[BioC] Normalizing a dataset from GEO to suit a classifier trained on another dataset

[Matthew McCall]

If all the preprocessing you're doing to the Illumina chips is
quantile normalization, you could use:

normalize.quantiles.determine.target()

followed by:

normalize.quantiles.use.target()

Both are in the preprocessCore package.

Best,
Matt






On Sun, Apr 14, 2013 at 11:53 AM, Nathan Skene <nathan.skene at gmail.com> wrote:
> Hi Matt,
>
> That looks like exactly the thing... but it wouldn't work on Illumina,
> right? RMA is Affy only?
>
> Thanks,
>
> Nathan
>
>
> On 14 April 2013 16:43, Matthew McCall <mccallm at gmail.com> wrote:
>>
>> Nathan,
>>
>> Take a look at the frma package and paper:
>> McCall MN, Bolstad BM, and Irizarry RA* (2010). Frozen Robust
>> Multi-Array Analysis (fRMA), Biostatistics, 11(2):242-253.
>>
>> Best,
>> Matt
>>
>>
>>
>> On Sun, Apr 14, 2013 at 10:40 AM, Nathan Skene [guest]
>> <guest at bioconductor.org> wrote:
>> >
>> > Hi,
>> >
>> > I have a built a classifier on a set of my own microarray data.
>> >
>> > I would like to be able to show that it works on new data, by taking
>> > some from GEO (same tissue, same array platform etc) and classifying that.
>> >
>> > However, the probe values in the new datasets are offset compared to
>> > those in my original dataset, presumably because of differences in how they
>> > were normalised.
>> >
>> > Is there a way to normalize datasets taken from GEO so that they are
>> > normalized with respect to an existing dataset?
>> >
>> > Thanks
>> >
>> >
>> >  -- output of sessionInfo():
>> >
>> > -
>> >
>> > --
>> > Sent via the guest posting facility at bioconductor.org.
>> >
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>>
>>
>>
>> --
>> Matthew N McCall, PhD
>> 112 Arvine Heights
>> Rochester, NY 14611
>> Cell: 202-222-5880
>
>



-- 
Matthew N McCall, PhD
112 Arvine Heights
Rochester, NY 14611
Cell: 202-222-5880