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?

 

Any help would be wonderful!

 

Wyatt

 

K. Wyatt McMahon, Ph.D.

Texas Tech University Health Sciences Center

Department of Internal Medicine

3601 4th St. 

Lubbock, TX - 79430

806-743-4072

"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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