[BioC] Merging microarray datasets

[Kort, Eric]

> -----Original Message-----
> From: bioconductor-bounces at 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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