James W. MacDonald wrote: > Robert Gentleman wrote: > >>Hi, >> >>Sean Davis wrote: >> >> >>>On 3/13/06 3:38 PM, "Glazko, Galina" <galina_glazko at="" urmc.rochester.edu=""> >>>wrote: >>> >>> >>> >>> >>>>Dear list, >>>> >>>> >>>> >>>>Is there a way to automatically select one probeset for one gene in Affy >>>>arrays? >>>> >>>>Say, if we have several probesets for a given gene, we select the one >>>>with the highest level of expression, or based on any other reasonable >>>>criteria...? >>>> >>>>I am sorry if this question was answered before, it seems to be very >>>>basic question and I hope there is the solution... >>> >>> >>>Galina, >>> >>>You can contrive a solution, I suppose. However, I'm not sure this is a >>>good idea. Whatever "reasonable criteria" you use are likely to lead to >>>bias. Filtering on unmeasured probesets or other quality measures applied >>>equally to all probesets is probably reasonable, but not applying on a >>>per-gene basis. There have been related discussions in the past, often >>>centering around "averaging" expression values. >>> >>>The more accepted way of dealing with multiple probesets is to do your >>>analysis based on the probeset; only after that is done do you then connect >>>your gene labels back to the probesets. >> >> >> >> Unfortunately that approach does not always work and something needs >>to be done a bit earlier in the process if a user wants to make use of >>data such as GO, chromosomal location etc where the mapping is based on >>Entrez Gene ID (for example, but other identifiers have very similar >>issues). Not removing the duplicates leads to often quite different >>results (in essence there is over counting if all probes are accurate). >>As users of GOstats know, you have to choose one candidate for each >>Entrez gene id (and probably what I have been doing there is not ideal - >>the suggestion below, due to Seth Falcon is, I think, better). But I >>would be interested to hear other points of view. >> >> I also do not like averaging for several reasons. Now, I have two >>kinds of measurements (averages and ordinary old probes) and that is >>problematic for some uses. Second, if not all of the probes work (which >>might be why there are several variants) then I am averaging the good >>with the bad, which also seems like a less than ideal way to go. > > > One inherent problem with using the Affy probesets is that there are > known issues with many of the probes; some measure related transcripts > and others measure unrelated transcripts, so what you are measuring is > not always clear. The MBNI cdfs which have been re-mapped may help with > at least two of these problems. First, all probes that no longer blast > to the transcript of interest are removed from consideration. Second, > all probes that do blast to the transcript of interest are piled > together into one probeset (I guess you could argue this is bad since > the expression measures are now based on variable numbers of probes, but > that is already true anyway...). Note that these cdfs are planned to be > part of the new release of BioC, but currently are only available from > the MBNI website > > http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/g enomic_curated_CDF.asp > > Since you now have only one probeset per gene (based on Entrez Gene, > UniGene, RefSeq, or Ensembl) you no longer have to decide which one to > use. The biggest downside to using these cdfs is the lack of > infrastructure in BioC that is tailored to their use, which requires a > higher level of understanding of R than one would need to use a 'stock' > cdf (which reminds me - I should be doing something about that ;-D). Hi, These are good points, but I think that they are complementary rather than a strict replacement. First, I might just have expression data, not CEL files, so this approach would not be an option. Second, I might decide to map to Unigene or RefSeq, and then would still have the same problem these do not necessarily have a 1-1 correspondence with Entrez gene. And finally, I might be working with cDNA arrays where there is no clear way to take this same approach. That is not to say that this is not a viable approach and it certainly does solve some problems, best wishes Robert > > HTH, > > Jim > > > >> One suggestion is to do non-specific filtering (say on variation, or >>for expressed versus not, or something of that ilk) and to then select >>the probe set that has the highest value. Thus, you are selecting the >>probe with the most information (but do be careful not to use any >>phenotypic information as this could cause problems). Your (Galina's) >>suggestion was to use level of expression, but that is generally a bad >>idea because that would involve a between probe within array comparison >>and these are not ideal; just because one spot is brighter does not mean >>it works better, or that there is more mRNA than a less bright spot. >> >> HTH >> Robert >> >> >> >> >>>Sean >>> >>>_______________________________________________ >>>Bioconductor mailing list >>>Bioconductor at stat.math.ethz.ch >>>https://stat.ethz.ch/mailman/listinfo/bioconductor >>> >> >> > > -- Robert Gentleman, PhD Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N, M2-B876 PO Box 19024 Seattle, Washington 98109-1024 206-667-7700 rgentlem at fhcrc.org