[BioC] Question about normalization of microarray data

[STKH (Steen Krogsgaard)]

Hi Johan,

I'm no stat wiz myself. It's just that common reference design and
balanced block design are two more or less equally powerful designs. In
your case I got the impression that you did not have a common reference
that were biologically relevant and the one you have will give you
problems during normalization. That's why I suggested balanced block
design. I'm not aware that BB design should be any different from other
designs when normalizing the individual slides (lowess or whatever).

Richard Simon from NCI gave an excellent talk on experimental desing at
the MGED-7 conference in Toronto this year, your can see his
presentation at ftp://linus.nci.nih.gov/pub/techreport/MGED-B.pdf.

cheers
Steen

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Johan
Lindberg
Sent: 18. november 2004 16:07
To: bioconductor at stat.math.ethz.ch
Subject: RE: [BioC] Question about normalization of microarray data


Dear I ask (from not being a statistician point of view), what do you
mean by "since you don't seem to have a suitable common reference, how
about 
using a balanced block design instead?" in this context?

As far as I now a balanced block design considers the individual groups
of measurements that are expected to be more homogeneous than others
(with the same amount of observations within each group) when doing an
Anova, or? Is this not something you would like to do after
normalization, when trying to identify differences in some context in
the data? Or did you refer to a model that considers dye-effects so you
won't have to do normalization?

Best regards

//Johan


-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Sean Davis
Sent: Thursday, November 18, 2004 12:57 PM
To: STKH (Steen Krogsgaard)
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] Question about normalization of microarray data


On Nov 18, 2004, at 6:42 AM, STKH ((Steen Krogsgaard)) wrote:

> Hi Johan,
>
> since you don't seem to have a suitable common reference, how about
> using a balanced block design instead?
>
> cheers
> Steen
>

Yep.  The true power of the two-color design is in comparing within 
slide two samples of interest.  If one is going to use a common 
reference, then the common reference should probably have SOME 
semblance to the test sample in terms of gene expression.  Even when 
using a commercially available reference, one presumably has some 
variation in expression that mimics that in the test sample better than 
using a non-biologic reference like PCR products.  It will be 
interesting to see what you end up doing here, but I do agree that sing 
within array contrasts whenever possible is a good idea.

Sean

> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch
> [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Johan 
> Lindberg
> Sent: 18. november 2004 09:15
> To: bioconductor at stat.math.ethz.ch
> Subject: [BioC] Question about normalization of microarray data
>
>
> Hi all. I have a question about normalization of microarray data.
>
> In our lab we use in-house spotted cDNA arrays. We have so far used
> commercial reference when doing reference design. We are now trying a 
> new approach but we have problems with normalization. What we have 
> done is to pool product from every spot on the chip and done in vitro 
> transcription on the PCR product. So we have RNA corresponding to 
> every spot on the chip. Then this is used as a reference. It is much 
> cheaper and we get signal from every spot on the chip instead of 
> having spots with no signal in both channels. But when one looks at an

> MA-plot the plot will be skewed towards the reference. There are about

> (in this pilot case) 2000 spots that only give signal in the reference

> channel (which will skew the MA-plot). This will make many assumptions

> not correct when normalizing the data, e.g. using lowess normalization

> assuming that the ratio R/G should be 1 for most spots.
> Since the case for this kind of data is that one channel should be
> much stronger than the other, and we want to keep the normalization 
> within slide (to be able to correct for spatial biases and intensity 
> dependent) the only way I could think of is by spotting a lot of 
> control spots (not present in the tested RNA or the reference RNA) and

> use these to normalize the data.

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

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