[BioC] miRNA normalisation

[Dermot Morris]

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 at 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
>
>
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-- 
James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
Department of Human Genetics
5912 Buhl
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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.