Hi Jim, Thanks for reply After trying various approaches similar to what you suggested I didn't think it was easy! I'm thinking that even though there are probes for other species on chip (other than the one of interest) that even ~specific and non specific signals from these would still be better than using a small set (~700 probe sets) for normalisation. When I do this and plot summarized "bta" probes only they look ok normalised for one tissue extract but not for another. Any comments or ideas on this ? Regards, Dermot -----Original Message----- From: James W. MacDonald [mailto:jmacdon@med.umich.edu] Sent: 23 January 2012 15:03 To: Dermot Morris Cc: bioconductor at r-project.org Subject: Re: [BioC] miRNA normalisation Hi Dermot, On 1/23/2012 9:10 AM, Dermot Morris wrote: > Dear all, > > > > I would like to extract the probesets corresponding to a single species > ("bta") from an Affymetrix > > miRNA array to examine the effects of normalisation using a single > species compared to using all probes on array. > > > > Is there a simple way to subset an affybatch at the probe level prior to > normalisation ? No. You can subset an AffyBatch at the chip level (essentially subsetting by column), but subsetting by row has been deprecated for years now. Hypothetically you could go through the AffyBatch and remove all non- bta probes by hand. You then might have to do something to the cdf package as well. I'm not sure without doing a close examination of the code. You could also hypothetically use the pm() function with LISTRUE = TRUE, to extract all the probesets into a named list, then normalize all the bta probes separately. You could then put the normalized bta probes back into that list, convert to a matrix that is correctly ordered (see showMethods(pm, class = "AffyBatch", includeDefs = TRUE)), and then use the pm() function to put it back into the AffyBatch. An alternative of this would be to extract as a list, then collect the row.names() of all the probesets that are bta probes. You could then re-extract using pm() without the LISTRUE argument, and use the row.names you extracted to remove those rows, normalize the data, and then put back into the original matrix. You could then use pm() to stick the data back into the AffyBatch and go from there. You could also hypothetically do the bg.correct() step on your AffyBatch, then extract the pm values into a list as above, then do the normalization and summarization step by hand on the list (the summarization step of rma() can be emulated using the medpolish() function). None of those possibilities is simple, however. Best, Jim > > > > Best Regards, > > > > Dermot > > ---------------------------------------------------------------------- -- ------ > Attention: > This e-mail is privileged and confidential. If you are not the intended recipient please delete the message and notify the sender. Any views or opinions presented are solely those of the author. > This email was scanned by Teagasc and has been certified virus free with the pattern file currently in use. This however cannot guarantee that it does not contain malicious content. > > Tabhair aire: > Ta an r-phost seo faoi phribhleid agus faoi run. 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Scanadh an r-phost seo le Teagasc agus deimhniodh go raibh se saor o vioras leis an bpatrunchomhad ata in usaid faoi lathair. Ni feidir a rathu leis seo afach nach bhfuil abhar mailiseach ann.