On Tue, Feb 18, 2014 at 7:30 PM, Damian Plichta
<damian.plic...@gmail.com> wrote:
> Thanks, that helped a lot. It took me less than 3 hours to perform the
> background correction.
>
> Now I'm wondering if for the next step, quantile normalization, I could do a
> similar trick. Is there a way to precompute the target empirical
> distribution based on all arrays and then do the normalization on chunks of
> data (thus in an independent manner)? I can see the option
> targetDistribution under QuantileNormalization.

# Calculate the target distribution based on *all* arrays [not parallalized]
qn <- QuantileNormalization(dsC, typesToUpdate="pm")
target <- getTargetDistribution(qn, verbose=verbose)

# Normalize array by array toward the same target distribution [in chucks]
dsCs <- extract(dsC, 1:100)
qn <- QuantileNormalization(dsCs, typesToUpdate="pm", targetDistribution=target)
csNs <- process(qn, verbose=verbose)

Hope this helps

/Henrik

>
> Kind regards,
>
> Damian Plichta
>
> On Monday, February 17, 2014 4:03:54 PM UTC-5, Henrik Bengtsson wrote:
>>
>> Hi.
>>
>> On Sun, Feb 16, 2014 at 6:53 PM, Damian Plichta
>> <damian....@gmail.com> wrote:
>> > Hi,
>> >
>> > I'm processing around 5500 affymetrix exon arrays. The
>> > RmaBackgroundCorrection() is pretty slow, 1-2 minutes/array. I played
>> > with
>> > setOption(aromaSettings, "memory/ram", X) and increased X up to 100 but
>> > it
>> > didn't have any effect on this stage of analysis.
>>
>> If you don't notice any difference in processing time by changing
>> "memory/ram" from the default (1.0) to 100, then the memory is not
>> your bottleneck.
>> >
>> > Any way to speed the process up?
>>
>> If you haven't already, make sure to read "How to: Improve processing
>> time":
>>
>>   http://aroma-project.org/howtos/ImproveProcessingTime
>>
>> If you have access to multiple machines on the same file system, you
>> can do poor mans parallel processing for the *background correction*,
>> because each array is corrected independently of the others.  You can
>> do this by processing a subset of arrays per computer, e.g.
>>
>> dsR <- AffymetrixCelSet$byName("MyDataSet", chipType="HuEx-1_0-st-v2")
>> dsR <- extract(dsR, 1:100)
>> bg <- RmaBackgroundCorrection(dsS)
>> dsC <- process(bg, verbose=verbose)
>>
>> Repeat on another machine with 101:200, and so on.
>>
>> When all arrays have been background corrected, you can move back to
>> your original script - all arrays background corrected are already
>> saved to file and will therefore not be redone.
>>
>> /Henrik
>>
>> >
>> > Kind regards,
>> >
>> > Damian Plichta
>> >
>> > --
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