[BioC] average difference and global scaling for normalizing affymetrix expression data?

Wolfgang Huber whuber at embl.de
Wed Aug 18 09:26:15 CEST 2010

Hi Alice

you could try vsn. It applies a logarithm-like transformation that 
avoids the steepness of the usual logarithm for small values and the 
singularity at 0. Its bottom-end behaviour is fit based on the data's 
background noise characteristics, and it has worked well in conjunction 
with subsequent linear modeling in many previous cases. Conceptually, 
this modification of the usual logarithm at the bottom end is equivalent 
to a "background correction" followed by usual logarithm.

	Best wishes

On 18/08/10 01:30, Alice Johnstone wrote:
> Hello,
> I have been asked to look at data from the GEO to make a comparison with current microarray data.  The GEO data is from an Affymetrix Murine Genome U74 Version 2 Array experiment, from 2003 and the description says that the data is the "average difference" with "global scaling" applied.
> This means there are negative values for some probesets and the values range from -831.8 to 12785, but the majority sit around 15-300.
> My question is what is the best way to proceed?  Do I take this as normalized data?  A boxplot of the data shows some variation between the samples.  Should I just remove the negative values and then proceed with limma with log2 of the data.
> Or will I need to make further transformations before I apply the linear model?  if so, which?
> Im wary of over manipulating the data as its only a 2x2 comparison to begin with.
> Thanks for your advice.
> Alice
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Wolfgang Huber

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