Thanks to Adai and Eric,

Well, I'm trying to bring back the discussion to the previous direction as
it apparently went to a different area : cross-platform integration. :)   I
was wondering about integration within the same platform – an issue when
there are multiple chips (in case of affymetrix)     OR        multiple
print layouts (cDNA .gal files).

"… have to normalize all raw data together. All data should also be of one
platform only. Then you can simply normalize all CEL files or all -----
files together and be done."  [courtesy: Balasubramanian]

So, if I have suppose MA1, MA2… as respective normalised datasets (same
platform. After doing normalization based on chip-types in case of
Affymetrix, OR, print layouts in case of cDNA), can I just normalize them
again for the final dataset, or I need to take care of some other issues too
(how to tackle!) ? Also, wonder if there's any smart package in this regard!


Also Eric, I didn't get you what project you were talking about : "…See for
example the oncomine project…."

Thanks.

Kathy




= = = =  = = = = =


On Thu, Apr 24, 2008 at 7:34 AM, Kort, Eric <Eric.Kort@vai.org> wrote:

> > -----Original Message-----
> > From: bioconductor-bounces@stat.math.ethz.ch
> > Subject: Re: [BioC] Merging microarray datasets
> >
> >
> > This is an interesting question and one that I like to
> > explore further.
> >
> > The papers I have seen on combining microarray datasets so
> > far select one algorithm for Affymetrix and one algorithm for cDNA.
> >
> > Has anyone investigated which combination of preprocessing
> > algorithm(s) make data from these two platforms comparable?
> > Indeed, how does one check if they are comparable? Any
> > references and suggestions would be very welcome.
>
> Platforms aside, cDNA arrays are usually two color and ratios, and Affy
> are one color and not ratios.
>
> So one approach is to turn everything into ratios after preprocessing and
> normalization (using, for example, a suitable set of  reference
> samples...e.g. normal kidney for kidney tumors).  Obviously, thoughtful
> selection of reference samples is required, and the reference chosen should
> be analagous to whatever was used as the reference in the two color arrays.
>
> Then, one can try to bring things further into line by converting the log
> transformed ratios into z scores.
>
> As far as verification, a place to start would be box and whisker plots to
> expose obvious abnormalities.  You can also perform unsupervised clustering
> to see if the samples cluster mainly according to platform/lab or mainly
> according to known phenotype.
>
> Then, as Robert Gentleman stated, you can use appropriate models to
> correct systematic biases.  But I will leave the details of that to the
> statisticians (and, indeed, the archives of this list).
>
> Obviously, the whole exercise is frought with difficulties, but it is
> done.  See for example the oncomine project. Whether it is is fruitfully
> done is open to argument. One thing to consider is to utilize down-stream
> analysis methods that care more about relative position of genes and less
> about magnitude of values (e.g. GSEA or PGSEA).
>
> >
> > Thank you.
> >
> > Regards, Adai
>

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