[BioC] Normalized microarray data and meta-analysis
Thomas.H.Hampton at Dartmouth.edu
Thu Dec 18 00:02:07 CET 2008
The question, I think, has to do with what sort of comparisons you
make. When people normalize using RMA, each slide ends up with a common
distribution -- the only variable being how the elements of the
to probes on any given slide. This is already some pretty hairy
but it seems to work ok for lining up arrays done by the same people
at the same
time and place so that you can meaningfully compare expression values
head, calculate averages, and do significance tests.
With or without raw data, the idea of a meaningful direct comparisons
between of say, an
expression value of 7.5 in one lab with an expression value of 8.3 in
seem very optimistic to me.
Saying something like gene X was in the top 1% in expression in both
On Dec 17, 2008, at 5:31 PM, Mcmahon, Kevin wrote:
> Hello Bioconductor-inos,
> I have more of a statistical/philosophical question regarding using
> vs. normalized data in a microarray meta-analysis. I've looked
> the bioconductor archives and have found some addressing of this
> but not exactly what I'm concerned with. I don't mean to waste
> 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
> directly comparable with eachother? If so, then why does GEO even
> such data?
> Any help would be wonderful!
> K. Wyatt McMahon, Ph.D.
> Texas Tech University Health Sciences Center
> Department of Internal Medicine
> 3601 4th St.
> Lubbock, TX - 79430
> "It's been a good year in the lab when three things work. . . and
> one of
> those is the lights." - Tom Maniatis
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