[BioC] normalization and analysis of connected designs

[Xavier Solé]

We have seen that the effect of the competitive hybridization is no so
relevant for cDNA microarrays. In fact, Affy arrays hybridize just one
sample per chip.

To perform quantile normalization, look at the LIMMA package, Ramon.

Cheers,

Xavi.

----- Original Message ----- 
From: "Ramon Diaz-Uriarte" <rdiaz at cnio.es>
To: "Xavier Solé" <x.sole at ico.scs.es>; <w.huber at dkfz-heidelberg.de>;
"bioconductor" <bioconductor at stat.math.ethz.ch>
Sent: Thursday, July 03, 2003 1:31 PM
Subject: Re: [BioC] normalization and analysis of connected designs


> 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
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor