Dear Dr. Robert,

Thanks for your reply.  I have put my effort to understand the method, which
is described in your paper (Synthesis of microarray experiments). Pls
correct me if my understanding is not correct.

It seems that for the 1st dataset, I can easily calculate the effect size
(by comparing fold change between treated and untreated). But for the 2nd
dataset, it is not possible to calculate the 'difference between mean',
hence there is no estimates of the effect (d).

Since I have 2 strains (A and B, both untreated) in two datasets, is it make
sense to combine the 2 datasets by estimating the effect for untreated
condition only?  Then, I can compare the combined data with the treated
condition of the 1st dataset.

Your suggestions are greatly appreciated.

Thanks again,
Sharon

On 6/11/06, Robert Gentleman <rgentlem@fhcrc.org> wrote:
>
>
>
> Sean Davis wrote:
> > Sharon wrote:
> >> Hi,
> >>
> >> I am trying to combine two Affy datasets (on rae230a chips), where
> >> experiments done one year apart. In the first dataset, we have 2
> >> strains with each strain treated and untreated.  But for the second
> >> dataset, we have just 2 strains untreated.
> >>
> >> Because of unequal levels in the 2 datasets, I am not able to use
> >> 'getdF'  in GeneMeta as it is.  Any suggestions for using 'getdF' for
> >> this situation?  or any alternate way of combining these 2 datasets?
> >
> > Are these datasets really that much different that you can't just
> > combine them?  They may be, but have you looked at affyPLM results,
> > density plots, etc., just to be sure?  If they aren't that much
> > different, perhaps you can just normalize them together and move on?
> > Just asking....
>
>   Sorry, but that is, IMHO, a bad idea. You should never jointly
> normalize separate experiments. Normalize separately and use a random
> effects model for the experiments. As, for how to handle different
> levels of factors/covariates, the issue then becomes one of what can be
> estimated from both. Once you identify that you can set up the
> appropriate model and then use tools like nlme and lmer (depending on
> the model) to estimate parameters. But this will require some
> statistical expertise and for that you will have to look locally, these
> things are too hard to do over the internet,  IMHO.
>   There is a BioC technical report on Synthesis of microarray
> experiments that outlines some of these details more completely.
>
>
>   best wishes
>    Robert
>
> >
> > Sean
> >
> > _______________________________________________
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> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >
>
> --
> Robert Gentleman, PhD
> Program in Computational Biology
> Division of Public Health Sciences
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N, M2-B876
> PO Box 19024
> Seattle, Washington 98109-1024
> 206-667-7700
> rgentlem@fhcrc.org
>

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