Hi Sebastien.
Interesting observation, I hadn't noticed that.
Since the major difference is in the fitting stage, my guess would be
that there are just more larger units (although less total units) in
the RaGene CDF. This is certainly true for say, the number of
probesets with more than 100 probes:
cdf1 <- AffymetrixCdfFile$fromChipType("RaGene-1_0-st-v1",tags="r3")
cdf2 <- AffymetrixCdfFile$fromChipType("HuGene-1_0-st-v1",tags="r3")
cpu1 <- nbrOfCellsPerUnit(cdf1)
cpu2 <- nbrOfCellsPerUnit(cdf2)
> sum(cpu1 > 100)
[1] 183
> sum(cpu2 > 100)
[1] 70
I haven't looked in close detail, but it may be worth removing some of
the large probesets in the interest of speed. Sometimes these are
just controls anyways. aroma.affymetrix already does this by default
for the super large probesets (it jumps to median polish instead of a
robust linear model).
> options()$aroma.affymetrix.settings$models$RmaPlm
$medianPolishThreshold
[1] 500 6
$skipThreshold
[1] 5000 1
Hope that helps.
Mark
On 27/02/2009, at 5:57 PM, Sebastien Gerega wrote:
>
> Hi,
> I have been playing around with the Aroma package and using sample
> data
> from the Affymetrix site. I've noticed that normalising ragene10st
> arrays takes about 10 times longer than it does for hugene10st. For
> example:
> ragene10st:
>
> Total time for complete data set: 20.31min = 0.34h
> Fraction of time spent on different tasks: Fitting: 96.5%, Reading:
> 0.9%, Writing: 2.6% (of which 60.78% is for encoding/writing
> chip-effects), Explicit garbage collection: 0.0%
>
>
> hugene10st:
> Total time for complete data set: 2.38min = 0.04h
> Fraction of time spent on different tasks: Fitting: 69.6%, Reading:
> 6.5%, Writing: 23.5% (of which 61.68% is for encoding/writing
> chip-effects), Explicit garbage collection: 0.4
>
> Both analyses are being run with 6 cel files. Is this expected and
> if so
> what is the reason for the difference?
> thanks,
> Sebastien
>
>
> >
------------------------------
Mark Robinson
Epigenetics Laboratory, Garvan
Bioinformatics Division, WEHI
e: [email protected]
e: [email protected]
p: +61 (0)3 9345 2628
f: +61 (0)3 9347 0852
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