[BioC] Most stable gene pairs in array experiment

[Naomi Altman]

You would conclude that the pairs of genes with very low variance 
track each other closely in the samples.
Since the analysis is on the log-scale, this means that the fold 
ratio is stable.  It does not mean that the two genes do not 
vary.  For that, you would want to compute the gene-wise variance.

Naomi

At 08:30 AM 10/20/2009, Marcelo Laia wrote:
>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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Naomi S. Altman                                814-865-3791 (voice)
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