[BioC] normalization and analysis of connected designs

[Ramon Diaz-Uriarte]

Dear Savi,

Thanks for the comment; that option (as well as Wolfgangs comments), seems to 
me a puzzling possibility... It would be really nice, but I am not sure I see 
how one would be able to do it (see also Gordon Smith's comments in this 
thread).

By the way, is there any package for quantile normalization for cDNA arrays? 

Best,

Ramón


On Wednesday 02 July 2003 18:04, Xavier Solé wrote:
> 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
> >
> > _______________________________________________
> > 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