[BioC] Genefilter parameters for mouse 430 2 #3

[Richard Friedman]

Jim,

	Thanks again for your quick and helpful  reply.
I have some disagreements and a further question.

On Mar 19, 2008, at 11:27 PM, James W. MacDonald wrote:

> Hi Rich,
>
> Richard Friedman wrote:
>> Jim,
>>     Thank you for your detailed and helpful reply.
>> On Mar 19, 2008, at 4:52 PM, James W. MacDonald wrote:
>>>
>>> That depends. If you are using rma(), then no ;-P
>> what about gcrma.
>
> Same diff. The maximum with either will be ~14, so filtering on 100  
> will remove everything.
>

I filtered on log2(100)=6.64,  which is well under 14. Based upon  
this filter alone I got
9681 probesets.

This as about 25% of the probesets. I guess I still am wondering if  
there is a way of taking the
intensity curve into account in setting the cutoff.



>>>
>>> You might try something like
>>>
>>> eset2 <- nsFilter(eset)$eset
>>>
>>> and see how many probesets you end up with.
>> I have tried
>>  > xen2nsSUB<-nsFilter(xen2dataeset)$xen2dataeset
>>  > sum(xen2nsSUB)
>> [1] 0
>>  > xen2nsSUB
>> NULL
>
> Yup. That should be
>
> xen2nsSUB <- nsFilter(xen2dataeset)$eset
>
> if you just want the resulting ExpressionSet.

Most helpful!
>
>>>
>>>> If you are just doing fold changes, you might consider filtering  
>>>> on each fold change rather than overall. For instance you could  
>>>> create a filter
>>>
>>> filt <- filterfun(kOverA(1, 100))
>>>
>>> that you would then use for each fold change comparison to ensure  
>>> that at least one of the samples had an expression > 100.  
>>> Shameless plug - see foldFilt() in affycoretools.
>> I think that that is basically what I did with genefilter pOverA 
>> (0.25,log2(100)
>> described in my first note (.25 of 4 =1). Or am I getting somehing  
>> wrong.
>
> Well, that isn't what you did (or maybe it is what you did, but you  
> didn't do what I am suggesting). If you are doing fold change  
> calculations then you (IMO) only care about the two things under  
> consideration. So if you have something like this:
>
> Samples	1	2	3	4
> expval	30	85	1500	2500
>
> Then what you did will nuke that probeset. However, the comparisons  
> for 1v3, 1v4, 2v3, 2v4 and 3v4 are probably quite useful. The only  
> one you don't care about is 1v2, which will give a high fold change  
> but it is probably not meaningful.
>

I fear that I don't understand filterfun. when I used kOverA(1,log2 
(100) instead of pOverA above, I get the same # of probesets as I did
with pOverA(.25,log2(100)) (9681).

As I understand pOverA(.25, 100) it would not elminate this probeset  
because at least 25% is above 100).

Best wishes,
Rich




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