Hi Anbarasu. Comments below.
> Hi Mark, > > Thanks for your suggestions. What I have tried so far is: I removed all outliers CEL files from rawData and re-run the analysis. I was expecting a > slightly different intensity distributions of chips (due to quantile normalization) but it seems I have the same distributions that I got with > all chips, including outliers. Amongst many chips, I would guess that removing a handful would have very little effect on the overall distribution that each sample is quantile-normalized to. So, this doesn't surprise me. Also, be sure that you run the fit() and process() with force=TRUE, otherwise the code *may* be going directly to cached results, regardless of your removal of files. > > I will try with what you have suggested. Do I need to use extract() for sub > setting before or after normalization? Are we ignoring the effect of these > outlier chips in normalization step (if I have to use extract() after normalization)? > I would do it after. And yes, this ignores the effects of outlier chips, which I suspect is minimal over a big dataset. Cheers, Mark > Thanks again. > > Kind regards, > Anbarasu > > On Thu, Aug 6, 2009 at 10:46 PM, Mark Robinson > <mrobin...@wehi.edu.au>wrote: > >> Hi Anbarasu. >> No, you don't have to remove all the files. What you can do is use extract() to extract the files that you are interested in, and create a new AffymetrixCelSet and fit the probe level modesl only on those samples. You do need to be careful though and I suggest you use *tags* so that the output results are sent to a different location on disk. Here is an example: >> [...] # preprocessing as before >> csN1 <- extract(csN,1:12) # take a subset >> plmTr <- ExonRmaPlm(csN1, mergeGroups=TRUE, tag="*,subsetmerged") # add a tag >> fit(plmTr, verbose=verbose) # fit as normal >> Hope that helps. >> Mark >> On 04/08/2009, at 8:22 PM, anbarasu wrote: >> > >> > Dear All, >> > >> > I was able to run the human exon array analysis with 120 chips. I have >> > identified few outlier chips and would like to re-run the analysis again without these outliers. Do I need to remove all files (in plmData, probeData, and reports) that are created by aroma.affymetrix? >> > >> > Thanks in advance. >> > >> > Kind regards, >> > Anbarasu >> > > >> ------------------------------ >> Mark Robinson, PhD (Melb) >> Epigenetics Laboratory, Garvan >> Bioinformatics Division, WEHI >> e: m.robin...@garvan.org.au >> e: mrobin...@wehi.edu.au >> p: +61 (0)3 9345 2628 >> f: +61 (0)3 9347 0852 >> ------------------------------ >> > > > > > --~--~---------~--~----~------------~-------~--~----~ When reporting problems on aroma.affymetrix, make sure 1) to run the latest version of the package, 2) to report the output of sessionInfo() and traceback(), and 3) to post a complete code example. You received this message because you are subscribed to the Google Groups "aroma.affymetrix" group. To post to this group, send email to aroma-affymetrix@googlegroups.com To unsubscribe from this group, send email to aroma-affymetrix-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/aroma-affymetrix?hl=en -~----------~----~----~----~------~----~------~--~---
