[BioC] Genefilter parameters for mouse 430 2 #3
friedman at cancercenter.columbia.edu
Thu Mar 20 17:15:07 CET 2008
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:
>> 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
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)
>>  0
>> > xen2nsSUB
> Yup. That should be
> xen2nsSUB <- nsFilter(xen2dataeset)$eset
> if you just want the resulting ExpressionSet.
>>>> 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
>> described in my first note (.25 of 4 =1). Or am I getting somehing
> 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).
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