[BioC] Most stable gene pairs in array experiment
Michael Dondrup
Michael.Dondrup at bccs.uib.no
Tue Oct 20 15:31:32 CEST 2009
Hi,
one can of course compute a lot of things the question is if that
makes sense. The term 'stability' is as far as i know not well defined
in the statistical sense, however there is a definition of numerical
stability for algorithms.
http://en.wikipedia.org/wiki/Numerical_stability
Therefor, use of 'stability' in this context is not too helpful. If
you have multivariate random variables (genesA,...) you can of course
define, that
you search for the minimum of of the variance estimate of the
difference (e.g. var(geneA - geneB)). Thereby creating a new random
variable from the difference of two
variables Z= X-Y. If application of the variance estimate makes sense
here, I cannot know.
If you need gene pairs that somewhat 'always react in the way', e.g.
for qPCR correlation might be a more appropriate concept, as mentioned
by others, and it's well defined.
If you look only at the difference between gene pairs, the result can
be influenced by the individual variance of both genes, even if they
otherwise have the same behaviour under each condition.
Furthermore, one could possibly apply a correlation test (corr.test)
and select those genes which are most significant. But this will
always depend on the desired application which is not clear to me.
One could also choose 'housekeeping' genes by prior knowledge. Maybe
there are even better methods for selecting controls for qPCR
experiments from other data in the literature?
Hope that helps even though it's maybe a bit off-topic.
Michael
Am 20.10.2009 um 14:30 schrieb Marcelo Laia:
> 2009/10/20 Michael Dondrup <Michael.Dondrup at bccs.uib.no>:
>> Hi,
>>
>> you can try something like this or use two for loops:
>>
>>
>>> apply (mygenes, 1 , function(row) { apply (mygenes, 1, function(x) {
>>> var(row-x) } ) } )
>>
>> geneA geneB geneC geneD geneE
>> geneA 0.00000 0.04108 0.06397 0.12217 0.08233
>> geneB 0.04108 0.00000 0.15543 0.12807 0.09365
>> geneC 0.06397 0.15543 0.00000 0.08628 0.08903
>> geneD 0.12217 0.12807 0.08628 0.00000 0.01517
>> geneE 0.08233 0.09365 0.08903 0.01517 0.00000
>>
>>
>> Cheers
>> Michael
>
> Hi,
>
> I am following the discussion and I'm finding very interesting.
> Congratulations!
>
> After this, I could compare the two genes, two-by-two, and I could
> conclude that the pair with minor variance are the two most stable
> genes of all?
>
> Is this genes appropriated for qPCR internal control? Or am I totally
> wrong here?
>
> Thank you very much!
>
> --
> Marcelo Luiz de Laia
> Universidade do Estado de Santa Catarina
> UDESC - www.cav.udesc.br
> Lages - SC - Brazil
> Linux user number 487797
>
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Michael Dondrup, Ph.D.
Bergen Center for Computational Science
Computational Biology Unit
Unifob AS - Thormøhlensgate 55, N-5008 Bergen, Norway
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