I have a set of Affy human gene expression arrays (HG-U133_Plus2.1) CEL files representing a treated sample over a number of time points. I'd like to compare the expression of these files not just on the genes they probe but on the exons. I know I should be using exon arrays for this, but what I have is a gene arrays, so I want to see how much I can do with that. If I can find the differential expression in an individual probe, I can map it to an exon. This should give me a limited view of which exons are changing expression between the arrays. I was planning to use the Rank Product method: http://www.bioconductor.org/packages/bioc/html/RankProd.html as the differential expression algorithm. Can anybody give me any advice on adapting this to work on individual probes, rather than whole genes? Or am I completely wasting my time on this? Peter [[alternative HTML version deleted]]

Hi Peter, My understanding of the probesets on the 3' biased arrays like the hgu133plus2 is that they primarily targeted the 3' UTR. For some genes with really short 3' UTRs and small first exons, you may be able to compare the expression of one or two exons. That said, I have yet to map a single probeset that didn't fall completely within the 3' UTR of the mRNA it was intended to interrogate. You can easily test this yourself; I wrote a simple function years ago to output the probe sequences for a probeset in FASTA format that can then be uploaded to blat at the UCSC genome browser: blatGene <- function(affyid, probe, filename){ ## affyid == Affy probeset ID ## probe == BioC probe package name ## filename == output file name require(probe, quietly = TRUE, character.only = TRUE) tmp <- data.frame(get(probe)) if(length(affyid) > 1){ seqnc <- vector() for(i in seq(along = affyid)) seqnc <- c(seqnc, tmp[tmp$Probe.Set.Name == affyid[i], 1]) }else{ seqnc <- tmp[tmp$Probe.Set.Name == affyid,1] } out <- vector() if(length(seqnc) > 25) warning("Blat will only return values for 25 or fewer sequences!", call. = FALSE) for(i in seq(along = seqnc)) out <- rbind(out, rbind(paste("> Probe", i, sep=""), seqnc[i])) write.table(out, filename, sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE) } You could do the same using a combination of the probe package, Biostrings, BSgenome.Hsapiens.UCSC.hg19, and rtracklayer if you wanted to be really cool. I have a function that did that as well, but given the speed with which rtracklayer has been changing, I doubt it works any longer. Best, Jim Peter Saffrey wrote: > I have a set of Affy human gene expression arrays (HG-U133_Plus2.1) > CEL files representing a treated sample over a number of time points. > I'd like to compare the expression of these files not just on the > genes they probe but on the exons. > > I know I should be using exon arrays for this, but what I have is a > gene arrays, so I want to see how much I can do with that. If I can > find the differential expression in an individual probe, I can map it > to an exon. This should give me a limited view of which exons are > changing expression between the arrays. > > I was planning to use the Rank Product method: > > http://www.bioconductor.org/packages/bioc/html/RankProd.html > > as the differential expression algorithm. Can anybody give me any > advice on adapting this to work on individual probes, rather than > whole genes? Or am I completely wasting my time on this? > > Peter > > [[alternative HTML version deleted]] > > _______________________________________________ Bioconductor mailing > list Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor Search the > archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826