[BioC] Normalized microarray data and meta-analysis

Sean Davis seandavi at gmail.com
Wed Dec 17 23:58:49 CET 2008

On Wed, Dec 17, 2008 at 5:31 PM, Mcmahon, Kevin
<kwyatt.mcmahon at ttuhsc.edu> wrote:
> Hello Bioconductor-inos,
> I have more of a statistical/philosophical question regarding using raw
> vs. normalized data in a microarray meta-analysis.  I've looked through
> the bioconductor archives and have found some addressing of this issue,
> but not exactly what I'm concerned with.  I don't mean to waste anyone's
> time, but I was hoping I could get some help here.
> I've performed a meta-analysis using the downloaded data from 3
> different GEO data sets (GDS).  It is my understanding that these are
> normalized data from the various microarray experiments.  Seems to me
> that the  data from those normalized results are normally distributed,
> those three experiments are perfectly comparable (if you think the
> author's respective normalization approaches  were reasonable).  All you
> need to do is calculate some sort of effect size/determine a
> p-value/etc. for all genes in the experimental conditions of interest
> and then combine these statistics across the different experiments.
> However, I consistently read things like "raw data are required for a
> microarray meta-analysis."  Does this mean that normalized data are not
> directly comparable with eachother?  If so, then why does GEO even host
> such data?

It depends entirely on what you want to do with the data.  However, I
think that many people like to have the raw data, not for
normalization purposes only, but for quality control, also.


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