I have realised that the annotations I have been using are incorrect so 
the information I mentioned regarding RT1-C113 is irrelavant. However, I 
would still like to know how to (if possible) change the options for 
probe skipping.
cheers,
Sebastien


Sebastien Gerega wrote:
> Thanks for the insight Mark. I have included the gene symbols for the 
> 183 probes with greater than 100 cells per unit. It turns out 147 of 
> them are for the same gene RT1-C113 This gene has been the following 
> summary at NCBI 
> (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=24151)
>  
> :
>
> Summary
>    DISCONTINUED: This record has been withdrawn by RGD
>
> What is the meaning of all this?
>
> And is there a way to change the options for aroma so that the probes 
> with greater than 100 cells per unit are skipped? Or would I have to 
> remove them manually?
> thanks again,
> Sebastien
>
>
>
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "C1r"
> "Mphosph10"
> "Ruvbl1"
> "Tmem34"
> "Pim2"
> "RGD1561513"
> "Fstl1"
> "Areg"
> "RT1-C113"
> "Id3"
> "RT1-C113"
> "RGD1308106"
> "Cops5"
> "Vil1"
> "Sppl3"
> "Mobkl1a"
> "Zfp143"
> "RGD1565370"
> "Qprt"
> "Rangap1"
> "Pde6d"
> "Tmem199"
> "Alcam"
> "RGD1311493"
> "Cpa6"
> "RGD1624210"
> "Cops5"
> "RT1-C113"
> "Pygo2"
> "RGD1562372"
> "Rb1cc1"
> "Kcne3"
> "Porcn"
> "Rras"
> "Cuta"
> "Zfp313"
> "Olr1333-ps"
> "RGD1562284"
> "Epb4.1l5"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
> "RT1-C113"
>
>
>
>
> Mark Robinson wrote:
>> 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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traceback(), and 3) to post a complete code example.


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