[BioC] normalization and analysis of connected designs

[Xavier Solé]

If you use a quantile normalization and have each channel replicated at
least twice you may be able to do comparisons of the intensities of
different channels, even though they are not connected.

Regards,

Xavi.

----- Original Message ----- 
From: "Ramon Diaz-Uriarte" <rdiaz at cnio.es>
To: <w.huber at dkfz-heidelberg.de>; "bioconductor"
<bioconductor at stat.math.ethz.ch>
Sent: Wednesday, July 02, 2003 5:52 PM
Subject: Re: [BioC] normalization and analysis of connected designs


> Dear Wolfgang,
>
> Thank you very much for your answer. A couple of things I don't see:
>
> > Another point: It may not always be true that
> >
> > [1] h_3G - h_3R + h_2G - h_2R + h_1G - h_1R
> >
> > is a better estimate for the D-A comparison than
> >
> > [2] h_3G - h_1R
> >
> > Here, h_3G is the green channel on array 3, h_1R the red on array 1, and
> > so on. For good arrays, [2] should have a three times lower variance.
> > However, [1] may be able to correct for spotting irregularities between
> > the chips. Thus which is better depends on the data and the quality of
the
> > chips. You may want to try both.
>
> I am not sure I follow this. I understand that, __if__ D and A had been
> hybridized in the same array, then the variance of their comparison would
be
> a third of the variance of the comparison having to use the (two-step)
> connectiion between A and D. But I am not sure I see how we can directly
do
> h_3G - h_1R
> (if this were possible, then, there would be no need to use connected
> designs.)
>
> They way I was seeing the above set up was:
> from h_3 we can estimate phi_3 = D - C (as the mean log ratio from the
arrays
> of type 3),
> from h_2, phi_2 = C - B
> from h_1, phi_1 = B - A
> phi_1, phi_2, and phi_3 are the three basic estimable effects.
>
> Since I want D - A, I estimate that from the linear combination of the
phis
> (which here is just the sum of the phis).
>
> This is doing it "by hand"; I think that if we use a set up such as the
ANOVA
> approach of Kerr, Churchill and collaborators (or Wolfinger et al), we end
up
> doing essentially the same (we eventually get the "VG" effects), and we
still
> need a connected design.
>
> So either way, I don't get to see how we can directly do
> h_3G - h_1R
>
> But then, maybe I am missing something obvious again...
>
>
> Best,
>
> Ramón
>
>
> >
> > Best regards
> >
> >   Wolfgang
> >
> > On Tue, 1 Jul 2003, Ramon Diaz wrote:
> > > Suppose we have an experiment with cDNA microarrays with the
structure:
> > > A -> B -> C -> D
> > > (i.e., A and B hybridized in the same array, A with Cy3, B with Cy5; B
> > > and C in the same array, with B with Cy3, etc).
> > >
> > > In this design, and if we use log_2(R/G), testing A == D is
> > > straightforward since A and D are connected and we can express D - A
as
> > > the sum of the log ratios in the three arrays.
> > >
> > > But suppose we use some non-linear normalization of the data, such as
> > > loess as in Yang et al. 2002 (package marrayNorm) or the variance
> > > stabilization method of Huber et al., 2002 (package vsn).  Now, the
> > > values we have after the normalization are no longer log_2(R/G) but
> > > something else (that changes with, e.g., log_2(R*G)).  Doesn't this
> > > preclude the simple "just add the ratios"? Is there something obvious
I
> > > am missing?
> > >
> > > Thanks,
> > >
> > > Ramón
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor at stat.math.ethz.ch
> > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
> -- 
> Ramón Díaz-Uriarte
> Bioinformatics Unit
> Centro Nacional de Investigaciones Oncológicas (CNIO)
> (Spanish National Cancer Center)
> Melchor Fernández Almagro, 3
> 28029 Madrid (Spain)
> Fax: +-34-91-224-6972
> Phone: +-34-91-224-6900
>
> http://bioinfo.cnio.es/~rdiaz
>
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