[BioC] filter on Human Gene U133 Plus 2

[Yuan Hao]

You might want to look at the 'genefilter' package which filters probe  
sets based on different requirements, and there is a function to  
filter out all control probe sets as well.

Also, given that there are over 50,000 probe sets on hgu133plus2  
array, cut it down to 500-3000 for me sounds too harsh, although I  
don't think there is a golden-standard about what would be a good  
number after filtering. It depends on the design and expectations of  
your experiment. However, for me ~26,000 probe sets afterwards have no  
harm.

Cheers,
Yuan

On 29 Oct 2010, at 20:42, cstrato wrote:

> Dear Naima,
>
> Usually I prefer to reduce the number of probesets using "median  
> absolute deviation" as prefilter (see e.g. ?madFilter). This reduces  
> the number of probesets to about 500-3000 (depending on the cutoff).  
> Afterwards I use either unitestFilter and fcFilter (see Chapter 5.2  
> of xps.pdf), or I use the package "limma" which most people use (see  
> Appendix A.3 how to create an ExpressionSet for use with limma).  
> Generally I do not consider MAS5 detection calls to be sufficient  
> for pre-filtering.
>
> I am sure that other people can give you a more detailed answer, but  
> using "moderated t-statistics" is generally a good idea.
>
> Best regards
> Christian
>
>
> On 10/29/10 4:45 PM, Naïma Oumouhou wrote:
>>  Dear Christian,
>>
>> I'm sorry to bother you again : I've got a question about filter on
>> Affymetrix Human gene U133 Plus 2 array.
>> I would like to find differentiallty expressed genes between 2  
>> groups of
>> patients (n1=7 and n2=6).
>> I have no experience in microarray analyses.
>> I read several publications and your xps vignettes but I don't know  
>> what
>> I have to do.
>> Some people filtered probesets using Detection MAS5 call:probesets  
>> that
>> aren't expressed in at least one sample using the Detection MAS5
>> algorithm are discarded.
>> What do you think about this filter?not tight enough?
>> After this filter and with my dataset,I still have 26 495 probesets? 
>> Is
>> it too much?
>> Furthermore, in these remaining probesets, there are affymetrixx  
>> control
>> probesets. These probesets have to be removed?At which step?
>> After, I use the moderated t-statistics with BH correction. I find no
>> differentiallty expressed genes.
>> I wonder if I have to reduce the number of probesets with another  
>> filter
>> or with a "Detection filter" tighter?
>> Thanks for any help.
>> Naïma
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>
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