes that passed the filter criteria and have a p-value lower than 0.01 mySelGenes.2 <- function(allScore) { returnGENE.INFO[allScore, "p.value"] < 0.01 & GENE.INFO[allScore, "filtered"]) } ## how many differentially expressed genes are: sum(mySelGenes(scoreIndex)) sum(mySelGenes.2(scoreIndex)) ## build the topGOdata class GOdata <- new("topGOdata", ontology = "BP", allGenes = scoreIndex, geneSel = mySelGenes, nodeSize = 10, annot = annFUN.db, affyLib = affyLib) ## display the GOdata object GOdata ## we can change the selection criteria, by updating the object with mySelGenes.2 geneSelectionFun(GOdata) <- mySelGenes.2 ## same gene universe but different number of interesting genes ... GOdata ## we can use both KS and Fisher's exact test on this object: geneSelectionFun(GOdata) <- mySelGenes resFisher <- runTest(GOdata, algorithm = "classic", statistic = "fisher") resKS <- runTest(GOdata, algorithm = "classic", statistic = "KS") ## Fisher's exact test accounting for the gene filter geneSelectionFun(GOdata) <- mySelGenes.2 resFisher.2 <- runTest(GOdata, algorithm = "classic", statistic = "fisher") ## we can see some summary for each result resFisher resFisher.2 resKS ## we can generate the table which summarises the results ## but the stats. in the table will depend on the current selection function stored in the GOdata object. allRes <- GenTable(GOdata, classic = resFisher, KS = resKS, classic.2 = resFisher.2, orderBy = "classic.2", ranksOf = "classic", topNodes = 20) allRes ---------------------------------------------------------------------- -------- R version 2.9.0 (2009-04-17) i686-pc-linux-gnu locale: LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US .UTF-8;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_N AME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTI FICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] multtest_2.1.1 genefilter_1.24.2 hgu95av2.db_2.2.12 [4] ALL_1.4.5 topGO_1.12.0 SparseM_0.80 [7] GO.db_2.2.11 RSQLite_0.7-2 DBI_0.2-4 [10] AnnotationDbi_1.6.1 Biobase_2.4.1 graph_1.22.2 loaded via a namespace (and not attached): [1] annotate_1.22.0 grid_2.9.0 lattice_0.17-25 MASS_7.2-48 [5] splines_2.9.0 survival_2.35-4 tools_2.9.0 xtable_1.5-5