Hi all, > gene.table <- read.table("/Users/oystercow/Desktop/11:07:2011workfol der/p-value_for_topGO_5d_1d_all", header = TRUE, row.names=1) > genelist_topGO_5d_1d_all <- as.numeric(gene.table$p.value) > names(genelist_topGO_5d_1d_all) <- as.character(row.names(gene.table)) #My geneList looks good, just like the example, e.g.: > head(genelist_topGO_5d_1d_all) comp0_c0_seq1 comp0_c0_seq10 comp0_c0_seq2 comp0_c0_seq3 comp0_c0_seq4 comp0_c0_seq5 1.742075e-03 3.160000e-159 1.453968e-02 9.230000e-06 3.300000e-14 1.710000e-65 #Yet when I try to define and use the topDiffGenes function, the results are unexpected > topDiffGenes <- function(allScore) { + return(allScore < 0.01) + } > sum(topDiffGenes(genelist_topGO_5d_1d_all)) [1] NA #this should be <58819, and certainly not 'NA' > length(topDiffGenes(genelist_topGO_5d_1d_all)) [1] 58819 #this is the total number of IDs, contigs in my case > head(topDiffGenes(genelist_topGO_5d_1d_all)) comp0_c0_seq1 comp0_c0_seq10 comp0_c0_seq2 comp0_c0_seq3 comp0_c0_seq4 comp0_c0_seq5 TRUE TRUE FALSE TRUE TRUE TRUE #If you think my error came from: > genelist_topGO_5d_1d_all <- as.numeric(gene.table$p.value) #and that I instead should import the p.values as.character (which I saw on a previous posting, https://stat.ethz.ch/pipermail/bioconductor/2007-November/020045.html) > genelist_topGO_5d_1d_all_2 <- as.character(gene.table$p.value) > names(genelist_topGO_5d_1d_all_2) <- as.character(row.names(gene.table)) > head(genelist_topGO_5d_1d_all_2) comp0_c0_seq1 comp0_c0_seq10 comp0_c0_seq2 comp0_c0_seq3 comp0_c0_seq4 comp0_c0_seq5 "0.001742075" "3.16e-159" "0.014539683" "9.23e-06" "3.3e-14" "1.71e-65" > sum(topDiffGenes(genelist_topGO_5d_1d_all_2)) [1] NA > length(topDiffGenes(genelist_topGO_5d_1d_all_2)) [1] 58819 #same results, except even worse , inaccurate comparisons: > head(topDiffGenes(genelist_topGO_5d_1d_all_2)) comp0_c0_seq1 comp0_c0_seq10 comp0_c0_seq2 comp0_c0_seq3 comp0_c0_seq4 comp0_c0_seq5 TRUE FALSE FALSE FALSE FALSE FALSE I would like to do this: > GOdata <- new("topGOdata", ontology = "BP", allGenes = genelist_topGO_5d_1d_all, geneSel = topDiffGenes(genelist_topGO_5d_1d_all), annot = annFUN.GO2genes, GO2genes = as.list(read.table("~/Desktop/annot_readyforR.annot", header = FALSE, sep = "\t"))) #using my own annotations #"~/Desktop/annot_readyforR.annot", is: comp517_c0_seq1 GO:0015850 comp517_c0_seq1 GO:0015665 comp517_c0_seq1 GO:0031224 comp517_c0_seq1 GO:0015291 comp517_c0_seq1 GO:0012501 comp517_c0_seq1 GO:0030001 comp1970_c0_seq1 GO:0004000 comp1970_c0_seq1 GO:0003676 comp1970_c0_seq1 GO:0031981 comp1970_c0_seq1 GO:0016553 comp1970_c0_seq1 GO:0019221 comp1970_c0_seq1 GO:0010467 comp1964_c0_seq1 GO:0005488 comp1964_c0_seq2 GO:0005488 ... My error message for the above is: Error in checkSlotAssignment(object, name, value) : assignment of an object of class "logical" is not valid for slot "geneSelectionFun" in an object of class "topGOdata"; is(value, "function") is not TRUE Any suggestions? topGO seems quite streamlined for microarray data but for "self-annotated" transcriptome data, any other hints would surely help. Thanks, Ian McDowell University of Rhode Island