[BioC] Normalized microarray data and meta-analysis

[Thomas Hampton]

I feel that p-values, corrected or otherwise, may be unsatisfactory for
detecting concordance between experiments. For example, an experiment  
with
higher N will show lower p-values for the same gene, even under
conditions that are otherwise precisely the same. So we can't compare
p values head to head across multiple experiments directly. Simple  
simulations show
that straight fold change can be more predictive of future behavior  
(say, in
somebody else's study) than statistics which place a high premium on
within-group consistency.

Check this out:

BMC Bioinformatics. 2008; 9(Suppl 9): S10.
Published online 2008 August 12. doi: 10.1186/1471-2105-9-S9-S10.
PMCID: PMC2537561
Copyright © 2008 Shi et al; licensee BioMed Central Ltd.

The balance of reproducibility, sensitivity, and specificity of lists  
of differentially expressed genes in microarray studies


Cheers

Tom


On Dec 17, 2008, at 7:06 PM, Paul Leo wrote:

> No you don't need the raw data. However, do you need to check that
> p-values were calculated the same way between experiments (will be
> consistent if you use GEO processed data ) - what if one group did a
> multiple testing correction and the other did not? Perhaps this is
> already accounted for in the method you mentioned?
>
> You may wish to consider if you will combine p-values at the gene  
> level
> the probe level. Most favour the probe level due to spline varients  
> etc
>
> If you comparing cross array platforms then you need to be very  
> careful;
> a conservative appraoch is blast probe-to-probe across array platforms
> to get the correspondence. Illumina provides "pre-basted" probes  
> sets on
> their ftp site for ilumina-affy comparisons.
>
> Best of luck.
>
> Cheers
> Paul
>
>
> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch
> [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Mcmahon,
> Kevin
> Sent: Thursday, 18 December 2008 8:31 AM
> To: bioconductor at stat.math.ethz.ch
> Subject: [BioC] Normalized microarray data and meta-analysis
>
> 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
>
>
>
>
> 	[[alternative HTML version deleted]]
>
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